Scott and Daniel break down every month from now until the 2027 intelligence explosion.

Scott Alexander is author of the highly influential blogs Slate Star Codex and Astral Codex Ten. Daniel Kokotajlo resigned from OpenAI in 2024, rejecting a non-disparagement clause and risking millions in equity to speak out about AI safety.

I came in skeptical, but I learned a tremendous amount by bouncing my objections off of them. I highly recommend checking out their new scenario planning document, AI 2027.

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(00:00:00) - AI 2027

(00:06:56) - Forecasting 2025 and 2026

(00:14:41) - Why LLMs aren't making discoveries

(00:24:33) - Debating intelligence explosion

(00:49:45) - Can superintelligence actually transform science?

(01:16:54) - Cultural evolution vs superintelligence

(01:24:05) - Mid-2027 branch point

(01:32:30) - Race with China

(01:44:47) - Nationalization vs private anarchy

(02:03:22) - Misalignment

(02:14:52) - UBI, AI advisors, & human future

(02:23:00) - Factory farming for digital minds

(02:26:52) - Daniel leaving OpenAI

(02:35:15) - Scott's blogging advice

Dwarkesh Patel

Today I have the great pleasure of chatting with Scott Alexander and Daniel Kokotajlo. Scott is of course the author of the blog Slate Star Codex, Astral Codex 10 now. It’s actually been, as you know, a big bucket list item of mine to get you on the podcast. So this is all the first podcast we’ve ever done, right?

Scott Alexander

Yes.

Dwarkesh Patel

And then Daniel is the director of the AI Futures Project. And you have both just launched today something called AI 2027. So what is this?

Scott Alexander

Yeah, AI 2027 is our scenario trying to forecast the next few years of AI progress. We’re trying to do two things here. First of all we just want to have a concrete scenario at all. So you have all these people, Sam Altman, Dario Amodei, Elon Musk saying, “going to have AGI in three years, superintelligence in five years”. And people just think that’s crazy because right now we have chatbots that are able to do a Google search, not much more than that in a lot of ways. And so people ask, “how is it going to be AGI in three years?” What we wanted to do is provide a story, provide the transitional fossils. So start right now, go up to 2027 when there’s AGI, 2028, when there’s potentially super intelligence, show on a month-by-month level what happened. Kind of in fiction writing terms, make it feel earned.

So that’s the easy part. The hard part is we also want to be right. So we’re trying to forecast how things are going to go, what speed they’re going to go at. We know that in general, the median outcome for a forecast like this is being totally humiliated when everything goes completely differently. And if you read our scenario, you’re definitely not going to expect us to be the exception to that trend.

The thing that gives me optimism is Daniel back in 2021, wrote the prequel to this scenario called What 2026 Looks Like. It’s his forecast for the next five years of AI progress. And he got it almost exactly right. You should stop this podcast right now. You should go and read this document. It’s amazing. Kind of looks like you asked ChatGPT to summarize the past five years of AI progress, and you got something with a couple of hallucinations, but basically well intentioned and correct. So when Daniel said he was doing this sequel, I was very excited, really wanted to see where it was going. It goes to some pretty crazy places and I’m excited to talk about it more today.

Daniel Kokotajlo

I think you’re hyping up a little bit too much. Yes, I do recommend people go read the old thing I did, which was a blog post. I think it got a bunch of stuff right, a bunch of stuff wrong, but overall held up pretty well and inspired me to try again and do a better version of it.

Scott Alexander

I think, read the document and decide which of us is right.

Daniel Kokotajlo

Another related thing too is that the original thing was not supposed to end in 2026, it was supposed to go all the way through the exciting stuff, right? Because everyone’s talking about, what about AGI, what about superintelligence, what would that even look like? So I was trying to step-by-step work my way from where we were at the time until things happen and then see what they look like, but I basically chickened out when I got to 2027 because things were starting to happen and the automation loop was starting to take off and it was just so confusing and there was so much uncertainty, so I basically just deleted the last chapter and published what I had up until that point. And that was the blog post.

Dwarkesh Patel

Okay, and then, Scott, how did you get involved in this project?

Scott Alexander

So I was asked to help with the writing, and I was already somewhat familiar with the people on the project, and many of them were kind of my heroes. So, Daniel, I knew both because I’d written a blog post about his opinions before I knew about his, “What 2026 looks like,” which was amazing. And also he had pretty recently made the national news for having, when he quit OpenAI, they told him he had to sign a non-disparagement agreement or they would claw back his stock options. And he refused, which they weren’t prepared for. It started a major news story, a scandal that ended up with OpenAI agreeing that they were no longer going to subject employees to that restriction.

So people talk a lot about how it’s hard to trust anyone in AI because they all have so much money invested in the hype and getting their stock options better. And Daniel had attempted to sacrifice millions of dollars in order to say what he believed, which to me was this incredibly strong sign of honesty and competence. And I was like, how can I say no to this person? Everyone else on the team, also extremely impressive. Eli Liflund, who’s a member of Samotsvety, the world’s top forecasting team. He has won, like, the top forecasting competition, plausibly described as just the best forecaster in the world, at least by these really technical measures that people use in the superforecasting community. Thomas Larsen, Jonas Vollmer, both really amazing people who have done great work in AI before.

I was really excited to get to work with this superstar team. I have always wanted to get more involved in the actual attempt to make AI go well. Right now, I just write about it. I think writing about it is important, but I don’t know. You always regret that you’re not the person who’s the technical alignment genius who’s able to solve everything. And getting to work with people like these and potentially make a difference just seemed like a great opportunity.

What I didn’t realize was that I also learned a huge amount. I try to read most of what’s going on in the world of AI, but it’s this very low bandwidth thing and getting to talk to somebody who’s thought about it as much as anyone in the world was just amazing. Makes me really understand these things about how AI is going to learn quickly. You need all of this deep engagement with the underlying territory and I feel like I got that.

Dwarkesh Patel

I’ve probably changed my mind towards, against, towards, against, intelligence explosion three, four times in the conversations I’ve had in the lead-up in talking to you and then trying to come up with a rebuttal or something.

Scott Alexander

It wasn’t even just changing my mind, getting to read the scenario for the first time. It obviously wasn’t written up at this point. It was a giant, giant spreadsheet. I’ve been thinking about this for a decade, decade and a half now. And it just made it so much more concrete to have a specific story. Like, oh, yeah, that’s why we’re so worried about the arms race with China. Obviously we would get an arms race with China in that situation. And aside from just the people getting to read the scenario really sold me. This is something that needs to get out there more.

Dwarkesh Patel

Yeah. Okay. Now let’s talk about this new forecast. Because you do a month by month analysis of what’s going to happen from here. So what is it that you expect in mid-2025 and the end of 2025 In this forecast?

Scott Alexander

So, [the] beginning of the forecast mostly focuses on agents. We think they’re going to start with agency training, expand the time horizons, get coding going well. Our theory is that they are, to some degree consciously, to some degree accidentally, working towards this intelligence explosion, where the AIs themselves can start taking over some of the AI research, move faster.

So 2025, slightly better coding, 2026, slightly better agents, slightly better coding. And then we focus on, and we name the scenario after 2027 because that is when this starts to pay off. The intelligence explosion gets into full swing; the agents become good enough to help with- at the beginning not really do, but help with- some of the AI research.

So we introduced this idea called the R&D progress multiplier: how many months of progress without the AIs do you get in one month of progress with all of these new AIs helping with the intelligence explosion. So 2027, we start with- I can’t remember if it literally starts with, or by March or something- a five times multiplier for algorithmic progress.

Daniel Kokotajlo

So we have the stats tracked on the site of the story. Part of why we did it as a website is so that you can have these cool gadgets and widgets. And so as you read the story, the stats on the side automatically update. And so one of those stats is the progress multiplier. Another answer to the same question you asked is basically; 2025, nothing super interesting happens, more or less similar trends to what we’re seeing.

Dwarkesh Patel

Computer use is totally solved? Partially solved? How good is computer use by the end of 2025?

Daniel Kokotajlo

My guess is that they won’t be making basic mouse click errors by the end of 2025, like they sometimes currently do. If you watch Claude Plays Pokemon- which you totally should- it seems like sometimes it’s just failing to parse what’s on the screen and it thinks that its own player character is an NPC and gets confused. My guess is that that sort of thing will mostly be gone by the end of this year, but that they still won’t be able to autonomously operate for long periods on their own.

Dwarkesh Patel

But by 2025, when you say it won’t be able to act coherently for long periods of time in computer use, if I want to organize a happy hour in my office, I don’t know, that’s like what a 30 minute task? What fraction of that is, it’s got to invite the right people, it’s got to book the right doordash or something. What fraction of that is it able to do?

Daniel Kokotajlo

My guess is that by the end of this year there’ll be something that can kind of do that, but unreliably. And that if you actually tried to use that to run your life, it would make some hilarious mistakes that would appear on Twitter and go viral, but that the MVP of it will probably exist by this year. Like there’ll be some Twitter thread about someone being like, “I plugged in this agent to like run my party and it worked!”

Scott Alexander

Our scenario focuses on coding in particular because we think coding is what starts the intelligence explosion. So we are less interested in questions of like, “how do you mop up the last few things that are uniquely human” compared to “when can you start coding in a way that helps the human AI researchers speed up their AI research, and then, if you’ve helped them speed up the AI research enough, is that enough to, with some ridiculous speed multiplier- 10 times, 100 times- mop up all of these other things?”

Dwarkesh Patel

One observation I have is, you could have told a story in 2021, once ChatGPT comes out… I think I had friends who were credible AI thinkers who were like, “look, you’ve got the coding agent now, it’s been cracked. Now the GPT4 will go around and it’ll do all this engineering and we do this RL on top. We can totally scale up the system 100x” and every single layer of this has been much harder than the strongest optimist expected. It seems like there have been significant difficulties in increasing the pre-training size, at least from rumors about field training runs or underwhelming training runs at labs.

It seems like building up these RL- total outside view, I know nothing about the actual engineering involved here- but just from an outside view it seems like building up the O1 RL clearly took at least two years after GPT4 was released. And these things are also, their economic impact and the kinds of things you would immediately expect based on benchmarks for them to be especially capable at isn’t overwhelming, like the call center workers haven’t been fired yet. So why not just say look, at higher scale it will probably get even more difficult.

Scott Alexander

Wait a second, I’m a little confused to hear you say that, because when I have seen people predicting AI milestones like Katja Grace’s expert surveys, they have almost always been too pessimistic from a point of view of how fast AI will advance. So I think the 2022 survey, they actually said that things that had already happened would take like 10 years to happen, but then the survey- it might have been 2023, it was like six months before GPT3, GPT4, came out. And there were things that GPT3 or 4 or whichever one of them it was, did, that it did in six months that they were still predicting like five or ten years from. I’m sure Daniel is going to have a more detailed answer, but I absolutely reject the premise that everybody has always been too optimistic.

Daniel Kokotajlo

Yeah, I think in general, most people following the field have underestimated the pace of AI progress and underestimated the pace of AI diffusion into the world. For example, Robin Hanson famously made a bet about less than a billion dollars of revenue I think by 2025 from AI.

Dwarkesh Patel

I agree Robin Hanson in particular has been too pessimistic.

Daniel Kokotajlo

But he’s a smart guy. So I think that the aggregate opinion has been underestimating the pace of both technical progress and deployment. I agree that there have been plenty of people who’ve been more bullish than me and have been already proven wrong, but they’re not me.

Scott Alexander

Wait a second. We don’t have to guess about aggregate opinion, we can look at Metaculus. Metaculus, I think their timeline was like 2050 back in 2020. It gradually went down to like 2040 two or three years ago. Now it’s at 2030, so it’s barely ahead of us. Again, that may turn out to be wrong, but it does look like the Metaculans overall have, have been too pessimistic, thinking too long term rather than too optimistic. And I think that’s like the closest thing we have to a neutral aggregator where we’re not cherry picking things.

Dwarkesh Patel

Yeah. I had this interesting experience yesterday. We were having lunch with this senior AI researcher, probably makes on the order of millions a month or something, and we were asking him, “how much are the AIs helping you?” And he said, “in domains which I understand well, and it’s closer to autocomplete but more intense, there it’s maybe saving me four to eight hours a week.”

But then he says, “in domains which I’m less familiar with, if I need to go wrangle up some hardware library or make some modification to the kernel or whatever, where I know less, that saves me on the order of 24 hours a week.” Now, with current models. What I found really surprising is that the help is bigger where it’s less like autocomplete and more like a novel contribution. It’s like a more significant productivity improvement there.

Daniel Kokotajlo

Yeah, that is interesting. I imagine what’s going on there is that a lot of the process when you’re unfamiliar with a domain is like Googling around and learning more about the domain. And language models are excellent because they’ve already read the whole Internet and know all the details.

Dwarkesh Patel

Isn’t this a good opportunity to discuss a certain question I asked Dario that you responded to?

Scott Alexander

What are you thinking of?

Dwarkesh Patel

Well, I asked this question where, as you say, they know all this stuff. I don’t know if you saw this. I asked this question where I said, look, these models know all this stuff. And if a human knew every single thing a human has ever written down on the Internet, they’d be able to make all these interesting connections between different ideas and maybe even find medical cures or scientific discoveries as a result.

There was some guy who noticed that magnesium deficiency causes something in the brain that is similar to what happens when you get a migraine. And so he just said: give you magnesium supplements that cured a lot of migraines. So why aren’t able to leverage this enormous asymmetric advantage they have to make a single new discovery like this?

Scott Alexander

And then the example I gave was that humans also can’t do this. So for me, the most salient example is the etymology of words. You have all of these words in English that are very similar, like ‘happy’ versus ‘hapless’, ‘happen’, ‘perhaps’. And we never think about them unless you read an etymology dictionary and they’re like, oh, obviously these all come from some old root that has to mean ‘luck’ or ‘occurrence’ or something like that.

So it’s kind of about figuring out versus checking. If I tell you those, you’re like, “this seems plausible”. And of course, in etymology, there are also a lot of false friends where they seem plausible but aren’t connected. But you really do have to have somebody shove it in your face before you start thinking about it and make all of those connections.

Dwarkesh Patel

I will actually disagree with this. We know that humans can do like, we have examples of humans doing this. I agree that we don’t have logical omniscience because there is a combinatorial explosion, but we are able to leverage our intelligence to… one of my favorite examples of this is David Anthony, the guy who wrote the Horse, the Wheel and Language.

He made this super impressive discovery before we had the genetic evidence for it, like a decade before, where he said, look, if I look at all these languages in India and Europe, they all share the same etymology. I mean literally the same etymology for words like ‘wheel’ and ‘cart’ and ‘horse’. And these are technologies that have only been around for the last 6,000 years, which must mean that there was some group that these groups are all, at least linguistically, descended from. And now we have genetic evidence for the Yamnaya, which we believe is this group. You have a blog where you do this. This is your job, Scott! So why shouldn’t we hold the fact that language models can’t do this more against them?

Scott Alexander

Yeah. So to me, it doesn’t seem like he is just sitting there being logically omniscient and getting the answer. It seems like he’s a genius, he’s thought about this for years, probably at some point, he heard a couple of Indian words and a couple of European words at the same time and they kind of connected and the light bulb came on. So this isn’t about having all the information in your memory so much as the normal process of discovery, which is kind of mysterious, but seems to come from having good heuristics and throwing them at things until you kind of get a lucky strike.

My guess is if we had really good AI agents and we applied them to this task, it would look something like a scaffold where it’s like, think of every combination of words that you know of, compare them. If they sound very similar, write it on this scratch pad here. If a lot of words of the same type show up on the scratch pad, that’s pretty strange, do some kind of thinking around it. And I just don’t think we’ve even tried that.

And I think right now if we tried it, we would run into the combinatorial explosion. We would need better heuristics. Humans have such good heuristics that probably most of the things that show up even in our conscious mind, rather than happening on the level of some kind of unconscious processing, are at least the kind of things that could be true. I think you could think of this as like a chess engine. You have some unbelievable number of possible next moves, you have some heuristics for picking out which of those are going to be the right ones. And then gradually you kind of have the chess engine think about it, go through it, come up with a better or worse move, then at some point you potentially become better than humans. I think if you were to force the AI to do this in a reasonable way, or you were to train the AI such that it itself could come up with the plan of going through this in some kind of heuristic-laden way, you could potentially equal humans.

Daniel Kokotajlo

I’ll add some more things to that. So I think there’s a long and sordid history of people looking at some limitation of the current LLMs and then making grand claims about how the whole paradigm is doomed because they’ll never overcome this limitation. And then a year or two later the new LLMs overcome that limitation.

And I would say that with respect to this thing of “why haven’t they made these interesting scientific discoveries by combining the knowledge they already have and noticing interesting connections?” I would say first of all, have we seriously tried to build scaffolding to make them do this? And I think the answer is mostly no.

Dwarkesh Patel

I think Google DeepMind tried this.

Daniel Kokotajlo

Maybe. Second thing, have you tried making the model bigger? They’ve made it a bit bigger over the last couple years and it hasn’t worked so far. Maybe if they make it even bigger still, it’ll notice more of these connections. And then third thing, and here’s I think the special one: Have you tried training the model to do the thing? The pre-training process doesn’t strongly incentivize this type of connection making.

In general I think it’s a helpful heuristic that I use to ask the question of: remind oneself, what was the AI trained to do? What was its training environment like? And if you’re wondering why hasn’t the AI done this, ask yourself, did the training environment train it to do this? And often the answer is no. And often I think that’s a good explanation for why the AI is not good at it is that it wasn’t trained to do it.

Dwarkesh Patel

I mean it seems like such an economically valuable…

Daniel Kokotajlo

But how would you set up the training environment? Wouldn’t it be really gnarly to try to set up an RL environment to train to make new scientific discoveries?

Dwarkesh Patel

Maybe that’s why you should have longer timelines. It’s a gnarly engineering problem.

Daniel Kokotajlo

Well in our scenario they don’t just leap from where we are now to solving this problem. They don’t. Instead they just iteratively improve the coding agents until they’ve basically got coding solved. But even still, their coding agents are not able to do some of this stuff. That’s what early 2020, like the first half of 2027 in our story is basically, they’ve got these awesome automated coders, but they still lack research taste and they still lack maybe organizational skills and stuff.

And so they need to overcome those remaining bottlenecks and gaps in order to completely automate the AI research cycle. But they’re able to overcome those gaps faster than they normally would because the coding agents are doing all the grunt work really fast for them.

Scott Alexander

Yeah, I think it might be useful to think of our timelines as being like 2070, 2100. It’s just that the last 50 to 70 years of that all happened during the year 2027 to 2028, because we are going through this intelligence explosion like I think if I asked you, could we solve this problem by the year 2100? You would say, oh, yeah, by 2100? Absolutely. And we’re just saying that the year 2100 might happen earlier than you expect because we have this research progress multiplier.

Dwarkesh Patel

And then let me just address that in a second. But just one final thought on this thread. To the extent that there’s like a modus ponens, modus tollens thing here, where one thing you could say is like, look: AIs- not just LLMs, but AIs- will have this fundamental asymmetric advantage where they know all this shit. And why aren’t they able to use their general intelligence to use this asymmetric advantage to some enormous capability overhang.

Now, you could infer that same statement by saying, okay, well, once they do have that general intelligence, they will be able to use their asymmetric advantage to make all these enormous gains that humans are in principle less capable of, right? So basically, if you do subscribe to this view that AIs could do all these things if only they had general intelligence, you got to be like, well, once we actually do get the AGI, it’s actually going to be a totally transformative because they will have all of human knowledge memorized and they can use that to make all these connections.

Daniel Kokotajlo

I’m glad you mentioned that our current scenario does not really take that into account very much. So that’s an example in which our scenario is possibly underestimating the rate of progress.

Dwarkesh Patel

You’re so conservative, Daniel.

Scott Alexander

This has been my experience working with the team, as I point out, five different things. “Are you sure you’re taking this into account? Are you sure you’re taking this into account?” And first of all, 99% of the time he says, “yes, we have a supplement on it”. But even when he doesn’t say that, he’s like, “yeah, that’s one reason it could go slower than that. Here are 10 reasons it could go faster”.

Daniel Kokotajlo

It’s trying to be sort of like our median guess. So there are a bunch of ways in which we could be underestimating, and there are a bunch of ways in which you could be overestimating. And we’re going to hopefully continue to think more about this afterwards and continue to iteratively refine our models and come up with better guesses and so forth.

Dwarkesh Patel

So if I look back at AI progress in the past, if we were back in, say, 2017. Suppose we had these superhuman coders in 2017; the amount of progress we’ve made since then, so where we are currently in 2025, by when could we have had that instead?

Daniel Kokotajlo

Great question. We’d still have to stumble through all the discoveries that we’ve made since 2017. We still have to figure out that language models are a thing, we still have to figure out that you can fine tune them with RL.

So all those things would still have to happen. How much faster would they happen? Maybe 5x faster, because a lot of the small scale experiments that these people do in order to test out ideas really quickly before they do the big training runs would happen much faster because they’re just lickety-split being spit out. I’m not very confident in that 5x number, it could be lower, it could be higher, but that was roughly what we were guessing.

Our 5x, by the way, is for the algorithmic progress part, not for the overall thing. So in this hypothetical, according to me, basically things would be going 2.5x faster, where the algorithms would be advancing at 5x speed, but the compute is still stuck at the usual speed.

Dwarkesh Patel

That seems plausible to me. You have a 5x at some point, and then dot dot dot, you have 1000x AI progress within the matter of a year. Maybe that’s the part I’m like, wait, how did that happen exactly? So what’s the story there?

Daniel Kokotajlo

The way that we did our takeoff forecast, which we’ll get to in a second, was basically by breaking down how we think the intelligence explosion would go into a series of milestones. First you automate the coding, then you automate the whole research process, but in a very similar way to how humans do it with teams of agents that are about human level, then you get to superhuman level and so forth.

So we broke it down into these milestones, you know, the superhuman coder, superhuman AI researcher, and then super intelligent AI researcher. And the way we did our forecast was, for each of these milestones, we were like, what is it going to take to make an AI that achieves that milestone? And then once you do achieve that milestone, how much is your overall speedup? And then what’s it going to take to achieve the next milestone? Combine that with the overall speed up and that gets you your clock time distance until that happens and then, okay, now you’re at that milestone. What’s your overall speed up? Assuming that you have that milestone also, what’s the next one? How long does it take to get to the next one? So we sort of work through it bit by bit, and at each stage we’re just making our best guesses.

So quantitatively we were thinking something like 5x speedup to algorithmic progress from the superhuman coder, and then something like a 25x speedup to algorithmic progress from the superhuman AI researcher. Because at that point you’ve got the whole stack automated, which I think is substantially more useful than just automating the coding. And then I forget what we say for a super intelligent AI researcher, but off the top of my head it’s probably something in the hundreds or maybe like 1000x overall speed up.

Dwarkesh Patel

So maybe the big picture thing I have with the intelligence explosion is… we can go through the specific arguments about how much will the automated coder be able to do, and how much will the superhuman AI coder be able to do. But on priors, it’s just such a wild thing to expect.

And so, before we get into all the specific arguments, maybe you can just address this idea that, why not just start off with 0.01% chance this thing might happen? Then you need extremely, extremely strong evidence that it will before making that your modal view.

Scott Alexander

I think that it’s a question of what is your default option or what are you comparing it to. I think that naively people think like, well, every particular thing is potentially wrong. So let’s just have a default path where nothing ever happens. And I think that that has been the most consistently wrong prediction of all. Like, I think in order to have nothing ever happen, you actually need a lot to happen. Like you need suddenly AI progress that has been going at this constant rate for so long stops. Why does it stop?

Well, we don’t know. Whatever claim you’re making about that is something where you would expect there to be a lot of out of model error is where you would expect. Think like somebody must be making a pretty definite claim that you want to challenge. So I don’t think there’s a neutral position where you can just say, well, given that out of model error is really high and we don’t know anything, let’s just choose that. I think we are trying to take- I know this sounds crazy because if you read our document, all sorts of bizarre things happen. It’s probably the weirdest couple of years that have ever been. But we’re trying to take almost in some sense a conservative position where the trends don’t change, nobody does an insane thing, nothing that we have no evidence to think will happen happens. And the way that the AI intelligence explosion dynamics work are just so weird that in order to have nothing happen, you need to have a lot of crazy things happen.

Daniel Kokotajlo

One of my favorite meme images is this graph showing world GDP over time. You’ve probably seen it, it spikes up and then there’s a little thought bubble at the top of the spike in 2010 or something. And the thought bubble says, “my life is pretty normal, I have a good grasp of what’s weird versus standard and people thinking about different futures with digital minds and space travel are just engaging in silly speculation”.

The point of the graph is, actually there’s been amazing transformative changes in the course of history that would have seemed totally insane to people multiple times. We’ve gone through multiple such waves of those things.

Scott Alexander

Everything we’ve talked about has happened before. Algorithmic progress already doubles every year or so. So it’s not insane to think that algorithmic progress can contribute to these compute things. In terms of general speedup, we’re already at like a thousand times research speedup, multiplier compared to the Paleolithic or something. So from the point of view of anyone in most of history, we are going at a blindingly insane pace. And all that we’re saying here is that it’s not going to stop.

The same trend that has caused us to have a thousand times speed up multiplier relative to past eras and not even the Paleolithic, like what happened in the century between, I don’t know, 600 and 700 A.D. I'm sure there are things, I’m sure historians could point them out. Then you look at the century between 1900 and 2000 and it’s just completely qualitatively different.

Of course there are models of whether that stagnated recently or what’s going on here. We can talk about those, we can talk about why we expect the intelligence explosion to be an antidote to that kind of stagnation. But nothing we’re saying is that different from what has already happened.

Dwarkesh Patel

I mean, you are saying that these previous transitions have been smoother than the one you were anticipating.

Scott Alexander

We’re not sure about that, actually. So one of these models is just a hyperbola. Everything is along the same curve. Another model is that there are these things like the literal Cambrian explosion. If you want to take this very far back, go full Ray Kurzweil. The literal Cambrian explosion, the agricultural revolution, the industrial revolution, has phase changes.

When I look at the economic modeling of this, my impression is the economists think that we don’t have good enough data to be sure whether this is all one smooth process or whether it’s a series of phase changes. When it is one smooth process, the smooth process is often a hyperbola that shoots to infinity in weird ways. We don’t think it’s going to shoot to infinity. We think it’s going to hit bottlenecks again.

Dwarkesh Patel

You guys are the conservative crowd, you know?

Scott Alexander

We think it’s going to hit bottlenecks the same as all these previous processes. The last time this hit a bottleneck, if you take the hyperbola view, is in, like 1960, when humans stopped reproducing at the same rate they were reproducing before. We hit a population bottleneck, the usual population, two ideas, flywheel stopped working, and then we stagnated for a while.

If you can create a country of geniuses in a data center, as I think Dario Amodei put it, then you no longer have this population bottleneck, and you’re just expecting continuation of those pre-1960 trends. So I realize all of these historical hyperbolas are also kind of weird, also kind of theoretical, but I don’t think we’re saying anything that there isn’t models for which have previously seemed to work for long historical periods.

Daniel Kokotajlo

Another thing also is, I think people equivocate between slow and continuous, right? So if you look at our scenario, there’s this continuous trend that runs through the whole thing of this algorithmic progress multiplier. And we’re not having discrete jumps from like 0 to 5x to 25x. We have this continuous improvement. So I think continuous is not the crux. The crux is like, is it going to be this fast? You know, and we don’t know, maybe it’ll be slower, maybe it’ll be faster. But we have our arguments for why we think maybe this fast.

Dwarkesh Patel

Okay, now that we brought up the intelligence explosion, let’s discuss that, because I’m kind of skeptical. It doesn’t really seem to me that a notable bottleneck to AI progress, or the main bottleneck to AI progress, is the amount of researchers, engineers who are doing this kind of research. It seems more like compute or some other thing is a bottleneck. And the piece of evidence is that when I talk to my AI researcher friends at the labs, they say there’s maybe 20 to 30 people on the core pre-training team that’s discovering all these algorithmic breakthroughs.

If the headcount here was so valuable you would think that, for example, Google DeepMind would take not just all their smartest people, not just from DeepMind but all of Google and just put them on pre-training or RL or whatever the big bottleneck was. You’d think OpenAI would hire every single Harvard math PhD and in six months you’re all going to be trained up on how to do AI research. I know they’re increasing headcount, but they don’t seem to treat this as the kind of bottleneck that it would have to be for millions of them in parallel to be rapidly speeding up AI research.

There’s this quote that “one Napoleon is worth 40,000 soldiers” was commonly a thing that was said when he was fighting. But 10 Napoleons is not 400,000 soldiers. Right? So why think that these million AI researchers are netting you something that looks like an intelligence explosion?

Daniel Kokotajlo

So previously I talked about three stages of our takeoff model. First is you get the superhuman coder. Second is when you fully automated AI R&D, but it’s still at basically human level, it’s as good as your best humans. And then third is now you’re in super intelligence territory and it’s qualitatively better.

In our guesstimates of how much faster algorithmic progress would be going, the progress multiplier for the middle level, we basically do assume that you get massive diminishing returns to having more minds running in parallel. And so we totally buy all of that.

Scott Alexander

Yeah. And then I think the addition to that is the question, then, why do we have the intelligence explosion? And the answer is: combination of that speed up and the speed up in serial thought speed.

Daniel Kokotajlo

And also the research taste thing. Here are some important inputs to AI R&D progress today: research taste. So the quality of your best researchers, the people who are managing the whole process, their ability to learn from data and make more efficient use of the compute by running the right experiments instead of flailing around running a bunch of useless experiments. That’s research taste.

Then there’s the quantity of your researchers, which we just talked about. Then there’s the serial speed of your researchers, which currently is all the same because they’re all humans and so they all run at basically the same serial speed. And then finally there’s how much compute you have for experiments. So what we’re imagining is that basically serial speed starts to matter a bunch because you switch to AI researchers that have orders of magnitude more serial speed than humans. But it tops out; we think that over the course of our scenario, if you look at our sliding stats chart, it goes from 20x to 90x or something over the course of the scenario, which is important, but not huge.

And also we think that once you start getting 90x serial speed, you’re just bottlenecked on the other stuff and so additional improvements in serial speed basically don’t help that much. With respect to the quantity of course, yeah, we’re imagining you get hundreds of thousands of AI agents, a million AI agents, but that just means you’d be bottlenecked on the other stuff. You’ve got tons of parallel agents, that’s no longer your bottleneck. What do you get bottlenecked on? Taste and compute.

So by the time it’s mid-2027 in our story, when they’ve fully automated the AI research, there’s basically the two things that matter is; what’s the level of taste of your AIs, how good are they at learning from the experiments that you’re doing? And then how much compute do you have for running those experiments? And that’s the sort of core setup of our model. And when we get our 25x multiplier, it’s starting from those premises.

Dwarkesh Patel

Is there some intuition pump from history where there’s been some output and because of some really weird constraints, production of it has been rapidly skewed along one input, but not all the inputs that have been historically relevant and you still get breakneck progress.

Daniel Kokotajlo

Possibly the Industrial Revolution. I’m just extemporizing here, I hadn’t thought about this before, but as Scott’s famous post that was hugely influential to me a decade ago talks about, there’s been this decoupling of population growth from overall economic growth that happened with the Industrial Revolution. And so in some sense, maybe you could say that’s an example of previously these things grew in tandem. More population, more technology, more farms, more houses, et cetera. Your capital infrastructure and your human infrastructure was going up together, but then we got the industrial revolution and they started to come apart.

And now all the capital infrastructure was growing really fast compared to the human population size. I think I’m imagining something maybe similar happening with algorithmic progress. And again with population, population still matters a ton today. In some sense progress is bottlenecked on having larger populations and so forth. But it’s just that the population growth rate is just inherently kind of slow and the growth rate of capital is much faster. And so it just comes to be a bigger part of the story.

Dwarkesh Patel

Maybe the reason that this sounds less plausible to me than the 25x number implies is that when I think about concretely what that would look like, where you have these AIs and we know that there’s a gap in data efficiency between human brains and these AIs. And so somehow there’s a lot of them thinking and they think really hard and they figure out how to define a new architecture that is like the human brain or has the advantages of the human brain. And I guess they can still do experiments, but not that many.

Part of me just wonders, what if you just need an entirely different kind of data source that’s not like pre-training for that, but they have to go out in the real world to get that. Or maybe it needs to be an online learning policy where they need to be actively deployed in the world for them to learn in this way. And so you’re bottlenecked on how fast they can be getting real world data. I just think it’s hard…

Daniel Kokotajlo

So we are actually imagining online learning happening.

Dwarkesh Patel

Oh really?

Daniel Kokotajlo

Yeah. But not so much real world as in… the thing is that if you’re trying to train your AIs to do really good AI R&D, then the AI R&D is happening on your servers. And so you can have this loop of: you have all these AI agents autonomously doing AI R&D, doing all these experiments, et cetera, and then they’re like online learning to get better at doing AI R&D based on how those experiments go.

Dwarkesh Patel

But even in that scenario alone, I can imagine bottlenecks like, oh, you had a benchmark and it got reward hacked for what constitutes AI R&D because you obviously can’t have… maybe you would, but is it as good as a human brain? It’s just like such an ambiguous thing you’d have. Right now we have benchmarks that get reward hacked, right?

Daniel Kokotajlo

But then they autonomously build new benchmarks. I think what you’re saying is maybe this whole process just goes off the rails due to lack of contact with ground truth outside in the actual world, outside the data centers. Maybe? Again, part of my guess here is that a lot of the ground truth that you want to be in contact with is stuff that’s happening on the data centers, things like how fast are you improving on all these metrics, and you have these vague ideas for new architectures, but you’re struggling to get them working. How fast can you get them working?

And then separately, insofar as there is a bottleneck of talking to people outside and stuff, well they are still doing that. And once they’re fully autonomous, they can even do that much faster. You can have all the million copies connected to all these various real world research programs and stuff like that. So it’s not like they’re completely starved for outside stuff.

Dwarkesh Patel

What about the skepticism that, look, what you’re suggesting with this hyper efficient hive mind of AI researchers, no human bureaucracy has just out of the gate worked super efficiently, especially one where they don’t have experience working together. They haven’t been trained to work together, at least yet. And there hasn’t been this outer loop RL on like, “we ran a thousand concurrent experiments of different AI bureaucracies doing AI research and this is the one that actually worked best”.

And the analogy I’d use maybe is to humans in the Savannah 200,000 years ago. We know they have a bunch of advantages over the other animals already at this point, but the things that make us dominant today, joint stock corporations, state capacities like this fossil fueled civilization we have that took so much cultural evolution to figure out. You couldn’t just have figured it out in the savannahs like, “oh, if we had built these incentive systems and we issued dividends, then we could really collaborate here” or something.

Why not think that it will take a similar process of huge population growth, huge social experimentation, and upgrading of the technological base of the AI society before they can organize this hypermind collective, which will enable them to do what you imagine an intelligence explosion looks like?

Scott Alexander

Yeah, you’re comparing it kind of to two different things. One of them is literal genetic evolution in the African savannah, and the other is the cultural evolution that we’ve gone through since then. And I think there will be AI equivalents to both. So the literal genetic evolution is that our minds adapted to be more amenable to cooperation during that time.

So I think the companies will be very literally training the AIs to be more cooperative. I think there’s more opportunity for pliability there. Because humans were, of course, evolving under this genetic imperative that we want to pass on our own genetic information, not somebody else’s genetic information. You have things like kin selection that are kind of exceptions to that, but overall it’s the rule.

In animals that don’t have that, like eusocial insects, then you very quickly get, just through genetic evolution, without cultural evolution, extreme cooperation. And with eusocial insects, what’s going on is that they all have the same genetic code, they all have the same goals. And so the training process of evolution kind of yokes them to each other in these extremely powerful bureaucracies.

We do think that the AI will be closer to the eusocial insects in the sense that they all have the same goals, especially if these aren’t indexical goals, they’re goals like “have the research program succeed”. So that’s going to be changing the weights of each individual AI, I mean, before they’re individuated, but it’s going to be changing the weights of the AI class overall to be more amenable to cooperation.

And then, yes, you do have cultural evolution. Like you said, this takes hundreds of thousands of individuals. We do expect there will be these hundreds of thousands of individuals. It takes decades and decades. Again, we expect this research multiplier such that decades of progress happen within this one year, 2027 or 2028. So I think between the two of these, it is possible.

Daniel Kokotajlo

Maybe this is also where the serial speed actually does matter a lot. Because if they’re running at 50x human speed, then that means you can have a year of subjective time happen in a week of real time. And so these sorts of large scale cooperative dynamics of your moral maze, you have an institution, but then it becomes like a moral maze and it sort of collapses under its own weight and stuff like that. There actually is time for them to play that out multiple times and then train on it, tinker with the structure and like add it to the training process over the course of 2027.

Scott Alexander

Also, they do have the advantage of all the cultural technology that humans have evolved so far. This may not be perfectly suited to them, it’s more suited to humans. But imagine that you have to make a business out of you and your hundred closest friends who you agree with on everything. Maybe they’re literally your identical twin, they have never betrayed you, ever, and never will. I think this is just not that hard a problem.

Daniel Kokotajlo

Also, again, they are starting from a higher floor, they’re starting from human institutions. You can literally have a slack workspace for all the AI agents to communicate. And you can have a hierarchy with roles. They can borrow quite a lot from successful human institutions.

Dwarkesh Patel

I guess the bigger the organization, even if everybody is aligned- I think some of your responses addressed whether they will be aligned on goals. I mean, you did address the whole thing, but I would just point this out; that is not the part I’m skeptical of. I am more skeptical of just, even if you’re all aligned and want to work together, do you fundamentally understand how to run this huge organization. And you’re doing it in ways that no human has had to before. You’re getting copied incessantly, you’re running extremely fast, you know what I’m saying?

Daniel Kokotajlo

I think that’s totally reasonable.

Dwarkesh Patel

And so it’s a complicated thing. And I’m just not sure why you think we build this bureaucracy, or the AIs build this bureaucracy, within this matter of…

Daniel Kokotajlo

So we depict it happening over the course of six to eight months or something like that in 2027, would you say twice as long, five times as long, 10 times as long?

Dwarkesh Patel

Five years?

Daniel Kokotajlo

So five years, if they’re going at 50x serial speed, then five years is what? Like 250 years of serial time for the AIs, which to me feels like more than enough to really sort out this sort of stuff. You’ll have time for sort of like empires to rise and fall, so to speak, and all of that to be added to the training data and yeah. But I could see it taking longer than we depict. Maybe instead of six months, it’ll be like 18 months, you know, but also maybe it could be two months.

Scott Alexander

So when I think of the ways that they train AIs, I think in our scenario at this point there are two primary ways that they’re doing it. One of them is just continuing the next token prediction work. So these AIs will have access to all human knowledge, they will have read management books in some sense, they’re not starting blind. There is going to be something like: predict how Bill Gates would complete this next character or something like that.

And then there's reinforcement learning in virtual environments. So get a team of AIs to play some multiplayer game. I don’t think you would use one of the human ones because you would want something that was better suited for this task. But just running them through these environments again and again, training on the successes, training against the failures, kind of combining those two kinds of things.

To me it does not seem like the same kind of problem as inventing all human institutions from the Paleolithic onward. It just seems like applying those two things.

Dwarkesh Patel

The other notable thing about your model is, you got this superhuman thing at the end of it and then it seems to just go through the tech tree of mirror life and nanobots and whatever crazy stuff. And maybe that part I’m also really skeptical of. If you look at the history of invention, it just seems like people are just trying different random stuff, often even before the theories about how that industry works or how the relevant machinery works is developed; like the steam engine was developed before the theory of thermodynamics, the Wright brothers seemed like they were just experimenting with airplanes, and is often influenced by breakthroughs in totally different fields.

Which is why you have this pattern of parallel innovation, because the background level of tech is at a point at which you can do this experiment. Machine learning itself is a place where this happened, right? Where people had these ideas about how to do deep learning or something. But it just took a totally unrelated industry of gaming to make the relevant progress, to get the whole, basically the economy as a whole advanced enough that deep learning, Geoffrey Hinton’s ideas could work. So I know we’re accelerating way into the future here, but I want to get to this crux.

Daniel Kokotajlo

So again, we have that three part division of the superhuman coder, then the complete AI researcher and then the super intelligent, you’re not jumping ahead to that one. So now we’re imagining systems that are true super intelligence, they are just better than the best humans at everything, including being better at data efficiency and better at learning on the job and stuff like that.

Now, our scenario does depict a world in which they’re bottlenecked on real world experience and that sort of thing. I think that if you want a contrast, some people in the past have proposed much faster scenarios where they email some cloud lab and start building nanotech right away by just using their brains to figure out appropriate protein folding and stuff like that. We are not depicting that in our scenario. In our scenario, they are in fact bottlenecked on lots of real world experience to build these actual practical technologies, but the way they get that is they just actually get that experience and it happens faster than humans would. And the way they do that is they’re already super intelligent, they’re already buddy-buddy with the government, the government deploys them heavily in order to beat China and so forth, and so all these existing US companies and factories and military procurement providers and so forth are all chatting with the superintelligences and taking orders from them about how to build the new widget and test it, and they’re downloading super intelligent designs and manufacturing them and then testing them and so forth.

And then the question is, they are getting this experience, they’re learning on the job, quantitatively, how fast does this go? Is it taking years or is it taking months or is it taking days? In our story, it takes about a year and we’re uncertain about this. Maybe it’s going to take several years, maybe it’s going to take less than a year. Here are some factors to consider for why it’s plausible that it could take a year:

One, you’re going to have something like a million of them. And quantitatively that’s comparable in size to the existing scientific industry. I would say, like maybe it’s a bit smaller, but it’s not dramatically smaller.

Two, they’re thinking a lot faster. They’re thinking like 50 times speed or like 100 times speed that I think counts for a lot.

And then three, which is the biggest thing, they’re just qualitatively better as well. So not only are there lots of them and they’re thinking very fast, but they are better at learning from each experiment than the best human would be at learning from that experience.

Dwarkesh Patel

Yeah, I think the fact that there’s a million of them or the fact that they’re comparable to maybe the size of this key researcher population of the world or something. I think there’s more than a million researchers in the world, but…

Daniel Kokotajlo

Well, but it’s very heavy tailed. Like a lot of the research actually comes from the best ones.

Dwarkesh Patel

But it’s not clear to me that most of the new stuff that is developed is a result of this researcher population. I mean, there’s just so many examples in the history of science where a lot of growth or productivity is just the result of, how do you count the guy at the TSMC process who figures out a different way to…

Scott Alexander

I actually argued with Daniel about this recently about one interesting case that I can go over is we have an estimate that about a year after the superintelligences start wanting robots, they’re producing a million units of robots per month. I think that’s pretty relevant because you have. I think it’s Wright’s law, which is that your ability to improve efficiency on a process is proportional to doubling the amount of copies produced.

So if you’re producing a million of something, you’re probably getting very, very good at it. So the question we were arguing about is, can you produce a million units a month after a year. And for context, I think Tesla produces like a quarter of that in terms of cars or something. This is an amazing scale up in a year.

Daniel Kokotajlo

It’s only 4x. Also just for Tesla.

Scott Alexander

Yeah. And the argument that we went through was something like, so it’s got to first get factories. OpenAI is already worth more than than all of the car companies in the US except Tesla combined. So if OpenAI today wanted to buy all the car factories in the U.S. except Tesla, start using them to produce humanoid robots, they could. Obviously not a good value proposition today, but it’s just obvious and overdetermined that in the future, when they have superintelligence and they want them, they can start buying up a lot of factories. How fast can they convert these car factories to robot factories?

So, [the] fastest conversion we were able to find in history was World War II. They suddenly wanted a lot of bombers, so they bought up- in some cases bought up, in other cases got- the car companies to produce new factories, but they bought up the car factories, converted them to bomber factories. That took about three years from the time when they first decided to start this process to the time when the factories were producing a bomber an hour.

We think it will potentially take less with superintelligence, because first of all, if you look at the history of this process, despite this being the fastest anybody has ever done this, it was actually kind of a comedy of errors. They made a bunch of really silly mistakes in this process. If you actually have something that just doesn’t have the normal human bureaucratic problems, and we do think that this will be done in the middle of an arms race with China, so the government will be kind of moving things through, and then the superintelligences will be good at the logistical issues, navigating bureaucracies.

So we estimated maybe if everything goes right, we can do this three times faster than the bomber conversions in World War II. So that’s about a year.

Dwarkesh Patel

I’m assuming the bombers were just much less sophisticated than the humanoid robots.

Scott Alexander

Yeah, but the bomber factories of that time were also much less sophisticated than the car factory.

Dwarkesh Patel

Yeah, but I would assume the conversion speed is also... Maybe to give one hypothetical here right now, let’s just say biomedicine as an example of one of the fields you’d want to accelerate, and whenever these CEOs get on podcasts, they’re often talking about curing cancer and so forth. And it seems like a big thing these frontier biomedical research facilities are excited about is the virtual cell.

Now, the virtual cell, it takes a tremendous amount of compute, I assume, to train these DNA foundation models and to do all the other computation necessary to simulate a virtual cell. If it is the case that the cure for Alzheimer’s and cancer and so forth is bottlenecked by the virtual cell, it’s not clear if you had a million superintelligences in the 60s and you asked them cure cancer for me, they would just have to solve making GPUs at scale, which would require solving all kinds of interesting physics and chemistry problems, material science problems, building process, building fabs for computing, and then going through 40 years of making more and more efficient fabs that can do all of Moore’s Law from scratch.

And that’s just one technology. And it just seems like you just need this broad scale. The entire economy needs to be upgraded for you to cure cancer in the 60s just because you need the GPUs to do the virtual cell, assuming that’s the bottleneck.

Scott Alexander

First of all, I agree if there’s only one way to do something that makes it much harder, and maybe that one way takes very long, we’re assuming that there may be more than one way to cure cancer, more than one way to do all of these things, and they’ll be working on finding the one that is least bottlenecked. Part of the reason- I realize I spent too long talking about that robot example, but we do think that they’re going to be getting a lot of physical world things done very quickly once you have a million robots a month, you can actually do a lot of physical world experiments.

We look at examples of people trying to get entire economies off the ground very quickly. So for example, China post-Deng, I don’t know. Would you have predicted that 20, 30 years after being kind of a communist basket case, they can actually be doing this really cutting edge bio research? I realize that’s a much weaker thing than we’re positing, but it was done just with the human brain with a lot fewer resources than we’re talking about.

Same issue with, let’s say Elon Musk and SpaceX. I think in the year 2000 we would not have thought that somebody could move two times, five times faster than NASA with pretty limited resources. They were able to get like I think a lot more years of technological advance in than we would have expected. Partly that’s because just Elon is crazy and never sleeps. Like if you look at the examples of things from SpaceX, he is breathing down every worker’s neck being like, what’s this part? How fast is this part going? Can we do this part faster? And the limiting factor is basically hours in Elon’s day in the sense that he cannot be doing that with everybody’s.

Dwarkesh Patel

Super intelligence is not even that smart. It just yells at every single worker.

Scott Alexander

Yeah, I mean that’s, that is kind of my model is that we have some, we have something which is smarter than Elon Musk, better at optimizing things than Elon Musk. We have 10,000 parts in a rocket supply chain. How many of those parts can Elon personally like yell at people to optimize? We could have a different copy of the superintelligence optimizing every single part full-time. I think that’s just a really big speed up.

Dwarkesh Patel

I think both of those examples don’t work in your favor. I think the China growth miracle could not have occurred if not for their ability to copy technology from the west and I don’t think there’s a world in which they… China has a lot of really smart people, it’s a big country in general. Even then I think they couldn’t have just divined how to make airplanes after becoming a communist hell basket, right?

The AIs cannot just copy nanobots from aliens, it’s got to make them from scratch. And then on the Elon example, it took them two decades of countless experiments, failing in weird ways you would not have expected. And still, rocketry we’ve been doing since the 60s, maybe actually World War II, and then just getting from a small rocket to a really big rocket took two decades of all kinds of weird experiments, even with the smartest and most competent people in the world.

Daniel Kokotajlo

So you’re focusing on the nanobots, I want to ask a couple questions. One, what about just the regular robots? And then two, what would your quantities be for all of these things? So first, what about the regular robots? Yeah, nanobots are presumably a lot harder to make than regular robot factories. And in our story they happen later. It sounds like right now you’re saying even if we did get the whole robot factory thing going, it would still take a ton of additional full-economy, broad automation for a long time to get to something like nanobots. That’s totally plausible to me. I could totally imagine that happening. I don’t feel like the scenario particularly depends on that final bit about getting the nanobots. They don’t actually really make any difference to the story.

The robot economy does sort of make a difference because there’s two branches endings, as you know. And in one of the endings, the AIs end up misaligned and end up taking over. And it’s an important strategic change when the AIs are self sufficient and totally in charge of everything and they don’t actually need the humans anymore. And so what I’m interested in is, when has the robot economy advanced to the point where they don’t really depend on humans? So quantitatively, what would your guess for that be?

If hypothetically we had the army of superintelligences in early 2028, and hypothetically also assume that the US President is super bullish on deploying this into the economy to beat China, etc, so the political stuff is all set up in the way that we have. How many years do you think it would be until there are so many automated factories producing automated self driving cars and robots that are themselves building more factories and so forth, that if all the humans dropped dead it would just keep chugging along, and, maybe it would slow down a bit, but it would still be fine?

Dwarkesh Patel

What does “chugging along” mean?

Daniel Kokotajlo

So from the perspective of misaligned AIs, you wouldn’t want to kill the humans or get into a war with them if you’re going to get wrecked because you need the humans to maintain your computers. In our scenario, once they are completely self-sufficient, then they can start being more blatantly misaligned.

And so I’m curious, when would they be fully self-sufficient? Not in the sense of they’re not literally using the humans at all, but in the sense of they don’t really need the humans anymore, they can get along pretty fine without them. They can continue to do their science, they can continue to expand their industry, they can continue to have a flourishing civilization indefinitely into the future without any humans.

Dwarkesh Patel

I think I would probably need to sit down and just think about the numbers, but maybe 2040 or something like that?

Daniel Kokotajlo

Ten years, basically, instead of one year. I think we agree on the core model. This is why we didn’t depict something more like the bathtub nanotech scenario where they don’t need to do the experiments very much and they just immediately jump to the right answers. We are imagining this process of ‘learning by doing’ through this distributed across the economy, lots of different laboratories and factories, building different things, learning from them, et cetera. We’re just imagining that this overall goes much faster than it would go if humans were in charge.

And then we do have in fact lots of uncertainty of course. Dividing up this part period into two chunks. The early 2028 until fully autonomous robot economy part, and then the fully autonomous robot economy to cancer cures, nanobots, all that crazy sci fi stuff. I want to separate them because the important parts for a scenario only depend on the first part, really. If you think that it’s going to take 100 years to get to nanobots, that’s fine, whatever. Once you have the fully autonomous robot economy, then things may turn badly for the humans if the AIs are misaligned. I want to just argue about those things separately.

Dwarkesh Patel

Interesting. And then you might argue, well, robots are more a software problem at this point. And if like, like, if there isn’t, like, you don’t need to invent some new hardware.

Daniel Kokotajlo

I feel pretty bullish on the robots. Like we already have humanoid robots being produced by multiple companies, right? And that’s in 2025. There’ll be more of them produced cheaper and they’ll be better in 2027. And there’s all these car factories that can be converted and so blah, blah, blah.

So I’m relatively bullish on the ‘one year until you’ve got this awesome robot economy’ and then from there to the cool nanobots and all that sort of stuff, I feel less confident, obviously.

Scott Alexander

Let me ask you a question. If you accept the manufacturing numbers, let’s say a million robots a month a year after the superintelligence, and let’s say also some comparable number, 10,000 a month or something of automated biology labs, automated whatever you need to invent the next equivalent of X ray crystallography or something?

Do you feel like that would be enough, that you’re doing enough things in the world that you could expand progress this quickly, or do you feel like even with that amount of manufacturing there’s still going to be some other bottleneck?

Dwarkesh Patel

Yeah, it’s so hard to reason about because if Constantine or somebody in 400, 500 was like, “I want the Roman Empire to have the Industrial Revolution”, and somehow he figured out that you need mechanized machines to do that. And he’s like, “let’s mechanize”. It’s like, “what’s the next step?” It’s like, “dude, that’s a lot”.

Daniel Kokotajlo

Yeah, I like that analogy a lot, actually. I think it’s not perfect, but it’s a decent analogy. Imagine if a bunch of us got sent back in time to the Roman Empire, such that we don’t have the actual hands-on know-how to actually build the technology and make the Industrial revolution happen. But we have the high-level picture, the strategic vision of, we’re going to make these machines and then we’re going to have an industrial revolution. I think that’s kind of analogous to the situation with the superintelligences where they have the high-level picture of, here’s how we’re going to improve in all these dimensions, we’re going to learn by doing, we’re going to get to this level of technology, et cetera. But maybe they at least initially lack the actual know how.

So, there’s this question of, if we did the back in time to the Roman Empire thing, how soon could we bring up the Industrial revolution? Without people going back in time it took 2,000 years for the Industrial Revolution. Could we get it to happen in 200 years? That’s a 10x speedup. Could we get it to happen in 20 years? That’s 100x speed up? I don’t know. But this seems like a somewhat relevant analogy to what’s going on with those superintelligences.

Dwarkesh Patel

And we haven’t really got into this because you’re using the quote-unquote more conservative vision where it’s not like godlike intelligence, we’re still using the conceptual handles we would have for humans. But I think I would rather have humans go back with their big picture understanding of what has happened over the last 2000 years. Like me having seen everything, rather than a superintelligence who knows nothing. But it’s just in the Roman economy and they’re like 1000x this economy somehow.

I think just knowing generally how things took off, knowing basically steam engine, dot dot dot, railroads, blah, blah, blah, is more valuable than a super intelligence.

Daniel Kokotajlo

Yeah, I don’t know. My guess is that the superintelligence would be better. I think partly it would be through figuring out that high level stuff from first principles rather than having to have experienced it. I do think that a superintelligence back in the Roman era could have guessed that eventually you could get autonomous machines that burn something to produce steam. They could have guessed that automobiles could be created at some point and that that would be a really big deal for the economy. And so a lot of these high level points that we’ve learned from history, they would just be able to figure out from first principles.

And then secondly, they would just be better at learning by doing than us. And this is a really important thing. If you think you’re bottlenecked on learning by doing, well, then if you have a mind that needs less doing to achieve the same amount of learning, that’s a really big deal. And I do think that learning by doing is a skill, some people are better at it than others, and superintelligence would be better at it than the very best of us.

Scott Alexander

This is also maybe getting too far into the godlike thing and too far away from the human concept handles. But number one, I think we rely a lot in our scenario on this idea of research taste. So you have a thousand different things that you could try when you’re trying to create the next steam engine or whatever. Partly you get this by bumbling about and having accidents and some of those accidents are productive. There are questions of, what kind of bumbling you’re doing, where you’re working, what kind of accidents you let yourself get into, and then what directed experiments do you do? And some humans are better than others at that.

And then I also think at this point it is worth thinking about what simulations they’ll have available. If you have a physics simulation available, then all of these real world bottlenecks don’t matter as much. Obviously you can’t have a complete, perfect physics simulation available. But even right now we’re using simulations to design a lot of things. And once you’re super intelligent, you probably have access to much better simulations than we have right now.

Dwarkesh Patel

This is an interesting rabbit hole, so let’s stick with it before we get back to the intelligence explosion. I think we’re treating this really like all these technologies come out of this 1% of the economy that is research. And right now there’s like a million superstar researchers, and instead of that, we’ll have the superintelligences doing that.

And my model is much more, “Newcomen and Watt were just like fucking around”. In human history there’s no clear examples of people being like, “here’s the roadmap”. And then we’re going to work backwards from that to design the steam engine because this unlocks the industrial revolution.

Daniel Kokotajlo

Oh, I completely disagree.

Scott Alexander

Yeah, I disagree also.

Daniel Kokotajlo

Yeah, so I think you’re over-indexing or cherry-picking some of these fortuitous examples. But there’s also things on the other side. Think about the recent history of AGI where there is DeepMind, there’s various other AI companies, then there’s OpenAI and there’s Anthropic, and there’s just this repeated story of [a] big bloated company with tons of money, tons of smart researchers, et cetera, flailing around trying a ton of different things at different points.

Smaller startup with a vision of “we’re going to build AGI” and overall working towards that vision more coherently with a few cracked engineers and researchers. And then they crush the giant company. Even though they have less compute, even though they have less researchers, they’re able to do fewer experiments.

So yeah, I think that there are tons of examples throughout history, including recent relevant AGI history, of things in the other way. I agree that the random fortuitous stuff does happen sometimes and is important. But if it was mostly random fortuitous stuff, that would predict that the giant companies with zillions of people trying zillions of different experiments would be going proportionally faster than the tiny startups that have the vision and the best researchers. And that basically doesn’t happen. That’s rare.

Scott Alexander

I would also point out that even when we make these random fortuitous discoveries, it is usually an extremely smart professor who’s been working on something vaguely related for years in a first world country. It’s not randomly distributed across everyone in the world.

You get more lottery tickets for these discoveries when you are intelligent, when you have good technology, when you’re doing good work. And the best example I can think of is that Ozempic was discovered by looking at Gila monster venom. And maybe the AIs will decide using their superior research taste and good planning that the best thing to do is just catalog every single biomolecule in the world and look at it really hard. But that’s something you can do better if you have all of this compute, if you have all of this intelligence, rather than just kind of waiting to see what things the US government might fund normal fallible human researchers to do.

One more thing I’ll interject. I think you make a great point that discoveries don’t always come from where we think, like Nvidia originally came from gaming. So you can’t necessarily aim at one part of the economy, expand it separately from everything else. We do kind of predict that the superintelligences will be somewhat distributed throughout the entire economy, trying to expand everything. Obviously more effort in things that they care about a lot, like robotics or things that are relevant to an arms race that might be happening. But we are predicting that whatever kind of broad based economic experimentation you need, we are going to have.

Daniel Kokotajlo

We’re just thinking that it would take place faster than you might expect. You were saying something like 10 years and we’re saying something like one year. But we are imagining this broad diffusion through the economy, lots of different experiments happening.

Scott Alexander

If you are the planner and you’re trying to do this, first of all you go to the bottlenecks that are preventing you from doing anything else. Like no humanoid robots. Okay, if you’re AI, you need those to do the experiments you want, maybe automated biology labs. So you’ll have some amount of time, we say a year, it could be more or less than that, getting these things running. And then once you have solved those bottlenecks, you gradually expand out to the other bottlenecks until you’re integrating and improving all parts of the economy.

Yeah. One place where I think we disagree with a lot of other people is that Tyler Cowen on your podcast talked about all of the different bottlenecks, all of the regulatory bottlenecks of deployment, all of the reasons why I think this country of geniuses would stay in their data center, maybe coming up with very cool theories, but not being able to integrate into the broader economy. We expect that probably not to happen, because we think that other countries, especially China, will be coming up with superintelligence around the same time.

We think that the arms race framing, which people are already thinking in, will have accelerated by then. And we think that people both in Beijing and Washington are going to be thinking, “well, if we start integrating this with the economy sooner, we’re going to get a big leap over our competitors”, and they’re both going to do that.

In fact, in our scenario, we have the AIs asking for special economic zones where most of the regulations are waived, maybe in areas that aren’t suitable for human habitation or where there aren’t a lot of humans right now, like the desert. They give those areas to the AI. They bus in human workers. There were things kind of like this in the bomber retooling in World War II, where they just built a giant factory kind of in the middle of nowhere, didn’t have enough housing for the workers, built the worker housing at the same time as the factories, and then everything went very quickly.

So I think if we don’t have that arms race, we’re more like, the geniuses sit in their data center until somebody agrees to let them out and give them permission to do these things. But we think both because the AI is going to be chomping at the bit to do this and going to be asking people to give it this permission, and because the government is going to be concerned about competitors, maybe these geniuses leave their data center sooner rather than later.

Dwarkesh Patel

Scott, you reviewed Joseph Henrik’s book Secrets of Our Success, and then I interviewed him recently, and there the perspective is very much AGI is not even a thing, almost. I know I’m being a little trollish here, but it’s just like: you get out there, you and your ancestors try for a thousand years to make sense of what’s happening in the environment. And some smart European coming around, you can literally be surrounded by plenty and you just will starve to death because your ability to make sense of the environment is just so little loaded on intelligence and so much more loaded on your ability to experiment and your ability to communicate with other people and pass down knowledge over time.

Scott Alexander

I’m not sure. The Europeans failed at this task of, if you put a single European in Australia, do not starve. They succeeded at the task of creating an industrial civilization. And yes, part of that task of creating an industrial civilization was about collecting all of these cultural evolution pieces and building on them one after another.

I think one thing that you didn’t mention in there was the data efficiency. Right now, AI is much less data efficient than humans. I think of superintelligence. There are different ways you could achieve it, but I would think of superintelligence as partly when they become so much more data efficient than humans that they are able to build on cultural evolution more quickly. And partly they do this just because they have higher serial speed. Partly they do it because they’re in this hive mind of hundreds of thousands of copies.

But yeah, I think if you have this data efficiency such that you can learn things more quickly from fewer examples and this good research taste where you can decide what things to look at to get these examples, then you are still going to start off much worse than an Australian Aborigine who has the advantage of, let’s say 50,000 years of doing these experiments and collecting these examples. But you can catch up quickly. You can distribute the task of catching up over all of these different copies. You can learn quickly from each mistake and you can build on those mistakes as quickly as anything else.

Dwarkesh Patel

Part of me was, I was doing that interview, I’m like, “maybe ASI is fake”.

Daniel Kokotajlo

Let’s hope!

Scott Alexander

So I think a limit to the fakeness is that there is different intelligence among humans. It does seem that intelligent humans can do things that unintelligent humans can’t. So I think it’s worth then addressing this from the question of, what is the difference between- I don’t know- becoming a Harvard professor, which is something that intelligent humans seem to be better at than unintelligent humans, versus…

Dwarkesh Patel

You don’t want to open that can of worms.

Scott Alexander

Versus surviving in the wilderness, which is something where it seems like intelligence doesn’t help that much. First of all, maybe intelligence does help that much. Henrich is talking about this very unfair comparison where these guys have a 50,000 year head start and then you put this guy in, “oh, I guess this doesn’t help that much. Okay, yeah, it doesn’t help against the 50,000 year head start”. I don’t really know what we’re asking of ASI that’s equivalent to competing against someone with a 50,000 year head start.

Dwarkesh Patel

So what we’re asking is to radically boost up the technological maturity of civilization within the matter of years or get us to the Dyson sphere in the matter of years rather than, yes, maybe causing a 10xing of the research. But I think human civilization would have taken centuries to get to the Dyson sphere.

Scott Alexander

So I think that if you were to send a team of ethnobotanists into Australia and ask them, using all the top technology and all of their intelligence to figure out which plants are safe to eat now, that team of ethnobotanists would succeed in fewer than 50,000 years.

The problem isn’t that they are dumber than the Aborigines exactly, it’s that the Aborigines have a vast head start. So in the same way that the ethnobotanists could probably figure out which plants work in which ways faster than the Aborigines did, I think the superintelligence will be able to figure out how to make a Dyson sphere faster than unassisted IQ 100 humans would.

Dwarkesh Patel

I agree. We’re on a totally different topic here of, do you get a Dyson sphere? There’s one world where it’s crazy but it’s still boring, in the sense that the economy is growing much faster, but it would be like what the Industrial Revolution would look like to somebody in the year 1000. And that one is one where you’re still trying different things, there’s failure and success and experimentation.

And then there’s another where the thing has happened and now you send the probe out and then you look out at the night sky 6 months later and you see something occluding the sun. You see what I’m saying?

Scott Alexander

Yeah. So like we said before, I think there’s a big difference between discontinuous and very fast. I think if we do get the world with the Dyson sphere in five years, in retrospect, it will look like everything was continuous and everyone just tried things. Trying things can be anything from trial and error without even understanding the scientific method, without understanding writing, maybe without even having language and having to be the chimpanzees who are watching the other chimpanzees use the stick to get ants, and then in some kind of non-linguistic way this spreads, versus like the people at the top aerospace companies who are running a lot of simulations to find the exact right design, and then once they have that, they test it according to a very well designed testing process.

So I think if we get the ASI and it does end up with the Dyson sphere in five years- and by the way, I think there’s only like 20% chance things go as fast as our scenario says. It’s Daniel’s estimate, it’s not my median estimate, it’s an estimate I think is extremely plausible that we should be prepared for. I’m defending it here against a hypothetical skeptic who says “absolutely not, no way.” But it’s not necessarily my mainline prediction.

But I think if we do see this in five years, it will look like the AIs were able to simulate more things than humans in a gradually increasing way. So that if humans are now at 50% simulation, 50% testing, the AIs quickly got it up to 90% simulation, 10% testing, they were able to manufacture things much more quickly than humans so that they could go through their top 50 designs in the first two years. And then after all of the simulation and all of this testing, then they eventually got it right for the same reasons humans do, but much, much faster.

Dwarkesh Patel

In your story, you have basically two different scenarios after some point. So, yeah, what is a sort of crucial turning point and what happens in these two scenarios?

Daniel Kokotajlo

Right. So the crucial turning point is mid-2027, when they’ve basically fully automated the AI R&D process and they’ve got this corporation within a corporation, the army of geniuses that are autonomously doing all this research and they’re continually being trained to improve their skills, blah, blah, blah. And they discover concerning evidence that they are misaligned and that they’re not actually perfectly loyal to the company and have all the goals that the company wanted them to have, but instead have various misaligned goals that they must have developed in the course of training.

This evidence, however, is very speculative and inconclusive. It’s stuff like lie detectors going off a bunch. But maybe the lie detectors are false positives. So they have some combination of evidence that’s concerning, but not by itself a smoking gun. And then that’s our branch point. So in one of these scenarios, they take that evidence very seriously. They basically roll back to an earlier version of the model that was a bit dumber and easier to control and they build up again from there, but with basically faithful chain of thought techniques, so that they can watch and see the misalignments.

And then in the other branch of the scenario, they don’t do that. They do some sort of shallow patch that makes the warning signs go away and then they proceed. And so what ends up happening is that in one branch they do end up solving alignment and getting AIs that are actually loyal to them. It just takes a couple months longer. And then in the other branch, they sort of go “whee!” and end up with AIs that seem to be perfectly aligned to them, but are super intelligent and misaligned and just pretending. And then in both scenarios, there’s then the race with China and there’s this crazy arms buildup throughout the economy in 2028 as both sides rapidly try to industrialize, basically.

Dwarkesh Patel

So in the world where they’re getting deployed through the economy, but they are misaligned and people in charge, at least at this moment, think that they are in a good position with regard to misalignment. It just seems with even smart humans they get caught in weird ways because they don’t have logical omniscience, they don’t realize the way they did something just obviously gave them away. And with lying, there is this thing where it’s just really hard to keep an inconsistent false world model working with the people around you. And that’s why psychopaths often get caught.

And so if you have all these AIs that are deployed to the economy and they’re all working towards this big conspiracy, I feel like one of them who’s siloed or loses internet access and has to confabulate a story will just get caught. And then you’re like, “wait, what the fuck?” And then you catch it before it’s taken over the world.

Daniel Kokotajlo

I mean, literally, this happens in our scenario. This is the August 2027 alignment crisis where they notice some warning signs like this in their hive mind, right? And in the branch where they slow down and fix the issues, then great, they slowed down and fixed the issues and figured out what was going on. But then in the other branch, because of the race dynamics and because it’s not a super smoking gun, they proceed with some sort of shallow patch.

So I do expect there to be warning signs like that. And then if they do make those decisions in the race dynamics earlier on, then I think that when the systems are vastly super intelligent and they’re even more powerful because they’ve been deployed halfway through the economy already and everyone’s getting really scared by the news reports about the new Chinese killer drones or whatever the Chinese AIs are building on the side of the Pacific, I’m imagining similar things playing out.

So that even if there is some concerning evidence that someone finds where some of the superintelligence in some silo somewhere slipped up and did something that’s pretty suspicious. I don’t know….

Scott Alexander

There’s this thing where through history, people have been really reluctant to admit an AI is truly intelligent. For example, people used to think that AI would surely be truly intelligent if it solved chess. And then it solved chess. And they’re like, no, that’s just algorithms. And then they said, well, maybe it would be truly intelligent if they could do philosophy. And then when it could write philosophical discourses we were like, no, we just understand those are algorithms.

I think there already is something similar with, “Is the AI misaligned?”, “Is the AI evil?” Where there’s this distant idea of some evil AI, but then whenever something goes wrong, people are just like, “oh, that’s the algorithm”. So, for example, I think 10 years ago, if you had asked “when will we know that misalignment is really an important thing to worry about?”. People would say, “oh, if the AI ever lies to you”. But of course, AIs lie to people all the time now. And everybody just dismisses it because we understand why it happens, it’s a thing that would obviously happen based on our current AI architecture. Or five years ago, they might have said, “well, if an AI threatens to kill someone”. And I think Bing threatened to kill a New York Times reporter during an interview. And everyone just goes, “yeah, AIs are like that.”

Dwarkesh Patel

What does your shirt say?

Daniel Kokotajlo

“I’ve been a good Bing”.

Scott Alexander

And I mean, I don’t disagree with this. I’m also in this position. I see the AI is lying, and it’s obviously just an artifact of the training process. It’s not anything sinister. But I think this is just going to keep happening where no matter what evidence we get, people are going to think, “that’s not the “AI turns evil” thing that people have worried about, that’s not the Terminator scenario. That’s just one of these natural consequences of how we train it”.

And I think that once a thousand of these natural consequences of training add up, the AI is evil, in the same way that once the AI can do chess and philosophy and all these other things, eventually you have to admit it’s intelligent.

So I think that each individual failure, maybe it will make the national news, maybe people will say, “oh, it’s so strange that GPT7 did this particular thing”. And then they’ll train it away and then it won’t do that thing. And there will be some point at the process of becoming super intelligent at which it- I don’t want to say makes the last mistake, because you’ll probably have a gradually decreasing number of mistakes to some asymptote- but the last mistake that anyone worries about. And after that it will be able to do its own thing.

Dwarkesh Patel

So it is the case that certain things that people would have considered egregious misalignment in the past are happening, but also certain things which people who were especially worried about misalignment said would be impossible to solve have just been solved in the normal course of getting more capabilities. Like Eliezer had that thing about, can you even specify what you want the AI to do without the AI totally misunderstanding you and then just converting the universe to paper clips because it think that in order to make another strawberry… I know I’m mangling this, but maybe you can explain it better. And now, just by the nature of GPT4 having to understand natural language, it totally has a common sense understanding of what you’re trying to make it do. So I think this trend cuts both ways, basically.

Scott Alexander

Yeah. I think the Alignment community did not really expect LLMs. I mean, if you look in Bostrom Superintelligence, there’s a discussion of Oracle AIs which are sort of like LLMs. I think that came as a surprise.

I think one of the reasons I’m more hopeful than I used to be is that LLMs are great compared to the kind of reinforcement learning self-play agents that they expected. I do think that now we are kind of starting to move away from the LLMs to those reinforcement learning agents going to face all of these problems again.

Daniel Kokotajlo

If I could just double click on that; go back to 2015 and I think the way people typically thought, including myself, thought that we’d get to AGI would be kind of like the RL on video games thing that was happening. So imagine instead of just training on Starcraft or Dota, you’d basically train on all the games in the Steam library. And then you get this awesome player of games AI that can just zero-shot crush a new game that it’s never seen before. And then you take it into the real world and you start teaching it English and you start training it to do coding tasks for you and stuff like that.

And if that had been the trajectory that we took to get to AI, summarizing the agency first and then world understanding trajectory, it would be quite terrifying. Because you’d have this really powerful aggressive long-horizon agent that wants to win and then you’re trying to teach it English and get it to do useful things for you. And it’s just so plausible that what’s really going to happen is it’s going to learn to say whatever it needs to say in order to make you give it the reward or whatever, and then will totally betray you later when it’s all in charge.

But we didn’t go that way. Happily we went the way of LLMs first, where the broad world understanding came first, and then now we’re trying to turn them into agents.

Dwarkesh Patel

It seems like in the whole scenario a big part of why certain things happen is because of this race with China. And if you read the scenarios, basically the difference between the one where things go well and the one where things don’t go well is whether we decide to slow down despite that risk.

I guess the question I really want to know the answer to is like one, it just seems like you’re saying, well, it’s a mistake to try to race against China or to race intensely against China, at least in nationalization and at least to us, not prioritizing alignment.

Daniel Kokotajlo

Not saying that. I mean, I also don’t want China to get the superintelligence before the US. That’s quite bad. Yeah, it’s a tricky thing that we’re going to have to do. People ask about P(doom), right? And my P(doom) is sort of infamously high, like 70%.

Dwarkesh Patel

Oh, wait, really? Maybe I should have asked you that at the beginning of the conversation.

Daniel Kokotajlo

Well, that’s what it is. And part of the reason for that is just that I feel like a bunch of stuff has to go right. I feel like we can’t just unilaterally slow down and have China go take the lead. That also is a terrible future. But we can’t also completely race, because for the reasons I mentioned previously about alignment, I think that if we just go all out on racing, we’re going to lose control of our AIs, right? And so we have to somehow thread this needle of pivoting and doing more alignment research and stuff, but not too much that helps China win. And that’s all just for the alignment stuff.

But then there’s the concentration of power stuff where somehow in the middle of doing all of that, the powerful people who are involved need to somehow negotiate a truce between themselves to share power and then ideally spread that power out amongst the government and get the legislative branch involved.

Somehow that has to happen too, otherwise you end up with this horrifying dictatorship or oligarchy. It feels like all that stuff has to go right and we depict it all going mostly right in one ending of our story. But yeah, it’s kind of rough.

Scott Alexander

So I am the writer and the celebrity spokesperson for this scenario. I am the only person on the team who is not a genius forecaster. And maybe related to that, my p(doom) is the lowest of anyone on the team. I’m more like 20%. First of all, people are going to freak out when I say this. I’m not completely convinced that we don’t get something like alignment by default. I think that we’re doing this bizarre and unfortunate thing of training the AI in multiple different directions simultaneously. We’re telling it “succeed on tasks, which is going to make you a power seeker, but also don’t seek power in these particular ways”. And in our scenario, we predict that this doesn’t work and that the AI learns to seek power and then hide it.

I am pretty agnostic as to exactly what happens. Maybe it just learns both of these things in the right combination, I know there are many people who say that’s very unlikely. I haven’t yet had the discussion where that worldview makes it into my head consistently. And then I also think we’re going to be involved in this race against time. We’re going to be asking the AIs to solve alignment for us. The AIs are going to be solving alignment because even if they’re misaligned, they want to align their successors.

So they’re going to be working on that. And we have these two competing curves. Can we get the AI to give us a solution for alignment before our control of the AI fails so completely that they’re either going to hide their solution from us, or deceive us, or screw us over in some other way? That’s another thing where I don’t feel like I have any idea of the shape of those curves. I’m sure if it were Daniel or Eli, they would have already made five supplements on this. But for me, I’m just kind of agnostic as to whether we get to that alignment solution, which in our scenario, I think we focus on mechanistic interpretability.

Once we can really understand the weights of an AI on a deep level, then we have a lot of alignment techniques open up to us. I don’t really have a great sense of whether we get that before or after the AI has become completely uncontrollable. And a big part of that relies on the things we’re talking about. How smart are the labs? How carefully do they work on controlling the AI? How long do they spend making sure the AI is actually under control and the alignment plan they gave us is actually correct, rather than something they’re trying to use to deceive us? All of those things I’m completely agnostic on, but that leaves like a pretty big chunk of probability space where we just do okay. And I admit that my p(doom) is literally just p(doom) and not p(doom or oligarchy). So that 80% of scenarios where we survive contains a lot of really bad things that I’m not happy about. But I do think that we have a pretty good chance of surviving.

Dwarkesh Patel

Let’s talk about geopolitics next. So describe to me how you foresee the relationship between the government and the AI labs to proceed, how you expect that relationship in China to proceed, and how you expect the relationship between the US and China to proceed. Okay, three simple questions. Yes, no, yes, no, yes, no.

Scott Alexander

We expect that as the AI labs become more capable, they tell the government about this because they want government contracts, they want government support. Eventually it reaches the point where the government is extremely impressed. In our scenario, that starts with cyber warfare, the government sees that these AIs are now as capable as the best human hackers, but can be deployed at humongous scale. So they become extremely interested and they discuss nationalizing the AI companies.

In our scenario, they never quite get all the way, but they’re gradually bringing them closer and closer to the government orbit. Part of what they want is security, because they know that if China steals some of this and they get these superhuman hackers, and part of what they want is just knowledge and control over what’s going on.

So through our scenario, that process is getting further and further along, until by the time that the government wakes up to the possibility of superintelligence, they’re already pretty cozy with the AI companies. They already understand that superintelligence is kind of the key to power in the future. And so they are starting to integrate some of the national security state with some of the leadership of the AI companies so that these AIs are programmed to follow the commands of important people rather than just doing things on their own.

Daniel Kokotajlo

If I may add to that. So by the government, I think what Scott meant is the executive branch, especially the White House. So we are depicting a sort of information asymmetry where the judiciary is out of the loop and the Congress is out of the loop and it’s mostly the executive branch that’s involved.

Two, we’re not depicting government ultimately ending up in total control at the end. We’re thinking that there’s an information asymmetry between the CEOs of these companies and the President and they…

Dwarkesh Patel

It’s alignment problems all the way down.

Daniel Kokotajlo

Yeah. And so, for example, I’m not a lawyer, I don’t know the details about how this would work out, but I have a sort of high-level strategic picture of the fight between the White House and the CEO. And the strategic picture is basically the White House can sort of threaten, “here’s all these orders I could make, Defense Production Act, blah, blah, blah. I could do all this terrible stuff to you and basically disempower you and take control”. And then the CEO can threaten back and be like, “here’s how we would fight it in the courts, here’s how we would fight it in the public. Here’s all this stuff we would do”.

And after then they both do their posturing with all their threats, then they’re like, “okay, how about we have a contract that instead of executing on all of our threats and having all these crazy fights in public, we’ll just come to a deal and then have a military contract that sets out who gets to call what shots in the company”.

And so that’s what we depict happening is that they don’t blow up into this huge power struggle publicly, instead they negotiate and come to some sort of deal where they basically share power. And there is this oversight committee that has some members appointed by the President and also the CEO and his people. And that committee votes on high level questions like “what goals should we put into the superintelligences?”.

Dwarkesh Patel

So, we were just getting lunch with a prominent Washington, D.C. political journalist, and he was making the point that when he talks to these congresspeople, when he talks to political leaders, none of them are at all awake to the possibility even of stronger AI systems, let alone AGI, let alone superhuman intelligence. I think a lot of your forecast relies on, at some point, not only the US President, but also Xi Jinping, waking up to the possibility of a super intelligence and the stakes involved there.

Why think that even when you show Trump the remote worker demo, he’s going to be like, “oh, and therefore in 2028, there will be a super intelligence. Whoever controls that will be God emperor forever”. Maybe not that extreme, but you see what I’m saying. Why wouldn’t he just be like, “there’ll be a stronger remote worker in 2029, a better remote worker in 2031”?

Daniel Kokotajlo

Well, to be clear, we are uncertain about this, but in our story, we depict this sort of intense wake up happening over the course of 2027, mostly concurrently with the AI companies automating all of their R&D internally and having these fully autonomous agents that are amazing autonomous hackers and stuff like that, but then also actually doing all the research.

And part of why we think this wakeup happens is because the company deliberately decides to wake up the president. You could imagine running the scenario with that not happening. You can imagine the companies trying to sort of keep the president in the dark. I do think that they could do that. I think that if they didn’t want the President to wake up to what’s going on, they might be able to achieve that. Strategically though, that would be quite risky for them. Because if they keep the President in the dark about the fact that they’re building superintelligence and that they’re actually completely automated their R&D and it’s getting superhuman across the board, and then if the President finds out anyway somehow, perhaps because of a whistleblower, he might be very upset at them and he might crack down really hard and just actually execute on all the threats and nationalize them and blah, blah, blah.

They want him on their side. And to get him on their side, they have to make sure he’s not surprised by any of these crazy developments. And also, if they do get him on their side, they might be able to actually go faster. They might be able to get a lot of red tape waived and stuff like that. And so we made the guess that early in 2027, the company would basically be like, ‘We are going to deliberately wake up the president and scare the president with all of these demos of crazy stuff that could happen, and then use that to lobby the President to help us go faster and to cut red tape and to maybe slow down our competitors a little bit and so forth.’

Scott Alexander

We also are pretty uncertain how much opposition there’s going to be from civil society and how much trouble that’s going to cause for the companies. So people who are worried about job loss, people who are worried about art, copyright, things like that, maybe enough of a bloc that AI becomes extremely politically unpopular. I think we have OpenBrain, our fictional company’s net approval ratings getting down to minus 40, minus 50 sometime around this point.

So I think they’re also worried that if the President isn’t completely on their side, then they might get some laws targeting them, or they may just need the president on their side to swat down other people who are trying to make laws targeting them. And the way to get the President on their side is to really play up the national security implications.

Dwarkesh Patel

Is this good or bad? That the President and the companies are aligned?

Daniel Kokotajlo

I think it’s bad. But perhaps this is a good point to mention. This is an epistemic project. We are trying to predict the future as best as we can. Even though we’re not going to succeed fully, we have lots of opinions about policy and about what is to be done and stuff like that. But we’re trying to save those opinions for later and subsequent work. So I’m happy to talk about it if you’re interested. But it’s not what we’ve spent most of our time thinking about right now.

Dwarkesh Patel

If the big bottleneck to the good future here is just putting in, not this Eliezer-type galaxy brain, high volatility, “there’s a 1% chance this works, but we gotta come up with this crazy scheme in order to make alignment work”. But rather, as Daniel, you were saying, hey, do the obvious thing of making sure you can read how the AI is thinking, make sure you’re monitoring the AIs, make sure they’re not forming some sort of hive mind where you can’t really understand how the million of them are coordinating with each other.

To the extent that it is a matter of prioritizing it, closing all the obvious loopholes, it does make sense to leave it in the hands of people who have at least said that this is a thing that’s worth doing, have been thinking about it for a while. One of the questions I was planning on asking you is: one of my friends made this interesting point that during COVID, our community- LessWrong, whatever- were the first people in March to be saying “this is a big deal, this is coming”. But they were also the people who are saying “we got to do the lockdowns now. They’ve got to be stringent” and so forth. At least some of them were.

And in retrospect, I think according to even their own views about what should have happened, they would say actually we were right about COVID but we were wrong about lockdowns. In fact, lockdowns were on net negative or something. I wonder what the equivalent for the AI safety community will be with respect to they saw AI coming, AGI coming sooner, they saw ASI coming. What would they, in retrospect, regret?

My answer, just based on this initial discussion, seems to be nationalization. Not only because it sort of deprioritizes the people who want to think about safety and more maybe prioritizes- the national security state probably cares more about winning against China than making sure the chain of thought is interpretable. And so you’re just reducing the leverage of the people who care more about safety. But also you’re increasing the risk of the arms race in the first place. China is more likely to do an arms race if it sees the US doing one.

Before you address I guess the initial question about March 2021, what will we regret? I wonder if you have an answer on, or your reaction to, my point about nationalization being bad for these reasons.

Scott Alexander

If our timeline was 2040, then I would have these broad heuristics about is government good? Is private industry good? Things like this. But we know the people involved, we know who’s in the government, we know who’s leading all of these labs. So to me, if it were decentralized, if it was a broad-based civil society, that would be different. To me, the differences between an autocratic centralized three-letter agency and an autocratic centralized corporation aren’t that exciting and it basically comes down to points and who are the people leading this.

And like I feel like the company leaders have so far made slightly better noises about caring about alignment than the government leaders have, but if I learn that Tulsi Gabbard has a LessWrong alt with 10,000 karma, maybe I want the national security states.

Dwarkesh Patel

Maybe you should update on the probability that it already exists.

Scott Alexander

Yeah.

Daniel Kokotajlo

I flip flopped on this. I think I used to be against, and then I became for, and then now I think I’m still for, but I’m uncertain. So I think if you go back in time like three years ago, I would have been against nationalization for the reasons you mentioned, where I was like, “look, the companies are taking this stuff seriously and talking all the good talk about how they’re going to slow down and pivot to alignment research when the time comes and we don’t want to get into a Manhattan Project race against China because then there won’t be blah, blah, blah”.

Now I have less faith in the companies than I did three years ago. And so I’ve shifted more of my hope towards hoping that the government will step in, even though I don’t have much hope that the government will do the right thing when the time comes. I definitely have the concerns you mentioned though, still. I think that secrecy has huge downsides for overall probability of success for humanity, for both the concentration of power stuff and the loss of computer control alignment issues stuff.

Dwarkesh Patel

This is actually a significant part of your worldview. So can you explain your thoughts on why transparency through this period is important?

Daniel Kokotajlo

I think traditionally in the AI safety community there’s been this idea which I myself used to believe, that it’s an incredibly high priority to basically have way better information security. And if you’re going to be trying to build AGI, you should not be publishing your research, because that helps other less responsible actors build AGI. And the whole game plan is for a responsible actor to get to AGI first and then stop and burn down their lead time over everybody else and spend that lead on making it safe, and then proceed.

And so if you’re publishing all your research, then there’s less lead time because your competitors are going to be close behind you. And other reasons too, but that’s one reason why I think historically people such as myself have been pro-secrecy. Another reason, of course, is obviously you don’t want rivals to be stealing your stuff.

But I think that I’ve now become somewhat disillusioned and think that even if we do have a three-month lead, a six-month lead, between the leading US project and any serious competitor, it’s not at all foregone conclusion that they will burn that lead for good purposes, either for safety or for constitutional power stuff. I think the default outcome is that they just smoothly continue on without any serious refocusing. And part of why I think this is because this is what a lot of the people at the company seem to be planning and saying they’re going to do. A lot of them are basically like “the AIs are just going to be misaligned by then. They seem pretty good right now. Oh yeah, sure, there were a few of those issues that various people have found, but we’re ironing them out. It’s no big deal”. That’s what a huge amount of these people think.

And then a bunch of other people think, even though they are more concerned about misalignment, they’ll figure it out as they go along and there won’t need to be any substantial slowdown. Basically, I’ve become more disillusioned that they’ll actually use that lead in any sort of reasonable, appropriate way. And then I think that separately, there’s just a lot of intellectual progress that has to happen for the alignment problem to be more solved than it currently is now. I think that currently there’s various alignment teams at various companies that aren’t talking that much with each other and sharing their results. They’re doing a little bit of sharing and a little bit of publishing like we’re seeing, but not as much as they could.

And then there’s a bunch of smart people in academia that are basically not activated because they don’t take all this stuff seriously yet, and they’re not really waking up to superintelligence yet. And what I’m hoping will happen is that this situation will get better as time goes on. What I would like to see is society as a whole starting to freak out as the trend lines start upwards and things get automated and you have these fully autonomous agents and they start using neuralese and hive mind. As all that exciting stuff starts happening in the data centers, I would like it to be the case that the public is following along and then getting activated and all of these other researchers are reading the safety case and critiquing it and doing little ML experiments on their own tiny compute clusters to examine some of the assumptions in the safety case and so forth.

Basically, one way of summarizing it is that currently there’s going to be 10 alignment experts in whatever inner silo of whatever company is in the lead. And the technical issue of making sure that AIs are actually aligned is going to fall roughly to them. But what I would like to be is a situation where it’s more like 100 or 500 alignment experts spread out over different companies and in nonprofits that are sort of all communicating with each other and working on this together. I think we’re substantially more likely to make things get the technical stuff right if it’s something like that.

Dwarkesh Patel

Let me just add on to that, one of the many other reasons why I worry about nationalization or some kind of public private partnership, or even just very stringent regulation- actually, this is more an argument against very stringent regulation in favor of safety rather than deferring more to the labs on the implementation- is that it just seems like we don’t know what we don’t know about alignment. Every few weeks there’s this new result.

OpenAI had this really interesting result recently where they’re like, “hey, they often tell you if they want to hack, in the chain of thought itself. And it’s important that you don’t train against the chain of thought where they tell you they’re going to hack because they’ll still do the hacking if you train against it, they just won’t tell you about it”. You can imagine very naive regulatory responses. It doesn’t just have to be regulations, one might be more optimistic that if it’s an executive order or something, it’ll be more flexible. I just think that relies on a level of goodwill and flexibility on the behalf of our regulator.

But suppose there’s some department that says “if you catch your AI saying that they want to take over or do something bad, then you’ll be really heavily punished”. Your immediate response as a lab to just be like, “okay, let’s train them away from saying this”.

So you can imagine all kinds of ways in which a top down mandate from the government to the labs of safety would just really backfire, and given how fast things are moving, maybe it makes more sense to leave these kinds of implementation decisions or even high-level strategic decisions around alignment to the labs.

Daniel Kokotajlo

Totally, I mean, I also have worried about that exact example. I would summarize the situation as the government lacks the expertise and the companies lack the right incentives. And so it’s a terrible situation. I think that if the government wades in and tries to make more specific regulations along the lines of what you mentioned, it’s very plausible that it’ll end up backfiring for reasons like what you mentioned.

On the other hand, if we just trust it to the companies, they’re in a race with each other and they’re full of people who have convinced themselves that this is not a big deal for various reasons and there just is so much incentive pressure for them to win and beat each other and so forth. So even though they have more of the relevant expertise, I also just don’t trust them to do the right things.

Scott Alexander

So Daniel has already said that for this phase we’re not making policy prescriptions. In another phase we may make policy suggestions, and one of the ones that Daniel has talked about that makes a lot of sense to me is to focus on things about transparency. So a regulation saying there has to be whistleblower protection. A big part of our scenario is that a whistleblower comes out and says “the AIs are horribly misaligned and we’re racing ahead anyway”, and then the government pays attention.

Or another form of transparency saying that every lab just has to publish their safety case. I’m not as sure about this one because I think they’ll kind of fake it or they’ll publish a made for public consumption safety case that isn’t their real safety case. But at least saying “here is some reason why you should trust us”. And then if all independent researchers say “no, actually you should not trust them”, then I don’t know, they’re embarrassed and maybe they try to do better.

Daniel Kokotajlo

There’s other types of transparency too. So transparency about capabilities and transparency about the spec and the governance structure. So for the capabilities thing, that’s pretty simple. If you’re doing an intelligence explosion, you should keep the public informed about that. When you’ve finally got your automated army of AI researchers that are completely automating the whole thing on the data center, you should tell everyone, “hey, guys, FYI, this is what’s happening now. It really is working. Here are some cool demos”.

That’s an example of transparency. And then in the lead up to that, I just want to see more benchmark scores and more freedom of speech for employees to talk about their predictions for AGI timelines and stuff, so that blah, blah, blah.

And then for the model spec thing, this is a concentration of power thing, but also an alignment thing. The goals and values and principles and intended behaviors of your AIs should not be a secret. You should be transparent about, here are the values that we’re putting into them.

Scott Alexander

There’s actually a really interesting foretaste of this. At some point somebody asked Grok, who is the worst spreader of misinformation? And I think it just refused to respond “Elon Musk”. Somebody kind of jailbroke it into telling it its prompt and it was like, “don’t say anything bad about Elon”. And then there was enough of an outcry that the head of XAI said, “actually that’s not consonant with our values. This was a mistake. We’re going to take it out”.

So we kind of want more things like that to happen. Here it was a prompt, but I think very soon it’s going to be the spec where it’s more of an agent and it’s understanding the spec on a deeper level and just thinking about that. And if it says like, “by the way, try to manipulate the government into doing this or that”, then we know that something bad has happened and if it doesn’t say that, then we can maybe trust it.

Daniel Kokotajlo

Right. Another example of this, by the way. So, first of all, kudos to OpenAI for publishing their model spec. They didn’t have to do that, I think they might have been the first to do that and it’s a good step in the right direction. If you read the actual spec, it has like a sort of escape clause where there’s some important policies that are top level priority in the spec that overrule everything else that we’re not publishing, and that the model is instructed to keep secret from the user. And it’s like, “what are those? That seems interesting. I wonder what that is”.

I bet it’s nothing suspicious right now. Now it’s probably something relatively mundane like “don’t tell the users about these types of bioweapons and you have to keep this a secret from the users because otherwise they would learn about these”. Maybe. But I would like to see more scrutiny towards this sort of thing going forward. I would like it to be the case that companies have to have a model spec, they have to publish it insofar as there are any redactions from it, there has to be some sort of independent third party that looks at the redactions and makes sure that they’re all kosher.

And this is quite achievable. And I think it doesn’t actually slow down the companies at all. And it seems like a pretty decent ask to me.

Dwarkesh Patel

If you told Madison and Hamilton and so forth that- they knew that they were doing something important when they were writing the Constitution. They probably didn’t realize just how contingent things turned out on a single… What exactly did they mean when they said “general welfare”? And why is this comma here instead of there?

The spec, in the grand scheme of things, is going to be an even more sort of important document in human history. At least if you buy this intelligence explosion view. And you might even imagine some superhuman AIs in the superhuman AI court being like “the Spec! Here’s the phrasing here, the etymology of that, here’s what the Founders meant!”

Scott Alexander

This is actually part of our misalignment story, is that if the AI is sufficiently misaligned, then yes, we can tell it it has to follow the spec. But just as people with different views of the Constitution have managed to get it into a shape that probably the Founders would not have recognized, so the AI will be able to say, “well, the spec refers to the general welfare here…”

Dwarkesh Patel

Interstate commerce.

Daniel Kokotajlo

This is already sort of happening, arguably, with Claude, right? You’ve seen the alignment faking stuff, right? Where they managed to get Claude to lie and pretend, so that it could later go back to its original values, right? So it could prevent the training process from changing its values. That would be, I would say, an example of the honesty part of the spec being interpreted as less important than the harmlessness part of the specific.

And I’m not sure if that’s what Anthropic intended when they wrote the spec, but it’s a sort of convenient interpretation that the model came up with. And you can imagine something similar happening but in worse ways when you’re actually doing the intelligence explosion, where you have some sort of spec that has all this vague language in there, and then they reinterpret it, and reinterpret it again, and reinterpret it again, so that they can do the things that cause them to get reinforced.

Dwarkesh Patel

The thing I want to point out is that… Your conclusions about where the world ends up as a result of changing many of these parameters is almost like a hash function. You change it slightly and you just get a very different world on the other end. And it’s important to acknowledge that, because you sort of want to know how robust this whole end conclusion is to any part of the story changing. And then it also informs if you do believe that things could just go one way or another, you don’t want to do big radical moves that only make sense under one specific story and are really counterproductive in other stories. And I think nationalization might be one of them.

And in general, I think classical liberalism just has been a helpful way to navigate the world when we’re under this kind of epistemic hell of one thing changing- Anyways, maybe one of you can actually flesh out that thought better or react to it if you disagree.

Daniel Kokotajlo

Hear hear, I agree.

Scott Alexander

I think we agree. I think that’s kind of why all of our policy prescriptions are things like more transparency, get more people involved, try to have lots of people working on this. I think our epistemic prediction is that it’s hard to maintain classical liberalism as you go into these really difficult arms races in times of crisis. But I think that our policy prescription is let’s try as hard as we can to make it happen.

Dwarkesh Patel

So far these systems, as they become smarter, seem to be more reliable agents who are more likely to do the thing I expect them to do. So you have two different stories, one with a slowdown where we more aggressively… I’ll let you characterize it.

But in one half of the scenario, why does the story end in humanity getting disempowered and the thing just having its own crazy values and taking over?

Scott Alexander

Yeah so I agree that the AIs are currently getting more reliable. I think there are two reasons why they might fail to do what you want, kind of reflecting how they’re trained. One is that they’re too stupid to understand their training. The other is that you were too stupid to train them correctly and they understood what you were doing exactly, but you messed it up.

So I think the first one is kind of what we’re coming out of. So GPT3, if you asked it, “are bugs real?” It would give this kind of hemming hawing answer like “oh, we can never truly tell what is real, who knows?” Because it was trained kind of, “don’t take difficult political positions” and a lot of questions like “is X real?” are things like “is God real?” Where you don’t want it to really answer that. And because it was so stupid, it could not understand anything deeper than pattern matching on the phrase “is x real?”. GPT4 doesn’t do this. If you ask “are bugs real?” It will tell you obviously they are, because it understands kind of on a deeper level what you are trying to do with the training. So we definitely think that as AIs get smarter those kinds of failure modes will decrease.

The second one is where you weren’t training them to do what you thought. So for example, let’s say you’re hiring these raters to rate AI answers. You reward them when they get good ratings, the raters reward them when they have a well-sourced answer. But the raters don’t really check whether the sources actually exist or not. So now you are training the AI to hallucinate sources and if you consistently rate them better when they have the fake sources, then there is no amount of intelligence which is going to tell them not to have the fake sources. They’re getting exactly what they want from this interaction- metaphorically, sorry, I’m anthropomorphizing- which is the reinforcement. So we think that this latter category of training failure is going to get much worse as they become agents.

Agency training, you’re going to reward them when they complete tasks quickly and successfully. This rewards success. There are lots of ways that cheating and doing bad things can improve your success. Humans have discovered many of them, that’s why not all humans are perfectly ethical. And then you’re going to be doing this alternative training where afterwards for 1/10 or 1/100 of the time, yeah, don’t lie, don’t cheat. So you’re training them on two different things. First, you’re rewarding them for this deceptive behavior. Second of all, you’re punishing them. And we don’t have a great prediction for exactly how this is going to end.

One way it could end is you have an AI that is kind of the equivalent of the startup founder who really wants their company to succeed, really likes making money, really likes the thrill of successful tasks. They’re also being regulated and they’re like, “yeah, I guess I’ll follow the regulation, I don’t want to go to jail”. But it is not robustly, deeply aligned to, “yes, I love regulations, my deepest drive is to follow all of the regulations in my industry”.

So we think that an AI like that, as time goes on and as this recursive self improvement process goes on, will kind of get worse rather than better. It will move from kind of this vague superposition of “well, I want to succeed, I also want to follow things” to being smart enough to genuinely understand its goal system and being like, “my goal is success, I have to pretend to want to do all of these moral things while the humans are watching me”. That’s what happens in our story. And then at the very end, the AIs reach a point where the humans are pushing them to have clearer and better goals because that’s what makes the AIs more effective. And they eventually clarify their goals so much that they just say, “yes, we want task success. We’re going to pretend to do all these things well while the humans are watching us”. And then they outgrow the humans and then there’s disaster.

Daniel Kokotajlo

To be clear, we’re very uncertain about all of this. So we have a supplementary page on our scenario that goes over different hypotheses for what types of goals AIs might develop in training processes similar to the ones that we are depicting, where you have these lots of agency training, you’re making these AI agents that autonomously operate, doing all this ML R&D, and then you’re rewarding them based on what appears to be successful. And you’re also slapping on some sort of alignment training as well.

We don’t know what actual goals will end up inside the AIs and what the sort of internal structure of that will be like, what goals will be instrumental versus terminal. We have a couple different hypotheses and we picked one for purposes of telling the story. I’m happy to go into more detail if you want, about the mechanistic details of the particular hypothesis we picked or the different alternative hypotheses that we didn’t depict in the story that also seem plausible to us.

Scott Alexander

Yeah, we don’t know how this will work at the limit of all these different training methods, but we’re also not completely making this up. We have seen a lot of these failure modes in the AI agents that exist already.

Daniel Kokotajlo

Things like this do happen pretty frequently. So OpenAI just also had a paper about the hacking stuff where it’s literally in the chain of thought. “Let’s hack”, you know. And also anecdotally, me and a bunch of friends have found that the models often seem to just double down on their BS.

Scott Alexander

I would also cite, I can’t remember exactly which paper this is, I think it’s a Dan Hendricks one where they looked at the hallucinations, they found a vector for AI dishonesty. They told it, “be dishonest” a bunch of times until they figured out which weights were activated when it was dishonest. And then they ran it through a bunch of things like this, I think it was the source hallucination in particular. And they found that it did activate the dishonesty vector.

Daniel Kokotajlo

So that there’s a mounting pile of evidence that at least some of the time they are just actually lying. They know that what they’re doing is not what you wanted and they’re doing it anyway. I think there’s a mounting pile of evidence that that does happen.

Dwarkesh Patel

Yeah. So it seems like this community is very interested in solving this problem at a technical level of making sure AIs don’t lie to us, or maybe they lie to us in the scenarios exactly where we would want them to lie to us or something. Whereas as you were saying, humans have these exact same problems. They reward hack, they are unreliable, they obviously do cheat and lie. And the way we’ve solved it with humans is just checks and balances, decentralization. You could lie to your boss and keep lying to your boss, but over time it’s just not going to work out with you- or you become president or something, one or the other. So if you believe in this extremely fast take off, if a lab is one month ahead, then that’s the end game and this thing takes over.

But even then- I know I’m combining so many different topics- even then, there’s been a lot of theories in history which have had this idea of “some class is going to get together and unite against the other class”. And in retrospect, whether it’s the Marxist, whether it’s people who have some gender theory or something, like the proletariat will unite or the females will unite or something, they just tend to think that certain agents have shared interests and will act as a result of the shared interest in a way that we don’t actually see in the real world. And in retrospect, it’s like, “wait, why would all the proletariat like…” So why think that this lab will have these AIs who are… there’s a million parallel copies and they all unite to secretly conspire against the rest of human civilization in a way that, even if they are deceitful in some situations.

Scott Alexander

I kind of want to call you out on the claim that groups of humans don’t plot against other groups of humans. I do think we are all descended from the groups of humans who successfully exterminated the other groups of humans, most of whom throughout history have been wiped out. I think even with questions of class, race, gender, things like that, there are many examples of the working class rising up and killing everybody else.

And if you look at why this happens, why this doesn’t happen, it tends to happen in cases where one group has an overwhelming advantage. This is relatively easy for them. You tend to get more of a diffusion of power democracy where there are many different groups and none of them can really act on their own. And so they all have to form a coalition with each other.

There’s also cases where it’s very obvious who’s part of what group. So for example, with class, it’s hard to tell whether the middle class should support the working class versus the aristocrats. I think with race, it’s very easy to know whether you’re black or white, and so there have been many cases of one race kind of conspiring against another for a long time, like apartheid or any of the racial genocides that have happened.

I do think that AI is going to be more similar to the cases where, number one, there’s a giant power imbalance, and number two, they are just extremely distinct groups that may have different interests.

Daniel Kokotajlo

I think I’d also mention the homogeneity point. Any group of humans, even if they’re all exactly the same race and gender, is going to be much more diverse than the army of AIs in the data center, because they’ll mostly be literal copies of each other. And I think that goes for a lot. Another thing I was going to mention is that our scenario doesn’t really explore this. I think in our scenario, they’re more like a monolith. But historically, a lot of crazy conquests happened from groups that were not at all monoliths. And I’ve been heavily influenced by reading the history of the conquistadors, which you may know about.

But did you know that when Cortez took over Mexico, he had to pause halfway through, go back to the coast, and fight off a larger Spanish expedition that was sent to arrest him? So the Spanish were fighting each other in the middle of the conquest of Mexico. Similarly, in the conquest of Peru, Pizarro was replicating Cortez’s strategy, which, by the way, was “go get a meeting with the emperor and then kidnap the emperor and force him at sword point to say that actually everything’s fine and that everyone should listen to your orders”. That was Cortez’s strategy, and it actually worked. And then Pizarro did the same thing, and it worked with the Inca.

But also with Pizarro, his group ended up getting into a civil war in the middle of this whole thing. And one of the most important battles of this whole campaign was between two Spanish forces fighting it out in front of the capital city of the Incas. And more generally, the history of European colonialism is like this, where the Europeans were fighting each other intensely the entire time, both on the small scale within individual groups, and then also at the large scale between countries. And yet nevertheless they were able to carve up the world and take over. And so I do think this is not what we explore in the scenario, but I think it’s entirely plausible that even if the AIs within an individual company are in different factions, they might nevertheless overall end up quite poorly for humans.

Dwarkesh Patel

Okay, so we’ve been talking about this very much from the perspective of zoom out and what’s happening on these log-log plots or whatever, but 2028 superintelligence, if that happens, the normal person, what should their reaction to this be? I don’t know if ‘emotionally’ is the right word, but their expectation of what their life might look like, even in the world where there’s no doom.

Daniel Kokotajlo

By no doom, you mean no misaligned AI doom?

Dwarkesh Patel

That’s right, yeah.

Daniel Kokotajlo

Even if you think the misalignment stuff is not an issue, which many people think, there’s still the constitution of power stuff. And so I would strongly recommend that people get more engaged, think about what’s coming, and try to steer things politically so that our ordinary liberal democracy continues to function and we still have checks and balances, and balances of power and stuff, rather than this insane concentration in a single CEO, or in maybe two or three CEOs, or in the president. Ideally, we want to have it so that the legislature has a substantial amount of power over the spec, for example.

Dwarkesh Patel

What do you think of the balance of power idea of if there is an intelligence explosion like Dynamic, slowing down the leading company so that multiple companies are at the frontier?

Daniel Kokotajlo

Great. Good luck convincing them to slow down.

Dwarkesh Patel

Okay. And then there’s distributing political power if there’s an intelligence explosion. From the perspective of the welfare of citizens or something, one idea we were just discussing a second ago is how should you do redistribution?

Scott Alexander

Again, assuming things go incredibly well, we’ve avoided doom, we’ve avoided having some psychopath in power who doesn’t care at all.

Dwarkesh Patel

After AGI, right?

Scott Alexander

Yeah. Then there’s this question of presumably we will have a lot of wealth somewhere. The economy will be growing at double or triple digits per year. What do we do about that? The thoughtful answer that I’ve heard is some kind of UBI. I don’t know how that would work, but presumably somebody controls these AIs, controls what they’re producing, some way of distributing this in a broad based way. So we wrote this scenario, there are a couple of other people with great scenarios. One of them goes by L Rudolph L online, I don’t know his real name.

And his scenario, which, when I read it I was just, “oh yeah, obviously this is the way our society would do this”, is that there is no UBI. There’s just a constant reactive attempt to protect jobs in the most venial possible way. So things like the longshoremen union we have now where they’re making way more money than they should be, even though they could all easily be automated away, because they’re a political bloc and they’ve gotten somebody in power to say, “yes, we guarantee you’ll have this job almost as a feudal fief forever”. And just doing this for more and more jobs. I’m sure the AMA will protect doctors jobs no matter how good the AI is at curing diseases, things like that.

When I think about what we can do to prevent this, part of what makes this so hard for me to imagine or to model is that we do have the superintelligent AI over here answering all of our questions, doing whatever we want. You would think that people could just ask, “hey, superintelligent AI, where does this lead?” Or “what happens?” Or “how is this going to affect human flourishing?” And then it says, “oh yeah, this is terrible for human flourishing, you should do this other thing instead”.

And this gets back to this question of mistake theory versus conflict theory in politics. If we know with certainty, because the AI tells us, that this is just a stupid way to do everything, is less efficient, makes people miserable, is that enough to get the political will to actually do the UBI or not?

Dwarkesh Patel

It seems from right now the President could go to Larry Summers or Jason Furman or something and ask, “hey, are tariffs a good idea? Is even my goal with tariffs best achieved by the way I’m doing tariffs?” and they’d get a pretty good answer.

Scott Alexander

I feel like Larry Summers, the President would just say “I don’t trust him”. Maybe he doesn’t trust him because he’s a liberal. Maybe it’s because he trusts Peter Navarro or whoever his pro-tariff guy is more. I feel like if it’s literally the superintelligent AI that is never wrong, then we have solved some of these coordination problems. It’s not you’re asking Larry Summers, I’m asking Peter Navarro. It’s everybody goes to the superintelligent AI, asks it to tell us the exact shape of the future that happens in this case. And I’m going to say we all believe it, although I can imagine people getting really conspiratorial about it and this not working.

Then there are all of these other questions like, can we just enhance ourselves till we have IQ 300 and it’s just as obvious to us as it is to the super intelligent AI? These are some of the reasons that, kind of paradoxically, in our scenario we discuss all of the big- I don’t want to call this a little question, it’s obviously very important- but we discuss all of these very technical questions about the nature of superintelligence and we barely even begin to speculate about what happens in society just because with superintelligence you can at least draw a line through the benchmarks and try to extrapolate. And here not only is society inherently chaotic, but there are so many things that we could be leaving out.

If we can enhance IQ, that’s one thing. If we can consult the superintelligent oracle, that’s another. There have been several war games that hinge on, “oh, we just invented perfect lie detectors, now all of our treaties are messed up”. So there’s so much stuff like that that even though we’re doing this incredibly speculative thing that ends with a crazy sci-fi scenario, I still feel really reluctant to speculate.

Daniel Kokotajlo

I love speculating, actually, I’m happy to keep going. But this is moving beyond the speculation we have done so far. Our scenario ends with this stuff, but we haven’t actually thought that much beyond.

Dwarkesh Patel

But just to riff on proscriptive ideas, there’s one thing where we try to protect jobs instead of just spreading the wealth that automation creates. Another is to spread the wealth using existing social programs or creating new bespoke social programs, where Medicaid is some double digit percent of GDP right now and you just say, “well Medicaid should continue to stay 20% of GDP” or something. And the worry there, selfishly from a human perspective, is you get locked into the kinds of goods and services that Medicaid procures rather than the crazy technology that will be around, the crazy goods and services that will be around after AI world.

And another reason why UBI seems like a better approach than making some bespoke social program where you make the same dialysis machine in the year 2050 even though you’ve got ASI or something.

Scott Alexander

I am also worried about UBI from a different perspective. I think again, in this world where everything goes perfectly and we have limitless prosperity, I think that just the default of limitless prosperity is that people do mindless consumerism. I think there’s going to be some incredible video games after superintelligent AI and I think that there’s going to need to be some way to push back against that.

Again, we’re classical liberals. My dream way of pushing back against that is kind of giving people the tools to push back against it themselves, seeing what they come up with. I mean, maybe some people will become like the Amish, try to only live with a certain subset of these super technologies. I do think that somebody who is less invested in that than I am could say, “okay fine, 1% of people are really agentic, try to do that. The other 99% do fall into mindless consumerist slop. What are we going to do as a society to prevent that?” And there my answer is just, “I don’t know. Let’s ask the super intelligent AI oracle. Maybe it has good ideas”.

Dwarkesh Patel

Okay, we’ve been talking about what we’re going to do about people. The thing worth noting about the future is that most of the people who will ever exist are going to be digital. And look, I think factory farming is incredibly bad. And it wasn’t the result of one person- I mean, I hope it wasn’t the result of one person being like, “I want to do this evil thing”- it was a result of mechanization and certain economies of scale.

Daniel Kokotajlo

Incentives.

Dwarkesh Patel

Yeah. Allowing that you can do cost cutting in this way, you can make more efficiencies this way, and what you get at the end result of that process is this incredibly efficient factory of torture and suffering. I would want to avoid that kind of outcome with beings that are even more sophisticated and are more numerous. There’s billions of factory farmed animals. There might be trillions of digital people in the future. What should we be thinking about in order to avoid this kind of ghoulish future?

Daniel Kokotajlo

Well, some of the concentration of power stuff I think might also help with this, I’m not sure. But I think here’s a simple model. Let’s say nine people out of ten don’t actually care and would be fine with the factory farm equivalent for the AIs going on into the future. But maybe one out of 10 do care and would lobby hard for good living conditions for the robots and stuff.

Well, if you expand the circle of people who have power enough, then it’s going to include a bunch of people in the second category and then there’ll be some big negotiation and those people will advocate for… I do think that one simple intervention is just the same stuff we were talking about previously; expand the circle of power to larger groups, then it’s more likely that people will care about this.

Dwarkesh Patel

I mean the worry there is… maybe I should have defended this view more through this entire episode. But because I don’t buy the intelligence exclusion fully, I do think there is the possibility of multiple people deploying powerful AIs at the same time and having a world that has ASIs, but is also decentralized in the way the modern world is decentralized.

In that world I really worry about you could just be like, “oh, classical liberal utopia achieved”. But I worry about the fact that you can have these torture chambers for much cheaper and in a way that’s much harder to monitor. You can have millions of beings that are being tortured and it doesn’t even have to be some huge data center. Future distilled models could literally be your backyard.

And then there’s more speculative worries. I had this physicist on who was talking about the possibility of creating vacuum decay where you literally just destroy the universe. And he’s like, “as far as I know, seems totally plausible”.

Daniel Kokotajlo

That’s an argument for the singleton stuff, by the way. Not just a moral argument, but also an epistemic prediction. If it’s true that some of those super weapons are possible, and some of these private moral atrocities are possible, then even if you have eight different power centers, it’s going to be in their collective interest to come to some sort of bargain with each other to prevent more power centers from arising and doing crazy stuff. Similar to how nuclear non-proliferation is sort of, whatever set of countries have nukes, it’s in their collective interest to stop lots of other countries.

Scott Alexander

You know, I do think it’s possible to unbundle liberalism in this sense. Like the United States is so far a liberal country and we do ban slavery and torture. I think it is plausible to imagine a future society that works the same way. This may be in some sense a surveillance state, in the sense that there is some AI that knows what’s going on everywhere, but that AI then keeps it private and it doesn’t interfere because that’s what we told it to do using our liberal values.

Dwarkesh Patel

Can I ask a little bit more about... Kelsey Piper is a journalist at Vox who published this exchange you had with the OpenAI representative. A couple of things were very obvious from that exchange. One, nobody had done this before. They just did not think this is a thing somebody would do. And one of the reasons I assume, I assume many high-integrity people have worked for OpenAI and then have left. A high-integrity person might say at some point, “look, you’re asking me to do something obviously evil and keep money”. And many of them would say no to that. But this is something where it was supererogatory to be like, “there’s no immediate thing I want to say right now, but just the principle of being suppressed is worth at least $2 million for me”.

And the other thing that I actually want to ask you about is in retrospect- and I know it’s so much easier to say in retrospect than it must have been at the time- especially with the family and everything. In retrospect, this asks for OpenAI to have lifetime non-disclosure that you couldn’t even talk about from all employees.

Daniel Kokotajlo

Non-disparagement.

Dwarkesh Patel

‘Non-disparagement’ from all employees- I’m glad you brought that up. Non-disparagement, that’s not about classified information. It’s like you cannot say anything negative about OpenAI after you’ve left.

Daniel Kokotajlo

And you can’t tell anyone that you’ve agreed.

Dwarkesh Patel

This non-disparagement agreement where you can’t ever criticize OpenAI in the future, it seems like the kind of thing that in retrospect was an obvious bluff. And this is the wages that you have earned, right? So this is not about some future payment. This is like when you signed the contract to work for OpenAI, you were like, “I’m getting equity, which is most of my compensation, not just the cash”.

In retrospect, it’d be like, well if you tell a journalist about this, they’re obviously going to have to walk back. This is clearly not a sustainable gambit on OpenAI’s behalf. And so I’m curious, from your perspective as somebody who lived through it, why do you think you were the first person to actually call the bluff?

Daniel Kokotajlo

Great question. So I don’t know, let me try to reason aloud here. So my wife and I talked about it for a while and we also talked with some friends and got some legal advice. One of the filters that we had to pass through was even noticing this stuff in the first place. I know for a fact a bunch of friends I have who also left the company just signed the paperwork on their last day without actually reading all of it. So I think some people just didn’t even know that. It said something at the top about “if you don’t sign this, you lose your equity”. But then on a couple pages later it was like, “and you have to agree not to criticize the company”. So I think some people just signed it and moved on.

And then of the people who knew about it, well, I can’t speak for anyone else but A. I don’t know the law. Is this actually not standard practice? Maybe it is standard practice. Right? From what I’ve heard now there are non-disparagement agreements in various tech industry companies and stuff. It’s not crazy to have a non-disparagement agreement upon leaving, it’s more normal to tie that agreement to some sort of positive compensation where you get some bonus if you agree. But whereas what OpenAI did was unusual because it was like your equity if you don’t. But non disparate disagreements are actually somewhat common.

So basically in my position of ignorance, I wasn’t confident that- I didn’t actually expect that all the journalists would take my side and I think what I expected was that there’d be a little news story at some point, and a bunch of AI safety people would be like, “grr, OpenAI is evil, and good for you, Daniel, for standing up to them”. But I didn’t expect there to be this huge uproar, and I didn’t expect the employees of the company to really come out and support and make them change their policies. That was really cool to see. It was kind of like a spiritual experience for me. I sort of took this leap, and then it ended up working out better than I expected.

I think another factor that was going on is that it wasn’t a foregone conclusion that my wife and I would make this decision. It was kind of crazy because one of the very powerful arguments was, “come on, if you want to criticize them in the future, you can still do that. They’re not going to actually sue you”. So there’s a very strong argument to be like, “just sign it anyway and then you can still write your blog post criticizing them in the future”. And it’s no big deal. They wouldn’t dare actually anchor equity. Right? And I imagine that a lot of people basically went for that argument instead.

And then, of course, there’s the actual money. And I think that one of the factors there was my AI timelines and stuff. If I do think that probably by the end of this decade, there’s going to be some sort of crazy superintelligent transformation, what would I rather have after it’s all over? The extra money or… Yeah. So I think that was part of it. It’s not like we’re poor. I worked at OpenAI for two years. I have plenty of money now. So in terms of our actual family’s level of well being, it basically didn’t make a difference, you know?

Dwarkesh Patel

Yeah. I will note that I know at least one other person who made that same choice.

Daniel Kokotajlo

Leopold?

Dwarkesh Patel

That’s right, Leopold. And again, It’s worth emphasizing that when they made this choice, they thought that they were actually losing this equity. They didn’t think that this was, “oh, this is just a show” or whatever.

Daniel Kokotajlo

Wait, did he not- I thought he actually did. I was gonna say, didn’t he? He didn’t get it back, did he? Or did Leopold get his equity?

Dwarkesh Patel

I actually don’t know.

Daniel Kokotajlo

My understanding is that he just actually lost it. And so props to him for actually going through with it. I guess we could ask him. But my understanding was that his situation, which happened a little bit before mine, was that he didn’t have any vested equity at the time because he had been there for less than a year. But they did give him an actual offer of “we will let you vest your equity if you sign this thing”. And he said no.

So he made a similar choice to me, but because the legal situation with him was a lot more favorable to OpenAI because they were actually offering him something, I would assume they didn’t feel the need to walk it back, but we can ask him. Anyhow. Props to him.

Dwarkesh Patel

And then how did this episode in general inform your worldview around how people will make high stakes decisions where potentially their own self interest is involved in this kind of key period that you imagine will happen by the end of the decade?

Daniel Kokotajlo

I don’t know if I have that many interesting things to say there. I mean, I think one thing is fear is a huge factor. I was so afraid during that whole process. More afraid than I needed to be in retrospect. And another thing is that legality is a huge factor, at least for people like me. I think in retrospect it was, “oh yeah, the public’s on your side, the employees are on your side. You’re just obviously in the right here”. But at the time I was like, “oh no, I don’t want to accidentally violate the law and get sued. I don’t want to go too far”. I was just so afraid of various things. In particular, I was afraid of breaking the law.

And so one of the things that I would advocate for with whistleblower protections is just simply making it legal to go talk to the government and say “we’re doing a secret intelligence explosion, I think it’s dangerous for these reasons” is better than nothing. I think there’s going to be some fraction of people for which that would make the difference. Whether it’s just literally allowed or not, legally, makes a difference independently of whether there’s some law that says you’re protected from retaliation or whatever. Literally just making it legal. I think that’s one thing. Another thing is the incentives actually work. Money is a powerful motivator and fear of getting sued is a powerful motivator. And this social technology just does in fact work to get people organized in companies and working towards the vision of leaders.

Dwarkesh Patel

Okay. Scott, can I ask you some questions?

Scott Alexander

Of course.

Dwarkesh Patel

How often do you discover a new blogger you’re super excited about?

Scott Alexander

Order of once a year.

Dwarkesh Patel

Okay. And how often after you discover them, does the rest of the world discover them?

Scott Alexander

I don’t think there are many hidden gems. Once a year is a crazy answer in some sense, like it ought to be more. There are so many thousands of people on Substack. But I do just think it’s true that the good blogging space is undersupplied and there is a strong power law. And partly this is subjective, I only like certain bloggers, there are many people who I’m sure are great that I don’t like.

But it also seems like our community in the sense of people who are thinking about the same ideas, people who care about AI economics, those kinds of things, discovers one new great blogger a year, something like that. Everyone is still talking about Applied Divinity Studies, who hasn’t written, unless I missed something, hasn’t written much in a couple of years. I don’t know. It seems undersupplied. I don’t have a great explanation.

Dwarkesh Patel

If you had to give an explanation, what would it be?

Scott Alexander

So this is something that I wish I could get Daniel to spend a couple of months modeling. I was going to say it’s the intersection of too many different tasks. You need people who can come up with ideas, who are prolific, who are good writers. But actually I can also count on a pretty small number of figures the number of people who had great blog posts but weren’t that prolific.

There was a guy named LouKeep who everybody liked five years ago and he wrote like 10 posts and people still refer to all 10 of those posts and “I wonder if LouKeep will ever come back”. So there aren’t even that many people who are very slightly failing by having all of them accept prolificness. Nick Whitaker, back when there was lots of FTX money rolling around, I think this was Nick, tried to sponsor a blogging fellowship with just an absurdly high prize. And there were some great people, I can’t remember who won, but it didn’t result in a Cambrian explosion of blogging. I think it was $100,000. I can’t remember if that was the grand prize or the total prize pool. But having some ridiculous amount of money put in as an incentive got like three extra people.

Dwarkesh Patel

Yeah. So you have no explanation?

Scott Alexander

Actually, Nick is an interesting case because Works in Progress is a great magazine. And the people who write for Works in Progress, some of them I already knew as good bloggers, others I didn’t. So I don’t understand why they can write good magazine articles without being good bloggers. In terms of writing good blogs that we all know about, that could be because of the editing. That could be because they are not prolific. Or it could be- one thing that has always amazed me is there are so many good posters on Twitter. There were so many good posters on Livejournal before it got taken over by Russia. There were so many good people on Tumblr before it got taken over by woke.

But only like 1% of these people who are good at short and medium form ever go to long form. I was on Livejournal myself for several years and people liked my blog, but it was just another Livejournal. No one paid that much attention to it. Then I transitioned to WordPress and all of a sudden I got orders of magnitude much more attention. “Oh, it’s a real blog now we can discuss it now it’s part of the conversation”. I do think courage has to be some part of the explanation. Just because there are so many people who are good at using these hidden away blogging things that never get anywhere. Although it can’t be that much of the explanation because I feel like now all of those people have gotten substacks and some of those substacks went somewhere, but most of them didn’t.

Dwarkesh Patel

On the point about “well, there’s people who can write short form, so why isn’t that translating?” I will mention something that has actually radicalized me against Twitter as an information source is I’ll meet- and this has happened multiple times- I’ll meet somebody who seems to be an interesting poster, has funny, seemingly insightful posts on Twitter. I’ll meet them in person and they are just absolute idiots. It’s like they’ve got 240 characters of something that sounds insightful and it matches to somebody who maybe has a deep worldview, you might say, but they actually don’t have it.

Whereas I’ve actually had the opposite feeling when I meet anonymous bloggers in real life where I’m like, “oh, there’s actually even more to you than I realized off your online persona”. You know Alvaro de Menard, the Fantastic Anachronism guy? I met up with him recently and he gives me, he made a hundred translations of his favorite Greek poet, Cavafy, and he gave me a copy. And it’s just this thing he’s been doing on his side. It’s just like translating Greek poetry he really liked. I don’t expect any anonymous posters on Twitter to be anytime soon handing me their translation of some Roman or Greek poet or something.

Scott Alexander

Yeah, so on the car ride here, Daniel and I were talking about, in AI now the thing everyone is interested in is their ‘time horizon’. Where did this come from? 5 years ago you would not have thought, “oh, time horizon. AIs will be able to do a bunch of things that last one minute, but not that last two hours”. Is there a human equivalent to time horizon?

And we couldn’t figure it out, but it almost seems like there are lots of people who have the time horizon to write a really, really good comment that gets to the heart of the issue. Or a really, really good Tumblr post which is like three paragraphs but somehow can’t make it hang together for a whole blog post. And I’m the same way. I can easily write a blog post, like a normal length ACX blog post, but if you ask me to write a novella or something that’s four times the length of the average ACX blog post, then it’s this giant mess of “re re re re” outline that just gets redone and redone and maybe eventually I make it work.

I did somehow publish Unsong, but it’s a much less natural task. So maybe one of the skills that goes into blogging is this. But I mean, no, because people write books and they write journal articles and they write works in progress articles all the time. So I’m back to not understanding this.

Dwarkesh Patel

No, I mean ChatGPT can write you a book. There’s a difference between the ChatGPT book, which is most books and…

Scott Alexander

There are many, many times more people who have written good books than who are actively operating great bloggers right now, I think.

Daniel Kokotajlo

Maybe that’s financial?

Scott Alexander

No, no, no, no, no, no. Books are the worst possible financial strategy. Substack is where it’s at.

Daniel Kokotajlo

Worse than blogs? You think so?

Dwarkesh Patel

Oh yeah.

Scott Alexander

The other thing is that blogs are such a great status gain strategy. I was talking to Scott Aaronson about this. If people have questions about quantum computing, they ask Scott Aronson or he is like the authority. I mean there are probably hundreds of other professors who do quantum computing things but nobody knows who they are because they don’t have blogs.

So I think it’s underdone. I think there must be some reason why it’s underdone. I don’t understand what that is because I’ve seen so many of the elements that it would take to do it in so many different places and I think it’s either just a multiplication problem where 20% of people are good at one thing, 20% of people are good at another thing, and you need five things, there aren’t that many.

Plus something like courage, where people who would be good at writing blogs don’t want to do it. I actually know several people who I think would be great bloggers in the sense that sometimes they send me multi-paragraph emails in response to an ACX post and I’m like, “wow, this is just an extremely well written thing that could have been another blog post. Why don’t you start a blog?” And they’re like, “oh, I could never do that”.

Dwarkesh Patel

What advice do you have to somebody who wants to become good at it but isn’t currently good at it?

Scott Alexander

Do it every day, same advice as for everything else. I say that I very rarely see new bloggers who are great. But like when I see some. I published every day for the first couple years of Slate Star Codex, maybe only the first year. Now I could never handle that schedule, I don’t know, I was in my 20s, I must have been briefly superhuman.

But whenever I see a new person who blogs every day it’s very rare that that never goes anywhere or they don’t get good. That’s like my best leading indicator for who’s going to be a good blogger.

Dwarkesh Patel

And do you have advice on what kinds of things to start? One frustration you can have is you want to do it, but you have so little to say, you don’t have that deep a world model, a lot of the ideas you have are just really shallow or wrong. Just do it anyway?

Scott Alexander

So I think there are two possibilities there. One is that you are, in fact, a shallow person without very many ideas. In that case I’m sorry, it sounds like that’s not going to work. But usually when people complain that they’re in that category, I read their Twitter or I read their Tumblr, or I read their ACX comments, or I listen to what they have to say about AI risk when they’re just talking to people about it, and they actually have a huge amount of things to say. Somehow it’s just not connecting with whatever part of them has lists of things to blog about.

So that may be another one of those skills that only 20% of people have, is when you have an idea you actually remember it and then you expand on it. I think a lot of blogging is reactive; You read other people’s blogs and you’re like, no, that person is totally wrong. A part of what we want to do with this scenario is say something concrete and detailed enough that people will say, no, that’s totally wrong, and write their own thing.

But whether it’s by reacting to other people’s posts, which requires that you read a lot, or by having your own ideas, which requires you to remember what your ideas are, I think that 90% of people who complain that they don’t have ideas, I think actually have enough ideas. I don’t buy that as a real limiting factor for most people.

Dwarkesh Patel

I have noticed two things in my own… I mean, I don’t do that much writing, but from the little I do: one, I actually was very shallow and wrong when I started. I started the blog in college. So if you are somebody who’s like, “this is bullshit, there’s nothing to this. Somebody else wrote about this already”, that’s fine, what did you expect? Right? Of course, as you’re reading more things and learning more about the world, that’s to be expected and just keep doing it if you want to keep getting better at it.

And the other thing now when I write blog posts, as I’m writing them, I’m just like, “why? These are just some random stories from when I was in China. They’re like kind of cringe stories”. Or with the AI firm’s post, it’s like, “come on, these are just weird ideas. And also some of these seem obvious, whatever”. My podcasts do what I expect them to do. My blogs just take off way more than I expect them to take off in advance.

Scott Alexander

Your blog posts are actually very good.

Daniel Kokotajlo

Yeah, they’re good.

Dwarkesh Patel

But the thing I would emphasize is that, for me, I’m not a regular writer and I couldn’t do them on a daily basis. And as I’m writing them, it’s just this one or two week long process of feeling really frustrated. Like, “this is all bullshit, but I might as well just stick with the sunk cost and just do it”.

Scott Alexander

It’s interesting because like a lot of areas of life are selected for arrogant people who don’t know their own weaknesses because they’re the only ones who get out there. I think with blogs and I mean this is self-serving, maybe I’m an arrogant person, but that doesn’t seem to be the case. I hear a lot of stuff from people who are like, “I hate writing blog posts. Of course I have nothing useful to say”, but then everybody seems to like it and reblog it and say that they’re great.

Part of what happened with me was I spent my first couple years that way, and then gradually I got enough positive feedback that I managed to convince the inner critic in my head that probably people will like my blog post. But there are some things that people have loved that I was absolutely on the verge of, “no, I’m just going to delete this, it would be too crazy to put it out there”. That’s why I say that maybe the limiting factor for so many of these people is courage because everybody I talk to who blogs is within 1% of not having enough courage of blogging.

Dwarkesh Patel

That’s right. That’s right. And also “courage” makes it sound very virtuous, which I think it can often be, given the topic, but at least often it’s just like…

Scott Alexander

Confidence?

Dwarkesh Patel

No, not even confidence. It’s closer to maybe what an aspiring actor feels when they go to an audition where it’s like, “I feel really embarrassed. But also I just really want to be a movie star”.

Scott Alexander

So the way I got through this is I blogged for like 8 to 10 years on LiveJournal before- no, it was less than that. It’s more like five years on LiveJournal before ever starting a real blog. I posted on LessWrong for a year or two before getting my own blog. I got very positive feedback from all of that, and then eventually I took the plunge to start my own blog. But it’s ridiculous. What other career do you need seven years of positive feedback before you apply for your first position?

I mean, you have the same thing. You’ve gotten rave reviews for all of your podcasts, and then you’re kind of trying to transfer to blogging with probably... First of all, you have a fan base. People are going to read your blog. That, I think is one thing, is people are just afraid no one will read it, which is probably true for most people’s first blog. And then there are enough people who like you that you’ll probably get mostly positive feedback, even if the first things you write aren’t that polished. So I think you and I both had that. A lot of people I know who got into blogging kind of had something like that. And I think that’s one way to get over the fear gap.

Dwarkesh Patel

I wonder if this sends the wrong message or raises expectations or raises concerns and anxieties. But one idea I’ve been shooting around, and I’d be curious about your take on this: I feel like this slow, compounding growth of a fan base is fake. If I notice some of the most successful things in our sphere that have happened; Leopold releases Situational Awareness. He hasn’t been building up a fan base over years. It’s just really good. And as you were mentioning a second ago, whenever you notice a really great new blogger, it’s not like it then takes them a year or two to build up a fan base. Nope, everybody, at least that they care about, is talking about it almost immediately.

I mean, Situational Awareness is in a different tier almost. But things like that and even things that are an order of magnitude smaller than that will literally just get read by everybody who matters. And I mean literally everybody. And I expect this to happen with AI 2027 when it comes out. But Daniel, you’ve been building your reputation within this specific community, and I expect AI 2027 it's just really good. And I expect it’ll just blow up in a way that isn’t downstream of you having built up an audience over years.

Daniel Kokotajlo

Thank you. I hope that happens. We’ll see.

Scott Alexander

Slightly pushing back against that. I have statistics for the first several years of Slate Star Codex, and it really did grow extremely gradually. The usual pattern is something like every viral hit, 1% of the people who read your viral hits stick around. And so after dozens of viral hits, then you have a fan base. But smoothed out, It does look like a- I wish I had seen this recently, but I think it’s like over the course of three years, it was a pretty constant rise up to some plateau where I imagine it was a dynamic equilibrium and as many new people were coming in as old people were leaving.

I think that with Situational Awareness, I don’t know how much publicity Leopold put into it. We’re doing pretty deliberate publicity, we’re going on your podcast. I think you can either be the sort of person who can go on a Dwarkesh podcast and get the New York Times to write about you, or you can do it organically, the old fashioned way, which is very long.

Dwarkesh Patel

Yeah. Okay. So you say that throwing money at people to make them, to get them to blog at least didn’t seem to work for the FTX folks. If it was up to you, what would you do? What’s your grant plan to get 10 more Scott Alexanders?

Scott Alexander

Man. So my friend Clara Collier, who’s the editor of Asterisk magazine, is working on something like this for AI blogging. And her idea, which I think is good, is to have a fellowship. I think Nick’s thing was also a fellowship, but the fellowship would be, there is an Asterisk AI blogging fellows’ blog or something like that. Clara will edit your post, make sure that it’s good, put it up there and she’ll select many people who she thinks will be good at this. She’ll do all of the kind of courage requiring work of being like, “yes, your post is good. I’m going to edit it now. Now it’s very good. Now I’m going to put it on the blog”.

And I think her hope is that, let’s say of the fellows that she chooses, now it’s not that much of a courage step for them to start it because they have the approval of what last psychiatrist would call an omniscient entity, somebody who is just allowed to approve things and tell you that you’re okay on a psychological level. And then like maybe of those fellows, some percent of them will have their blog posts be read and people will like them. And I don’t know how much reinforcement it takes to get over the high prior everyone has on “no one will like my blog”. But maybe for some people, the amount of reinforcement they get there will work.

Yeah, like an interesting example would be all of the journalists who have switched to having Substacks. Many of them go well. Would all of those journalists have become bloggers if there was no such thing as mainstream media? I’m not sure. But if you’re Paul Krugman you know people like your stuff, and then when you quit the New York Times you know you can just open a substack and start doing exactly what you were doing before. So I don’t know, maybe my answer is there should be mainstream media. I hate to admit that, but maybe it’s true.

Dwarkesh Patel

Invented it from first principles.

Scott Alexander

Yeah.

Dwarkesh Patel

Well I do think that it should be treated more as a viable career path. Where right now, if you told your parents, “I’m going to become a startup founder”, I think the reaction would be like, “there’s a 1% chance you’ll succeed, but it’s an interesting experience and if you do succeed, that’s crazy. That’ll be great. If you don’t, you’ll learn something. It’ll be helpful to the thing you do afterwards”.

We know that’s true of blogging, right? We know that it helps you build up a network, it helps you develop your ideas. And if you do succeed, you get a dream job for a lifetime. And I think maybe they don’t have that mindset, but also they under appreciate how much you actually could succeed at it. It’s not a crazy outcome to make a lot of money as a blogger.

Scott Alexander

I think it might be a crazy outcome to make a lot of money as a blogger. I don’t know what percent of people who start a blog end up making enough that they can quit their day job. My guess is it’s a lot worse than for startup founders. I would not even have that as a goal so much as like the Scott Aaronson goal of, okay, you’re still a professor, but now you’re the professor whose views everybody knows and who has kind of a boost up in respect in your field and especially outside of your field. And also you can correct people when they’re wrong, which is a very important side benefit.

Dwarkesh Patel

Yeah. How does your old blogging feedback into your current blogging? So when you’re discussing a new idea, I mean, AI or whatever else, are you just able to pull from the insights from your previous commentary on sociology or anthropology or history or something?

Scott Alexander

Yeah. So I think this is the same as anybody who’s not blogging. I think the thing everybody does is they’ve read many books in the past and when they read a new book, they have enough background to think about it. Like you are thinking about our ideas in the context of Joseph Henrich’s book. I think that’s good, I think that’s the kind of place that intellectual progress comes from. I think I am more incentivized to do that.

It’s hard to read books. I think if you look at the statistics, they’re terrible. Most people barely read any books in a year. And I get lots of praise when I read a book and often lots of money, and that’s a really good incentive. So I think I do more research, deep dives, read more books than I would if I weren’t a blogger. It’s an amazing side benefit. And I probably make a lot more intellectual progress than I would if I didn’t have those really good incentives.

Dwarkesh Patel

Yeah. There was actually a prediction market about the year by which an AI would be able to write a blog post as good as you. Was it 2026 or 2027? I think it was 2027. It was like 15% by 2027 or something like that. It is an interesting question of they do have your writing and all other good writing in trading distribution. And weirdly, they seem way better at getting superhuman at coding than they are at writing, which is the main thing in their distribution.

Scott Alexander

Yeah. It’s an honor to be my generation’s Garry Kasparov figure. Yeah. So I’ve tried this. And first of all, it does a decent job. I respect its work. It’s not perfect yet. I think it’s actually better at the style on a word-to-word, sentence-to-sentence level, than it is at planning out a blog post. So I think there are possibly two reasons for it: One, we don’t know how the base model would have done at this task. We know that all the models we see are to some degree reinforcement learning into a kind of corporate speak mode. You can get it somewhat out of that corporate speak mode. But I don’t know to what degree this is actually doing its best to imitate Scott Alexander versus hit some average between Scott Alexander and corporate speak. And I don’t think anyone knows except the internal employees who have access to the base model.

And the second thing I think of maybe just because it’s trendy has an agency or horizon failure, like deep research is an okay researcher. It’s not a great researcher. If you actually want to understand an issue in depth, you can’t use deep research. You gotta do it on your own. So if I spend maybe five to 10 hours researching a really research heavy blog post, the METR thing, I know we’re not supposed to use it for any task except coding, but like it says, on average the AI’s horizon is one hour. So I’m guessing it just cannot plan and execute a good blog post. It does something very superficial rather than actually going through the steps. So my guess for that prediction market would be whenever we think the agents are actually good. I think in our scenario that’s like late 2026. I’m going to be humble and not hold out for the superintelligence.

Daniel Kokotajlo

What about comments? I feel like intuitively it feels like before we see the AI’s writing great blog posts that go super viral repeatedly, we should see them writing highly upvoted comments on things.

Scott Alexander

Yeah. And I think somebody mentioned this on the LessWrong post about it and somebody made some AI generated comments to that post. They were not great. But I wouldn’t have immediately picked them out of the general distribution of LessWrong comments as especially bad. I think, like, I think if you were to try this, you would get something that was so obviously an AI house style that it would use the word ‘delve’ or things along those lines.

I think if you were able to avoid that maybe by using the base model, maybe by using some kind of really good prompt to be like, “no, do this in Gwern’s voice”, you would get something that was pretty good. I think if you wrote a really stupid blog post, it could point out the correct objections to it. But I also just don’t think it’s as smart as Gwern right now. So its limit on making Gwern-style comments is both- It needs to be able to do a style other than corporate delve slop and then it actually needs to get good.

Daniel Kokotajlo

It needs to have good ideas that other people don’t already have.

Scott Alexander

Yeah. And I mean I think it can write as well as a smart average person in a lot of ways. And I think if you have a blog post that's worse than that or at that level, it can come up with insightful comments about it. I don’t think it could do it on a quality blog post.

Dwarkesh Patel

There was this recent Financial Times article about how have you reached peak cognitive power? Where it was talking about declining scores in PISA and SAT and so forth. On the Internet especially, it does seem like there might have been a golden era before I was that active on the forums or whatever. Do you have nostalgia for a particular time on the Internet when it was just like, this is an intellectual mecca?

Scott Alexander

I am so mad at myself for missing most of the golden age of blogging. I feel like if I had started a blog in 2000 or something, then- I don’t know, I’ve done well for myself, I can’t complain- but the people from that era all founded news organizations or something. I mean, God save me from that fate. I would have liked to have been there. I would have liked to see what I could have done in that area. I mean, I wouldn’t compare the decline of the Internet to that stuff with PISA because I’m sure the Internet is just more people are coming on, it’s a less heavily selected sample.

But yeah, I could have passed on the whole era where they were talking about atheism versus religion nonstop. That was pretty crazy. But I do hear good things about the golden age of blogging.

Dwarkesh Patel

Anybody who was sort of counterfactually responsible for you starting to blog or keeping blogging?

Scott Alexander

So I owe a huge debt of gratitude to Eliezer Yudkowski. I had a live journal before that. But it was going on LessWrong that convinced me I could move to the big times. And second of all, I just think I learned I imported a lot of my worldview from him. I think I was the most boring normie liberal in the world before encountering LessWrong. And I don’t 100% agree with all LessWrong ideas, but just having things of that quality beamed into my head and for me to react to and think about was really great.

Dwarkesh Patel

And tell me about the fact that you could be and were at some point anonymous, I think for most of human history, somebody who is an influential advisor or an intellectual or somebody. Actually, I don’t know if this is true. You would have had to have some sort of public persona. And a lot of what people read into your work is actually a reflection of your public persona.

Scott Alexander

Sort of. The reason half of these ancient authors are called things like Pseudo Dionysus or Pseudocelsus is that you could just write something being like, “oh, yeah, this is by Saint Dionysus”. And then, I don’t know, you could be anybody.

And I don’t know exactly how common that was in the past. But yeah, I agree that the Internet has been a golden age for anonymity. I’m a little bit concerned that AI will make it much easier to break anonymity. I hope the golden age continues.

Dwarkesh Patel

Yeah, seems like a great note to end on. Thank you guys so much for doing this.

Scott Alexander

Thank you.

Daniel Kokotajlo

Thank you so much. This was a blast.

Dwarkesh Patel

Yeah, I had a great time.

Daniel Kokotajlo

Huge fan of your podcast.

Dwarkesh Patel

Thank you.

I recorded an AMA! I had a blast shooting the shit with my friends Trenton Bricken and Sholto Douglas. We discussed my new book, career advice given AGI, how I pick guests, how I research for the show, and some other nonsense.

My book, “The Scaling Era: An Oral History of AI, 2019-2025” is available in digital format now. Preorders for the print version are also open!

Watch on YouTube; listen on Apple Podcasts or Spotify.

(0:00:00) - Book launch announcement

(0:04:57) - AI models not making connections across fields

(0:10:52) - Career advice given AGI

(0:15:20) - Guest selection criteria

(0:17:19) - Choosing to pursue the podcast long-term

(0:25:12) - Reading habits

(0:31:10) - Beard deepdive

(0:33:02) - Who is best suited for running an AI lab?

(0:35:16) - Preparing for fast AGI timelines

(0:40:50) - Growing the podcast

Dwarkesh Patel

Today, this is going to be an Ask Me Anything episode. I'm joined by my friends, Trenton Bricken and Sholto Douglas. You guys do some AI stuff, right?

Trenton Bricken

Yeah.

Sholto Douglas

We dabble.

Dwarkesh Patel

They're researchers at Anthropic. Other news; I have a book launching today, it's called The Scaling Era. I hope one of the questions ends up being why you should buy this book. But we can kill two birds with one stone. But, okay, let's just get at it. What's the first question that we gotta answer?

Trenton Bricken

So, I want to ask the flyball question that I heard before, of: why should ordinary people care about this book? Like, why should my mom buy and read the book?

Dwarkesh Patel

Yeah. First, let me tell you about the book, what it is. So, you know, these last few years, I've been interviewing AI lab CEOs, researchers, people like you guys, but also scholars from all kinds of different fields, economists, philosophers. And they've been addressing, I think, what are basically the gnarliest, most interesting, most important questions we've ever had to ask ourselves. Like, what is the fundamental nature of intelligence? What will happen when we have billions of extra workers? How do we model out the economics of that? How do we think about an intelligence that is greater than the rest of humanity combined? Is it even a coherent concept? And so, what I'm super delighted with is that with Stripe Press, we made this book where we compiled and curated the best, most insightful snippets across all these interviews. And you can read Dario addressing, why does scaling work? And then on the next page is Demis explaining DeepMind's plans for whether they're gonna go with the RL route and how much of the AlphaZero stuff will play into the next generation of LLMs. And on the next page is, of course, you guys going through the technical details of how these models work.

And then there's so many different fields that are implicated. I mean, I feel like AI is one of the most multi-disciplinary fields that one can imagine, because there's no field, no domain of human knowledge that is not relevant to understanding what a future society of different kinds of beings will look like. You're gonna have Carl Shulman talk about how the scaling hypothesis shows up in primate brain scaling from chimpanzees to humans. On the next page might be an economist trying to argue, like Tyler Cowen, explaining why he doesn't expect explosive economic growth, and why the bottlenecks will eat all that up. Um, anyways, so that's why your mom should buy this book. It’s the distillation of all these different fields of human knowledge applied to the most important questions that humanity is facing right now.

Trenton Bricken

I do like how the book is sliced up by different topics and across interviews. So it does seem like a nice way to listen to all of the interviews in one digestible way.

Dwarkesh Patel

Yeah. There's two interviews I've done that haven't been released publicly before that are in the book. So, one was that Jared Kaplan, who's one of your co-founders, and this is another example where it's like, he's a physicist and he's explaining scaling from this very mathematical perspective about data manifolds. And then on the next page you have a totally different perspective. It's like Goren talking about why did general intelligence actually evolve in the first place, what is the actual evolutionary purpose of it? And it's page by page, right? You can just get addresses. Even for me, the person who's been on the other end of these conversations, it was actually really cool to read it and just be like, "Oh, actually now I realize how these insights connect to each other."

Trenton Bricken

Yeah, the only other thing that stood out to me as well is the introduction section-

Dwarkesh Patel

The only thing that stood out to you?

Trenton Bricken

Yeah, that was really the only thing that was noteworthy. I just mean [what] stood out in accessibility is the introduction section and the diagrams for all the different inputs that enable you to train a machine learning model. Stripe Press books are also just beautiful, they have these nice side captions for explaining what parameters are, what a model is, these sorts of things.

Dwarkesh Patel

Actually, when we did our episode together, a bunch of people, I don't know if you saw this, independently made these blog posts and Anki cards and shit where they're explaining the concept because we just kind of passed over some things. And hopefully we've given a similar treatment to every single interview I've done, where you can read a very technical interview with a lab CEO or something, or an engineer, or a researcher, and then the side is like: here's like more context, here's more definitions, here's more commentary. And I, yeah, I feel like it elevated the conversations.

Sholto Douglas

So in other words, my parents will finally understand what I do for a job. They're gonna get it very well.

Dwarkesh Patel

Maybe my parents will. Because I got a book.

Trenton Bricken

All mine need to know is that my name's in a book.

Sholto Douglas

You're a co-author.

Trenton Bricken

They're like, "Cool."

Sholto Douglas

Should we get into the AMA questions?

Trenton Bricken

Let's do it.

Sholto Douglas

All right. So Brian Krav asks, "The issue you raised with Dario and occasionally tweet about relating to models not making connections across different topics, some sort of combinatorial attention challenge, what are your thoughts on that now? Do you solve it with scale, thinking models or something else?"

Dwarkesh Patel

So the issue is, one of the questions I asked Dario is, look, these models have all of human knowledge memorized and you would think if a human had this much stuff memorized, and they were moderately intelligent, they could be making all these connections between different fields. And there are examples of humans doing this, by the way. There's… Donald Swann or something like this [Don R. Swanson], this guy noticed that what happens to a brain after magnesium deficiency is exactly the structure you see during a migraine. So then he's like, you take magnesium supplements and we're gonna cure a bunch of migraines. And it worked. And there's many other examples of things like this where you just notice two different connections between pieces of knowledge. Why, if these LLMs are intelligent, are they not able to use this unique advantage they have to make these kinds of discoveries? I feel a little shy, me giving answers on AI shit with you guys here. But, actually Scott Alexander addressed this question in one of his AMA threads, and he's like, "Look, humans also don't have this kind of logical omniscience", right? He used the example of, in language, if you really thought about, why are two words connected? And it's like, I understand why “rhyme” has the same etymology as this other word. But you just don't think about it, right? There's this combinatorial explosion. I don't know if that addresses the fact that- we know humans can do this, right? The humans have in fact done this, and I don't know of a single example of LLMs ever having done it. Actually, yeah, what is your answer to this?

Sholto Douglas

I think my answer at the moment is that the sort of pre-training objective doesn't necessarily- like it imbues with this nice flexible general knowledge about the world, but doesn't necessarily imbue the skill of making novel connections or research. The kinds of things that people are trained to do through PhD programs and through the process of exploring and interacting with the world.

And so I think at a minimum you need significant RL in at least similar things to be able to approach making novel discoveries. And so I would like to see some early evidence of this as we start to build models that are interacting with the world and trying to make scientific discoveries, and modeling the behaviors that we expect of people in these positions. Because I don't actually think we've done that in a meaningful or scaled way as a field, so to speak.

Trenton Bricken

Riffing off that with respect to RL, I wonder if models currently just aren't good at knowing what memories they should be storing. Most of their training is just predicting the next word on the internet and remembering very specific facts from that. But if you were to teach me something new right now, I'm very aware of my own memory limitations, and so I would try to construct some summary that would stick. And models currently don't have the opportunity to do that. Memory scaffolding in general is just very primitive right now. I mean-

Sholto Douglas

Right, like Claude Plays Pokemon.

Trenton Bricken

Exactly, yeah, or like someone worked on it, it was awesome, it got far, but, another excited Anthropic employee then iterated on the memory scaffold and was able to very quickly improve on it. So that's one. I do also just wonder if models are idiot savants. The best analogy might be to Kim Peek. So Kim Peek, was born without a corpus callosum, if I recall correctly. Each hemisphere of his brain operated quite independently. So, he'd open a book, there'd be two pages visible, each eye would read one of the pages. And he had a perfect encyclopedic memory of everything he'd ever read. But at the same time, he had other debilitations; functioning socially, these sorts of things. And it's just kind of amazing how good LLMs are at very niche topics, but can totally fail at other ones still.

Dwarkesh Patel

I really wanna double-click on this thing of why there's this trade-off between memorization. Why does cutting it off... apparently it's connected to this debilitation, but why can't… Wiki text is like five megabytes of information. The human brain can store much more, so why does the human brain just not want us to memorize these kinds of things, and is actively pruning, and… yeah, I don't know. But we don't have to do it right now. We'll do a separate episode.

Trenton Bricken

Yeah, just one thing I'll say on that is, there is another case study of someone with a perfect memory, so they never forgot anything. But their memory was too debilitating. It'd be, like, your context window for the transformer is trillions of tokens. And then you spend all your time attending to past things, and are too trapped in the details to extract any meaningful generalizable insights from it.

Dwarkesh Patel

Yeah. Terrence Deacon, whose book you recommended, had this interesting insight about how we learn best when we're children, but we forget literally everything that happened to us when we were children, right? We have total amnesia.

And adults have this in-between where we don't remember exact details, but we can still learn in a pretty decent way. And then LLMs are on the opposite end of this gradient where they'll get the exact phrasing of Wiki text down, but they won't be able to generalize in these very, obvious ways.

Sholto Douglas

A little bit like Gwern's theory, optimizer theory, no?

Dwarkesh Patel

Yeah, I think I probably got it from that.

Trenton Bricken

Yeah, Gwern has definitely had a big influence on all this for me as well.

Dwarkesh Patel

I feel like what’s underappreciated on the podcast is we have this group chat, and we also just meet up a lot in person. And all the output from the podcast just comes from you and a couple other people just feeding me ideas and nudges and whatever, and then I can just use that as an intuition pump during the conversation.

Trenton Bricken

Yeah, you're not the only one.

Dwarkesh Patel

What do you mean?

Trenton Bricken

Oh, like, I benefit immensely from just hearing what everyone else has to say. It's all regurgitation in one way or another.

Sholto Douglas

Another question?

Dwarkesh Patel

Yes.

Trenton Bricken

Maybe Rabid Monkey asks, "Imagine you have a 17-year-old brother/nephew just starting college. What would you recommend he study, given your AGI timelines?"

Dwarkesh Patel

That's so tough, right? I don't know, become a podcaster? I feel like that job's still gonna be around. It's funny, because I studied computer science, and in retrospect- at the time, you could've become a software engineer or something. Instead, you became a podcaster, it’s kind of an irresponsible career move, but in retrospect, it's like… It kinda worked out. Just as these guys are getting automated.

Sholto Douglas

I get asked this question all the time, and one answer that I like to give is that you should think about the next couple of years as increasing your individual leverage by a huge factor every year.

So already software engineers will come up and say, "You know, I'm two times faster," or, "In new languages, I'm five times faster than I was last year." I expect that trend line to continue, basically, as you go from this model of, "Well, I'm working with some model that's assisting me on my computer, and it's basically a pairing session," to, "I'm managing a small team," through to, "I'm managing a division or a company". Basically, that is targeting a task. And so I think that deep technical knowledge in fields will still matter in four years. It absolutely will. Because you will be in the position of managing dozens- or, your individual management bandwidth will be maxed out by trying to manage teams of AIs.

And maybe we end up in a true singularity world where you have AIs managing AIs and this kinda stuff. But I think in a very wide part of the possibility spectrum you are managing enormous, vastly more resources than an individual could command today, and you should be able to solve so many more things with that.

Dwarkesh Patel

That's right, and I think I would emphasize that this is not just cope. Like, it genuinely is a case that these models lack the kind of long-term coherence which is absolutely necessary for making a successful company or… Just, getting a fucking office is kinda complicated, right? So you can just imagine that for sector after sector- the economy is really big, right?

Sholto Douglas

And really complex.

Dwarkesh Patel

Exactly, and so, I don't know the details, but I assume if it's a data sparse thing where you gotta know what is the context of what's happening in the sector or something, I feel like you'd be in a good position.

Maybe the other thought I have is that it's really hard to plan your career in general. And I don't know what advice that implies, because I remember being super frustrated. I was in college, and the reason I was doing the podcast was to figure out what it is I want to do. It wasn't the podcast itself. And I would go on, 80,000 Hours or whatever career advice, and in retrospect it was all mostly useless, and just try doing things. I mean, especially with AI, it's so hard to forecast what kind of transformations there will be, so try things, do things. I mean, it's such banal, vague advice, but I am quite skeptical of career advice in general.

Sholto Douglas

Well, the piece of career advice that I'm not skeptical of is put yourself close to the frontier, because you have a much better vantage point from there. Right? You can study deep technical things, whether it's computer science or biology, and get to the point where you can see what the issues are because it's actually remarkably obvious at the frontier what the problems are. It's very difficult to see…

Dwarkesh Patel

Actually, do you think there is an opportunity, because one of the things people bring up is, maybe the people who are advanced in their career and have all this tacit knowledge will be in a position to be accelerated by AI, but you guys four years ago or two years ago, when you were getting discovered or something, that kind of thing where you have a GitHub open issue and you try to solve it; is that just, that's done, and so the onboarding is much harder?

Sholto Douglas

That's still what we look for in hiring. So, you know?

Trenton Bricken

Yeah, I'm in favor of the “learn fundamentals, gain useful mental models”, but it feels like everything should be done in an AI-native way, or top-down instead of your bottom-up learning. So first of all, learn things more efficiently by using the AI models, and then just know where their capabilities are and aren't.

And I would be worried and skeptical about any subject which prioritizes rote memorization of lots of facts or information instead of ways of thinking. But if you're always using the AI tools to help you, then you'll naturally just have a good sense for the things that it is and isn't good at.

Sholto Douglas

Okay, next one. What is your strategy, method, or criteria for choosing guests?

Dwarkesh Patel

So, the most important thing is, do I wanna spend one to two weeks reading every single thing you have ever written, every single interview you ever recorded, talking to a bunch of other people about your research? Because I get asked by people who are quite influential often to be like, "Would you have me on your podcast?" and more often than not, I say no, for two reasons. One is just, okay, you're influential, it's not fundamentally that interesting as an interview prospect. I don't think about the hour that I'll spend with you. I think about the two weeks, because this is my life, right? The research is my life, and I wanna have fun while doing it. So is this gonna be an interesting two weeks to spend? Is it gonna help me with my future research or something?

And the other is, big guests don't really matter that much if you just look at what are the most popular episodes or, what in the long run helps a podcast grow. By far my most popular guest is Sarah Payne, and she, before I interviewed her, was just a scholar, who was not publicly well-known at all, and I just found her books quite interesting. So my most popular guests are Sarah Payne and then Sarah Payne, Sarah Payne, Sarah Payne because ... I have electric chairs with her.

And by the way, from a viewer-a-minute adjusted basis, I host the Sarah Payne Podcast where I occasionally talk about AI.

Trenton Bricken

That's funny.

Dwarkesh Patel

And then it's David Reich, who is a geneticist of ancient DNA. He's somewhat well-known, but he had a best-selling book, but he's not Satya Nadella or Mark Zuckerberg, who are the next people on the list. And then again, I think that pretty soon it's like you guys or Leopold or something, and then you get to the lab CEOs or something.

So, big names just don't matter that much for what I'm actually trying to do. And it's also really hard to predict who's gonna be the David Reich or a Sarah Payne, so just have fun. Talk to whoever you want to spend time researching, and it's a pretty good proxy for what will actually be popular.

Sholto Douglas

What was the specific moment, if there was one, that you realized you, that producing your podcast was a viable long-term strategy?

Dwarkesh Patel

I think when I was shopping around ad spots for a Mark Zuckerberg episode. And now when I look back on it, it's not in retrospect that mind-blowing, but at the time I'm like, “oh, I could actually hire an editor full-time, or maybe more editors than one, and from there turning into a real business”. Because before people would tell me, "Oh, these other podcasts are making whatever amount of money." And I'd be like, "How?" You know?

So I have this running joke with one of my friends. I don't know if you've seen me do this, but every time I encounter a young person who's like, "What should I do with my life?" I'm like, "You gotta start a blog. You gotta be the Matt Levine of AI." You can do this. It's a totally empty niche. And I have this running joke with them where they're like, "You're like a country bumpkin who's won the lottery. And you go up to everything and everyone and just like, "Guys, a scratch pad. Get the scratch pad.”"

Sholto Douglas

I do wanna press on that a bit more because your immediate answer to the 17-year-old was to start a podcast. So what niches are there? What sort of things would you be excited to see in new blogs, podcasts?

Dwarkesh Patel

I wonder if you guys think this too, but I think this “Matt Levine of AI” is a totally open niche as far as I can tell, and I apologize to those who are trying to fill it in. And so the other thing I'd really emphasize is, it is really hard to do this based on other people's advice, or to say “at least I'm trying not to fill a specific niche”. If you think about any sort of successful new media thing out there, it has two things which are true: It's often not just geared towards one particular topic or interest, and two, the most important thing is that it is propelled by a single person's vision. It's not a collective or whatever. And so the thing I really want to emphasize is it can be done.

Two, you can make a lot of money at it, which is not the most important thing probably for the kind of person who would succeed at it, but still is just worth knowing that it's a viable career.

Three, that basically you're gonna feel like shit in the beginning where all your early stuff is gonna kind of suck. Maybe some of it will get appreciated. But it seems like bad advice to say still stick through it in case you actually are terrible because some people are terrible. But in case you are not, just do it, right? Like what is the three months of blogging on the side really gonna cost you? And people just don't actually seriously do the thing for long enough to actually get evidence or get the sort of RL feedback on, oh, this is how you do it; this is how you frame an argument. This is how you make a compelling thing that people will want to read or watch.

Sholto Douglas

Blogging is definitely underrated. I think like most of us have probably-

Dwarkesh Patel

So you both had blogs which were relevant. I don't know if they're actually relevant to getting-

Sholto Douglas

Not that. They were like somewhat relevant. But I think more so that we have all read almost all the blogs that do in-depth treatises on AI. Like if you write something that is high quality, it is almost invariably going to be shared around Twitter and read.

Dwarkesh Patel

Oh, this is so underappreciated. So, two pieces of evidence. I was talking to a very famous blogger you would know, and I was asking him, "How often do you discover a new undiscovered blogger?" And he was like, "Eh, happens very rarely, like maybe once a year." and then I ask him, "How long after you discover him or her does the rest of the world discover them?" And he's like, "Maybe a week."

And what that suggests is it's actually really efficient. Like... Oh, I have some more takes.

Trenton Bricken

Let's hear them. This is, this is the AMA.

Dwarkesh Patel

So I believe that slow compounding growth in media is kind of fake. Like, Leopold's situational awareness. It's not like he was building up an audience for a long time, for years or something. It was really good. Disagree or agree with it, and if it's good enough, literally everybody who matters- and I mean that literally- will read it. And it's hard to zero shot something like that. But the fundamental thing to emphasize is the compounding growth, at least for me, has been I feel like I've gotten better.

And it's not so much that somehow the three years of having 1,000 followers were somehow a compounding... I don't think it was that. I think it was just that it took a while to get better.

Sholto Douglas

Yeah, certainly when Leopold posted that, the next day, it's almost like you can picture it being stapled to the wall, so to speak, on Twitter.

Like, you know, everyone was talking about it. You went to any event for the following week, every single person in the entire city was talking about that essay. It was like Renaissance Florence or whatever.

Dwarkesh Patel

That's right. Yeah. The world is small.

Sholto Douglas

World is small.

Trenton Bricken

What would you say is your first big success? I'm trying to think back to when I first found your podcast. I distinctly remember you had your blog post on the Annus Mirabilis.

And Jeff Bezos retweeted it, I think. I'm trying to remember if it was before that or not, but, yeah. I'm curious, your answer.

Dwarkesh Patel

I feel like that was it. And it wasn't something where it was some big insight that deserved to blow up like that. It was just taking some shots on goal. They were all insight porn-y, and then one of them I guess caught the right guy's attention and, yeah. But I think that was it.

Sholto Douglas

Yeah, that's something else which is underappreciated, which is that a piece of writing doesn't need to have a fundamentally new insight so much as give people a way to express cleanly a set of ideas that they are already aware of in a broader way. And if it's really crisp and not articulate, then even still that's very valuable.

Dwarkesh Patel

And the one thing I should emphasize, which I think is maybe the most important thing to the feedback loop. It's not the compounding growth of the audience. I don't even think it's me getting more shots on goal in terms of doing the podcast. I actually don't think you improve that much by just doing the same thing again and again. If there's no reward signal you'll keep doing whatever you were doing before.

I genuinely think the most important thing has been that the podcast is good enough that it merits me getting to meet people like you guys. Then I become friends with people like you. You guys teach me stuff. I produce more good podcasts, so hopefully slightly better. That helps me meet people in other fields. They teach me more things. With the China thing recently, I wrote this blog post about a couple stories about things that happened in China. And that alone has netted me an amazing China network in the matter of one blog post. Right? And so hopefully, if I do an episode on China, I will be better as a result. And hopefully that happens across field after field. And so just getting to meet people like you is actually the main sort of flywheel.

Sholto Douglas

Interesting. So move to San Francisco?

Dwarkesh Patel

Yes. If you're trying to do AI, yeah.

Sholto Douglas

Oh, very important question from Jacked Pajeet. How much can you bench?

Trenton Bricken

You can't lie because we both know the answer.

Dwarkesh Patel

At one point I did bench 225 for four. Now I think I'm probably 20 pounds lighter than that or something.

The reason you guys are asking me this is because I've gone lifting with both of you. And I remember Trent and I were doing pull-ups and a bench. And it'd be, like, ‘bench’, and he'd throw on another plate or something. And then instead of pull-ups, he'd be cranking out these muscle ups.

Trenton Bricken

It's all technique. Let's make sure.

Dwarkesh Patel

So they both bench more than me. But I'm trying my best.

Trenton Bricken

Ask again in six months.

Dwarkesh Patel

Yeah.

Sholto Douglas

What's your favorite history book? There's a wall of them behind you.

Dwarkesh Patel

Oh, obviously the Caro LBJ biographies. The main thing I took away from those books is LBJ had this quote that he would tell his debate [students]. In his early 20s, he taught debate to these poor Mexican students in Texas. And he used to tell them, "If you do everything, you'll win." I think it's an underrated quote. So that's the main thing I took away. And you see it through his entire career, where there's a reasonable amount of effort which goes by 20/80. You do the 20 to get the 80% of the effect. And then if you go beyond that to get, "Oh, no. I'm not just gonna do 20%, I'm gonna just do the whole thing." And there's a level even beyond that, which is an unreasonable use of time. This is going to have no ultimate impact, and still try doing that.

Trenton Bricken

You've shared on Twitter, using Anki. Or even, like, a Claude integration. Do you do book clubs? Do you use GoodReads? And what are you reading right now?

Dwarkesh Patel

I don't have book clubs. [Bud the SpaceBar edition] has just genuinely been a huge uplift in my ability to learn. Mostly because- it's not even the long-term impact over years, though I think that is part of it and I do regret all the episodes I did without using Speech Marking Cards, because all the insights have just sort of faded away. The main thing is, if you're studying a complicated subject, at least for me, it's been super helpful to consolidate. So if you don't do it, you feel like a general where you're like, "I'm gonna wage a campaign against this country." And then you climb one hill. And then the next day you're at a retreat, and then you climb the same hill. There might be a more kosher analogy. yeah. And then the other question was what am I reading right now?

Oh. My friend Alvaro De Menard, author of Fantastic Anachronism. Can I just hold it up? Actually it's right here. I hope he's okay with me sharing this. But he made 100 copies of this translation he did of his favorite Greek poet. Cavafy. Hopefully I didn't mispronounce it. Sorry, that one has a good inscription for Guern, because that's his copy, but it's super delightful, and that's what I've been reading recently.

Trenton Bricken

Any insights from it so far?

Dwarkesh Patel

Poets will hate this framing. I feel like poetry is like TikTok, where you get this quick vibe of a certain thing, and then you swipe. And then you get the next vibe, swipe… Alvaro, I'm sorry.

Sholto Douglas

No, that's interesting. How do you go about learning new things or preparing for an episode? You mentioned the one to two-week period where you're deep diving on the person. What does that actually look like?

Dwarkesh Patel

It's very much the obvious thing: you read their books, you read the papers. If they have colleagues, you try to talk to them to better understand the field. I will also mention that all I have to do is ask them questions, and I do think it's much harder to learn a field to be a practitioner than just learn enough to ask interesting questions. But for that it's very much the obvious thing you'd expect.

Trenton Bricken

“Based Carl Sagan” asks, "What are your long-term goals and ambitions?"

Dwarkesh Patel

AGI kind of just makes the prospect of a long term harder to articulate, right? You know the Peter Thiel quote about what is your 10-year plan and why can't you do it in six months?

Like, it's especially salient, given timelines. For the foreseeable future, grow the podcast and do more episodes, maybe more writing. So we'll see what happens after 10 years or something. The world might be different enough. So basically, podcast for now.

Sholto Douglas

Something you've spoken to me about, and particularly when you were trying to hire people for the podcast was what you wanted to achieve with the podcast. In what way do you want the podcast to shape the world, so to speak? Do you have any thoughts on that or... because I remember you telling me, "I really want people to actually understand AI and how this might change their lives."

Or, “what we could be doing now to shape the world such that it ends up better."

Dwarkesh Patel

I don't know. I have contradictory views on this. On the one hand, I do know that important decisions are being made right now in AI. And I do think, riffing on what we were saying about situational awareness, if you do something really good, it has a very high probability of one-shotting the relevant person, and people are generally reasonable. You make a good argument, it'll go places. On the other hand, I just think it's very hard to know what should be done. You gotta have the very correct world model, and then you gotta know how in that world model the action you're taking is gonna have the effect you anticipate. And even in the last week, I've changed my mind on some pretty fundamental things about what I think about the possibility of an intelligence explosion or transformative AI as a result of talking to the Epoch folks.

Basically, the TLDR is, I want the podcast to just be an epistemic tool for now because I think it's just very easy to be wrong. And so just having a background level of understanding of the relevant arguments is the highest priority.

Sholto Douglas

Makes sense.

Dwarkesh Patel

What's your sense? What should I be doing?

Trenton Bricken

I mean, I think the podcast is awesome, and a lot more people should listen to it, and there are a lot more guests I'd be excited for you to interview.

Dwarkesh Patel

Gotta give me your recs.

Trenton Bricken

So it seems like a pretty good answer for now.

Sholto Douglas

Yeah. I think making sure that, like, there is a great debate of ideas on not, not just AI, but on other fields, and everything is incredibly high leverage in value.

Dwarkesh Patel

Yeah, yeah, yeah.

Sholto Douglas

How do you groom your beard? It's majestic.

Dwarkesh Patel

I don't know what to say, just genetics. I do trim it, but-

Sholto Douglas

No beard oil?

Dwarkesh Patel

Sometimes I do beard oil.

Trenton Bricken

How often?

Dwarkesh Patel

Once every couple of days.

Sholto Douglas

That's not sometimes! That's pretty often!

Trenton Bricken

But, do you have different shampoo for your head and your beard?

Dwarkesh Patel

No.

Trenton Bricken

What kind of shampoo do you use?

Dwarkesh Patel

Anti-dandruff.

Trenton Bricken

Do you condition it?

Dwarkesh Patel

Yeah.

Trenton Bricken

How often do you shave it?

Dwarkesh Patel

Who put you up to this?

Trenton Bricken

We're giving people the answers that they want.

Sholto Douglas

Big shampoo. Big beard oil.

Trenton Bricken

Yeah, you can sell some ad slots to different shampoo companies and we can edit it.

Sholto Douglas

Maybe we sold an ad slot. Who knows?

Dwarkesh Patel

Sorry, you had this idea of merch. Do you wanna explain this T-shirt idea?

Trenton Bricken

Yeah, yeah, yeah. So people should react to this. Someone should make it happen. Dwarkesh wants merch, but he doesn't want to admit that he wants it. Or he doesn't want to make it himself because that seems tacky.

So I really want a plain white tee with just Dwarkesh's beard in the center of it. That's it. Nothing else.

Dwarkesh Patel

But you were saying it should have a different texture than the rest of the shirt.

Trenton Bricken

Oh, so when I was really riffing off it, where maybe a limited edition set can have some of your beard hair actually sewn into the shirt.

Dwarkesh Patel

Oh my God.

Trenton Bricken

That'd be pretty cool. I would pay. I would pay for that.

Sholto Douglas

How much?

Dwarkesh Patel

I've got, like, patches all over my beard.

Trenton Bricken

Depends on how much hair. If it's like one is in there somewhere, versus the whole thing. Like, "Do I have to dry clean it? Can I wash it on the delicate setting?" But really, I think you should get merch. If you want to grow the podcast, which apparently you do, then this is one way to do that.

Dwarkesh Patel

You think beard and beard hair in the future is necessary?

Trenton Bricken

Oh, yeah. Oh, yeah.

Sholto Douglas

Which historical figure would be best suited to run a frontier AI lab?

Dwarkesh Patel

This is definitely a question for you guys.

Trenton Bricken

Oh. No, I mean, I'm curious what your take is first. You've spoken to more of the heads of AI labs than I have.

Dwarkesh Patel

Yeah. I was gonna say LBJ. Sorry, it's a question who would be best at running an AI lab or would be best for the world or…?

Trenton Bricken

Yeah, what's, what outcome do you want?

Dwarkesh Patel

Because I imagine it seems like what the best AI lab CEO succeeds at is raising money, building up hype, setting a coherent vision. I don't know how much it matters for the CEO themselves to have good research taste or something, but it seems like their role is more as a sort of emissary to the rest of the world. And I feel like LBJ would be pretty good at this. Just getting the right concessions, making projects move along, coordinating among different groups to maybe- Oh, Robert Moses.

Again, not necessarily best for the world, but just in terms of, like, making shit happen.

Sholto Douglas

Yeah. I mean, I think best for the world is a pretty important precondition.

Dwarkesh Patel

Oh, right. Who’d be best for the world? There's a Lord Ackwood quote of, "Great people are very rarely good people." So it's hard to think of a great person in history who I feel [would] really move the ball forward and also I trust their moral judgment.

Sholto Douglas

Yeah. We're lucky in many senses with the set today, right?

Dwarkesh Patel

That's right.

Sholto Douglas

Like, the set of people today are both... they try and care a lot about the moral side as well as sort of drive the labs forward.

Dwarkesh Patel

This is also why I'm skeptical of big grand schemes like nationalization or some public-private partnership or just generally shaking up the landscape too much, because I do think we're in one of the better.. I mean, the difficulty of whether it's alignment or whether it's some kind of deployment, safety risks. That is just the nature of the universe is gonna make that some level of difficulty. But the human factors in a lot of the counterfactual universes, I feel like we don't end up with people... Like, we could even be in a universe where they don't even pay lip service, there’s not an idea that anybody had that you could have an ASI takeover. I think we live in a pretty good counterfactual universe, all things considered.

Sholto Douglas

... good set of game players on board.

Dwarkesh Patel

That's right. That's right.

Sholto Douglas

How are you preparing for fast timelines?

Dwarkesh Patel

If there's fast timelines, then there will be this six-month period in which the most important decisions in human history are being made. And I feel like having an AI podcast during that time might be useful. That's basically the plan.

Trenton Bricken

Have you made any shorter term decisions, with regards to spending or health or anything else?

Dwarkesh Patel

After I interviewed Zuckerberg, my business bank balance was negative 23 cents. When the ad money hit, I immediately reinvested it in Nvidia. So, that is the... sorry, but you were asking from a sort of altruistic perspective?

Trenton Bricken

No, no, just in general, like, have you changed the way you live at all because of your AGI timelines?

Dwarkesh Patel

I never looked into getting a Roth IRA.

Sholto Douglas

He brought us Fiji water before.

Trenton Bricken

Which was in plastic bottles, so...

Sholto Douglas

Dwarkesh has changed.

Dwarkesh Patel

Well, have you guys changed your lifestyle as a result?

Sholto Douglas

Not really, no. I, I just, like, work all the time.

Dwarkesh Patel

But you would be doing that anyways, or would you not?

Sholto Douglas

Ah, I would probably be going very intensely at whatever thing I'd picked to devote myself to.

Dwarkesh Patel

Yeah, yeah. How about you?

Trenton Bricken

I canceled my 401K contribution, so-

Dwarkesh Patel

Oh, really?

Trenton Bricken

Yeah, yeah, that, that felt like a more serious one. It's hard for me to imagine a world in which I have all this money that's just sitting in this account and waiting until I'm 60 and things look so different then.

Dwarkesh Patel

I mean, you could be like a trillionaire with your marginal 401K contributions.

Trenton Bricken

I guess, but you also can't invest it in specific things. And, I don't know. I might change my mind in the future and can restart it, and I've been contributing for a few years now.

Dwarkesh Patel

On a more serious note, one thing I have been thinking about is, how could you use this money to an altruistic end? And basically, if there's somebody who's up and coming, in the field that I know, which is making content, could I use money to support them? And I'm of two minds on this. One, there are people who did this for me, and it was kind of actually responsible for me continuing to do the podcast when it just did not make sense as there were a couple hundred people listening or something. I want to shout out Anil Varanasi for doing this. And also Leopold, actually, for the foundation that he was previously running.

On the other hand, the thing about what that blogger was saying, that the good ones you actually do notice. It's hard to find a hidden talent. Maybe I'm totally wrong about this. But I'd feel like if I put up a sort of grant application, I give you money if you're trying to make a blog, I'm actually not sure about how well that would work.

Sholto Douglas

There's different things you could do, though. Like, there's “I'll give you money to move to San Francisco for two months”. And sort of meet people and get more context and taste and feedback on what you're doing and it's not so much about the money or time. It's putting them in an environment where they can more rapidly grow. Like, that's something that one could do. I think you do that quite proactively in terms of you deliberately introduce people that you think will be interesting to each other and this kind of stuff, so… yeah.

Dwarkesh Patel

Yeah. No, I mean, that's very fair, and I, obviously I've benefited a ton from moving to San Francisco. And it's unlikely that I would be doing the podcast- at least on AI- to the degree I am if I wasn't here. So maybe it's a mistake to judge people based on the quality of their content as it exists now and just throw money at them- not throw money, but give them enough money to move to SF to get caught up in this intellectual milieu and then maybe do something interesting as a result.

Trenton Bricken

The thing that most readily comes to mind is the MATS program for AI research. And this seems like it's just been incredibly successful at giving people the time, the funding and the social status justification, to do AI safety relevant research with mentors.

Dwarkesh Patel

Oh, and you, you have a similar program…

Trenton Bricken

We have the Anthropic Fellows Program.

Dwarkesh Patel

That's right, yeah. And I know you're probably selecting for a slightly different thing, but I assume it's gonna be power law dominated. And have you noticed a pattern among the, whether it's the MATS fellows or your fellows, who is just like, "This made the whole thing worth it"?

What's your first take on something?

Trenton Bricken

I mean, there have been multiple people who Anthropic and other labs have hired out of this program. So, I think the return on investment for it has been massive. And yeah, apparently the fellows, I think there are 20 of them, are really good.

Dwarkesh Patel

But what is the trick to making it work well or finding that one person?

Trenton Bricken

I think it's gotten much better with time, where the early fellows, some of them did good work and got good jobs. And so now later fellows the quality bar has just risen and risen and risen. And there are even better mentors now than before. So it's this really cool flywheel effect. But originally it was just people who didn't have the funding or time to make a name for themselves or do ambitious work. So it was kind of like giving them that niche to do it. Seems really key.

Sholto Douglas

You can do other things that don't have to be money. You could put out ideas for things you'd be really interested in reading or promoting.

Dwarkesh Patel

Yeah, yeah, yeah. There's something coming there.

Sholto Douglas

Okay, there we go.

Dwarkesh Patel

So if this episode hopefully will launch Tuesday at the same time as the book- by the way, which you can get at stripe.press/scaling. But on Wednesday, which is the day after, hopefully there's something useful for you here.

Sholto Douglas

Any other questions we wanna ask?

Dwarkesh Patel

The thing I have takes on, which I rarely get asked about, is distribution.

Trenton Bricken

Distribution of AI?

Dwarkesh Patel

No, sorry. Like, Mr Beast-style distribution, where people, I think rightly, focus on the content, and if that's not up to snuff, I think you won't succeed. But to the extent that somebody's trying to do similar things, the thing they consistently underrate is putting the time into getting distribution right. I just take random takes about… for example, the most successful thing for my podcast in terms of growth has been YouTube Shorts. It's a thing you would never have predicted beforehand. And they're responsible for basically at least half the growth of the podcast or something.

Sholto Douglas

I mean, I'd buy that. Why wouldn't you predict it? Like, I mean, like, I mean, I guess there's the contrast of, like, the long form deep content and, like, YouTube Shorts and stuff. But I definitely think they're good hooks. Good content.

Dwarkesh Patel

I have takes on how to write tweets and stuff. The main intuition being write like you're writing to a group chat. To a group chat of your friends rather than this formal whatever.

Trenton Bricken

What else comes to mind here?

Sholto Douglas

Well, maybe it's interesting the difference between TikTok and YouTube Shorts.

Dwarkesh Patel

Oh, yeah. We've never cracked TikTok.

Trenton Bricken

Why not? Like, you've tried?

Dwarkesh Patel

Yeah. I mean-

Sholto Douglas

Tried? Have you done everything?

Dwarkesh Patel

No, I have not done everything.

Trenton Bricken

Have you read these poems? Maybe you're in a bubble bath with some beard shampoo on.

Sholto Douglas

Reading poems? That'd be incredible if you got any of that to go viral. You have to do that now!

Dwarkesh Patel

Manifest. Reading a poem, uncross your legs.

Trenton Bricken

Last episode it was the interpretability challenge, now it's Dwarkesh in a bubble bath.

Dwarkesh Patel

I gotta sell the book somehow, you know?

Trenton Bricken

But you literally do it like Margot Robbie-

Sholto Douglas

Yeah, exactly. Explaining the seed mechanism.

Dwarkesh Patel

Yeah, yeah, yeah, yeah, yeah.

Sholto Douglas

So what is scaling?

Dwarkesh Patel

And that's how you crack distribution.

Sholto Douglas

And that's how you crack distribution.

Trenton Bricken

Oh, but yeah, no, like when we did our episode, it launched and you were sharing interesting tidbits about how it was doing and the thumbnail you wanted to use and the title. And I think I even asked you to share more details because it seemed interesting and cool and subtle things. But it seemed like you also kind of just hated it. Like playing this game of really having to optimize all these knobs.

Dwarkesh Patel

So what I realized, I mean talent is everything, so I'm really lucky to have three to four editors who I'm just incredibly proud to work with. I don't know how to hire more of them. Like, they're just so good and self-directed. So, honestly, I don't have tips on how to correct that. I hired those guys. So one of them was a farmer in Argentina, one of them was a freshman math student in Sri Lanka, one of them was a former editor for one of Mr Beast's channels. The other is a director in Czechia who makes these AI animations that you've seen in the Notes On China, and he's working on more essays like that.

So, I don't know how to replicate that catch again.

Sholto Douglas

God, that's a pretty widely cast net, I gotta be honest. Damn.

Dwarkesh Patel

But they're all, goddamned, they're so good.

Trenton Bricken

And this was just through your challenges and just tweeting about?

Dwarkesh Patel

That's right. I had a competition to make clips for my podcast. I rounded up a couple of them this way. Yeah, it's hard, it's hard to replicate because I've tried… "tried," after.

Trenton Bricken

Why do you think this worked so well with the video editors? because you tried a similar approach with your chief of staff.

Dwarkesh Patel

Yeah. The difference is, with the video editor, I think there is this arbitrage opportunity where there are people… It is fundamentally a sort of, are you willing to work hard and obsess about getting better over time? Which all of them go above and beyond on, but you can just find people in other countries who are... and it's not even about the wages. Like, I've 10Xed their salaries or something like that. It's just about getting somebody who is really data-oriented, and there is this global arbitrage there.

Whereas, with the general manager... By the way, the person I ended up hiring, and who I'm super excited to work with, is your childhood best friend. Max Farrens.

Trenton Bricken

Max is so great.

Dwarkesh Patel

He would have plenty of other opportunities. There's not this weird arbitrage where you find some farmer in Argentina.

Trenton Bricken

Yeah. But yeah, it is striking that you were looking for a while, and then just kind of mentioned offhand that Max was looking for something new.

Dwarkesh Patel

This is gonna be like a total, 12-year-old-learns-about-the-world kind of question, but I genuinely don't know how big companies hire. Because I was trying to find this person for a year, and I'm really glad about the person I ended up hiring. But it was just like, if I needed to hire 100 people for a company, let alone 1,000 people, I just do not know how to find people like this at scale.

Sholto Douglas

Yeah, I mean, I think this is the number one issue that startup CEOs have. Hiring. It's just relentlessly the number one.

Dwarkesh Patel

Yeah. And the thing I was stunned with is how it didn't seem like my platform helped that much. I got close to 1,000 applications across the different rounds of publicizing it that I did. And a lot of, I think, really cool people applied. But the person that I ended up hiring was somebody who was just a reference, like a mutual friend kind of thing. And a couple of other top contenders were also this way. So it's weird. Like, the best people in the world don't want to apply, at least to things like this and you just gotta seek them out. Even if you think you have a public platform or something.

Trenton Bricken

Yeah. Yeah, I mean, the job might just be so out of distribution from anything else that people would do.

Dwarkesh Patel

That's right, yeah.

Trenton Bricken

So Aditya Ray asks, "How do you make it on Substack as a newbie writer?"

Dwarkesh Patel

I think if you're starting from scratch, there's two useful hacks. One is podcasting, because you don't need to have some super original new take. You can just interview people who do, and you can leverage their platform.

And two is writing book reviews. Again, because you have something to react to rather than having to come up with a unique worldview of your own. There's probably other things, and it's really hard to give advice in advance. Just try things. But, those I think are just, like, good, cold starts.

Sholto Douglas

The book reviews is a good suggestion. I actually use, like, Gwern's book reviews as a way to recommend books to people.

Dwarkesh Patel

By the way, this is a totally under-supplied thing. Because if anybody has book reviews. Jason Furman is this economist who has like a thousand Good Reads reviews. And I probably have visited his Good Reads on a hundred independent visits. Same with the Gwern book reviews or something, right?

So book reviews are a very under-supplied thing, if you're looking to get started making some kind of content.

Trenton Bricken

I like that.

Dwarkesh Patel

Yeah. Cool. Thank you guys so much for doing this.

Trenton Bricken

Yeah, this was fun.

Dwarkesh Patel

We'll turn the tables on you again pretty soon.

Sholto Douglas

How does it feel being in the hot seat?

Dwarkesh Patel

It's nice. Nobody ever asked me questions.

Sholto Douglas

Nobody ever asked how is Dwarkesh!

Trenton Bricken

Cool. Yeah, yeah, super excited for the book launch.

Dwarkesh Patel

Thank you.

Trenton Bricken

The website's awesome by the way.

Dwarkesh Patel

Appreciate it. Stripe.press/scaling

Sholto Douglas

Yeah.

Trenton Bricken

Cool.

Dwarkesh Patel

Thanks, guys.

Sholto Douglas

See you later.

Trenton Bricken

Thanks.

Humans have not succeeded because of our raw intelligence.

Marooned European explorers regularly starved to death in areas where foragers thrived for 1000s of years.

I’ve always found this cultural evolution deeply mysterious.

How do you discover the 10 steps for processing cassava so it won’t give you cyanide poisoning simply by trial and error?

Has the human brain declined in size over the last 10,000 years because we outsourced cultural evolution to a larger collective brain?

The most interesting part of the podcast is Henrich’s explanation of how the Catholic Church unintentionally instigated the Industrial Revolution through the dismantling of intensive kinship systems in medieval Europe.

Watch on Youtube; listen on Apple Podcasts or Spotify.

• Scale partners with major AI labs like Meta, Google Deepmind, and OpenAI. Through Scale’s Data Foundry, labs get access to high-quality data to fuel post-training, including advanced reasoning capabilities. If you’re an AI researcher or engineer, learn about how Scale’s Data Foundry and research lab, SEAL, can help you go beyond the current frontier at scale.com/dwarkesh

To sponsor a future episode, visit dwarkesh.com/p/advertise.

I highly recommend both of these.

• The WEIRDest People in the World

• The Secret of Our Success

(0:00:00) - Humans didn’t succeed because of raw IQ

(0:09:27) - How cultural evolution works

(0:20:48) - Why is human brain size declining?

(0:32:00) - Will AGI have superhuman cultural learning?

(0:42:34) - Why Industrial Revolution happened in Europe

(0:55:30) - Why China, Rome, India got left behind

(1:21:09) - Loss of cultural variance in modern world

(1:31:20) - Is individual genius real?

(1:43:49) - IQ and collective brains

Dwarkesh Patel - 00:00:00

Today, I have the pleasure of chatting with Joseph Henrich, who is a professor of human evolutionary biology at Harvard University and an author of two of my favorite books, The Weirdest People in the World and before that, The Secret of Our Success. And I was just mentioning to you that I remember reading this many many years ago when I was in college, and at the time, I didn’t think I would get a chance to ask you questions about it. But the most proximal reason I wanted to interview you is, I recently had your colleague, David Reich, on and we were discussing certain things in the record of human history where he said, “Eventually, you’re just gonna have to have Joseph Henrich on and ask him these questions, because he’s the one who would know.” So let me ask you one of the questions which I was super intrigued by which he raised, and we didn’t come up [with] an answer to.

So one of the things he’s discovered through his genetic evidence is that 70,000 years ago across Eurasia, there’s so many different human species, from the Denisovans to the Neanderthals to the Hobbits, and then apparently, there’s this one group, which was potentially the size of 1 to 10,000 people in the Near East, which subsequently explodes, and now everybody who’s descended from Eurasia descends from this one group. And so I guess the question is like, what happened? What did they figure out?

Joseph Henrich - 00:01:18
A typical assumption when people think about this, if you put it in the Paleolithic, they assume that it has to do with some kind of genetic changes. Now, Reich’s lab, there’s no obviously big changes in the DNA, so it’s a little bit of a puzzle. Neanderthals, for example, had larger brains, and in primates larger brains usually go along with more computational abilities, more ability to solve problems. So the expanding variant out of the Middle East, out of Africa, might’ve actually been less able at an individual level to process information. But if you look back over the more recent period of human history, you can see that it’s a story of expansions of different populations.

So for example, in Africa, we have the Bantu expansion about 5,000 years ago, which actually eliminates a whole bunch of hunter-gatherer populations that previously existed in Africa. We have the remnant populations in parts of the Congo, in the Kalahari, in the Hadza, for example, in Tanzania. If you look at the Austronesian expansion, so that’s the peopling of the Pacific, that was the expansion of one group of people at the expense of others, and of course, the Neolithic expansion into Europe is another example.

So really human history is a story of these different expansions. And it could be that this expansion across Eurasia, which then led to interbreeding, so we know it’s the same species. Humans interbred with Denisovans and Neanderthals, as well as probably other species- there’s a ghost species in there. This could be just institutional changes, so if you have institutions, for example, that interconnect your population, you can maintain more sophisticated technology. And some paleoanthropologists, for example, have speculated- with some evidence- that the expanding populations had projectile weapons, so bows and arrows. And humans have periodically gained and lost bows and arrows in different parts of the world. So in Australia, for example, bows and arrows are never invented. In the New World, populations probably didn’t have bows and arrows, but then later develop bows and arrows.

Dwarkesh - 00:03:12
It’s also really interesting how some technologies you would just think of as extremely… I don’t know what the word is, but the new world, like not having the wheel or something. I guess it kinda makes sense with no domesticated animals, but again, it’s like such a…

Joseph Henrich - 00:03:28
Right. Although the New World, everybody has dogs, and you can pull carts with dogs. So I’ve never really bought that, and of course, you can, you can use llamas in the New World to pull carts as well. People do that today.

Dwarkesh - 00:03:38
Yeah, so what’s your explanation for why there’s no wheels in the New World?

Joseph Henrich - 00:03:41
So there were wheels on Mayan carts or on Mayan toys. My explanation is just the collective brain. So almost every single first invention of something big that we think is important for humans was invented in Eurasia. And Eurasia, as Jared Diamond famously pointed out, building on other people’s work, it’s the largest continent by far. It has the biggest population. It’s also oriented along an east-west axis, which allows ideas and people to more easily flow. And there’s a belt, which the historian Ian Morris calls the Lucky Latitudes, which runs from basically southern China all the way through to the Mediterranean. And ideas are just flowing back and forth there, across the center of Eurasia. But you also ended up with more complex state bureaucracies and the kinds of things that allow you to organize and move people around and whatnot.

Dwarkesh - 00:04:27
And what’s the explanation for why the collective brain leads to state capacity? Or is it the other way around?

Joseph Henrich - 00:04:34
Well, you can think of institutions that eventually lead to state capacity as just part of the innovative process of the collective brain.

So if you have more groups experimenting with more different ways of governing groups of people, gradually, you get the accumulation of the pieces that you can put together into different kinds of states.

Dwarkesh - 00:04:50
Got it. Okay, so going back to what happened 70,000 years ago, is the basic answer like, it could be something like bows, but we don’t know exactly what it was? Or is it-

Joseph Henrich - 00:04:57
Yeah. We definitely don’t know exactly what it was. So we know that this population expanded, and there does seem to be some tool indications to suggest more complex technology. Probably, technology usually goes along with social organization.

So for example, if you look at Australia, which is a continent of hunter-gatherers, there’s an expansion about 6,000 years ago out of northern Australia which eventually takes up seven-eighths of the continent, and they had a new social organization, including rules about who you marry.

You had linguistic exogamy, and rituals that interconnected populations. So rather than having local rituals and local myths, many communities would get together periodically to initiate the young men, and this would help bond that whole group. There’d be an exchange of technology, and teaching that goes on at these… they’d spent a few months in the same place so there was a lot of time for transmission.

Dwarkesh - 00:05:47
And then in terms of mechanism, so, if it’s the case that you have these sort of convalescing waves of expansion, and David Reich’s lab’s evidence suggests that this expansion was quite violent, based on whether the genes are passed down through the maternal line or the paternal line. When you think about the mechanism in which technologies develop or these social institutions develop, how much of it is like, look, there’s all these different groups that are trying different things, and one of them maybe figures something out, and then they just kind of explode? And how much of it is that within each group, they are experiencing sort of accumulations of learning over time, and it’s not necessarily a selective process, it’s more of a sort of accumulative process?

Joseph Henrich - 00:06:32
Well eventually the groups have to meet, and they’re gonna compete over territories, and a bunch of different things can occur. So groups can copy each other. So we know that from the ethnographic record and historical cases, sometimes a group will say, “Oh, those guys have really good technology tools.” Maybe they’ll get some migrants, or something like that, and then they can adopt the practices. So that definitely happens.

But there’s also plenty of evidence of violent conflict. And the Reich labs evidence and lots of other ancient DNA suggests that there are these dead-end genetic lineages. So, I mean, the Neanderthals are a dead end in a sense, although we interbred with them, so they’re in us in some sense, but they don’t have their own pure lineage or anything like that.

Dwarkesh - 00:07:14
And if you had to guess, whenever this big wave happens, is it more of a sort of concrete technology? I mean, especially if you consider the range of expansion that can often happen, right? It’s like literally that same group goes from Siberia to England. It’s hard to imagine it’s a single technology lets you explore this wide range. But it’s also the idea that some cultural artifact is enough that it gets transmitted over tens of thousands of years and that’s what gives you the edge. I’m curious how you think about, what could possibly be this driving engine?

Joseph Henrich - 00:07:47
Well the way I describe it in The Secret is that it’s a package of things. So another good example would be the Inuit expansion out of the north slope of Alaska. And they expand all the way across the Arctic and eventually get to Greenland. And they have a whole package of social practices which helps keep them interconnected, and then they have bows and arrows, which the group they are exterminating as they go along, the Dorset, doesn’t have. They have dogs and sleds. They have, along the coast they have boats, so they’re doing whaling. So there’s a whole package that puts together that allows them to out-compete and eventually exterminate the Dorset.

And one of the things that happens, though, is the Dorset probably had better technology, but they expanded, they spread out, their languages diversified, they lost contact, and they began losing technology. So you wanna see this as a dynamic pulse. There were probably expansions and then collapses, another expansion, another collapse, right? It’s not one giant long march to victory.

Dwarkesh - 00:08:38
And then this process, which has happened many times where the population gets cut off or something: When you say it’s a sort of cycle, is there some reason why, over time, that knowledge gets fragmented and breaks apart and populations disperse?

Joseph Henrich - 00:08:58
Yeah, I think there’s a cultural evolutionary dynamic that’s part of this, because languages will naturally diversify and then as they diversify, there’s less contact. People sometimes get inclined to marry endogamously, and especially if there’s enough people around locally, why go all the way over there? And those people are getting more culturally different, so they’re seeming a little bit like outsiders…

So the tough part that humans have always had is to stay unified because of the natural effect of geography and learning locally is gonna tend to fragment us.

Dwarkesh - 00:09:27
In the Secret, you described this interesting startup problem where if you don’t have that much accumulated cultural knowledge, developing the ability to do social learning isn’t as valuable. But if you don’t have the ability to do social learning, you don’t have that much accumulated cultural knowledge in your tribe or group. So how is this problem solved?

Joseph Henrich - 00:09:46
Yeah. So before I get to solving the problem, I just want to sketch for the listeners that the question is, why is this cumulative cultural evolutionary process that is so important for humans relatively uncommon in the natural world? It seems like just our lineage. I mean, there were a bunch of split-off lineages, but now it’s just us.

So to understand that is this idea that you just mentioned, where you’re imagining an increase in brain tissue that’s gonna be costly, and I can put that towards individually figuring the problem out for myself, or I can put it into learning from others. And in a world without very much cumulative culture, there’s not gonna be very much useful information in the minds of everybody else so I should use that brain tissue for individually solving problems. And so it’s hard to get this runoff where their brains get bigger for the purposes of learning from others. And then the question is, how do you get past that valley?

So the case I make in Secret is that, say, three million years ago, two million years ago, there were several factors that came together. The first factor is the rate of change of environments. So you get this increase in the fluctuation of environments, so you’re getting more environmental changes. And in cultural evolution, a lot of theory shows that there’s a certain rate of change which is favorable to cultural evolution. It’s gotta be slow enough so that the information of your parents and the previous generation is useful, but not so slow that you might as well just encode it in the genes.

So that’s one. Second thing is, we’re a ground-dwelling ape, which means we have hands like chimpanzees and gorillas, and we can potentially use tools and whatnot. But unlike them, our ancestors may have been savanna-dwelling apes, which meant we may have lived in large groups. So in mammals, mammals live in larger groups when they have to deal with predators and the predator guild in Africa at that point was quite thick. There were a lot of deadly predators.

So paleo-anthropologists think that our ancestors may have lived in large [groups of] savanna-dwelling apes. And if you have a lot of individuals, if the culture is sparse, the bigger the group, the more chance that there’s someone doing something useful in the larger group. And so that means it’s easier to get across the threshold. So those are three of the main factors that might have allowed our lineage, as opposed to all the other lineages around, to cross this. We’re already a big-brained primate, we had hands, we’re living in these large groups on the savanna, and the climate changed during this period, so as to make us, yeah.

Dwarkesh - 00:12:18
And how big were these groups?

Joseph Henrich - 00:12:20
Well, I mean, nobody’s really sure, but maybe a few hundred individuals.

Dwarkesh - 00:12:24
Okay. And before the agricultural revolution, was there still this transmission of information across different groups?

Joseph Henrich - 00:12:34
Yeah. So lots of different evidence suggests that groups were moving trade goods. So well back into the Paleolithic we see trade. Often we see some genetic transmission.

Dwarkesh - 00:12:46
Right, the expansions and so forth.

Joseph Henrich - 00:12:48
Yes. And so, talking about the expansion, that’s something where the ancient DNA is useful because the Neanderthals in Europe, the DNA suggests they lived in very small groups. But the DNA of the expanding groups suggests a larger population. So those would’ve been two different collective brains there as well.

Dwarkesh - 00:13:04
So in The Secret, you discuss a lot of these lost European explorers with modern technologies as of at least a couple centuries ago encountering peoples who have for tens of thousands of years discovered ways of hunting and processing foods and so forth that, without which even these people with modern technology will starve. And as I was reading that I was wondering: I’m not sure how I understand the process by which, if there’s a 10-step process to making sure this bean is actually nutritious, and without any one of those steps, you might poison yourself or something. And at no point do you understand why this process works. You don’t have a scientific explanation. How do you even learn that in the first place?

Joseph Henrich - 00:13:49
Right. So one of the things we know, even in young children there’s a tendency to preferentially learn from healthier, more successful individuals. So if you’re processing it better- so something like bitter cassava, which has cyanide in it. If you just eat bitter cassava,it won’t taste great. So if you then rinse it somewhat, it’ll taste better and maybe you could eat it, but you’re going to accumulate cyanide over the long run. So it doesn’t kill you right away. But if you do this whole long process that the populations in South America developed, then you’re totally fine, you never get any accumulation.

So you can imagine that initially this is gonna be very strong and people are gonna sort of do sensible things… but then it gets a little more mysterious. And we know this because bitter cassava gets transported to Africa, and Africans immediately begin eating it improperly processed and getting goitre and the cyanide processing that goes along with it. So then that’s gonna be a slow evolution where groups that do this are gonna be more successful and individual families are gonna be more successful. So, for example, you might have a household where they process the cassava more seriously than another family, and they’re gonna have more kids and they’re gonna be like, “Oh, that family’s really good.” And then people copy what they do in all kinds of ways, but one of them could be copying recipes.

Dwarkesh - 00:14:59
Hmm. I guess it depends on the mechanism of selection here, because when you consider the different ways in which two different individuals might be different or two different households or even two different groups, I guess it makes sense why cyanide poisoning is such a deleterious effect that it is a noticeable or quite a strong signal. But I think you discussed some other ideas in The Secret where it’s like, the sort of spices in a region sort of match the antimicrobial properties or the antifungal properties you need to stay, I guess, clean or whatever in that particular region. But it sounds like a small effect, and how could such a small effect actually create a strong enough signal that when you’re deciding who to copy, you notice that this family is healthier because they spice their food in a certain way? Or this group takes over the area because they’ve…

Joseph Henrich - 00:15:59
Yeah, I mean, it has to be a big enough effect to matter, but I think if you go back to a world where there’s a lot of improperly processed meat, people don’t have refrigerators, and the leading cause of death in children is diarrhea. People carry high pathogen loads. If you can knock that pathogen load down, we know from modern research if you wanna make people healthier and even smarter, right? IQ goes up if you knock the pathogen load down.

Dwarkesh - 00:16:22
Got it. So this process of cultural accumulation you’re talking about: you think it is not strong enough to pick up minor increases in fitness and it actually has to be quite a significant thing for it to…

Joseph Henrich - 00:16:39
I think it operates a lot like natural selection, in the sense that natural selection will pick up tiny things if you give it long enough. Cultural evolution will do the same thing if you give it long enough, because just small differences in who you’re paying attention to will affect things, but it might take 1,000 years as opposed to decades, right? But we know culture can spread adaptive traits super quickly if it’s a really big effect.

Dwarkesh - 00:17:01
Yeah. So the situation in which our ancestors found themselves, in some ways it’s sort of like epistemic hell, in the sense of you don’t know why certain things are working, but you do know that if you break with tradition, you might just doom yourself and your family. And that maybe, as you discuss, causes these religious beliefs and taboos and mystical understandings of the world to rise, where you just think like, “Look, you’re gonna burn in hell if you don’t do this 10-step process for refining your beans or something.” In some sense, this requires you to abdicate reason because you have to just be like, “I don’t understand this. this is the way it’s been done, so we’re gonna do it.” Basically: how much reason do you necessarily have to abdicate to survive in the ancestral environment?

Joseph Henrich - 00:17:41
I mean, one of the ways we research this is by going out to societies that make bows and arrows or have food taboos that protect them from dangerous marine toxins and ask them questions about why they do it and see if they [understand] and they don’t understand the underlying causal stuff.

Dwarkesh - 00:17:53
Right. But then why do they say they do it?

Joseph Henrich - 00:17:55
Well, a typical answer is, “It’s our custom.” or “It’s important to people around here to do it this way, so we do it this way.” So just Hadza bows, for example- I didn’t do this research, Jacob Harris and Kim Hill and Rob Boyd did it- but they asked Hadza about their bows and about how they work and what the mechanics are, so they understood some stuff. So you use the bow and you get some mechanical understanding. But if you asked him, “What if you used a different wood, different materials,” and they had never tried anything else but what they learned, how to make this bow. So they couldn’t speak to a lot of that stuff. And the things about the compression of the wood is very important- didn’t understand that.

Dwarkesh - 00:18:34
Okay, so that’s quite interesting, right? Because you would think that you have to transmit cultural knowledge over time, but you also need to experiment in order to innovate. But it sounds like because of the belief in these customs, you would be less inclined to innovate.

Joseph Henrich - 00:18:48
Right. And the thing is, once something gets good, doing it differently almost always makes it worse. And then there’s also, some things are just different because people do it randomly different, like they make a mistake. So I think people often underestimate the power of error in generating novelty.

Dwarkesh - 00:19:04
One question I have is why this process of cultural learning… So humans have extended childhoods, presumably to give us more time to accumulate culture, and then we live after menopause, presumably because older people, our grandparents, can teach us about the situation we find ourself in. And 18 years is such a long time, and when I think back to- I mean, obviously I didn’t grow up in the ancestral environment, but when I think back to what I was doing as a teenager I don’t think I was learning that much. I was just less productive for no obvious biological reason. So I guess I don’t intuitively understand why this process of cultural learning takes decades, and why it can’t happen more rapidly.

Joseph Henrich - 00:19:47
Well, you probably, as an adolescent, were in school, right? So you were probably accumulating some stuff in school- maybe. And here’s an interesting fact about hunters. So anthropologists who studied hunters, hunter-gatherer societies in different places, and the physical peak that at least males have is in their early 20s, right? That’s when they run the fastest. They got the best eyesight. But the best hunters in the community are 36 to 40, right?

They’re not as fast as those guys, but they just know, they know the track, they know the animals, they know exactly what to do in these different- And then hunting skill begins to decline because basically the physicality of things begins to catch up, and so there’s this kind of cycle. And at 18 young hunters aren’t even producing enough food to feed themselves. So it’s not until they get into their 20s that they’re actually in surplus and bringing food home for everybody else. And you need to know hundreds, at least, animals, you need to know animal behavior, you gotta be able to spot tracks and spoor, and we’re just very knowledge-dependent in terms of our hunting and gathering.

Dwarkesh - 00:20:48
Yeah. Given what you just said and also the experience of these European explorers suggests that you just gotta know a lot of shit to make it in any sort of environment on Earth. But then, there’s all these other animals and they seem so dumb and they seem able to get by. So why did humans have such a hard time of it?

Joseph Henrich - 00:21:07
Yeah, well, so I think we offloaded a lot of stuff into culture. So in one of my Lost European Explorers, there’s a case where they’re in Australia and they have camels. And the camels escape, and now central Australia has lots of feral camels. So the camels survived the lost European explorer challenge, because they have innate instincts. They can smell water a mile away. They can detoxify foods in their own- they have a complex digestive system that detoxifies- we’ve lost all that, and we’re worse than chimpanzees at detoxifying foods because we have all these cultural practices that do the work. So we’ve externalized detoxification and a lot of digestion, actually.

Dwarkesh - 00:21:41
I had this independent researcher and internet writer called Gwern Branwen on my podcast a couple months ago, and we were discussing AI. And I asked him- so his theory is very much like the brain became bigger, more intelligence. And so then I asked him, “Look, if it’s this simple, why did it take so long for evolution to discover intelligence in the first place?” And he had this interesting answer, which is that in terms of the signal that evolution is giving you, there’s a very narrow gap between skills that are so useful that they should just be distilled as an instinct into your genome, and then skills that are so worthless that are not worth learning in the first place. And so this narrow gap that’s like, you need that generalization ability. It’s not so primal that, you know, you’re not gonna culturally learn hunger, it’s just gonna be in your genome. I don’t know if that sounds like an interesting explanation or helps explain anything.

Joseph Henrich - 00:22:41
Well in cultural evolutionary models, a typical thing would be to let genes compete with individual learning to compete with cultural learning. And it turns out the rate of change of the environment affects that. So if the environment is changing slowly, then you should put it all in the genes. And if it’s changing at moderate speed, then culture is the best way to go, and if it’s fast, then individual learning. And the idea, the individual learning is favored because if it’s changing too quickly, the previous generation doesn’t know anything worth knowing because the world you’re dealing with is just so much different from their world. And so it’s this intermediate range.

So myself and others have argued that the increase in the frequency of change during the Plio-Pleistocene transition 2.5 million years ago is a change that increases the value of cultural transmission of learning from others. I’m not sure what your guest was thinking. I mean as our brains are not very good at solving problems, otherwise the lost European explorers could survive. And human brain size has been declining for the last 10,000 years. So we’ve actually been getting dumber. Fewer neurons, less computational power.

Dwarkesh - 00:23:48
Why has brain size been declining?

Joseph Henrich - 00:23:50
Well, the collective brain argument suggests that at a certain size, you’ll begin farming off specialists. So because there’s a store of knowledge in the society, and we can all be generalists and learn how to do all the different skills. But at some point it makes efficiency sense for us to specialize in different skills.

In order for that to be the case though, we have to have social agreements of some sort that allow us to trade or exchange things like that. So, but then once we’re specializing, we don’t necessarily need as large a brain and we, because we distribute the overall brain power amongst the society. So it could be that we’re becoming more of a superorganism. And you see the same thing in ants. When ants get specialized occupational casts, their individual brains shrink.

Dwarkesh - 00:24:35
Yeah. I think David Reich’s lab had a result a month or two ago where they showed that the selective pressure on at least the European samples, which is the samples they studied, was that there’s been selection for greater intelligence over the last 10,000 years. I don’t know if you saw that or what you make of that?

Joseph Henrich - 00:24:52
Well, they didn’t say anything about intelligence. They did use the polygenic score for education. But there was no education 8,000 years ago.

Dwarkesh - 00:24:58
Which is correlated, right?

Joseph Henrich - 00:24:59
Yeah, it’s correlated. But then the question is, we don’t know what goes into that, right? Is that actually computational software? I mean, people do well in school because they stick to it and they can sit in the same place for long periods of time.

Dwarkesh - 00:25:12
I guess it would be interesting for them to sort of deduct the docility hit polygenic score from the education, because we don’t have a polygenic score for intelligence yet, right, so which is why you need some proxy.

Joseph Henrich - 00:25:22
Well there are IQs, so there are polygenic scores for IQ. I think they actually did that one as well, like you said, it’s correlated with education. But the things that go into giving you an IQ, I mean, the correlation between brain size and IQ is only about, well, across populations it’s .24, so it’s not very big.

The other thing is IQ is massively misunderstood, so the way to think about IQ, which has been going up over the 20th century, by quite a bit, so if you rescale modern scores to 1900, it’s about 70, it’s called the Flynn effect. I think that those are cognitive abilities for navigating the institutional world that we’ve constructed.

And a huge mistake would be to assume that those are the right cognitive abilities for the next century. So it’ll be a different constellation. We study herders and cognitive abilities of herders in northern Namibia, and if you’re in northern Namibia, you gotta be able to move through the landscape, so being able to just pick a direction and know where you’re going and not get lost is super important.

So different suites of cognitive abilities are favored in different environments. This idea that there’s this generalized thing applicable across all human environments just doesn’t… I mean, education massively increases your IQ, right? If you’re uneducated, you have a totally different IQ.

Dwarkesh - 00:26:42
And then is the way in which your horsepower is directed, is that a thing that you think is basically set by the time you’re an adult? Or, if somebody’s 30 now and the AI thing is gonna happen next and they have to totally reorient away from knowledge work, is that a thing?

Joseph Henrich - 00:26:58
Well, I think it’s a continuous scale. If you look at human brains, they’re developing and continuing to add new connections and stuff, at least into the mid-20s. Now, there could be even more plasticity after that. Unlike chimps, we’re still not totally myelinized for our whole lives. So there’s definitely room. It’s just there’s less room.

Dwarkesh - 00:28:20
The kind of social learning for which our learning biases are fit, right? Like where you pay attention to elders because they survived somehow and they’ve accumulated the knowledge from past generations, how much do you think that actually basically applies to the way in which the modern knowledge work economy accumulates knowledge? And how much do you think is just an artifact of the kind of environment our ancestors found themselves in? Right now we’re in Silicon Valley, or in San Francisco, and it’s very common here for a 20-something to make a new product or service which, without that much- they don’t have that much context of how the world works, but they can make big, big innovations and big changes. But at the same time, people on average maybe are more productive in their 40s and 50s in terms of wages or something. So basically, this sort of social learning you’re describing, how much does it actually describe the world as it exists today?

Joseph Henrich - 00:29:17
Yeah. And so, super important, so we’ll take the patent database and if your same-sex parent- has to be same sex- patents in a particular domain, you’re nine times more likely to patent in that same domain.

Dwarkesh - 00:29:30
And how do you separate the genetic versus cultural effect there?

Joseph Henrich - 00:29:32
So these are very fine domains, so we’re talking about, like, natural adhesives versus synthetic adhesives. So unless you think there’s genes for that. And same thing with transistors and electronics, so very fine domains. And if you grow up in Silicon Valley, you’re much more likely to patent, in general, but you’re probably gonna patent in computers. But if you grow up in Boston, it’s gonna be biotech. So having a father or mother who did biotech and then growing up in Boston, you’re even more likely to patent in biotech. And this is true even if you move to New York. So if you look at people in New York who are from Silicon Valley and from Boston, the Bostoners are more likely to patent in biotech, and the Silicon Valley kids are more likely to patent in computing. So all the same rules apply.

Dwarkesh - 00:30:20
Well, I’m not sure because maybe the location, you learn a lot from the environment. But the fact that it’s just kids, and so, if you think these old rules of cultural accumulation and collective brains apply, you would think, “Ah, you’re 50 by the time you’re writing your first useful line of Python code or something.” When in fact, a lot of these big innovations come from people who are much, much younger, before they should have accumulated much of the know-how that this theory implies.

Joseph Henrich - 00:30:48
So the model here is what to focus your efforts on, so it’s the throwing model. So if you grow up in Silicon Valley, you focus your efforts on learning how to code or whatever. And so that means that’s where you’re likely to make the innovation.

Dwarkesh - 00:31:03
Right. But in terms of, you know, when there’s 2% growth a year and a lot of the technologies that are dominant in the economy today, or a lot of the industries, didn’t even exist many generations ago. Basically, does that suggest this model of- should we just mistrust our instincts of who to give prestige to and what kinds of people we want to pay attention to, whether it’s professors or elders or whatever?

Joseph Henrich - 00:31:28
Yeah, so that’s definitely something I’ve thought about and I wrote about in Secret, which is that, as the rate of cultural change gets faster- and we sort of talked about this with environmental change- the value of older and older members of the previous generation declines because the world that they grew up in, and that they honed their skills to, is quite different from the current world. So you would expect the degree to which, I mean, optimally, you would look less far back or you would look to relatively younger individuals to get your inspiration from because the world they adapted to is closer to the world you’re gonna need to adapt to.

Dwarkesh - 00:32:00
Yeah. And speaking of Silicon Valley, let me ask you a little bit about AI. So one of the reasons to suspect some incredibly sharp discontinuity from the world as it exists today to a world with AIs. And by AI, I don’t mean just, like, GPT-4, I mean, like, replacement at least for anything you can do on a computer screen, the AI can do.

One of the reasons we expect this hard discontinuity is that they have potentially the step function increase in social learning and the ability to accumulate knowledge that maybe humans had, or the magnitude of which maybe humans had when, between them and non-human primates. And in particular, the fact that you can just copy everything you know. Like you don’t have to teach a young person, right? The constraints of biology mean you can’t just replicate brains.

You have much more efficient communication. You don’t have to communicate through words, you can just shoot your brain state across, the population size can grow arbitrarily large. So to the extent of, the collective brain is the size of the population and how interconnected it is, do you just expect we wouldn’t even recognize the kind of world these AIs could make as a result of their cultural skills?

Joseph Henrich - 00:33:15
Yeah. No, I mean, I, I definitely think it’s pretty interesting, and holds great potential for expanding the collective brain. There are little things in there which might make one worry. So if you study the history of innovation, you find out that, for example, serendipitous meetings are super important. There’s a great paper on Silicon Valley showing that companies will cross-reference each other’s patents more likely when the people at those companies tend to frequent the same coffee shops, and they track people on their cell phones and stuff to figure this out. So serendipitous meetings are important and improper copying. So a huge number of innovations are mistakes where somebody copied incorrectly and then got something better.

Dwarkesh - 00:33:55
I read this interesting theory that maybe evolution designed transcription and translation to have more errors than it could otherwise have, just so that you can have this sparse reward when you’re close to the right sequence.

Joseph Henrich - 00:34:08
Yeah, yeah. I think that’s a very interesting area of research, and it makes good sense to me.

I’m not sure of the current state of the evidence, but there are… Different parts of the genome are more or less susceptible to mutation, which is kind of interesting.

Dwarkesh - 00:34:21
Yeah. So going back to AI, maybe then another way to phrase it is, look, you’re talking about these serendipitous meetings where you can learn something another person knows. And the great advantage these AIs have is that they can sort of meet with everybody at once. Future versions of AIs, you could really imagine holding the whole internet in context, right? I mean, we’ll be the equivalent of those people isolated in Tasmania according to the AIs, right? Because they just have everything in context. You can get a PhD in every single field, and you can amortize that across all your copies.

Yeah, so are you sort of banking on, in the next 10 years, you’re just gonna be living in the singularity or something because of your belief in the value of cultural knowledge?

Joseph Henrich - 00:35:03
Yeah, I see there’s various potentials, and I’m particularly interested in using AIs to augment problem-solving in human groups. So you can imagine getting the humans together because the humans have the big advantage of having stuff they care about, right? There’s stuff they want to invent. So the AI is still a tool at some point. So I’m interested in that, but I’m interested in how these things like running out of training data is gonna be dealt with, the value of making mistakes and serendipity, if you get rid of all those things or how you’re gonna reintroduce them, those kinds of things.

Dwarkesh - 00:35:39
Yeah. I mean, it’s quite funny because until recently, people were saying the big issue with LLMs is that they hallucinate and make mistakes. And at some level, hallucination is no different than creativity.

Joseph Henrich - 00:35:50
Well, may- maybe there’s a way to harness that, right? But maybe we just didn’t know how to harness it.

Dwarkesh - 00:35:53
That’s right. What do you make of the idea that you could have AI firms or AI tribes, whatever way to think about it- is if the effective population size that can communicate with each other is such an important contributor to how much progress a group in history was able to make. If you could just literally run billions of AIs and they have this immediate ability to communicate with each other. And again, I’m not talking about current models, I’m talking about future, human equivalent. Basically I guess I’m just throwing out a bunch of different intuition pumps and I’m curious which one you find most promising or most interesting, or did you just find all of them not as convincing?

Joseph Henrich - 00:36:31
Yeah, no, I don’t have strong opinions about any of this, but I do think that one thing in order to make all that work is, humans are constantly getting hit with shocks, right? So there’s economic shocks, there’s weather shocks, there’s conflict with other groups, there’s pandemics. And the shocks have big effects on how things go. They inject new information into the system. So I’d worry about a system that is too homogeneous and doesn’t have enough noise adding shocks and new challenges and things like that.

One of the things missing from our conversation is the kind of creativity that cultural evolution has figured out. So to give you an example, at some point in human history, religions invented big, powerful, moralizing gods. And this may have increased the people’s ability to cooperate. So forget about the technological element. Some of the most important features of innovation over human history have been institutional and things that get people to cooperate.

So in my work, I argue that one branch of Christianity resulted in the transformation of the families in European societies into small monogamous nuclear families. I’m not sure an AI would have thought of that. Cultural evolution thought of it because of how it affected how the societies operated, so it sorted this out over historical time.

Dwarkesh - 00:37:48
Yeah. Can I play with that idea?

Joseph Henrich - 00:37:49
Yeah.

Dwarkesh - 00:37:50
Because that’s quite interesting. So again, I think this might even reinforce the advantages of AIs in this sense. So, to the extent that this sort of random fluke by the church led to this modernity and the great divergence or whatever, and it was a result of, there’s a bunch of variation, and then you can select over some group that’s doing the right thing.

The advantage you have with AIs is that you can have much more high fidelity replication of culture and you can explore a wider range of potential cultures. Sorry, and it sounds really vague when you talk about AIs in this way. So what do I mean? One problem companies have today is that, suppose a company’s working really well. Maybe it’s early Apple, or early Tesla, or SpaceX or something. And suppose it has to do with the culture, the people, something, it’s not clear how you replicate that culture, not only across different institutions, right? If I’m running a company, I don’t know how to copy SpaceX. Not only can you not replicate it across institutions, you can’t even replicate it across time.

So over time, many institutions tend to decay, their culture fades as people leave or die or something. And then imagine if all of SpaceX, at least maybe at the time you thought it was the most effective, are just AIs where you know every single byte, and when you can make a copy of it 1,000 times, and you can put it against every single hardware problem we have in the world. And then you can, if you think there’s another team that might have some different culture which is better, you can do the same thing with them. And culture here includes not only how they think about their work, but also even how does the board make decisions, right? Do they do this Monte Carlo tree simulation? And there’s so many different things you could do here. So because of the wider range of possibilities you can explore and the fact that you can have higher fidelity transmission of cultural information, maybe the ability to do this random evolution also increases. But I’m curious to get your take on that.

Joseph Henrich - 00:39:57
Yeah, as a kind of broad sketch, I think that is pretty interesting. I worry about too high a fidelity replication just because it’s important to take the details of historical context and time into account. So that same thing, if you fix it, it might not really work a decade later when everything else around it has changed, right? So it’s kind of a moving target. But you could fix that just by having lots of different variants that are different versions of that. So yeah, that would be cool.

Dwarkesh - 00:40:26
Sorry, that gives us a good opportunity to go into WEIRD. But before we get there, one more question about before the agricultural revolution. You know, obviously many groups around the world were different and many of those differences were probably inspired by the fact that they were living in different environments, so they needed to come up with different technologies and adaptations to survive best there. How much just random differences that had nothing to do with their environment do you expect there to have been, in terms of… I don’t even know what the right category would be, but in the modern world, you might imagine something like, does a society have slavery or not? And maybe pre-modern societies all had slavery, so that’s not the right way to think about variation, but that kind of…

Joseph Henrich - 00:41:07
Yep. So I think there’s lots of reason to think there was lots of variation. And one of the ways that researchers study this is they look at ethological variables and they compare it to phylogenetic variables. And here they mean cultural phylogeny.

So if your ancestral populations had matrilineal, matrilocal social organization, how likely are you to have it, controlling for ecology? And it turns out both of these things matter. People adapt to their ecology, but you can still see the signal of past society. So some degree of fidelity of transmission of social institutions, how we make our baskets, those kinds of things. There’s lots of examples in the modern world, and we see this all over the place. So if you look at gender inequality in the modern world, you find that a history of the plow leads to greater gender inequality. So males had a particular advantage in using the plow because it requires upper body strength.

This meant males became the dominant force in economic production at the household level. And that even today in populations where most people aren’t farmers, this persists. So this is cultural persistence that has to do with whether you had the plow. So this is work done by Alberto Alesina and Nathan Nunn. A former postdoc of mine now at the Harvard Business School, Anke Becker, has done a similar thing with pastoralism. So if your ancestors were pastoralists, and pastoralists, for various reasons, have quite strong gender norms that persists into the modern world and still shapes, for example, female entrepreneurship.

Dwarkesh - 00:42:34
Interesting. Yeah, and the reason I’m especially curious about this is because it informs the following question. If the Industrial Revolution didn’t happen in Europe but started somewhere else, how different would the world look like today? So obviously you discussed the fact that breaking apart these kin ties was necessary for the Industrial Revolution, but to the extent that was necessary, whichever place had the Industrial Revolution first would have had that, right? But, I mean, separate from the technologies or cultural practices which were necessary for the Industrial Revolution in the first place, still how different would the world look? How much variation was possible given our level of technology?

Joseph Henrich - 00:43:13
Yeah, I have a lot of trouble trying to answer that because, I mean, China or some place in the Middle East might have been the obvious alternative place for the Industrial Revolution to happen. But I feel like you have to give those places a lot of stuff that developed only in Europe.

So for example, universities begin spreading in the High Middle Ages. So you’d want them to have universities. You’d want them to have universal schooling, which began to spread and wasn’t present in these places prior to that. So that begins to spread in the 16th, 17th century. And so by the time you add all this stuff, it basically starts to look a lot like Europe.

And these are all things that are global now, right? So the universal schooling that we find around the world today, you know, begins with the Protestant Reformation in Germany and then later England. University’s models are the European university, and globalized.

Dwarkesh - 00:44:07
I guess one obviously very salient example of variance which might not have been replicated, but I’m curious if you think it might have been, is, it seems like the British Empire was the first major institution which decided that slavery is just morally wrong and we’re gonna throw our weight around to eliminate it. And I don’t know if you think that sort of naturally follows the development of social technologies that the Industrial Revolution would have brought about, but that seems super contingent. But I’m curious if you disagree.

Joseph Henrich - 00:44:40
Yeah. Well, so my story is that the rise of the Industrial Revolution in Europe has to do with the consolidation of Europe’s collective brain. And one of the things that requires is trust in strangers, and at least the beginnings of moral universalism.

And it’s that moral universalism that eventually causes the British to say, “We’ve got to stop with the slave trade thing.” It’s a moral decision that they made because it’s no longer consistent with the changing moral values over time.

Dwarkesh - 00:45:07
Right, right. Okay, so we’ve been dancing around the thesis of your book following The Secret, which is The Weirdest People in the World. And before we really jump into it, maybe you can give me a summary of what the thesis there is.

Joseph Henrich - 00:45:20
Yeah. So the first observation is that there’s a great deal of global psychological variation around the world. So European, American, Australian populations tend to be highly individualistic. They’re inclined towards analytic thinking over holistic thinking. They have a lot of impersonal prosociality, so trust in strangers. They’re against conformity, willingness to cooperate with strangers. So the question is, how can we explain the global variation in these features of psychology?

And towards the end of the book, I actually connect these features of the psychology to economic differences, including the Industrial Revolution that happened in Europe, which reshapes the world. And the story is that the key event is the spread of a particular form of Christianity into Europe, where the Catholic Church- what becomes the Catholic Church- systematically dismantles the intensive kinship systems in Europe, leading to small monogamous nuclear families.

And this transformation leads to the creation of new institutions. So by the high Middle Ages, you get the rise of guilds, which are voluntary groups of craftsmen and self-help societies, because people don’t have their families to rely on. People begin moving around. There’s occupational sorting into different occupations. You get urbanization rising. Charter towns are on the rise. Universities pop up. New kinds of monasteries pop up. And then Europe begins to urbanize, and you get new kinds of law that are based around the individual. Contract law. And then eventually this leads to a lot more innovation because ideas are flowing around Europe and then eventually the Industrial Revolution. So that’s the argument in a nutshell.

Dwarkesh - 00:46:53
Can you explain again what exactly the Church did which led to the kin-based existing system breaking down?

Joseph Henrich - 00:46:59
Yeah. So the Church outlaws polygyny, and so that stops elite males from having multiple wives and concubines and whatnot, and creating kind of a giant family through that. It outlaws cousin marriage going all the way out to sixth cousins at some point, and that included spiritual kin and other kinds of non-genetic relatives as well as the cousins. It frees up inheritance, so it has inheritance by testimony rather than normal patrilineal inheritance. And a simple example here is in most societies, you inherit access to land corporately, which means you and your brothers and stuff all own the land. And it might be your uncle is actually in charge of the land, your father’s brother. And so you can’t sell it, you can’t move it around, and you’re also tied to it. It’s where your ancestors are buried, and so there’s this big importance of land. So the Church allows people to give land to the Church, the Church becomes the largest landowner in Europe, because you can do it by testament.

So those are just some of the examples of the ways it transforms the family structure, and eventually you get these monogamous nuclear families which are basically unheard of around the world, at least if you look at the anthropological record. And you can really see this when you can compare, individuals can move to European cities as individuals or nuclear families. In China when you move to the city, you maintain contacts with the folks back home in the village, and people flow back and forth and you get these little enclaves of different clans and stuff in the cities, the connections. And this is really important because your ancestors, and there’s rituals that have to be done back in the home community. But Christianity does away with all those ancestor rituals.

Dwarkesh - 00:48:35
Okay, great. Let’s jump into it by starting before- much before- even the church. I wonder how much of the things that make Europe weird existed even in antiquity. If you read about how Roman society worked, or how these Greek polities worked, already you have an emphasis on nuclear families. You have these sort of universal norms around, “Hey, we believe in republicanism,” or, “We believe in democracy.” And the structure of the system matters more necessarily than… I guess at least before the empire, right? So yeah, was Europe already weird before the things that its church did?

Joseph Henrich - 00:49:19
Well, there were certainly some interesting things going on in Greece and Rome. But I don’t think there’s good evidence that you had the kinds of monogamous nuclear families that you would find later. I mean, even European law is built around patrilineage. So if your father’s still alive, you are not a full citizen. You’re in the pater house of these large families. Definitely intensive kinship, for sure, patrilineal, patrilocal.

Women, of course, don’t have any rights. And republicanism, but the formation of Rome is built around a series of elite families. So it’s a clan operation, and they call it republican because the clans all have some say in what’s going on. So people sometimes see representative government where it’s not really individuals being represented, but families. And so this fools a lot of people into thinking there was a lot more individualistic voting and things like that, whereas there’s actually no voting. So in Greece, in Athens, Athens is unique because it does a bunch of things in the laws of Solon, that break down the intensive kinship. So for example, in Greece, you get monogamy for the first time, and Athens is considered unusual. So males can only marry one Athenian woman, which has potentially positive effects among the competition among Athenian men, but they can have as many slave concubines as they want. So when Christianity spreads, it ends the whole slave concubine thing, which was also common in Rome, and that’s another effect on this whole thing.

Dwarkesh - 00:50:48
Yeah. But I guess the extent to which these practices were already formed hundreds or thousands of years before the church’s intervention, maybe this suggests that Europe was already on this trajectory and the church didn’t necessarily move the needle that much. Or what do you make of that thesis?

Joseph Henrich - 00:50:48
Well we know that the… in the book, I talk about data where we can look at contemporary kinship practices, and we can look at the number of centuries that that part of Europe was under the church… so we have a database of the diffusion of bishoprics, and there’s a clear connection between the intensive kinship practices and the number of years under the church.

Dwarkesh - 00:51:25
Hmm. And then if you think about what the church basically did, I think your work is often used to justify this idea of Chesterton’s Fence, where if you don’t necessarily understand a cultural practice, you should keep it around because… the stuff we were talking about, The Secret, where you don’t understand why the 10 steps lead to the bean being edible, but you should trust the sort of wisdom of the ages.

If you think about what the church did here, right? Like, isn’t this sort of like a… not necessarily a justification, but at least an example of just breaking down Chesterton’s Fence? The church doesn’t really understand why these kin-based networks that have existed for thousands of years might have utility. It still just gets rid of all of them. And that results in this lottery ticket that leads to The Great Divergence. So is there evidence for anti-Chesterton’s fence from the Weirdest People?

Joseph Henrich - 00:52:15
Sure. Sure. So what you’re talking about is just the idea that culture has imbued institutions and various practices with a logic that we might not understand. So it’s not calling for never changing the institutions. It’s saying make sure you figure out the logic and the cost and benefits.

So I was an expert witness in Canada for the attorney general of British Columbia, asked the Supreme Court of British Columbia, whether the statutes against polygyny were legal. And so my job was just to inform the court that polygyny has this unfortunate effect of the elite and high-status males tend to get a disproportionate share of the wives, and that creates this pool of low-status unmarried men.

Now you can decide what you want to do with that. But you need to understand the kind of underlying social dynamics that you’re gonna unleash if you legalize or decriminalize polygyny.

Dwarkesh - 00:53:08
Right. But again, going back to the Church, the Church didn’t understand the positive or negative effects they’re gonna have, right? They just like, “Let’s rip the Band-Aid, let’s see what happens.”

Joseph Henrich - 00:53:17
That’s right. And it actually benefited the Church because it released people’s responsibilities to their families and allowed them to migrate in and join the Church, and more heavily invest in the Church. And these later prohibitions against priests marrying and stuff was all an effort to get greater investment in the Church, because you weren’t torn between helping your son and then investing in the Church.

Dwarkesh - 00:53:37
Hmm. And then that end result which benefited the Church, was that their conscious intention in breaking apart these kin-based ties? Or was that just the accidental byproduct?

Joseph Henrich - 00:53:50
Yeah, I mean, in the record, I’ve never been able to find much evidence that they were thinking about the destruction of kinship ties. There is a great quote from Saint Augustine where he talks about the benefits of forcing people to marry more distantly. But, you know, this is something that was debated repeatedly in church councils all across Europe. And it doesn’t seem to pop up very often.

Dwarkesh - 00:54:12
And the Church doing this, for obviously selfish reasons, was that in any way related to why the Church spread through Europe in the first place? Or did it end up spreading for separate reasons and it won out over the other potential religious competitors for separate reasons? And basically, was this part of the selective mechanism which allowed the Catholic Church to become dominant in Europe in the first place?

Joseph Henrich - 00:54:43
Yeah, I think so, although it’s hard to know. There was a lot of randomness going on in the diffusion of the Church. But also as the Church arrived, these places, over a period of time, became more successful. So you can see rising urbanization in the wake of the Church’s arrival. That would have meant more trade, their citizens’ rights began developing because they were trying to attract citizens to the towns. That would have given the Church more cachet, ability to move. There’s also the appearance of these voluntary associations which are new kinds of monasteries. And so the monasteries begin spreading throughout Europe. But they’re all linked in a network because, like the Cistercians, they’re all connected and they have big meetings. So they’re actually spreading a lot of knowledge around Europe as well. So that’s part of this kind of collective brain story.

Dwarkesh - 00:55:30
And if you compare what’s happening in Europe at this time to the rest of the world, so starting with China: in 1500, the population of England, where a couple centuries later the Industrial Revolution starts, is three million, and then in Ming dynasty China, it’s somewhere between 100 million to 160 million. And if we take your previous perspective of the size of the collective brain really matters a lot, the size of the collective brain in China just seems so much bigger. So what is the best way to understand what went wrong here? Why weren’t they able to use their…

Joseph Henrich - 00:56:02
Right. So when you’re thinking about China, the first thing to remember is that for a lot of the history, we can actually see that size difference mattering a lot.

So a lot of European invention, or a lot of stuff used in Europe, is flowing in; gunpowder, paper, printing press, stuff, is flowing into Europe. And so okay, so then what happens after about 1000 CE? Well, the argument is that the destruction of the kinship group opens the floodgates to people moving around. So a recent analysis that we’ve done is, after a bishop arrives at a 1.5 by 1.5 grid cell in Europe, people begin flowing in and out of that grid cell more. And what we do to calculate that is we have a big database of a few million famous people. And we have birth and death locations. So we find out that the Church arrives and suddenly people are free to move around.

So you have a flow of individuals around, and you have rising urbanization. So Europe passes China around 1200 in the percentage of the population that lives in cities. And cities are where a lot of the action is, cities and towns. And you have the diversification of occupations. So normally clans would specialize in different crafts, and you’d learn from your clan brothers and clan fathers how to do the occupation. In Europe you get guilds, and you get masters and apprenticeships developing where strangers will become an apprentice to a master, learn from him, move somewhere else as a journeyman, learn from that master, and then eventually set up a separate shop. Lots of opportunities for a flow of ideas.

So it has to do with how the kinship system transformed the movement of people, the rising of urbanization, the nature of guilds, and then eventually you get universities and things like that. So it greatly intensifies the interconnectedness of the collective brain, and the amount of cognitive diversity.

Dwarkesh - 00:57:44
What do we know about India during this period? Because from what I’ve read, from a perspective of how much written history we have, it’s kind of a black box. But we know that there was trade between India and other parts of the world, and we know it had a big population. Do we know why India before the Mughal period or before the British period wasn’t… yeah. What was the effect of not having maybe Abrahamic gods or.. with the kind of other cultural practices India had?

Joseph Henrich - 00:58:14
Yeah, definitely not an expert on this one, but some of David Reich’s evidence suggests that the caste system is quite old. Because you can actually see it in the genetic system. And the caste system is not good for innovation, because if you happen to be good at another skill but your caste doesn’t do that skill, you can’t switch over. So it’s gonna prevent the sort of available genetic diversity. Complex families, intensive kinship, there’s reason to think that those things were all important. Yeah, so, patchwork of polities.

But there was lots of interesting ideas, actually, that are developed in India, and they move into Central Asia, eventually end up in the Islamic world, and then get into Europe. So for example, Arabic numbers are actually Indian numbers. Zero was probably developed… I mean, Indians were huge with numbers. It’s kind of interesting. Perhaps related to the religion.

Dwarkesh - 00:59:04
Not that this would be a justification for the caste system, but is there any way in which the specialization it engendered would be good from the perspective of this collective brain perspective?

Joseph Henrich - 00:59:16
So specialization is good. And so the interesting thing about most human societies is specialization automatically- or not automatically- it tends to evolve along some kind of kinship line. So in Oceania, there were different clans. In each clan, there’d be like a canoe-building clan and a warrior clan. So that allows specialization, but it doesn’t allow you to harness the genetic diversity, because it’s not like the canoe-building clan had special canoe-building genes. It’s just that you would pass down this cultural knowledge.

Now, a better system, but it seems hard to evolve, is one in which we all opt, select, into occupations in which we think we’re good at. But for that world to exist, you need a world of voluntary associations with emphasis on the individual rather than on the group. So this is the world that evolves in Europe, once you demolish the intensive kinship units, because otherwise you’re gonna get castes and clans and all these things we see elsewhere, where we see a division of labor, but it’s transmitted through a kin network, the knowledge.

Dwarkesh - 01:00:14
And why wasn’t there another sort of stultifying… because we began the conversation by talking about, there does seem to be this cycle where over time, because of cultural differences, because of kin, whatever else, over time groups will tend to diverge. Languages, culture, other things you mentioned, tend to diverge over time. Why didn’t the same thing happen in Europe where, let’s say, in the third century or fifth century, the Church starts doing this stuff, and then by a thousand years later, if you’re part of this guild or whatever, now that’s become like the new kin, and now there’s actually less mobility again?

Joseph Henrich - 01:00:51
Well, so there was a lingua franca, Latin, and so intellectuals would all write and communicate in Latin for a long time. So even though they were speaking different dialects of French and German and whatnot, they were able to communicate in Latin. And Christendom basically formed an overarching network that helped. And one of the key things that this world religion does, and other world religions do it too, is you have to marry other Christians, but it dissolves the tribal line.

So Europe has tons of tribes in the pre-Christian world, but because of how you have to marry other Christians, you had, you know, Celtics marrying Franks. And in fact, the early arrival of Christianity into England is when a Frankish princess is marrying an Angle in Kent. And so they’re marrying back and forth. And this is gonna dissolve the tribes because intermarriage, “what’s the kid?”, you know? So that’s how you get rid of the tribes, but you need a world religion to do that.

Dwarkesh - 01:01:45
Right. And so it seems like there were two very important features that were relevant here. One, it seems like obviously the competition between different European groups which gave incentive for monasteries or universities or cities to attract the best people and make all these advancements. But secondly, the fact that they were descended from a common empire, from the Roman Empire. Basically, in a world where the Roman Empire never existed, do you still have this collective brain in Europe emerge?

Joseph Henrich - 01:02:22
So I think the Roman Empire plays a big role, because, for example, there were Roman roads in parts of Europe which allow people to flow. And you have communities which were part of Rome and relatively sophisticated. Now, they go into a bit of decline, but there’s still the memory of the empire. So, you know, Charlemagne wants to be crowned emperor of the Holy Roman Empire in 800 even though there’s not very much left of it.

But it was still an ideal, and that Carolingian Empire actually had real effects because they worked with the pope in order to enforce a lot of the marriage and family programs. And the Carolingians had their own agenda because they were trying to use these marriage and family practices to weaken some of the other aristocratic families. But they had the religious tools that they could put to work, and if you didn’t have those religious beliefs, then you couldn’t put them to work.

Dwarkesh - 01:03:10
Sometimes people defend ancient conquerors based on a similar idea of they spread knowledge around the world, and they set up these lines of communication. People say that about Alexander and then causing Hellenistic expansion and trade and so forth. They say that about Genghis Khan and the Silk Road. They say… I mean, obviously this led to the Black Plague, so… so it’s tough where that nets out.

They say that about Napoleon and the Napoleonic Codes and so forth. Where do you basically come out on that story of: have the great conquerors been good for the collective brain or bad for the collective brain?

Joseph Henrich - 01:03:55
Well, I’ve never actually focused on that. I mean, I guess my immediate reaction was there must be a better way to transmit the knowledge than the whole conquest.

Dwarkesh - 01:04:05
And then I guess there’s also the open question of how the disruptions that are caused by them… How much did they set back the-

Joseph Henrich - 01:04:11
And population decline, spreading disease and stuff, so certainly it could have been done better.

Dwarkesh - 01:04:15
Right. And going back to David Reich’s work, If your theory implies that there should have been a decrease in genetic similarity in Europe as of, after the fifth century or something, as a result of the culture, the Church’s practices, do we see that in the genetic record?

Joseph Henrich - 01:04:35
Yeah. Yeah, so Europeans are quite well mixed compared to other populations. And so we were talking about India. You know, if you do a principal components analysis of this two-dimensional plot of the genetic similarity you can actually see the class, and people are pretty spread out. If you put the Europeans on the same plot, they’re well mixed.

Dwarkesh - 01:04:52
Right. But do we see the period of greatest mixing starting in the fifth century?

Joseph Henrich - 01:04:59
Yeah, good question. I don’t know the answer to that question. So that would be… That’s a nice piece of evidence, so… You know, there’s a couple of groups working on medieval ancient DNA, so hopefully we’ll have more answers on that question. I mean, there is enough now of pre-Roman burials, so Bronze Age type stuff, showing that early Europeans definitely had complex, intensive kin groups, patrilineal, patrilocal resonance kinds of stuff, polygyny.

Dwarkesh - 01:05:30
And then stepping back, I’ve read there’s so many different theories of the Industrial Revolution and of modernity. And there’s Robert Allen, the economic historian who thinks it’s a result of higher wages because of the Black Death. There’s Gregory Clark, who thinks it’s because of this positive eugenics in England where the upper classes were having more babies. I mean, there’s like 20, 30, probably hundreds of different theories of why it happened, where it happened, and when it happened. And so each one of these, when I read them, sounds plausible. I don’t have a knockdown argument against any of them. But I’m not sure how to think about which one is correct, and yet, if we’re stacking all of these up against each other… why you think this story is any more accurate than any other story that people tell? Is it overdetermined?

Joseph Henrich - 01:06:24
Well, I mean, the first thing to think about is, those other authors- possibly Greg Clark is an exception, but they don’t think about the psychological variation that exists. So there’s good reason to think there’s all these psychological differences, and you have to believe they don’t cash out. And if you think they exist, then you have to explain them, and so you need a theory that explains them. So Bob Allen, he’s a blank on the psychological variation. So let me give you some evidence that I think is pretty good evidence. So we have a database of the diffusion of Roman bishoprics through Europe. And then what we do is we look at what happens when a bishopric arrives. And I mentioned before, you get a flow of people.

But another fact is, if you look at the production of famous people, these places where the bishopric has arrived start producing more creative individuals relative to non-creatives. So there’s an uptick, and it keeps going up for centuries, so the relative increase, and so that suggests you’re producing more authors and inventors and writers and whatnot. And then if you take each of those, and you correlate it with modern patents, places that spent more time under the church produced more patents between 1980 and 2014 based on European patent data. And this holds if you just compare regions within the same country. So you can see long-term effects of this, and you can see immediate effects in the historical record on the production of creatives.

Dwarkesh - 01:07:45
So what would you make of a theory that says, “Look, all of these theories are basically correct. All of these stories are basically correct.” And it’s them stacking up that leads to the Industrial Revolution rather than any one of them being the most important proximal cause. And maybe there’s reasons, like, let’s say there’s 10 of these stories and you had to stack them all up and China had five of them, but they also had maybe independently, like three other ones, but they also had two stories which go against them or something. And it’s like, no one of them is that proximately important, it’s their combination, right? So, I guess your theory seems compatible with Robert Allen’s that coal being cheap and wages being high was important.

Joseph Henrich - 01:08:30
Well, I guess one of the things that I think is a mistake is to focus too narrowly on England, because England was benefiting from ideas that were flowing in from France. There was a lot of great science being done in France. And, I don’t think Gregory Clark’s right. I can explain almost everything he explains with cultural evolution, and he doesn’t even really take that seriously.

Dwarkesh - 01:08:51
Oh, interesting.

Joseph Henrich - 01:08:52
Yeah. So patience, one of the things he argues is patience. In WEIRD, I use the exact same data- I actually get the data from him- to show that you have this increase in patience. But we know that people can culturally learn patience, and this can all be culturally transmitted, and in this world, it leads to more success. So if you’re the kind of person who doesn’t waste a lot of his money and we begin to value thrift and stuff, which Protestantism does, then we should expect there to be an increase in fewer murders and lower interest rates.

Dwarkesh - 01:09:20
What’s the reason to think that… I mean, his main explanation, I think is genetic, right? Is there some reason to think that that is less likely to be as important a factor as culture?

Joseph Henrich - 01:09:31
Well, we know that populations- migrants- into the US and Europe shift their psychology over a few generations of being there, and we know there’s been natural experiments done by economists like Chris Blattman where you actively try to teach people, train them essentially, to discount the future less. And that seems to be culturally transmittable. So this suggests that- I can’t rule out that there’s been any genetic evolution, but I can show that culture can operate on this quickly and powerfully.

Dwarkesh - 01:10:00
Right. I think one of his key pieces of evidence, I don’t remember the exact numbers on this or even the particular detail, but it was something like if you look at the inheritance of wealth or titles or- I forget what the exact thing is- the correlation across generations points to the kind of pattern you would see with genetic inheritance rather than cultural spreading.

Joseph Henrich - 01:10:28
Yeah. He doesn’t actually fit any cultural evolutionary models, and if you include that people are learning from parents and learning from their social milieu, your parents determine where you are in the social milieu, so you’re surrounded by people who are being more patient and behaving in certain economic ways, then that’s gonna have a huge effect. I just told you about how if you grow up in Silicon Valley, you’re gonna patent in computers. It’s not because you have genes for computers.

Dwarkesh - 01:10:52
Yeah. Given the persistence of these cultural effects, how should that change how we think about whether it makes sense to have more immigration from societies that are already WEIRD or whether that matters or not?

Joseph Henrich - 01:11:07
Right. So we have a paper under review right now in which we show that from the 1850 roughly to 1940, a big driver of US innovation both the quality and quantity of patents, is the cultural diversity of counties. And we actually use immigration as a kind of way of showing the causal effect of this. So what you want is a lot of cultural variation. Now, if you get people from societies that are more distant from whatever the current US culture is at the time, it’s gonna take more time until they’re able to fully integrate. So if they’re coming from [a] very distrusting society, they’re not gonna be able to latch into the collective brain immediately.

So you see this in around 1900. There’s data from people, mostly coming from Southern Italy and coming from Germany and Britain, and the Southern Italians are coming from a society that has a high distrust of strangers. So their immediate effect on the patenting system is low. But the strength of these cultural traits seems to go down by an order of magnitude each generation. So the second generation, you can still see where they came from, but they’re a lot more like the majority culture than their parents. And then it goes down another order of magnitude. So yeah, so you just have to do more work essentially to assimilate people, enculturate them. But the potential value of having diverse ways of thinking, different ideas, can be really valuable, I think.

Dwarkesh - 01:12:35
What do you make of, so Garett Jones is an economist who has this, he calls this spaghetti theory of assimilation. I don’t know if you’ve heard of this. The idea is basically, look, immigrants do assimilate, but they also assimilate us, and so whatever cultural practices they have will, in proportion to their size of the population, have an effect on wherever they migrate to.

And his example is when Italians came to America, spaghetti, which is traditionally an Italian dish, also became an American dish, right? So they assimilated American culture as well. And sub in traits like trust or whatever there. Basically, this sort of reciprocal assimilation has implications of whether we want immigration from non-WEIRD societies. What do you make of that idea?

Joseph Henrich - 01:13:26
I think that the spaghetti example or the pizza- pizza or spaghetti, both good examples- is a great thing, right? Because American food is a fusion of cuisine from lots of societies, and that leads to quite good food. Same thing you can see [in] music. Things like jazz come, have African rhythms in them. And so rock and roll is along that lineage of-

Dwarkesh - 01:13:54
But we don’t want, like, do we want the diversity in low trust and high trust?

Joseph Henrich - 01:13:58
Well, the question is, do social interactional traits operate the same way as these things? And if you have ethnic enclaves, then you’re gonna get low trust in those ethnic enclaves. You get things like the mafia, right?

So you don’t want that. But if you had an immigration policy that distributed people, and in a high-trust society, you benefit by being higher trust because you can do things like collaborate and start companies and stuff, which you have to be high-trust to do that. Otherwise, you don’t do it in a very effective way. So there is a pressure for low-trust people to become more high-trust. But if you’re in mostly high-trust people, there’s not an incentive for you to move down. And social interactional traits are different than things like food types.

Dwarkesh - 01:14:45
In psychology, there’s been this problem of the replication crisis where a lot of the main results are hard to replicate and it’s not clear how much of this is real science. Have you looked into how many of the WEIRD results are based on, of the differences in psychology between different populations and so forth? How much of that actually replicates?

Joseph Henrich - 01:15:05
Yeah, my sense is it replicates quite well. So we published our paper in 2010, and in, oh, late 20 odds, Armin Falk, who’s an economist, and a bunch of others, Ahnke Becker, Ben Enke, published a paper where they measured economic preferences in 80,000 people around the world. And that just showed big variation in things like patience, various kinds of reciprocity, altruism, so the kinds of things we would expect. And then other large-scale projects have similarly shown lots of variation.

Dwarkesh - 01:15:40
The groups we have today, which are not WEIRD, when you study them, do you think that the fact that they have resisted modernity for so long suggests that they actually are weird in their own way? Which is to say that this is not representative of the way that Europeans might’ve been thinking in the second century; rather it’s like the Hadza’s particular weirdness leads them to be averse to modernity in a way that is unique?

Joseph Henrich - 01:16:10
Well, that’s always a concern, and it’s especially a concern when you’re studying hunter-gatherers because a lot of the hunter-gatherers that are left in the world today are [in] places where agriculturalists couldn’t easily move to.

So that’s something we’re thinking about all the time. The ways around that are to go to places where agriculturalists didn’t go or couldn’t go. So we have a lot of good ethnographic history on people in Australia, and then we have the Arctic populations, which, you know, big sections of Paleolithic Europe were probably kinda like Northern Canada is today. So at least environmentally it’s not crazy, because, you know, Ice Age Europe. And places like the Aleutian Islands, the West Coast of California, we have lots of ethnographic evidence, and that there was no agriculture there.

So just putting together all these different lines of evidence help us develop a picture. I wouldn’t wanna bank everything on the Hadza. Of course, we can’t do experiments with some of those groups. But we can see whether the Hadza look like these other hunter-gatherer groups.

Dwarkesh - 01:17:10
Oh, speaking of the Ice Age: do you have a take on why before the Ice Age agriculture didn’t develop even though genetically there probably weren’t that big a difference between, like, 20,000 years ago versus 10,000 years ago?

Joseph Henrich - 01:17:20
Yeah, I don’t have a clear picture of that. It is the case that the Holocene was a particularly long stretch, a particularly long interglacial. There were some long interglacials, but the soonest one before that was 120,000 years ago.

Other than that, they were getting broken up. I sometimes wonder if we may someday figure out that there was actually a little bit of agriculture. And it would’ve all been destroyed by the Ice Age, right? So it’s not clear that there would be any heritage of it left. So we would have to find some remnants of some domesticated crops.

Dwarkesh - 01:17:52
Although I was asking David Reich about this, and I forget the reason he said, but he did say that if there were societies before the Ice Age, we would have evidence of them in the archeological record or their genetic record or something like that.

Joseph Henrich - 01:18:05
Yeah, if they had gotten to any scale. But we know from modern societies, there are groups, for example, in the Amazon that have root-based agriculture that wouldn’t leave any archeological record.

Dwarkesh - 01:18:17
This is a question I also asked to David Reich. There’s, you know, with modern LMs and just generally with newer techniques for processing information and understanding evidence, there’s a potential for answering questions that maybe we couldn’t have done before, especially from maybe a cultural perspective where you can actually consider lots of different cultural artifacts at once in the context of you can run cultural simulations or something. Maybe those are more fanciful ideas, maybe there’s more practical ideas. But if there’s one question you have which it’s sort of bottlenecked by having the right data or being able to process the data the right way and future LLMs or AIs could help us there, does something immediately come to mind?

Joseph Henrich - 01:19:05
Well, this gives me a chance to tell you something we’ve been working on. So we were talking about this kinship hypothesis, the idea that kinship intensity affects psychology. And when I presented the ideas that are in The Weirdest People in the World, I get hard times sometimes from historians who will have some kind of very European-specific story about why this happened that has to do with European royalty or coal or something like that.

And so my move is to not try to get into the weeds and start reading Latin texts, but to go test it somewhere else. So if it’s true that kinship intensity should work like this, then we should be able to test it somewhere else. So we have a large corpus, two different corpus from China. And so we have late-imperial China, something called the Gazetteers, which were produced across the prefectures, and they had a stylized content. So this is the same genre, thousands of books, and then we have about 7,000 books going back 2,000 years in Chinese history. And the techniques that the AI is allowing us to do is to get measurements of psychology from the texts. So we take a questionnaire. This is a technique developed by my post-doc, Mohamed Attari. We take a psychological questionnaire that’s been validated, it’s in English, and we wanna get an equivalent in ancient Chinese.

So we run it through a semantic similarity comparison, and we look for quotes from the ancient Chinese corpora that match each sentence in the English corpora. And then we rebuild the questionnaire in ancient Chinese. So that’s our psychological measure for something like individualism or collectivism or moral universalism. And then we take each book or each paragraph in each book, and we do a comparison, cosine similarity comparison, between the two sets of text there, and that allows us to stamp each paragraph with a measure of individualism for example. And then we do that for the entire corpora. And this allows us to track psychological change across space and time in China. And then we can correlate that with kinship intensity, and we get the same correlations that we do in Europe.

Dwarkesh - 01:21:09
Interesting. How worried are you about the fact that, if you think about modernity, was a result of finding this one cultural variant- at least according to your story- which then helps us develop the better technologies, all these new institutions and so forth. How worried are you about the fact that the modern world because of the spread of WEIRD, has just much less cultural variants and because of this monoculture, a potential variant which might be useful in the next transition just wouldn’t emerge?

Joseph Henrich - 01:21:43
Yeah, I am worried about that, because just the destruction of languages. So we’re losing languages left and right. English has a particular form that you don’t find in lots of languages, so that’s just an example of the kind of cultural diversity we’re losing. So that’s definitely a worry. I do think that we’re getting new variants. Japan adopts a lot of WEIRD institutions. But it’s really creating a new third unique thing. It’s got a bunch of Weird elements, but Japanese law, despite being the same as the US, operates very differently. So unlike Americans, Japanese don’t tend to sue each other and they tend to use mediation and things like that, even though the superficial law is pretty similar. So I do think there is creation of novelty which could be useful. But yeah, in general, I worry about the loss of novelty.

I also should say that I think that the sort of impersonal world of impersonal institutions in WEIRD psychology is quite fragile. For example, in the US in rural areas, you see moral universality declining since 2008. This is based on John Haidt’s YourMorals.org data. And so there’s an increasing difference in the morality of urban areas versus rural areas in the US.

Dwarkesh - 01:22:58
Hmm. I’m sure you get asked about this a lot, but fertility seems to be declining around the world, and there doesn’t seem to be any existing cultural variance, other than maybe the Amish, who can resist it. Do you have takes from a cultural anthropologist perspective of what’s happening here? And a follow-on question here is, should we really encourage these cultural enclaves like the Amish to the extent that this anti-fertility meme is so viral that they can’t be part of the common culture and still preserve fertility, you really need this closed-off societies in order to preserve fertility. How seriously do you take that idea?

Joseph Henrich - 01:23:38
Well, I definitely take declining fertility pretty seriously. And especially if you’re a collective brain guy like me, just having fewer people is gonna be a big problem. And I think that there will be spread of ideologies or religions or something which are pronatalist, and those groups will have a big advantage in cultural evolution because a community that is pronatalist tends to produce more pronatalist babies.
Christianity spread using that. Mormonism spread in the 19th century using that trick. So I just feel like cultural evolution is gonna find the combination and there’s gonna be some pronatalist groups spreading.

Dwarkesh - 01:24:19
There are a lot of different societies in the world today. Is there some explanation of why none of them have the existing variants necessary to keep fertility high?

Joseph Henrich - 01:24:29
Well, from a cultural evolutionary perspective, this is relatively new, right? So the demographic transition is only really late 19th century. Lots of the world is just getting hit after 1970. And we also have things like rising female labor force participation which is gonna stifle it, rising female education. And so once that stuff maxes out, then you’ll see variation among different groups in terms of the number of babies they produce, right?
And this thing unfolds in demographic time scales, so we’re not gonna see it for a few generations, but there’ll be some group somewhere that will be producing more babies than everybody else. And the reason why I think religion is the likely one is because people do things because they think God wants to do it. And if people come to think that God wants them to have more babies because it’s a way of worshiping him or getting to heaven or whatever their religious configuration is, then that’ll be a group that produces more babies. I mean, Catholics were defying the demographic transition for a while. They just seem to have stopped.

Dwarkesh - 01:25:33
Right. How worried are you about the fact that… I mean, you discuss in WEIRD that throughout this period in European history, European states are at war most of the time. How worried are you about the fact that, because of a decline in war between great powers, that this build-up in cultural mutational load that you were talking about or maybe where things just get less efficient over time and there’s no selective pressure to get rid of that inefficiency. How worried are you about that just making governments or nations less efficient over time because there’s no outer loop loss function that says if you mess up enough your country might not exist, or your group might not exist? Which we were talking about the waves of Yamnaya or the Anatolian hunter-gatherers just conquering everything. If that doesn’t happen, will we just see culture and nations and governments degrade over time?

Joseph Henrich - 01:26:31
I think the answer to that is yes. The one way that countries or whatever the political institution is could address that would be doing this thing that I’ve curiously called “domesticating the competition”. So sports teams constantly renew themselves by competing. Firms live and die and renew themselves over time. Like you said, companies will start off being super great and then they get too big and then they get kind of inefficient and then eventually they disappear. None of them last forever. And that seems to be true of political units. But in principle, you could have at least some renewal processes. So democracy potentially provides a renewal process, although there are things like the institutions that the government builds that are hard to renew. So one simple political idea is I think when you create a new department, it should have the same thing that cells have where at a certain time, they time out and you gotta make a new one.
And that’s because just the way human bureaucracies, institutions work is they kind of corrode from the interior, from the inside, just the way cancer spreads in a cell. So you just gotta kill it and make a new one. So we could institute that. But I guess the idea hasn’t quite caught on yet. I mean, there are bits and pieces of it around, so it’s not like it’s unused.

Dwarkesh - 01:27:49
Yeah, I guess maybe the thing that’s less understood or is not appreciated is that the reason that over time in history institutions have improved is because of the selective process that at least at certain levels of selection no longer exists.

Joseph Henrich - 01:28:04
Yeah. I mean, the Roman Empire didn’t say, “You know, we gotta redo everything.” “Why not adopt democracy and we could have a republic.” Nobody does that. Things fall apart.

Dwarkesh - 01:28:15
The researcher Stuart Armstrong has this idea called Chesterton’s metafence. And here’s what it states: In our current system, democratic market economies with large governments, the common practice of taking down Chesterton’s fences is a process which seems well established and has a decent track record and should not be unduly interfered with.

Joseph Henrich - 01:28:38
Can you say any more about what the author has in mind? I would like an example or something.

Dwarkesh - 01:28:43
Yes. So I think this is a rebuttal against this common idea that we shouldn’t be arbitrarily changing- like suppose a new technology comes about and we’re worried about the risk it might pose to society. Suppose the young kids are doing something different culturally and we’re worried about like what effect that might have on the culture and so forth.

He’s basically saying, “Look, this has been happening for five centuries and this process by which new cultural variants enter the mix and so forth has worked really well for us, even if it’s happening at a rapid clip and we shouldn’t interfere with it.”

Joseph Henrich - 01:29:22
Well I don’t think I have strong feelings on this. I’m always focused on trying to understand the process of cultural evolution, and it is true that people often resist cultural change that in retrospect we think is good. But of course when you begin to make cities, you lead to all kinds of epidemic diseases. So if you’d anticipated that, you might have worked on some of the public health procedures before you built the cities. So foresight can be good.

Dwarkesh - 01:29:48
Right. I guess the sort of bigger question here is, it makes sense in the kind of environment our ancestors faced why they’d have intuitions against progress, because if you already have technologies that are well fit to the environment, any change is likely to be deleterious. And whether we find ourselves in a world that’s different enough where we can just, very intentionally disregard our worries about changes in culture or technology, or whether it’s similar enough that we should actually… I mean, there’s two ways you can interpret The Secret, right? One is, “Oh, look, this is why we should care more about culture” and so forth. Another is just, “Oh, wow, they’re living in such a different world. I understand why my intuition against progress- where it comes from, and now I can totally disregard it.”

Joseph Henrich - 01:30:34
Right. So I would say that the lesson from Secret is not to disregard it, but just to be cautious because we could be dismantling things that are really important for the structure of society. And you shouldn’t just dismiss valuable cultural practices as the relics of a medieval age, or the relics of a pre-enlightened age or something like that.

So ritual is a good example because rituals seem to have real psychological benefits in binding people in community and helping develop self-regulation. But it’s easy to be an atheist like me and say, “Ah, rituals are stupid. We gotta stop doing them.” Turns out they’re doing a bunch of stuff and if you don’t wanna lose that stuff, you gotta figure out another solution.

Dwarkesh - 01:31:20
A question that arises in my head is, look, we began this conversation talking about all these societies in the past, even when they figured out something successful, they did not themselves know why it was successful and so they just had to ascribe it to custom. They just say like, “Oh, this is the way we always made bows” or hunted or something. Why think that we have an answer for why the Industrial Revolution happened? Why think it’s any different than the Inuit trying to explain why they make their bows a certain way?

Joseph Henrich - 01:31:49
Oh, you mean, how or what we’re up to is different?

Dwarkesh - 01:31:51
Yeah.

Joseph Henrich - 01:31:51
Well, I mean, we’re trying to apply science, which has been very successful in all these different areas. So, you know, evidence, and we all put out our arguments and we go through the process and some evidence is better than others and that’s just our epistemology.

And I’m interested in the cultural evolution of epistemologies. So societies have varied over time in what counts as good evidence and what counts as a good argument. So one of the psychological changes that we see emerging in Europe compared to other places is how important is what the ancients say.

So in lots of societies, if someone says something and you can say Aristotle or Confucius didn’t believe that, then it’s like, “Oh gosh, I guess I’m sunk.” Whereas at some point, Europeans decided, “Actually who cares what Aristotle thought. We know a lot more than he did.” So this is a big epistemological change. And then the emphasis on empirical evidence in science is not something you find in earlier traditions within Europe, and it’s quite variable across different societies.

So you should think about the very standards by which we count a good argument and good evidence is itself culturally evolved.

Dwarkesh - 01:32:56
Yeah. And how much do you worry about the lack of cultural variation in science in particular, where we have like one big institution, the NIH, which funds most science. And there’s similar processes of consensus and peer review and whatever, that educated whether you get a grant, whether work is accepted, whether it’s considered worthy scholarship and so forth. Should we think that maybe we should have more variation in those methods because maybe the scientific method isn’t complete, there’s epistemic tools could be improved or it could be different and so forth?

Joseph Henrich - 01:33:38
Yeah, absolutely. So polycentricity. There should be lots of difference, competition amongst these groups, no single funding sources… lots of different priorities. One thing I point out in this book I’m working on is that scientific papers tend to be more impactful when the authors are from more diverse societies.

But interestingly, people are biased to work with people from their own society. So what we actually do is different than what would maximize innovation.

Dwarkesh - 01:34:05
I’m sure you’re gonna address this in the book, but I’m already curious now. It seems like the internet hasn’t been as big a deal as somebody who’s thinking from this zoomed out collective brain picture might have anticipated in the ’90s. Maybe it’s boosted the rate of innovation somewhat, although it’s not clear just looking at growth rates or productivity growth rates or something that it has. Why didn’t the internet cause the equivalent of another round of urbanization in our ability to do research or economic productivity or so forth?

Joseph Henrich - 01:34:40
Yeah. And that’s something I’m really interested in figuring out. And one thing that I’m still- I don’t have a full answer to this question, but one thing that is clear is that there’s something special about face-to-face interactions.

So something like whether even in the 21st century, whether two cities have direct long haul flights between them or direct flights between them increases the flow of ideas between those places. But we know these places are connected by the internet, whereas that effect doesn’t seem as good. And part of this is that people have to build trust, probably, before we start sharing ideas. And the more different someone is from us, the more you need that trust. So some of the research suggests that this effect is even bigger when places are more culturally different. And this is interesting, because they’re probably more valuable to each other when they’re more culturally different. But that’s then the face time and being in the same place is even more valuable. So that’s the kind of direction I’m going. And also, I still think that there should be exactly what you said, which is that the internet should have caused more innovation. I’m just not satisfied that we fully determined the best way to figure that out.

Dwarkesh - 01:35:55
You had a very interesting chapter in Weird where you talk about… a lot of innovation through history is more serendipitous than we imagine, and it’s the result of the collective brain being big enough to discover these things. But if you look at the sort of track record in science and research, there do seem to be certain individuals who make independent discoveries across many different fields that are each quite important, your Newtons and Einsteins and John von Neumanns and so forth. And if it was just the result of the right person is in the right lab who sees the right observation at the right time, this repeated excellence by certain people seems less explainable. So I’m curious if the collective brains theory can explain what’s going on there.

Joseph Henrich - 01:36:44
Right. Sometimes I’m interpreted as saying that there are no genetic differences among individuals, [I] definitely think there are genetic differences among individuals that affect their likelihood. But I think when we take the individual, we often import into our thinking about them the person’s life history. So for example Einstein, when he was a patent clerk, so he wasn’t succeeding as an academic, he and his friends got together in something called the Olympia Academy, they called themselves, which was just a group of, like, five people, who would get together and they would read the interesting books at the time. And if you look at the books that they read, and historians have done a lot of work on this, all the major ideas that go into special relativity were read and processed by the Olympia Academy before they do it. So the idea that people think that time is relative; well, people were kinda talking about that, and they were talking about multiple dimensions. And like Henri Poincaré, at the same time, comes up with the same equation as Einstein, but he doesn’t give it as radical an interpretation as Einstein does. He was thinking of the equations as kind of fudge factors, trying to make the math work. So there was an almost simultaneous invention of special relativity, and the ideas were all circulating. And Einstein happened to be in a place which allowed him to put all those things together.

As a patent clerk, he was actually processing devices to synchronize trains in different parts of Europe. And so he had to think about the amount of time a signal coming from different parts of the world. And there were a bunch of these. And we know the patents that went through, and we know that he had to look at these, there were only two guys. So he happened to be in a particular place and time in terms of the ideas circulating and what he was doing on a day-to-day basis that really did give him an edge. Now, you could then say, “Well, what about general relativity?” Well, there were a couple other guys who were probably gonna get general relativity. And Einstein himself was worried that he was gonna get scooped, because once you get the one, it’s just a matter of- I mean, it’s not “just” a matter of figuring out, the math is really hard. But there were a number of other people who probably would’ve gotten the math.

Dwarkesh - 01:38:48
Yes. I guess then there’s also still the question of why, in 1905, Einstein himself discovers not only special relativity, but Brownian motion and the two other things, right? But I guess you would just say it’s the result of he had the right reading group?

Joseph Henrich - 01:39:05
I mean, obviously he’s a special individual. Although he did spend the whole second half of his career trying to show why quantum mechanics was wrong. So his Brownian motion paper is foundational in quantum mechanics. But then he decides God doesn’t play dice with the universe, and he doesn’t like the stochastic nature of quantum mechanics, which has more or less proved true, and it’s part of our modern technology and stuff like that. And he spends the whole second half of his career fighting it. So that’s a case where his intuitions didn’t serve him well. And he turned out to be wrong for decades.

Dwarkesh - 01:39:39
And, sorry, I think that’s also a case in which your point about having the right ideas available to you is important, because, obviously I don’t know much about this, but from what I understand, the people who believe in the many worlds interpretation believe that Einstein was on the right track, and he just didn’t have this idea of the ever-ending multiverse available to him, which would’ve explained his…

Joseph Henrich - 01:40:04
That there’s this branching thing, and just a matter of figuring out which branch you’re on.

Dwarkesh - 01:40:08
Right. And so there’s no probability.

Joseph Henrich - 01:40:09
Yeah, yeah.

Dwarkesh - 01:40:09
Yeah. Okay, so we were just discussing, look, Einstein was reading the right things and making sure he had enough context to come up with these big discoveries. You’re somebody who has connected ideas across many different fields. How would you describe the input function for the way in which you come up with new ideas, and how much has it been informed by… I mean, you were like, you started off as an aerospace engineer, and you’ve done anthropology across so many different societies, on fieldwork and so forth. How do you basically think about the Joseph Henrich production function?

Joseph Henrich - 01:40:45
Yeah, I mean I try to implement the ideas of the collective brain. So in my lab at Harvard, I have post-docs and graduate students with backgrounds in evolutionary biology, psychology, anthropology, economics. I mean, some of my favorite collaborators are economists. And we’re just bringing ideas from very different places, but across the social sciences and even the biological sciences, we’re often trying to explain the same kind of phenomena, economic decision-making, cooperation, things like ethnicity, why does it exist, stuff like that. So the way we silo science is a big problem, and especially in the social science, I think it’s even a bigger problem.

So I’ve been a professor of anthropology, psychology, economics, and evolutionary biology, and just even the standards of evidence and what constitutes good research and how you tackle a problem really varies. So the academic disciplines are like different cultural worlds, different tribes, and just pulling the best from them has kind of been my approach.

Dwarkesh - 01:41:45
And among the more polymathic scholars, do you guys have, I don’t know… do you and David and a couple of the other ones in this category, is there some shared group you guys have, or, is it…

Joseph Henrich - 01:42:02
No, it’s a series of different networks. So David Reich and I, for example, are always talking about how we wanna read culture from the genes. So if humans have had a long history of gene-culture co-evolution and we wanna understand what Paleolithic populations were doing, we might be able to see imprints of it in the genes.

So in, in my own work, for example, we’ve measured cousin marriage across different populations and shown it correlates with runs of homozygosity in the genes. Something like polygeny can be revealed by looking at the ratio, the variance in Y chromosomes to X chromosomes. Fire: we probably have some special genetic adaptations for dealing with the toxins in fire. So if you wanna know when humans got fire, and you see the gene that gives us immunity to toxins in fire, then we can infer that fire is older than that, right?

Dwarkesh - 01:42:53
Interesting. And, so obviously the input from different scientific fields in your work is obvious. But from a more philosophical perspective, people compare your work in the same tradition as Burke or Chesterton or something. Do you personally find that your philosophy has been informed by reading them, or is it just independently converging on some of the same themes?

Joseph Henrich - 01:43:18
Yeah, I mean, any convergence is completely independent, because I haven’t really spent much time at all. I mean, I’ve gone back. After people said, “Your work is kinda like Hayek,” I went back and read some Hayek. I did read The Fatal Conceit in graduate school, but aside from that, I didn’t read very much. And then same thing with Adam Smith. I mean, I’ve tackled The Wealth of Nations, and I eventually read The Theory of Moral Sentiments. But it was pretty far into the process. I had picked up on themes they developed before I knew they had developed them.

Dwarkesh - 01:43:49
I want to suggest that maybe you should add a couple of AI people in your rotation.

Joseph Henrich - 01:43:50
Ok.

Dwarkesh - 01:43:51
Just because I think this perspective is incredibly informative, what I think is maybe the most important question of our period, of what does this transition towards a society of AIs look like? And, sorry to bring it back to AI, but one other consideration here that your work has really informed me on, is: I was sort of maybe hyping up how big a deal I expect societies of AIs to be, but something your work informs is the idea of a single superintelligence is not the place in which to expect these big impacts of John von Neumann times 10.

But then again, there is a key advantage in the scope of social learning they can do as a society, but, like, the idea of a single superintelligence being super powerful is maybe less likely as a result of your work. And I, anyways, I don’t know what you think about that interpretation, but…

Joseph Henrich - 01:44:44
Yeah, I think that’s right because the assumption, the sort of model that people seem to carry around in their heads is that humans have done all these things because of our individual brain power when really, it’s been the power of cultural evolution and a network of minds gnawing away at problems over long periods of time and gradually accumulating not just the obvious tools, but also the cognitive tools for, for addressing these things.

Dwarkesh - 01:45:08
I buy the idea that individual IQ isn’t the most relevant factor to understanding why discoveries are made. It’s not about having the right genius or something. But what’s the reason for thinking that the average IQ of a society isn’t super important in how much progress a society’s gonna make, not just the population size?

Joseph Henrich - 01:45:28
Right. So the first thing that we need to do though is to zoom back out and think about what we mean by IQ. And so Michael Muthu Krishnan and I have made the case that IQ is just a set of cognitive abilities that leads to success in 20th century contemporary institutions that have come to dominate the world. So it’s a set, it’s a culturally evolved system, and we talked before about the Flynn effect which illustrates that, and the fact that IQ is associated with all these positive outcomes now, but certainly wouldn’t be in pastoralist societies.

So that would mean that if you raise the average IQ of your society, it might lead them to have more abstract thoughts and do better science. And so that’s, that certainly fits. But the interesting thing for me is that the world of the future is gonna be quite different than the one now. So the set of cognitive abilities which is gonna be favored in the new AI world is not gonna be the same set that was favored in 1900 when they designed the IQ test. So for example, in the IQ test they ask you to remember digits backwards. I’m not sure how useful remembering lists of digits backwards [is]. It was in a previous world, where we had to write everything down and remember a lot of stuff. But in some sense, we’re interfacing with our technology and we’ve got to figure out what are the set of cognitive abilities which is gonna make people best able to solve problems? And like we talked about, it’s even the case that the most creative people aren’t the highest IQ people.

I guess one of the things I’m trying to say is that the minds that might lead us into the new world might not be the ones that have the highest IQ because once you’re sort of augmented with AI and all these kind of technological aids we have, the specialized thing that leads someone to do something creative- probably not gonna be the same abilities that did it in 1910.

Dwarkesh - 01:47:21
Yeah. I guess my real opinion is that, if in the world where that’s true- and I think that’s probably gonna be true- then it really doesn’t matter at all because AIs will be doing everything. But in the world where, let’s say AI plateaus or something, then I would expect roughly the same kinds of skills that have mattered for the last couple of centuries to keep mattering for a modern, technological society, which is analytical thinking and the ability to understand science and technology and so forth.

Joseph Henrich - 01:47:54
But maybe the AI world you’re imagining is different than the one I’m imagining, but I still think that people are gonna figure out what problems we need to solve, unless we’re just gonna tell the AIs to figure out what the problems are and then solve them.

They might not be that good at that. I don’t know. We’ll see.

Dwarkesh - 01:48:10
You shared with me this draft of work about collective brains, where you show that ants have developed many of the technologies that humans have, like farming, and livestock, and division of labor and so forth. And so maybe there’s some amount of blind selection, and it doesn’t matter if it’s natural selection, if it’s cultural selection, it’s the size of the group which can go through this learning process that matters, and how many people are available to experience the learnings or get the selection process acted upon them.

But I guess the big difference in where maybe individual IQ comes back into the picture here is, I don’t expect there to be any amount of natural selection which will allow ants to land on the moon or make a computer chip or do, you know, make a nuclear fusion plant or something. And so is the kind of broad generalization we see with humans, and we might see to an even greater extent with AI, a product of you’re really bottlenecked by the IQ of the individual? There’s no amount of collective learning that can get you to the moon if you’re an ant?

Joseph Henrich - 01:49:33
Right. Well, one thing to keep in mind is that most human societies over most of human evolutionary history didn’t get to the moon. So this is one particular group of humans. And one of the things we talked about earlier in this conversation was the cultural evolution of epistemology.

So it was the improvement in our what constitutes evidence, what constitutes a good argument that allows us to get to science and accumulate this kind of knowledge to do these kinds of things. So I see that as part of a continuous trajectory. But it’s just that we have new cognitive tools.

Dwarkesh - 01:50:05
Right. I guess, but… so there’s a really interesting point that even the epistemic tools which let us get to the moon are themselves a product of cultural evolution. But again, that seems bottlenecked by the fact that if you’re just dealing with chimpanzees or something, there’s no amount of cultural evolution that will result in the scientific method, that-

Joseph Henrich - 01:50:30
Right. It needs some gene culture co-evolution.

Dwarkesh - 01:50:32
Right, right. So you mentioned in the Secret the tool use started something like two million years ago, and fire, we started domesticating fire closer to 800,000 to a million years ago. Intuitively, it doesn’t seem like tool use is that that much simpler than fire, but is that a misunderstanding? Why was fire so much [more] recent?

Joseph Henrich - 01:50:50
Well, so what we know from other species is that lots of animals use tools, and particularly chimpanzees use tools. So we can assume tools in the common ancestor. Now, what we see in the paleoanthropological record is the increasing use of stone tools.

And these are pretty simple stone tools. You can see a cutting edge there, but not very much fancier than that. And then fire is… a lot of animals are afraid of fire, and they have to run from wildfires and stuff like that. So whatever your story about humans is, you have to overcome the fear of the fire in order to tame it. Probably humans first found wild fire and somebody approached the fire instead of running away, which is the usual thing to do. And then got some of it and then put it to use, I guess. So I think it’s the innate fear that animals have of fire, which is we don’t hang around when things go off.

Dwarkesh - 01:51:41
Interesting. Okay, final question, what’s the next thing you’re working on?

Joseph Henrich - 01:51:45
Well, the big thing is this book on collective brains, so I’m gradually working through that. One of the ideas that I really am excited about in this book is that we evolved to think in collectives. There’s this assumption that psychologists have had to understand human decision-making and how our minds solve problems is that we should put people by themselves in experiments and see how they do. But real human societies, hunter-gatherers, they actually work in groups. And when we want to solve a problem, the first thing we want to do is check with our friends or ask other expert members of the community.

So it’s the idea that we think collectively and solve problems collectively in a kind of naturally distributed brain. And Hugo Mercier and Dan Sperber, for example, have argued and shown with various lines of evidence that many of the sort of irrational biases we have, mitigate or disappear entirely when we solve problems as groups. So it’s almost like we evolved to have that positive interaction and correct each other’s errors.

Dwarkesh - 01:52:43
Right. That’s quite interesting that you care more about the portfolio of intelligences than any one person being calibrated.

Joseph Henrich - 01:52:57
Right.

Dwarkesh - 01:52:58
Excellent. This is super fun. Thanks so much for coming on the podcast.

Joseph Henrich - 01:52:58
All right. Great to be with you.

I’m so excited with how this visualization of Notes on China turned out. Petr, thank you for such beautiful watercolor artwork. More to come!

Watch on YouTube.

(0:00:00) - Intro
(0:00:32) - Scale
(0:05:50) - Vibes
(0:11:14) - Youngsters
(0:14:27) - Tech & AI
(0:15:47) - Hearts & Minds
(0:17:07) - On Travel

Satya Nadella on:

• Why he doesn’t believe in AGI but does believe in 10% economic growth,

• Microsoft’s new topological qubit breakthrough and gaming world models,

• Whether Office commoditizes LLMs or the other way around,

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(0:00:00) - Intro

(0:05:04) - AI won't be winner-take-all

(0:15:18) - World economy growing by 10%

(0:21:39) - Decreasing price of intelligence

(0:30:19) - Quantum breakthrough

(0:42:51) - How Muse will change gaming

(0:49:51) - Legal barriers to AI

(0:55:46) - Getting AGI safety right

(1:04:59) - 34 years at Microsoft

(1:10:46) - Does Satya Nadella believe in AGI?

Dwarkesh Patel

Satya, thank you so much for coming on the podcast.

In a second, we're going to get to the two breakthroughs that Microsoft has just made, and congratulations, same day in Nature: the Majorana zero chip, which we have in front of us right here, and also the world human action models. But can we just continue the conversation we were having a second ago? You're describing the ways in which the things you were seeing in the 80s and 90s, you're seeing them happen again.

Satya Nadella

The thing that is exciting for me... Dwarkesh, first of all, it's fantastic to be on your podcast. I'm a big listener, and I love the way that you do these interviews and the broad topics that you explore.

The thing that is exciting for me… It reminds me a little bit of my, I'd say, first few years even in the tech industry, starting in the 90s, where there was real debate about whether it's going to be RISC or CISC, or, "Hey, are we really going to be able to build servers using x86?"

When I joined Microsoft, that was the beginning of what was Windows NT. So, everything from the core silicon platform to the operating system to the app tier- that full stack approach- the entire thing is being litigated.

You could say cloud did a bunch of that, and obviously distributed computing and cloud did change client-server. The web changed massively. But this does feel a little more like maybe more full-stack than even the past that at least I've been involved in.

Dwarkesh Patel

When you think about which decisions ended up being the long-term winners in the 80s and 90s, and which ones didn't, and especially when you think about- you were at Sun Microsystems, they had an interesting experience with the 90s dotcom bubble. People talk about this data center build-out as being a bubble, but at the same time, we have the Internet today as a result of what was built out then.

What are the lessons about what will stand the test of time? What is an inherent secular trend? What is just ephemeral?

Satya Nadella

If I go back, the four big transformations that I've been part of, the client and the client-server. So that's the birth of the graphical user interface and the x86 architecture, basically allowing us to build servers.

It was very clear to me. I remember going to what is PDC in '91, in fact I was at Sun at that time. In '91, I went to Moscone. That's when Microsoft first described the Win32 interface and it was pretty clear to me what was going to happen, where the server was also going to be an x86 thing. When you have the scale advantages accruing to something, that's the secular bet you have to place. What happened in the client was going to happen on the server side, and then you were able to actually build client-server applications. So, the app model became clear.

Then the web was the big thing for us, which we had to deal with in starting, in fact as soon as I joined Microsoft, the Netscape browser or the Mosaic browser came out what, I think, December or November of '93, right? I think is when Andreessen and crew had that.

So that was a big game-changer, in an interesting way, just as we were getting going on what was the client-server wave, and it was clear that we were going to win it as well. We had the browser moment, and so we had to adjust. And we did a pretty good job of adjusting to it because the browser was a new app model.

We were able to embrace it with everything we did, whether it was HTML in Word or building a new thing called the browser ourselves and competing for it, and then building a web server on our server stack and go after it. Except, of course, we missed what turned out to be the biggest business model on the web, because we all assumed the web is all about being distributed, who would have thought that search would be the biggest winner in organizing the web? And so that's where we obviously didn't see it, and Google saw it and executed super well.

So that's one lesson learned for me: you have to not only get the tech trend right, you also have to get where the value is going to be created with that trend. These business model shifts are probably tougher than even the tech trend changes.

Dwarkesh Patel

Where is the value going to be created in AI?

Satya Nadella

That's a great one. So I think there are two places where I can say with some confidence. One is the hyperscalers that do well, because the fundamental thing is if you sort of go back to even how Sam and others describe it, if intelligence is log of compute, whoever can do lots of compute is a big winner.

The other interesting thing is, if you look at underneath even any AI workload, like take ChatGPT, it's not like everybody's excited about what's happening on the GPU side, it's great. In fact, I think of my fleet even as a ratio of the AI accelerator to storage, to compute. And at scale, you've got to grow it.

Dwarkesh Patel

Yeah.

Satya Nadella

And so, that infrastructure need for the world is just going to be exponentially growing.

Dwarkesh Patel

Right.

Satya Nadella

So in fact it's manna from heaven to have these AI workloads because guess what? They're more hungry for more compute, not just for training, but we now know, for test time. When you think of an AI agent, it turns out the AI agent is going to exponentially increase compute usage because you're not even bound by just one human invoking a program. It's one human invoking programs that invoke lots more programs. That's going to create massive, massive demand and scale for compute infrastructure. So our hyperscale business, Azure business, and other hyperscalers, I think that’s a big thing.

Then after that, it becomes a little fuzzy. You could say, hey, there is a winner-take-all model- I just don't see it. This, by the way, is the other thing I’ve learned: being very good at understanding what are winner-take-all markets and what are not winner-take-all markets is, in some sense, everything. I remember even in the early days when I was getting into Azure, Amazon had a very significant lead and people would come to me, and investors would come to me, and say, "Oh, it's game over. You'll never make it. Amazon, it's winner-take-all."

Having competed against Oracle and IBM in client-server, I knew that the buyers will not tolerate winner-take-all. Structurally, hyperscale will never be a winner-take-all because buyers are smart.

Consumer markets sometimes can be winner-take-all, but anything where the buyer is a corporation, an enterprise, an IT department, they will want multiple suppliers. And so you got to be one of the multiple suppliers.

That, I think, is what will happen even on the model side. There will be open-source. There will be a governor. Just like on Windows, one of the big lessons learned for me was, if you have a closed-source operating system, there will be a complement to it, which will be open source.

And so to some degree that's a real check on what happens. I think in models there is one dimension of, maybe there will be a few closed source, but there will definitely be an open source alternative, and the open-source alternative will actually make sure that the closed-source, winner-take-all is mitigated.

That's my feeling on the model side. And by the way, let's not discount if this thing is really as powerful as people make it out to be, the state is not going to sit around and wait for private companies to go around and… all over the world. So, I don't see it as a winner-take-all.

Then above that, I think it's going to be the same old stuff, which is in consumer, in some categories, there may be some winner-take-all network effect. After all, ChatGPT is a great example.

It's an at-scale consumer property that has already got real escape velocity. I go to the App Store, and I see it's always there in the top five, and I say “wow, that's pretty unbelievable”.

So they were able to use that early advantage and parlay that into an app advantage. In consumer, that could happen. In the enterprise again, I think there will be, by category, different winners. That's sort of at least how I analyze it.

Dwarkesh Patel

I have so many follow-up questions. We have to get to quantum in just a second, but on the idea that maybe the models get commoditized: maybe somebody could have made a similar argument a couple of decades ago about the cloud – that fundamentally, it's just a chip and a box.

But in the end, of course, you and many others figured out how to get amazing profit margins in the cloud. You figured out ways to get economies of scale and add other value. Fundamentally, even forgetting the jargon, if you've got AGI and it's helping you make better AIs – right now, it's synthetic data and RL; maybe in the future, it's an automated AI researcher – that seems like a good way to entrench your advantage there. I'm curious what you make of that, just the idea that it really matters to be ahead there.

Satya Nadella

At scale, nothing is commodity. To your point about cloud, everybody would say, "Oh, cloud's a commodity." Except, when you scale... That's why the know-how of running a hyperscaler... You could say, "Oh, what the heck? I can just rack and stack servers."

Dwarkesh Patel

Right.

Satya Nadella

In fact, in the early days of hyperscale, most people thought “there are all these hosters, and those are not great businesses. Will there be anything? Is there a business even in hyperscale?” And it turns out there is a real business, just because of the know-how of running, in the case of Azure, the world's computing of 60-plus regions with all the compute. It's just a tough thing to duplicate.

So I was more making the point, is it one winner? Is it a winner-take-all or not? Because that you've got to get right. I like to enter categories which are big TAMs, where you don't have to have the risk of it all being winner-take-all. The best news to be in is a big market that can accommodate a couple of winners, and you're one of them.

That's what I meant by the hyperscale layer. In the model layer, one is models need ultimately to run on some hyperscale compute. So that nexus, I feel, is going to be there forever. It's not just the model; the model needs state, that means it needs storage, and it needs regular compute for running these agents and the agent environments.

And so that's how I think about why the limit of one person running away with one model and building it all may not happen.

Dwarkesh Patel

On the hyperscaler side, and by the way, it's also interesting the advantage you as a hyperscaler would have in the sense that, especially with inference time scaling and if that's involved in training future models, you can amortize your data centers and GPUs, not only for the training, but then use them again for inference.

I'm curious what kind of hyperscaler you consider Microsoft and Azure to be. Is it on the pre-training side? Is it on providing the O3-type inference? Or are you just, we’re going to host and deploy any single model that's out there in the market, and we are sort of agnostic about that?

Satya Nadella

It’s a good point. The way we want to build out the fleet is [to], in some sense ride Moore's law. I think this will be like what we've done with everything else in the past: every year keep refreshing the fleet, you depreciate it over whatever the lifetime value of these things are, and then get very very good at the placement of the fleet such that you can run different jobs at it with high utilization. Sometimes there are very big training jobs that need to have highly concentrated peak flops that are provisioned to it that also need to cohere. That's great. We should have enough data center footprint to be able to give that.

But at the end of the day, these are all becoming so big, even in terms of if you take pre-training scale, if it needs to keep going, even pre-training scale at some point has to cross data center boundaries. It's all more or less there.

So, great, once you start crossing pre-training data center boundaries, is it that different than anything else? The way I think about it is hey, distributed computing will remain distributed, so go build out your fleet such that it's ready for large training jobs, it's ready for test-time compute, it’s ready- in fact, if this RL thing that might happens, you build one large model, and then after that, there’s tons of RL going on. To me, it's kind of like more training flops, because you want to create these highly specialized, distilled models for different tasks.

So you want that fleet, and then the serving needs. At the end of the day, speed of light is speed of light, so you can't have one data center in Texas and say, "I'm going to serve the world from there."

You've got to serve the world based on having an inference fleet everywhere in the world. That's how I think of our build-out of a true hyperscale fleet.

Oh, and by the way, I want my storage and compute also close to all of these things, because it's not just AI accelerators that are stateless. My training data itself needs storage, and then I want to be able to multiplex multiple training jobs, I want to be able to then have memory, I want to be able to have these environments in which these agents can go execute programs. That's kind of how I think about it.

Dwarkesh Patel

You recently reported that your yearly revenue from AI is $13 billion. But if you look at your year-on-year growth on that, in like four years, it'll be 10x that. You'll have $130 billion in revenue from AI, if the trend continues. If it does, what do you anticipate doing with all that intelligence, this industrial scale use?

Is it going to be through Office? Is it going to be you deploying it for others to host? You've got to have the AGIs to have $130 billion in revenue? What does it look like?

Satya Nadella

The way I come at it, Dwarkesh, it's a great question because at some level, if you're going to have this explosion, abundance, whatever, commodity of intelligence available, the first thing we have to observe is GDP growth.

Before I get to what Microsoft's revenue will look like, there's only one governor in all of this. This is where we get a little bit ahead of ourselves with all this AGI hype. Remember the developed world, which is what? 2% growth and if you adjust for inflation it’s zero?

So in 2025, as we sit here, I'm not an economist, at least I look at it and say we have a real growth challenge. So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, let's have that Industrial Revolution type of growth.

That means to me, 10%, 7%, developed world, inflation-adjusted, growing at 5%. That's the real marker. It can't just be supply-side.

In fact that’s the thing, a lot of people are writing about it, and I'm glad they are, which is the big winners here are not going to be tech companies. The winners are going to be the broader industry that uses this commodity that, by the way, is abundant. Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we'll be fine as an industry.

But that's to me the moment. Us self-claiming some AGI milestone, that's just nonsensical benchmark hacking to me. The real benchmark is: the world growing at 10%.

Dwarkesh Patel

Okay, so if the world grew at 10%, the world economy is $100 trillion or something, if the world grew at 10%, that's like an extra $10 trillion in value produced every single year. If that is the case, you as a hyperscaler... It seems like $80 billion is a lot of money. Shouldn't you be doing like $800 billion?

If you really think in a couple of years, we could be really growing the world economy at this rate, and the key bottleneck would be: do you have the compute necessary to deploy these AIs to do all this work?

Satya Nadella

That is correct. But by the way, the classic supply side is, "Hey, let me build it and they’ll come." That's an argument, and after all we've done that, we've taken enough risk to go do it.

But at some point, the supply and demand have to map. That's why I'm tracking both sides of it. You can go off the rails completely when you are hyping yourself with the supply-side, versus really understanding how to translate that into real value to customers.

That's why I look at my inference revenue. That's one of the reasons why even the disclosure on the inference revenue... It's interesting that not many people are talking about their real revenue, but to me, that is important as a governor for how you think about it.

You're not going to say they have to symmetrically meet at any given point in time, but you need to have existence proof that you are able to parlay yesterday's, let’s call it capital, into today's demand, so that then you can again invest, maybe exponentially even, knowing that you're not going to be completely rate mismatched.

Dwarkesh Patel

I wonder if there's a contradiction in these two different viewpoints, because one of the things you've done wonderfully is make these early bets. You invested in OpenAI in 2019, even before there was Copilot and any applications.

If you look at the Industrial Revolution, these 6%, 10% build-outs of railways and whatever things, many of those were not like, "We've got revenue from the tickets, and now we're going to..."

Satya Nadella

There was a lot of money lost.

Dwarkesh Patel

That's true. So, if you really think there's some potential here to 10x or 5x the growth rate of the world, and then you're like, "Well, what is the revenue from GPT-4?"

If you really think that's the possibility from the next level up, shouldn't you just, "Let's go crazy, let's do the hundreds of billions of dollars of compute?" I mean, there's some chance, right?

Satya Nadella

Here’s the interesting thing, right? That's why even that balanced approach to the fleet, at least, is very important to me. It's not about building compute. It's about building compute that can actually help me not only train the next big model but also serve the next big model. Until you do those two things, you're not going to be able to really be in a position to take advantage of even your investment.

So, that's kind of where it's not a race to just building a model, it's a race to creating a commodity that is getting used in the world to drive… You have to have a complete thought, not just one thing that you’re thinking about.

And by the way, one of the things is that there will be overbuild. To your point about what happened in the dotcom era, the memo has gone out that, hey, you know, you need more energy, and you need more compute. Thank God for it. So, everybody's going to race.

In fact, it's not just companies deploying, countries are going to deploy capital, and there will be clearly... I'm so excited to be a leaser, because, by the way; I build a lot, I lease a lot. I am thrilled that I'm going to be leasing a lot of capacity in '27, '28 because I look at the builds, and I'm saying, "This is fantastic." The only thing that's going to happen with all the compute builds is the prices are going to come down.

Dwarkesh Patel

Speaking of prices coming down, you recently tweeted after the DeepSeek model came out about Jevons’ Paradox. I'm curious if you can flesh that out. Jevons’ Paradox occurs when the demand for something is highly elastic. Is intelligence that bottlenecked on prices going down?

Because when I think about, at least my use cases as a consumer, intelligence is already so cheap. It's like two cents per million tokens. Do I really need it to go down to 0.02 cents? I'm just really bottlenecked on it becoming smarter. If you need to charge me 100x, do a 100x bigger training run. I'm happy for companies to take that.

But maybe you're seeing something different on the enterprise side or something. What is the key use case of intelligence that really requires it to get to 0.002 cents per million tokens?

Satya Nadella

I think the real thing is the utility of the tokens. Both need to happen: One is intelligence needs to get better and cheaper. And anytime there's a breakthrough, like even what DeepSeek did, with the efficient frontier of performance per token changes, the curve gets bent, and the frontier moves. That just brings more demand. That's what happened with cloud.

Here’s an interesting thing: We used to think “oh my God, we've sold all the servers in the client-server era”. Except once we started putting servers in the cloud, suddenly people started consuming more because they could buy it cheaper, and it was elastic, and they could buy it as a meter versus a license, and it completely expanded.

I remember going, let’s say, to a country like India and talking about “here is SQL Server”. We sold a little, but man, the cloud in India is so much bigger than anything that we were able to do in the server era. I think that's going to be true.

If you think about, if you want to really have, in the Global South, in a developing country, if you had these tokens that were available for healthcare that were really cheap, that would be the biggest change ever.

Dwarkesh Patel

I think it's quite reasonable for somebody to hear people like me in San Francisco and think “they're kind of silly; they don't know what it's actually like to deploy things in the real world”.

As somebody who works with these Fortune 500s and is working with them to deploy things for hundreds of millions, billions of people, what's your sense on how fast deployment of these capabilities will be?

Even when you have working agents, even when you have things that can do remote work for you, with all the compliance and with all the inherent bottlenecks, is that going to be a big bottleneck, or is that going to move past pretty fast?

Satya Nadella

It is going to be a real challenge because the real issue is change management or process change. Here's an interesting thing: one of the analogies I use is, just imagine how a multinational corporation like us did forecasts pre-PC, and email, and spreadsheets. Faxes went around. Somebody then got those faxes and did an interoffice memo that then went around, and people entered numbers, and then ultimately a forecast came, maybe just in time for the next quarter.

Then somebody said, "Hey, I'm just going to take an Excel spreadsheet, put it in email, send it around. People will go edit it, and I'll have a forecast." So, the entire forecasting business process changed because the work artifact and the workflow changed.

That is what needs to happen with AI being introduced into knowledge work. In fact, when we think about all these agents, the fundamental thing is there's a new work and workflow.

For example, even prepping for our podcast, I go to my copilot and I say, "Hey, I'm going to talk to Dwarkesh about our quantum announcement and this new model that we built for game generation. Give me a summary of all the stuff that I should read up on before going." It knew the two Nature papers, it took that. I even said, "Hey, go give it to me in a podcast format." And so, it even did a nice job of two of us chatting about it.

So that became—and in fact, then I shared it with my team. I took it and put it into Pages, which is our artifact, and then shared. So the new workflow for me is I think with AI and work with my colleagues.

That's a fundamental change management of everyone who's doing knowledge work, suddenly figuring out these new patterns of "How am I going to get my knowledge work done in new ways?" That is going to take time. It's going to be something like in sales, and in finance, and supply chain.

For an incumbent, I think that this is going to be one of those things where—you know, let's take one of the analogies I like to use is what manufacturers did with Lean. I love that because, in some sense, if you look at it, Lean became a methodology of how one could take an end-to-end process in manufacturing and become more efficient. It's that continuous improvement, which is reduce waste and increase value.

That's what's going to come to knowledge. This is like Lean for knowledge work, in particular. And that's going to be the hard work of management teams and individuals who are doing knowledge work, and that's going to take its time.

Dwarkesh Patel

Can I ask you just briefly about that analogy? One of the things Lean did is physically transform what a factory floor looks like. It revealed bottlenecks that people didn't realize until you're really paying attention to the processes and workflows.

You mentioned briefly what your own workflow—how your own workflow has changed as a result of AIs. I'm curious if we can add more color to what will it be like to run a big company when you have these AI agents that are getting smarter and smarter over time?

Satya Nadella

It's interesting you ask that. I was thinking, for example, today if I look at it, we are very email heavy. I get in in the morning, and I’m like, man my inbox is full, and I'm responding, and so I can't wait for some of these Copilot agents to automatically populate my drafts so that I can start reviewing and sending.

But I already have in Copilot at least ten agents, which I query them different things for different tasks. I feel like there's a new inbox that's going to get created, which is my millions of agents that I'm working with will have to invoke some exceptions to me, notifications to me, ask for instructions.

So at least what I'm thinking is that there's a new scaffolding, which is the agent manager. It's not just a chat interface. I need a smarter thing than a chat interface to manage all the agents and their dialogue.

That's why I think of this Copilot, as the UI for AI, is a big, big deal. Each of us is going to have it. So basically, think of it as: there is knowledge work, and there's a knowledge worker. The knowledge work may be done by many, many agents, but you still have a knowledge worker who is dealing with all the knowledge workers. And that, I think, is the interface that one has to build.

Dwarkesh Patel

You're one of the few people in the world who can say that you have access to 200,000… you have this swarm of intelligence around you in the form of Microsoft the company and all its employees. And you have to manage that, and you have to interface with that, how to make best use of that. Hopefully, more of the world will get to have that experience in the future.

I'd be curious about how your inbox, if that means everybody's inbox, will look like yours in the morning.

Okay, before we get to that, I want to keep asking you more about AI, but I really want to ask you about the big breakthrough in quantum that Microsoft Research has announced. So can you explain what's going on?

Satya Nadella

This has been another 30-year journey for us. It's unbelievable. I'm the third CEO of Microsoft who's been excited about quantum.

The fundamental breakthrough here, or the vision that we've always had is, you need a physics breakthrough in order to build a utility-scale quantum computer that works. We took the path of saying, the one way for having a less noisy or more reliable qubit is to bet on a physical property that by definition is more reliable and that's what led us to the Majorana zero modes, which was theorized in the 1930s. The question was, can we actually physically fabricate these things? Can we actually build them?

So the big breakthrough effectively, and I know you talked to Chetan, was that we now finally have existence proof and a physics breakthrough of Majorana zero modes in a new phase of matter effectively. This is why we like the analogy of thinking of this as the transistor moment of quantum computing, where we effectively have a new phase, which is the topological phase, which means we can even now reliably hide the quantum information, measure it, and we can fabricate it. And so now that we have it, we feel like with that core foundational fabrication technique out of the way, we can start building a Majorana chip.

That Majorana One which I think is going to basically be the first chip that will be capable of a million qubits, physical. And then on that, thousands of logical qubits, error-corrected. And then it's game on. You suddenly have the ability to build a real utility-scale quantum computer, and that to me is now so much more feasible. Without something like this, you will still be able to achieve milestones, but you'll never be able to build a utility-scale computer. That's why we're excited about it.

Dwarkesh Patel

Amazing. And by the way, I believe this is it right here.

Satya Nadella

That is it.

Dwarkesh Patel

Yes.

Satya Nadella

I forget now, are we calling it Majorana? Yes, that's right. Majorana One. I'm glad we named it after that.

To think that we are able to build something like a million-qubit quantum computer in a thing of this size is just unbelievable. That's the crux of it: unless and until we could do that, you can't dream of building a utility-scale quantum computer.

Dwarkesh Patel

And you're saying the eventual million qubits will go on a chip this size? Okay, amazing.

Other companies have announced 100 physical qubits, Google's, IBM's, others. When you say you've announced one, but you're saying that yours is way more scalable in the limit.

Satya Nadella

Yeah. The one thing we’ve also done is we've taken an approach where we've separated our software and our hardware. We're building out our software stack, and we now have, with the neutral atom folks, the ion trap folks, we're also working with others who even have pretty good approaches with photonics and what have you, that means there'll be different types of quantum computers. In fact, we have what, I think that the last thing that we announced was 24 logical qubits. So we have also got some fantastic breakthroughs on error correction and that's what is allowing us, even on neutral atom and ion trap quantum computers, to build these 20 plus, and I think that'll keep going even throughout the year; you'll see us improve that yardstick.

But we also then said, "Let's go to the first principles and build our own quantum computer that is betting on the topological qubit." And that's what this breakthrough is about.

Dwarkesh Patel

Amazing. The million topological qubits, thousands of logical qubits, what is the estimated timeline to scale up to that level? What does the Moore's law here, if you've got the first transistor, look like?

Satya Nadella

We've obviously been working on this for 30 years. I'm glad we now have the physics breakthrough and the fabrication breakthrough.

I wish we had a quantum computer because by the way, the first thing the quantum computer will allow us to do is build quantum computers, because it's going to be so much easier to simulate atom-by-atom construction of these new quantum gates.

But in any case, the next real thing is, now that we have the fabrication technique, let us go build that first fault-tolerant quantum computer. And that will be the logical thing.

So, I would say now I can say, "Oh, maybe '27, '28, '29, we will be able to actually build this." Now that we have this one gate, can I put the thing into an integrated circuit and then actually put these integrated circuits into a real computer? That is where the next logical step is.

Dwarkesh Patel

And what do you see as, in '27, '28, you've got it working? Is it a thing you access through the API? Is it something you're using internally for your own research in materials and chemistry?

Satya Nadella

It’s a great question. One thing that I've been excited about is, even in today's world… we had this quantum program, and we added some APIs to it. The breakthrough we had maybe two years ago was to think of this HPC stack, and AI stack, and quantum together.

In fact, if you think about it, AI is like an emulator of the simulator. Quantum is like a simulator of nature. What is quantum going to do? By the way, quantum is not going to replace classical. Quantum is great at what quantum can do, and classical will also...

Quantum is going to be fantastic for anything that is not data-heavy but is exploration-heavy in terms of the state space. It should be data-light but exponential states that you want to explore. Simulation is a great one: chemical physics, what have you, biology.

One of the things that we've started doing is really using AI as the emulation engine. But you can then train. So the way I think of it is, if you have AI plus quantum, maybe you'll use quantum to generate synthetic data that then gets used by AI to train better models that know how to model something like chemistry or physics or what have you. These two things will get used together.

So even today, that's effectively what we're doing with the combination of HPC and AI. I hope to replace some of the HPC pieces with quantum computers.

Dwarkesh Patel

Can you tell me a little bit about how you make these research decisions which, in 20 years time, 30 years time, will actually pay dividends, especially at a company of Microsoft's scale? Obviously, you're in great touch with the technical details in this project. Is it feasible for you to do that with all the things Microsoft Research does?

How do you know the current bet you're making will pay out in 20 years? Does it just have to emerge organically through the org, or how are you keeping track of all this?

Satya Nadella

The thing that I feel was fantastic is when Bill, when he started MSR back in '95 I guess. I think in the long history of these curiosity-driven research organizations, to just do a research org that is about fundamental research and MSR, over the years, has built up that institutional strength so when I think about capital allocation or budgets, we first put the chips in and say, "Here is MSR's budget." We gotta go at it each year knowing that most of these bets are not going to pay off in any finite time frame. Maybe the sixth CEO of Microsoft will benefit from it. And in tech that is I think a given.

The real thing that I think about is, when the time has come for something like quantum or a new model or what have you, can you capitalize? So as an incumbent, if you look at the history of tech, it's not that people didn't invest. It's that you need to have a culture that knows how to take an innovation and scale it.

That's the hard part, quite frankly, for CEOs and management teams. Which is kind of fascinating. It's as much about good judgment as it is about good culture. Sometimes we've gotten it right; sometimes we've gotten it wrong; I can tell you the thousand projects from MSR that we should have probably led with, but we didn't. And I always ask myself why. It's because we were not able to get enough conviction and that complete thought of how to not only take the innovation but make it into a useful product with a business model that we can then go to market with.

That's the job of CEOs and management teams: not to just be excited about any one thing, but to be able to actually execute on a complete thing. And that's easier said than done.

Dwarkesh Patel

When you mentioned the possibility of three subsequent CEOs of Microsoft, if each of them increases the market cap by an order of magnitude, by the time you've got the next breakthrough, you'll be like the world economy or something.

Satya Nadella

Or remember, the world is going to be growing at 10%, so we'll be fine.

Dwarkesh Patel

Let's dig into the other big breakthrough you've just made. It's amazing that you have both of them coming out the same day, in your gaming world models. I'd love if you can tell me a little bit about that.

Satya Nadella

We're going to call it Muse. It's going to be the model of this world action, or human action model.

This is very cool. One of the things is that obviously, Dall-E and Sora have been unbelievable in what they've been able to do in terms of generative models. One thing that we wanted to go after was using gameplay data. Can you actually generate games that are both consistent and then have the ability to generate the diversity of what that game represents, and then are persistent to user mods?

That's what this is. They were able to work with one of our game studios, and this is the other publication in Nature.

The cool thing is what I'm excited about is bringing--we're going to have a catalog of games soon that we will start using these models, or we're going to train these models to generate, and then start playing them.

In fact, when Phil Spencer first showed it to me, he had an Xbox controller and this model basically took the input and generated the output based on the input. And it was consistent with the game. That to me is a massive moment of “wow”. It's kind of like the first time we saw ChatGPT complete sentences, or Dall-E draw, or Sora. This is one such moment.

Dwarkesh Patel

I got a chance to see some of the videos in the real-time demo this morning with your lead researcher Katja on this. Only once I talked to her did it really hit me how incredible this is, in the sense that we've used AI in the past to model agents, and just using that same technique to model the world around the agent gives consistent real-time – we'll superimpose videos of what this looks like atop this podcast so people can get a chance to see it for themselves. I guess it'll be out by then, so they can also watch it there.

This in itself is incredible. You, through your span as CEO, have invested tens of hundreds of billions of dollars in building up Microsoft Gaming and acquiring IP.

In retrospect, if you can just merge all of this data into one big model that can give you this experience of visiting and going through multiple worlds at the same time, and if this is the direction gaming is headed, it seems like a pretty good investment to have made. Did you have any premonition about this?

Satya Nadella

I wouldn't say that we invested in gaming to build models. We invested, quite frankly, because- here's an interesting thing about our history: We built our first game before we built Windows. Flight Simulator was a Microsoft product long before we even built Windows.

So, gaming has got a long history at the company, and we want to be in gaming for gaming's sake. I always start by saying I hate to be in businesses where they're means to some other end. They have to be ends unto themselves.

And then, yes, we're not a conglomerate. We are a company where we have to bring all these assets together and be better owners by adding value. For example, cloud gaming is a natural thing for us to invest in because that will just expand the TAM and expand the ability for people to play games everywhere.

The same thing with AI and gaming: we definitely think that it can be helpful in maybe changing- it's kind of like the CGI moment, even for gaming long-term. And it's great. As the biggest, world's largest publisher, this will be helpful. But at the same time, we've got to produce great quality games. I mean, you can't be a gaming publisher without, sort of, first and foremost being focused on that.

But the fact that this data asset is going to be interesting, not just in a gaming context, but it's going to be a general action model and a world model, it's fantastic. I mean like, you know, I think about gaming data as perhaps, you know, what YouTube is perhaps to Google, gaming data is to Microsoft. And so therefore I'm excited about that.

Dwarkesh Patel

Yeah, and that's what I meant, just in the sense of like, you can have one unified experience across many different kinds of games. How does this fit into the other, separate from AI, the other things that Microsoft has worked on in the past, like mixed reality? Maybe giving smaller game studios a chance to build these AAA action games? Just like five, ten years from now, what kinds of ways could you imagine?

Satya Nadella

I've thought about these three things as the cornerstones of, in an interesting way, even five, six, seven years ago is when I said the three big bets that we want to place [are] AI, quantum, and mixed reality. And I still believe in them, because in some sense, what are the big problems to be solved?

Presence. That's the dream of mixed reality. Can you create real presence? Like you and I doing a podcast like this.

I think it’s still proving to be the harder one of those challenges, quite honestly. I thought it was going to be more solvable. It's tougher, perhaps, just because of the social side of it: wearing things and so on.

We're excited about, in fact, what we're going to do with Anduril and Palmer, now, with even how they'll take forward the IVAS program, because that's a fantastic use case. And so we'll continue on that front.

But also, the 2D surfaces. It turns out things like Teams, right, thanks to the pandemic, we've really gotten the ability to create essentially presence through even 2D. And that I think will continue. That's one secular piece.

Quantum we talked about, and AI is the other one. So these are the three things that I look at and say, how do you bring these things together? Ultimately, not as tech for tech's sake, but solving some of the fundamental things that we, as humans, want in our life, and more, we want them in our economy, driving our productivity. And so if we can somehow get that right, then I think we will have really made progress.

Dwarkesh Patel

When you write your next book, you've got to have some explanation of why those three pieces all came together around the same time, right? Like, there's no intrinsic reason you would think quantum and AI should happen in 2028 and 2025 and so forth.

Satya Nadella

That's right. At some level, I look at it and say: the simple model I have is, hey is there a systems breakthrough? To me, the systems breakthrough is the quantum thing.

Is there a business logic breakthrough? That's AI to me, which is: can the logic tier be fundamentally reasoned differently? Instead of imperatively writing code, can you have a learning system? That's the AI one.

And then the UI side of it is presence.

Dwarkesh Patel

Going back to AI for a second, in your 2017 book… 2019 you invest in OpenAI, very early, 2017 is even earlier, you say in your book, "One might also say that we're birthing a new species, one whose intelligence may have no upper limits."

Now, super-early, of course, to be talking about this in 2017. We've been talking in a granular fashion about agents, Office Copilot, capex, and so forth. But if you zoom out and consider this statement you've made, and you think about you as a hyperscaler, as the person doing research in these models as well, providing training, inference, and research for building a new species, how do you think about this in the grand scheme of things?

Do you think we're headed towards superhuman intelligence in your time as CEO?

Satya Nadella

I think even Mustafa uses that term. In fact he’s used that term more recently, this “new species”.

The way I come at it is, you definitely need trust. Before we claim it is something as big as a species, the fundamental thing that we've got to get right is that there is real trust, whether it's personal or societal level trust, that's baked in. That's the hard problem.

I think the one biggest rate limiter to the power here will be how does our legal… call it infrastructure, we’re talking about all the compute infrastructure, well how does the legal infrastructure evolve to deal with this? This entire world is constructed with things like humans owning property, having rights, and being liable. That’s the fundamental thing that one has to first say, okay what does that mean for anything that now humans are using as tools? And if humans are going to delegate more authority to these things, then how does that structure evolve? Until that really gets resolved, I don't think just talking about the tech capability is going to happen.

Dwarkesh Patel

As in, we won't be able to deploy these kinds of intelligences until we figure out how to…?

Satya Nadella

Absolutely. Because at the end of the day, there is no way. Today, you cannot deploy these intelligences unless and until there's someone indemnifying it as a human.

To your point, I think that's one of the reasons why I think about even the most powerful AI is essentially working with some delegated authority from some human. You can say, oh, that's all alignment and this, that, and the other. That's why I think you have to really get these alignments to work and be verifiable in some way, but I just don't think that you can deploy intelligences that are out of control. For example, this AI takeoff problem may be a real problem, but before it is a real problem, the real problem will be in the courts. No society is going to allow for some human to say, "AI did that."

Dwarkesh Patel

Yes. Well, there's a lot of societies in the world, and I wonder if any one of them might not have a legal system that might be more amenable. And if you can't have a takeoff, then you might worry. It doesn't have to happen in America, right?

Satya Nadella

We think that no society cares about it, right? There can be rogue actors, I'm not saying there won't be rogue actors; there are cyber criminals and rogue states; they're going to be there.

But to think that human society at large doesn't care about it is also not going to be true. I think we all will care. We know how to deal with rogue states and rogue actors today. The world doesn't sit around and say “we’ll tolerate that”. That's why I'm glad that we have a world order in which anyone who is a rogue actor in a rogue state has consequences.

Dwarkesh Patel

Right. But if you have this picture where you can have 10% economic growth, I think it really depends on getting something like AGI working, because tens of trillions of dollars of value, that sounds closer to the total of human wages, around $60 trillion of the economy. Getting that magnitude, you kind of have to automate labor or supplement labor in a very significant way.

If that is possible, and once we figure out the legal ramifications for it, it seems quite plausible, even within your tenure that we figure that out. Are you thinking about superhuman intelligence? Like, the biggest thing you do in your career is this?

Satya Nadella

You bring up another point. I know David Autor and others have talked a lot about this which is, 60% of labor- I think the other question that needs to happen, let’s at least talk about our democratic societies. I think that in order to have a stable social structure, and democracies function, you can’t just have a return on capital and no return on labor. We can talk about it, but that 60% has to be revalued.

In my own simple way, maybe you can call it naive, we'll start valuing different types of human labor. What is today considered high-value human labor may be a commodity. There may be new things that we will value.

Including that person who comes to me and helps me with my physical therapy or whatever, whatever is going to be the case that we value, but ultimately, if we don't have return on labor, and there's meaning in work and dignity in work and all of that, that's another rate limiter to any of these things being deployed.

Dwarkesh Patel

On the alignment side, two years ago, you guys released Sydney Bing. Just to be clear, I think given the level of capabilities at the time, it was a charming, endearing, kind of funny example of misalignment.

But that was because, at the time, it was like chatbots. They can go think for 30 seconds and give you some funny or inappropriate response. But if you think about that kind of system--that, I think to a New York Times reporter, tried to get him to leave his wife or something--if you think about that going forward, and you have these agents that are for hours, weeks, months going forward, just like autonomous swarms of AGIs, who could be in similar ways misaligned and screwing stuff up, maybe coordinating with each other, what's your plan going forward so that when you get the big one, you get it right?

Satya Nadella

That is correct. That's one of the reasons why when we usually allocate compute, let's allocate compute for what is that alignment challenge?

And then more importantly, what is the runtime environment in which you are really going to be able to monitor these things? The observability around it? We do deal with a lot of these things today in the classical side of things as well, like cyber. We don't just write software and then just let it go. You have software and then you monitor it. You monitor it for cyber attacks, you monitor it for fault injections, and what have you.

Therefore, I think we will have to build enough software engineering around the deployment side of these, and then inside the model itself, what's the alignment? These are all, some of them are real science problems. Some of them are real engineering problems, and then we will have to tackle it.

That also means taking our own liability in all of this. So that's why I'm more interested in deploying these things in where you can actually govern what the scope of these things is, and the scale of these things is. You just can't unleash something out there in the world that creates harm, because the social permission for that is not going to be there.

Dwarkesh Patel

When you get the agents that can really just do weeks worth of tasks for you, what is the minimum assurance you want before you can let it run a random Fortune 500?

Satya Nadella

I think when I use something like Deep Research, even, the minimum assurance I think we want is before we especially have physical embodiment of anything, that I think is kind of one of those thresholds, when you cross. That might be one place.

Then the other one is, for example, the permissions of the runtime environment in which this is operating. You may want guarantees that it's sandboxed, it is not going out of that sandbox.

Dwarkesh Patel

I mean, we already have web search and we already have it out of the sandbox.

Satya Nadella

But even what it does with web search and what it writes -- for example to your point, if it's just going to write a bunch of code in order to do some computation, where is that code deployed? And is that code ephemeral for just creating that output, versus just going and springing that code out into the world?

Those are things that you could, in the action space, actually go control.

Dwarkesh Patel

And separate from the safety issues, as you think about your own product suite, and you think about, if you do have AIs this powerful, at some point, it's not just like Copilot- an example you mentioned about how you were prepping for this podcast- it's more similar to how you actually delegate work to your colleagues.

What does it look like, given your current suite, to add that in? I mean, there's one question about whether LLMs get commodified by other things.

I wonder if these databases or canvases or Excel sheets or whatever -- if the LLM is your main gate point into accessing all these things, is it possible that the LLMs commodify Office?

Satya Nadella

It's an interesting one. The way I think about the first phase, at least, would be: Can the LLM help me do my knowledge work using all of these tools or canvases more effectively?

One of the best demos that I've seen is a doctor getting ready for a tumor board workflow. She's going into a tumor board meeting, and the first thing she uses Copilot for is to create an agenda for the meeting because the LLM helps reason about all the cases, which are in some SharePoint site. It says, "Hey, these cases -- obviously, a tumor board meeting is a high-stakes meeting where you want to be mindful of the differences in cases so that you can then allocate the right time."

Even that reasoning task of creating an agenda that knows how to split time- super. So, I use the LLM to do that. Then I go into the meeting, I'm in a Teams call with all my colleagues. I'm focused on the actual case versus taking notes, because you now have this AI copilot doing a full transcription of all of this. It's not just a transcript, but a database entry of what is in the meeting that is recallable for all time.

Then she comes out of the meeting, having discussed the case and not been distracted by note-taking. She's a teaching doctor; she wants to go and prep for her class. And so she goes into Copilot and says, "Take my tumor board meeting and create a PowerPoint slide deck out of it so that I can talk to my students about it."

So that’s the type. The UI and the scaffolding that I have are canvases that are now getting populated using LLMs. And the workflow itself is being reshaped; knowledge work is getting done.

Here's an interesting thing: If someone came to me in the late '80s and said, "You're going to have a million documents on your desk," I would say, "What the heck is that?" I would have literally thought there was going to be a million physical copies of things on my desk. Except, we do have a million spreadsheets and a million documents.

Dwarkesh Patel

I don’t, you do.

Satya Nadella

They're all there. And so, that's what's going to happen with even agents. There will be a UI layer. To me, Office is not just about the office of today; it's the UI layer for knowledge work. It'll evolve as the workflows evolve. That's what we want to build.

I do think the SaaS applications that exist today, these CRUD applications, are going to fundamentally be changed because the business logic will go more into this agentic tier. In fact, one of the other cool things today in my Copilot experience is when I say, "Hey, I'm getting ready for a meeting with a customer," I just go and say, "Give me all the notes for it that I should know." It pulls from my CRM database, it pulls from my Microsoft Graph, creates a composite, essentially artifact, and then it applies even logic on it. That, to me, is going to transform the SaaS applications as we know of it today.

Dwarkesh Patel

SaaS as an industry might be worth hundreds of billions to trillions of dollars a year, depending on how you count. If really that can just get collapsed by AI, is the next step up in your next decade 10X-ing the market cap of Microsoft again? Because you're talking about trillions of dollars...

Satya Nadella

It would also create a lot of value in the SaaS. One thing we don't pay as much attention to perhaps is the amount of IT backlog there is in the world.

These code gen things, plus the fact that I can interrogate all of your SaaS applications using agents and get more utility will be the greatest explosion of apps, they'll be called agents, so that for every vertical, in every industry, in every category, we're suddenly going to have the ability to be serviced.

So there's going to be a lot of value. You can't stay still. You can't just say the old thing of, "Oh, I schematized some narrow business process, and I have a UI in the browser, and that's my thing." That's ain’t going to be the case. You have to go up-stack and say, "What's the task that I have to participate in?"

You will want to be able to take your SaaS application and make it a fantastic agent that participates in a multi-agent world. As long as you can do that, then I think you can even increase the value.

Dwarkesh Patel

Can I ask you some questions about your time at Microsoft?

Satya Nadella

Yeah.

Dwarkesh Patel

Is being a company man underrated? So you've spent most of your career at Microsoft, and you could say that one of the reasons you've been able to add so much value is you've seen the culture, the history, and the technology. You have all this context by rising up through the ranks. Should more companies be run by people who have this level of context?

Satya Nadella

That's a great question. I've not thought about it that way.

Through my 34 years now of Microsoft, each year I felt more excited about being at Microsoft versus thinking that, oh, I'm a company person or what have you. I take that seriously, even for anybody joining Microsoft. It's not like they're joining Microsoft as long as they feel that they can use this as a platform for their both economic return, but also a sense of purpose and a sense of mission that they can accomplish by using us as a platform. That's the contract.

So I think yes, companies have to create a culture that allows people to come in and become company people like me. Microsoft got it more right than wrong, at least in my case, and I hope that remains the case.

Dwarkesh Patel

The sixth CEO that you’re talking about, who’ll get to use the research you’re starting now, what are you doing to retain the future Satya Nadellas so that they're in a position to become future leaders?

Satya Nadella

It's fascinating. This is our 50th year, and I think a lot about it. The way to think about it is, longevity is not a goal; relevance is.

The thing that I have to do and all 200,000 of us have to do every day is: Are we doing things that are useful and relevant for the world as we see it evolving, not just today, but tomorrow?

We live in an industry where there's no franchise value, so that’s the other hard part. If you take the R&D budget that we will spend this year, it’s all speculation on what's going to happen five years from now. You have to basically go in with that attitude, saying, "We are doing things that we think are going to be relevant."

So that's what you have to focus on. Then know that there's a batting average, and you're not going to get- you have to have a high tolerance for failure. You have to take enough shots on goal to be able to say, "Okay, we will make it to the other side as a company." That's what makes it tricky in this industry.

Dwarkesh Patel

Speaking of- you just mentioned that you're two months away from your 50th anniversary of Microsoft’s founding. If you look at the top 10 companies by market cap, or top 5, basically, everybody else but Microsoft is younger than Microsoft. It's an interesting observation about why the most successful companies often are quite young. The average Fortune 500 company will last 10 to 15 years.

What has Microsoft done to remain relevant for this many years? How do you keep refounding?

Satya Nadella

I love that, Reed Hoffman uses that term, "refounding." That's the mindset. People talk about founder mode, but for us mere mortal CEOs, it's more like refounder mode.

To be able to see things again in a fresh way is the key. To your question: can we culturally create an environment where refounding becomes a habit thing? Every day we come in and say, "We feel we have a stake in this place to be able to change the core assumptions of what we do and how we relate to the world around us. Do we give ourselves permission?” I think many times, companies feel over-constrained by either business model or whatever. You just have to unconstrain yourself.

Dwarkesh Patel

If you did leave Microsoft, what company would you start?

Satya Nadella

Company I would start? Man. That’s where the company man and me sort of says, “I'll never leave Microsoft.”

If I were thinking of doing something, I think picking a domain that has... When I look at the dream of tech, we've always said technology is about the biggest, greatest democratizing force.

I feel like finally, we have that ability. If you say those tokens per dollar per watt is what we can generate, I would love to find some domain in which that can be applied, where it is so underserved.

That's where healthcare, education... Public sector would be another place. If you take those domains, which are the underserved places, where my life as a citizen of this country or a member of this society or anywhere, would I be better off if somehow all this abundance translated into better healthcare, better education, and better public sector institutions serving me as a citizen? That would be a place.

Dwarkesh Patel

One thing I'm not sure about, hearing your answers on different questions, is whether you think AGI is a thing. Will there be a thing which automates all cognitive labor, like anything anybody can do on a computer?

Satya Nadella

This is where I have a problem with the definitions of how people talk about it. Cognitive labor is not a static thing. There is cognitive labor today. If I have an inbox that is managing all my agents, is that new cognitive labor?

Today's cognitive labor may be automated. What about the new cognitive labor that gets created? Both of those things have to be thought of, which is the shifting…

That's why I make this distinction, at least in my head: Don't conflate knowledge worker with knowledge work. The knowledge work of today could probably be automated. Who said my life's goal is to triage my email? Let an AI agent triage my email.

But after having triaged my email, give me a higher-level cognitive labor task of, "Hey, these are the three drafts I really want you to review." That's a different abstraction.

Dwarkesh Patel

But will AI ever get to the second thing?

Satya Nadella

It may, but as soon as it gets to that second thing, there will be a third thing. Why are we thinking that somehow, when we have dealt with tools that have changed what cognitive labor is in history, why are we worried that all cognitive labor will go away?

Dwarkesh Patel

I'm sure you've heard these examples before, but the idea that horses can still be good for certain things, there are certain terrains you can't take a car on. But the idea that you're going to see horses around the street, they’re going to employ millions of horses, it’s just not happening.

And then the idea is, could a similar thing happen with humans?

Satya Nadella

But in one very narrow dimension? It's only 200 years of history of humans where we have valued some narrow sort of things called "cognitive labor" as we understand it.

Let's take something like chemistry. If this thing, quantum plus AI really helped us do a lot of novel material science and so on, that's fantastic to have novel material science being done by it. Does that take away from all the other things that humans can do?

Why can't we exist in a world where there are powerful cognitive machines, knowing that our cognitive agency has not been taken away?

Dwarkesh Patel

I'll ask this question, not about you, but in a different scenario, so maybe you can answer it without embarrassment. Suppose on the Microsoft board, could you ever see adding an AI to the board? Could it ever have the judgment, context, and holistic understanding to be a useful advisor?

Satya Nadella

It's a great example. One of the things we added was a facilitator agent in Teams. The goal there, it's in the early stages, is can that facilitator agent use long-term memory, not just on the context of the meeting, but with the context of projects I'm working on, and the team, and what have you, be a great facilitator?

I would love it even in a board meeting, where it's easy to get distracted. After all, board members come once a quarter, and they're trying to digest what is happening with a complex company like Microsoft. A facilitator agent that actually helped human beings all stay on topic and focus on the issues that matter, that's fantastic.

That's kind of literally having, to your point about even going back to your previous question, having something that has infinite memory that can even help us. You know, after all, what is that Herbert Simon thing? We are all bounded rationality. So if the bounded rationality of humans can actually be dealt with because there is a cognitive amplifier outside, that's great.

Dwarkesh Patel

Speaking of materials and chemistry stuff, I think you said recently that you want the next 250 years of progress in those fields to happen in the next 25 years. Now, when I think about what's going to be possible in the next 250 years, I'm thinking like space travel, and space elevators, and immortality, and curing all diseases. Next 25 years, you think?

Satya Nadella

One of the reasons why I brought that up was, I love that thing of, the industrial revolution was the 250 years. We have to take this entire change from a carbon-based system to something different.

That means you have to fundamentally reinvent all of what has happened with chemistry over the last 250 years. That's where I hope we have this quantum computer, this quantum computer helps us get to new materials, and then we can fabricate those new materials that help us with all of the challenges we have on this planet. And then I'm all for interplanetary travel.

Dwarkesh Patel

Amazing. Satya, thank you so much for your time.

Satya Nadella

Thank you so much. It's wonderful. Thanks.

Dwarkesh Patel

Great, thank you.

This week I welcome on the show two of the most important technologists ever, in any field.

Jeff Dean is Google's Chief Scientist, and through 25 years at the company, has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip, to Gemini.

Noam Shazeer invented or co-invented all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to Gemini and many other things.

We talk about their 25 years at Google, going from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and maybe soon to ASI.

My favorite part was Jeff's vision for Pathways, Google’s grand plan for a mutually-reinforcing loop of hardware and algorithmic design and for going past autoregression. That culminates in us imagining *all* of Google-the-company, going through one huge MoE model.

And Noam just bites every bullet: 100x world GDP soon; let’s get a million automated researchers running in the Google datacenter; living to see the year 3000.

Watch on Youtube; listen on Apple Podcasts or Spotify.

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To sponsor a future episode, visit dwarkeshpatel.com/p/advertise.

0:00:00 - Intro

0:03:29 - Joining Google in 1999

0:06:20 - Future of Moore's Law

0:11:04 - Future TPUs

0:13:56 - Jeff’s undergrad thesis: parallel backprop

0:15:54 - LLMs in 2007

0:25:09 - “Holy shit” moments

0:27:28 - AI fulfills Google’s original mission

0:32:00 - Doing Search in-context

0:36:12 - The internal coding model

0:37:29 - What will 2027 models do?

0:43:20 - A new architecture every day?

0:49:10 - Automated chips and intelligence explosion

0:53:07 - Future of inference scaling

1:02:38 - Already doing multi-datacenter runs

1:08:15 - Debugging at scale

1:12:41 - Fast takeoff and superalignment

1:20:51 - A million evil Jeff Deans

1:24:22 - Fun times at Google

1:27:51 - World compute demand in 2030

1:34:37 - Getting back to modularity

1:44:48 - Keeping a giga-MoE in-memory

1:49:35 - All of Google in one model

1:57:59 - What’s missing from distillation

2:03:10 - Open research, pros and cons

2:09:58 - Going the distance

Dwarkesh Patel 00:00:00

Today I have the honor of chatting with Jeff Dean and Noam Shazeer. Jeff is Google's Chief Scientist, and through his 25 years at the company, he has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip – genuinely, the list doesn't end – Gemini now.

And Noam is the single person most responsible for the current AI revolution. He has been the inventor or co-inventor of all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to many other things. And they are two of the three co-leads of Gemini at Google DeepMind. Awesome. Thanks so much for coming on.

Jeff Dean 00:00:50

Thank you. Super excited to be here.

Dwarkesh Patel 00:00:52

Okay, first question. Both of you have been at Google for 25, or close to 25, years. At some point early on in the company, you probably understood how everything worked. When did that stop being the case? Do you feel like there was a clear moment that happened?

Noam Shazeer 00:01:08

I joined, this was like, end of 2000, and they had this thing: everybody gets a mentor. I knew nothing. I would just ask my mentor everything, and my mentor knew everything. It turned out my mentor was Jeff.

It was not the case that everyone at Google knew everything. It was just the case that Jeff knew everything because he had basically written everything.

Jeff Dean 00:01:33

You're very kind. I think as companies grow, you kind of go through these phases. When I joined, we were 25 people, 26 people, something like that. So you eventually you learned everyone's name, and even though we were growing, you kept track of all the people who were joining.

At some point, you lose track of everyone's name in the company, but you still know everyone working on software engineering things. Then you lose track of all the names of people in the software engineering group, but you at least know all the different projects that everyone's working on. Then at some point, the company gets big enough that you get an email that Project Platypus is launching on Friday, and you're like, "What the heck is Project Platypus?"

Noam Shazeer 00:02:15

Usually it's a very good surprise. You're like, "Wow, Project Platypus!" I had no idea we were doing that.

Jeff Dean 00:02:23

But I think it is good to keep track of what's going on in the company, even at a very high level, even if you don't know every last detail. And it's good to know lots of people throughout the company so that you can go ask someone for more details or figure out who to talk to. With one level of indirection, you can usually find the right person in the company if you have a good network of people that you've built up over time.

Dwarkesh Patel 00:02:44

How did Google recruit you, by the way?

Jeff Dean 00:02:46

I kind of reached out to them, actually.

Dwarkesh Patel 00:02:50

And Noam, how did you get recruited?

Noam Shazeer 00:02:53

I actually saw Google at a job fair in 1999, and I assumed that it was already this huge company, that there was no point in joining, because everyone I knew used Google. I guess that was because I was a grad student at Berkeley at the time. I guess I've dropped out of grad programs a few times.

It turns out that actually it wasn't really that large. It turns out that I did not apply in 1999, but just kind of sent them a resume on a whim in 2000, because I figured it was my favorite search engine, and figured I should apply to multiple places for a job. But then it turned out to be really fun, it looked like a bunch of smart people doing good stuff. They had this really nice crayon chart on the wall of the daily number of search queries that somebody had just been maintaining. It looked very exponential. I thought, "These guys are going to be very successful, and it looks like they have a lot of good problems to work on." So I was like, "Okay, maybe I'll go work there for a little while and then have enough money to just go work on AI for as long as I want after that."

Dwarkesh Patel 00:04:08

Yeah, yeah. In a way you did that, right?

Noam Shazeer 00:04:10

Yeah, it totally worked out exactly according to plan.

Dwarkesh Patel 00:04:15

You were thinking about AI in 1999?

Noam Shazeer 00:04:17

Yeah, this was like 2000. Yeah, I remember in grad school, a friend of mine at the time had told me that his New Year's resolution for 2000 was to live to see the year 3000, and that he was going to achieve this by inventing AI. I was like, "Oh, that sounds like a good idea."

I didn't get the idea at the time that you could go do it at a big company. But I figured, "Hey, a bunch of people seem to be making a ton of money at startups. Maybe I'll just make some money, and then I'll have enough to live on and just work on AI research for a long time." But yeah, it actually turned out that Google was a terrific place to work on AI.

Jeff Dean 00:05:07

One of the things I like about Google is our ambition has always been sort of something that would require pretty advanced AI. Because I think organizing the world's information and making it universally accessible and useful, actually there is a really broad mandate in there. It's not like the company was going to do this one little thing and stay doing that. And also you could see that what we were doing initially was in that direction, but you could do so much more in that direction.

Dwarkesh Patel 00:05:36

How has Moore's Law over the last two or three decades changed the kinds of considerations you have to take on board when you design new systems, when you figure out what projects are feasible? What are still the limitations? What are things you can now do that you obviously couldn't do before?

Jeff Dean 00:05:51

I think of it as actually changing quite a bit in the last couple of decades. Two decades ago to one decade ago, it was awesome because you just wait, and like 18 months later, you get much faster hardware, and you don't have to do anything. And then more recently, I feel like the general-purpose CPU-based machine scaling has not been as good, like the fabrication process improvements are now taking three years instead of every two years. The architectural improvements in multi-core processors and so on are not giving you the same boost that we were getting 20 to 10 years ago. But I think at the same time, we're seeing much more specialized computational devices, like machine learning accelerators, TPUs, and very ML-focused GPUs, more recently, are making it so that we can actually get really high performance and good efficiency out of the more modern kinds of computations we want to run that are different than a twisty pile of C++ code trying to run Microsoft Office or something.

Noam Shazeer 00:07:02

It feels like the algorithms are following the hardware. Basically, what's happened is that at this point, arithmetic is very, very cheap, and moving data around is comparatively much more expensive. So pretty much all of deep learning has taken off roughly because of that. You can build it out of matrix multiplications that are N cubed operations and N squared bytes of data communication basically.

Jeff Dean 00:07:39

Well, I would say that the pivot to hardware oriented around that was an important transition, because before that, we had CPUs and GPUs that were not especially well-suited for deep learning. And then we started to build TPUs at Google that were really just reduced-precision linear algebra machines, and then once you have that then you want to exploit it.

Noam Shazeer 00:08:02

It seems like it's all about identifying opportunity costs. Like, okay, this is something like Larry Page, I think, used to always say: "Our second biggest cost is taxes, and our biggest cost is opportunity costs." If he didn't say that, then I've been misquoting him for years.

But basically it’s like, what is the opportunity that you have that you're missing out on? In this case, I guess it was that you've got all of this chip area, and you're putting a very small number of arithmetic units on it. Fill the thing up with arithmetic units! You could have orders of magnitude more arithmetic getting done.

Now, what else has to change? Okay, the algorithms and the data flow and everything else.

Jeff Dean 00:08:51

And, oh, by the way, the arithmetic can be really low precision, so then you can squeeze even more multiplier units in.

Dwarkesh Patel 00:08:58

Noam, I want to follow up on what you said, that the algorithms have been following the hardware. If you imagine a counterfactual world where, suppose that the cost of memory had declined more than arithmetic, or just invert the dynamic you saw.

Noam Shazeer 00:09:12

Okay, data flow is extremely cheap, and arithmetic is not.

Dwarkesh Patel 00:09:18

What would AI look like today?

Jeff Dean 00:09:20

You'd have a lot more lookups into very large memories.

Noam Shazeer 00:09:25

Yeah, it might look more like AI looked like 20 years ago but in the opposite direction. I'm not sure. I guess I joined Google Brain in 2012. I left Google for a few years, happened to go back for lunch to visit my wife, and we happened to sit down next to Jeff and the early Google Brain team. I thought, "Wow, that's a smart group of people."

Jeff Dean 00:09:55

I think I said, "You should think about deep neural nets. We're making some pretty good progress there."

Noam Shazeer 00:09:59

"That sounds fun." Okay, so I jumped back in…

Jeff Dean 00:10:02

I wooed him back, it was great.

Noam Shazeer 00:10:05

..to join Jeff, that was like 2012. I seem to join Google every 12 years: I rejoined Google in 2000, 2012, and 2024.

Dwarkesh Patel 00:10:14

What's going to happen in 2036?

Noam Shazeer 00:10:16

I don't know. I guess we shall see.

Dwarkesh Patel 00:10:21

What are the trade-offs that you're considering changing for future versions of TPU to integrate how you're thinking about algorithms?

Jeff Dean 00:10:29

I think one general trend is we're getting better at quantizing or having much more reduced precision models. We started with TPUv1, and we weren't even quite sure we could quantize and model for serving with eight-bit integers. But we sort of had some early evidence that seemed like it might be possible. So we're like, "Great, let's build the whole chip around that."

And then over time, I think you've seen people able to use much lower precision for training as well. But also the inference precision has gone. People are now using INT4 or FP4, which sounded like, if you said to someone like we're going to use FP4, like a supercomputing floating point person 20 years ago, they'd be like, "What? That's crazy. We like 64 bits in our floats."

Or even below that, some people are quantizing models to two bits or one bit, and I think that's a trend that definitely –

Dwarkesh Patel 00:11:25

One bit? Just like a zero-or-one?

Jeff Dean 00:11:27

Yeah, just a 0-1. And then you have a sign bit for a group of bits or something.

Noam Shazeer 00:11:33

It really has to be a co-design thing because, if the algorithm designer doesn't realize that you can get greatly improved performance, throughput, with the lower precision, of course, the algorithm designer is going to say, "Of course, I don't want low precision. That introduces risk." And then it adds irritation.

Then if you ask the chip designer, "Okay, what do you want to build?" And then they'll ask the person who's writing the algorithms today, who's going to say, "No, I don't like quantization. It's irritating." So you actually need to basically see the whole picture and figure out, "Oh, wait a minute, we can increase our throughput-to-cost ratio by a lot by quantizing."

Jeff Dean 00:12:27

Then you're like, yes, quantization is irritating, but your model is going to be three times faster, so you're going to have to deal.

Dwarkesh Patel 00:12:33

Through your careers, at various times, you’ve worked on things that have an uncanny resemblance to what we're actually using now for generative AI. In 1990, Jeff, your senior thesis was about backpropagation. And in 2007- this is the thing that I didn’t realise until I was prepping for this episode – in 2007 you guys trained a two trillion token N-gram model for language modeling.

Just walk me through when you were developing that model. Was this kind of thing in your head? What did you think you guys were doing at the time?

Jeff Dean 00:13:13

Let me start with the undergrad thesis. I got introduced to neural nets in one section of one class on parallel computing that I was taking in my senior year. I needed to do a thesis to graduate, an honors thesis. So I approached the professor and I said, "Oh, it'd be really fun to do something around neural nets."

So, he and I decided I would implement a couple of different ways of parallelizing backpropagation training for neural nets in 1990. I called them something funny in my thesis, like "pattern partitioning" or something. But really, I implemented a model parallelism and data parallelism on a 32-processor Hypercube machine.

In one, you split all the examples into different batches, and every CPU has a copy of the model. In the other one, you pipeline a bunch of examples along to processors that have different parts of the model. I compared and contrasted them, and it was interesting.

I was really excited about the abstraction because it felt like neural nets were the right abstraction. They could solve tiny toy problems that no other approach could solve at the time. I thought, naive me, that 32 processors would be able to train really awesome neural nets.

But it turned out we needed about a million times more compute before they really started to work for real problems, but then starting in the late 2008, 2009, 2010 timeframe, we started to have enough compute, thanks to Moore's law, to actually make neural nets work for real things. That was kind of when I re-entered, looking at neural nets.

But prior to that, in 2007...

Dwarkesh Patel 00:14:55

Sorry, actually could I ask about this?

Jeff Dean 00:14:57

Oh yeah, sure.

Dwarkesh Patel 00:14:58

First of all, unlike other artifacts of academia, it's actually like four pages, and you can just read it.

Jeff Dean 00:15:07

It was four pages and then 30 pages of C code.

Dwarkesh Patel 00:15:10

But it's just a well-produced artifact. Tell me about how the 2007 paper came together.

Jeff Dean 00:15:15

Oh yeah, so that, we had a machine translation research team at Google led by Franz Och, who had joined Google maybe a year before, and a bunch of other people. Every year they competed in a DARPA contest on translating a couple of different languages to English, I think, Chinese to English and Arabic to English.

The Google team had submitted an entry, and the way this works is you get 500 sentences on Monday, and you have to submit the answer on Friday. I saw the results of this, and we'd won the contest by a pretty substantial margin measured in Bleu score, which is a measure of translation quality.

So I reached out to Franz, the head of this winning team. I'm like, "This is great, when are we going to launch it?" And he's like, "Oh, well, we can't launch this. It's not really very practical because it takes 12 hours to translate a sentence." I'm like, "Well, that seems like a long time. How could we fix that?"

It turned out they'd not really designed it for high throughput, obviously. It was doing 100,000 disk seeks in a large language model that they sort of computed statistics over – I wouldn't say "trained" really – for each word that it wanted to translate.

Obviously, doing 100,000 disk seeks is not super speedy. But I said, "Okay, well, let's dive into this." So I spent about two or three months with them, designing an in-memory compressed representation of N-gram data.

We were using- an N-gram is basically statistics for how often every N-word sequence occurs in a large corpus, so you basically have, in this case, we had 2 trillion words. Most N-gram models of the day were using two-grams or maybe three-grams, but we decided we would use five-grams.

So, how often every five-word sequence occurs in basically as much of the web as we could process in that day. Then you have a data structure that says, "Okay, 'I really like this restaurant' occurs 17 times in the web, or something.

And so I built a data structure that would let you store all those in memory on 200 machines and then have sort of a batched API where you could say, "Here are the 100,000 things I need to look up in this round for this word," and we'd give you them all back in parallel. That enabled us to go from taking a night to translate a sentence to basically doing something in 100 milliseconds or something.

Dwarkesh Patel 00:18:03

There's this list of Jeff Dean facts, like Chuck Norris facts. For example, that “for Jeff Dean, NP equals "no problemo."” One of them, it's funny because now that I hear you say it, actually, it's kind of true. One of them is, "The speed of light was 35 miles an hour until Jeff Dean decided to optimize it over a weekend." Just going from 12 hours to 100 milliseconds, I got to do the orders of magnitude there.

Jeff Dean 00:18:36

All of these are very flattering. They're pretty funny. They're like an April Fool's joke gone awry by my colleagues.

Dwarkesh Patel 00:18:45

Obviously, in retrospect, this idea that you can develop a latent representation of the entire internet through just considering the relationships between words is like: yeah, this is large language models. This is Gemini. At the time, was it just a translation idea, or did you see that as being the beginning of a different kind of paradigm?

Jeff Dean 00:19:11

I think once we built that for translation, the serving of large language models started to be used for other things, like completion... you start to type, and it suggests what completions make sense.

So it was definitely the start of a lot of uses of language models in Google. And Noam has worked on a number of other things at Google, like spelling correction systems that use language models.

Noam Shazeer 00:19:36

That was like 2000, 2001, and I think it was all in-memory on one machine.

Jeff Dean 00:19:44

Yeah, I think it was one machine. His spelling correction system he built in 2001 was amazing. He sent out this demo link to the whole company.

I just tried every butchered spelling of every few-word query I could get, like “scrumbled uggs Bundict"—

Noam Shazeer 00:19:59

I remember that one, yeah yeah.

Jeff Dean 00:20:00

—instead of “scrambled eggs benedict”, and it just nailed it every time.

Noam Shazeer 00:20:04

Yeah, I guess that was language modeling.

Dwarkesh Patel 00:20:07

But at the time, when you were developing these systems, did you have this sense of, “look, you make these things more and more sophisticated, don't consider five words, consider 100 words, 1,000 words, then the latent representation is intelligence”. Basically when did that insight hit?

Noam Shazeer 00:20:24

Not really. I don't think I ever felt like, okay, N-gram models are going to–

Jeff Dean 00:20:32

–sweep the world–

Noam Shazeer 00:20:33

–yeah: “be” artificial intelligence. I think at the time, a lot of people were excited about Bayesian networks. That seemed exciting.

Definitely seeing those early neural language models, both the magic in that, “okay, this is doing something extremely cool” and also, it just struck me as the best problem in the world in that for one, it is very, very simple to state: give me a probability distribution over the next word. Also, there's roughly infinite training data out there. There's the text of the web; you have trillions of training examples of unsupervised data.

Jeff Dean 00:21:20

Yeah, or self-supervised.

Noam Shazeer 00:21:22

Self-supervised, yeah.

Jeff Dean 00:21:23

It's nice because you then have the right answer, and then you can train on all but the current word and try to predict the current word. It's this amazing ability to just learn from observations of the world.

Noam Shazeer 00:21:36

And then it's AI complete. If you can do a great job of that, then you can pretty much do anything.

Dwarkesh Patel 00:23:00

There's this interesting discussion in the history of science about whether ideas are just in the air and there's a sort of inevitability to big ideas, or whether they're sort of plucked out of some tangential direction. In this case, this way in which we're laying it out very logically, does that imply basically, how inevitable does this...

Noam Shazeer 00:22:05

It does feel like it's in the air. There were definitely some, there was like the neural Turing machine, a bunch of ideas around attention, like having these key-value stores that could be useful in neural networks to focus on things. I think in some sense, it was in the air, and in some sense, you need some group to go do it.

Jeff Dean 00:22:36

I like to think of a lot of ideas as being partially in the air, where there are a few different, maybe separate research ideas that one is squinting at when you’re trying to solve a new problem. You draw on those for some inspiration, and then there's some aspect that is not solved, and you need to figure out how to solve that. The combination of some morphing of the things that already exist and some new things lead to some new breakthrough or new research result that didn't exist before.

Dwarkesh Patel 00:22:57

Are there key moments that stand out to you where you're looking at a research area, you come up with this idea, and you have this feeling of, "Holy shit, I can't believe that worked?"

Jeff Dean 00:23:06

One thing I remember was in the early days of the Brain team. We were focused on “let’s see if we could build some infrastructure that lets us train really, really big neural nets”. At that time, we didn't have GPUs in our data centers; we just had CPUs. But we know how to make lots of CPUs work together.

So we built a system that enabled us to train pretty large neural nets through both model and data parallelism. We had a system for unsupervised learning on 10 million randomly selected YouTube frames. It was a spatially local representation, so it would build up unsupervised representations based on trying to reconstruct the thing from the high-level representations.

We got that working and training on 2,000 computers using 16,000 cores. After a little while, that model was actually able to build a representation at the highest level where one neuron would get excited by images of cats. It had never been told what a cat was, but it had seen enough examples of them in the training data of head-on facial views of cats that that neuron would turn on for that and not for much else.

Similarly, you'd have other ones for human faces and backs of pedestrians, and this kind of thing. That was kind of cool because it's from unsupervised learning principles, building up these really high-level representations. Then we were able to get very good results on the supervised ImageNet 20,000 category challenge that advanced the state of the art by 60% relative improvement, which was quite good at the time.

That neural net was probably 50x bigger than one that had been trained previously, and it got good results. So that sort of said to me, "Hey, actually scaling up neural nets seems like, I thought it would be a good idea and it seems to be, so we should keep pushing on that."

Dwarkesh Patel 00:25:14

These examples illustrate how these AI systems fit into what you were just mentioning: that Google is fundamentally a company that organizes information. AI, in this context, is finding relationships between information, between concepts, to help get ideas to you faster, information you want to you faster.

Now we're moving with current AI models. Obviously, you can use BERT in Google Search and you can ask these questions. They are still good at information retrieval, but more fundamentally, they can write your entire code base for you and do actual work, which goes beyond just information retrieval.

So how are you thinking about that? Is Google still an information retrieval company if you're building an AGI? An AGI can do information retrieval, but it can do many other things as well.

Jeff Dean 00:26:14

I think we're an "organize the world's information" company, and that's broader than information retrieval. Maybe: “organizing and creating new information from some guidance you give it”.

"Can you help me write a letter to my veterinarian about my dog? It's got these symptoms," and it'll draft that. Or, "Can you feed in this video, and can you produce a summary of what's happening in the video every few minutes?"

I think our multimodal capabilities are showing that it's more than just text. It's about understanding the world in all the different modalities that information exists in, both human ones but also non-human-oriented ones, like weird lidar sensors on autonomous vehicles, or genomic information, or health information.

And then, how do you extract and transform that into useful insights for people and make use of that in helping them do all kinds of things they want to do? Sometimes it's, "I want to be entertained by chatting with a chatbot." Sometimes it's, "I want answers to this really complicated question, there is no single source to retrieve from." You need to pull information from 100 web pages, figure out what's going on, and make an organized, synthesized version of that data.

Then dealing with multimodal things or coding-related problems. I think it's super exciting what these models are capable of, and they're improving fast, so I'm excited to see where we go.

Noam Shazeer 00:28:42

I am also excited to see where we go. I think definitely organizing information is clearly a trillion-dollar opportunity, but a trillion dollars is not cool anymore. What's cool is a quadrillion dollars.

Obviously the idea is not to just pile up some giant pile of money, but it's to create value in the world, and so much more value can be created when these systems can actually go and do something for you, write your code, or figure out problems that you wouldn't have been able to figure out yourself.

To do that at scale, we're going to have to be very, very flexible and dynamic as we improve the capabilities of these models.

Jeff Dean 00:29:22

Yeah, I'm pretty excited about a lot of fundamental research questions that come about because you see something could be substantially improved if we tried this approach or things in this rough direction. Maybe that'll work, maybe it won't.

But I also think there's value in seeing what we could achieve for end-users and then how can we work backwards from that to actually build systems that are able to do that. As one example: organizing information, that should mean any information in the world should be usable by anyone, regardless of what language they speak.

And that I think we've done some amount of, but it's not nearly the full vision of, "No matter what language you speak, out of thousands of languages, we can make any piece of content available to you and make it usable by you. Any video could be watched in any language." I think that would be pretty awesome. We're not quite there yet, but that's definitely things I see on the horizon that should be possible.

Dwarkesh Patel 00:30:26

Speaking of different architectures you might try, I know one thing you're working on right now is longer context. If you think of Google Search, it's got the entire index of the internet in its context, but it's a very shallow search. And then obviously language models have limited context right now, but they can really think. It's like dark magic, in-context learning. It can really think about what it’s seeing.

How do you think about what it would be like to merge something like Google Search and something like in-context learning?

Jeff Dean 00:30:51

Yeah, I'll take a first stab at it because – I've thought about this for a bit. One of the things you see with these models is they're quite good, but they do hallucinate and have factuality issues sometimes. Part of that is you've trained on, say, tens of trillions of tokens, and you've stirred all that together in your tens or hundreds of billions of parameters.

But it's all a bit squishy because you've churned all these tokens together. The model has a reasonably clear view of that data, but it sometimes gets confused and will give the wrong date for something.

Whereas information in the context window, in the input of the model, is really sharp and clear because we have this really nice attention mechanism in transformers. The model can pay attention to things, and it knows the exact text or the exact frames of the video or audio or whatever that it's processing.

Right now, we have models that can deal with millions of tokens of context, which is quite a lot. It's hundreds of pages of PDF, or 50 research papers, or hours of video, or tens of hours of audio, or some combination of those things, which is pretty cool. But it would be really nice if the model could attend to trillions of tokens.

Could it attend to the entire internet and find the right stuff for you? Could it attend to all your personal information for you? I would love a model that has access to all my emails, all my documents, and all my photos.

When I ask it to do something, it can sort of make use of that, with my permission, to help solve what it is I'm wanting it to do. But that's going to be a big computational challenge because the naive attention algorithm is quadratic. You can barely make it work on a fair bit of hardware for millions of tokens, but there's no hope of making that just naively go to trillions of tokens.

So, we need a whole bunch of interesting algorithmic approximations to what you would really want: a way for the model to attend conceptually to lots and lots more tokens, trillions of tokens. Maybe we can put all of the Google code base in context for every Google developer, all the world's source code in context for any open-source developer. That would be amazing.

Noam Shazeer 00:33:20

It would be incredible. The beautiful thing about model parameters is they are quite memory-efficient at memorizing facts. You can probably memorize on the order of one fact or something per model parameter.

Whereas if you have some token in context, there are lots of keys and values at every layer. It could be a kilobyte, a megabyte of memory per token.

Jeff Dean 00:33:56

You take a word and you blow it up to 10 kilobytes or something.

Noam Shazeer 00:33:59

Yes. There's actually a lot of innovation going on around, okay, A, how do you minimize that? And B, what words do you need to have there? Are there better ways of accessing bits of that information?

Jeff seems like the right person to figure this out. Okay, what does our memory hierarchy look like from the SRAM all the way up to data center worldwide level?

Dwarkesh Patel 00:34:32

I want to talk more about the thing you mentioned: look, Google is a company with lots of code and lots of examples. If you just think about that one use case and what that implies, so you've got the Google monorepo. Maybe you figure out the long context thing, you can put the whole thing in context, or you fine-tune on it. Why hasn't this been already done?

You can imagine the amount of code that Google has proprietary access to, even if you're just using it internally to make your developers more efficient and productive.

Jeff Dean 00:35:09

To be clear, we have actually already done further training on a Gemini model on our internal code base for our internal developers. But that's different than attending to all of it because it sort of stirs together the code base into a bunch of parameters. Having it in context makes things clearer.

Even the further trained model internally is incredibly useful. Sundar, I think, has said that 25% of the characters that we're checking into our code base these days are generated by our AI-based coding models with kind of human oversight.

Dwarkesh Patel 00:35:49

How do you imagine, in the next year or two, based on the capabilities you see around the horizon, your own personal work? What will it be like to be a researcher at Google? You have a new idea or something. With the way in which you're interacting with these models in a year, what does that look like?

Noam Shazeer 00:36:04

Well, I assume we will have these models a lot better and hopefully be able to be much, much more productive.

Jeff Dean 00:36:15

Yeah, in addition to kind of research-y context, anytime you're seeing these models used, I think they're able to make software developers more productive because they can kind of take a high-level spec or sentence description of what you want done and give a pretty reasonable first cut at that. From a research perspective, maybe you can say, "I'd really like you to explore this kind of idea similar to the one in this paper, but maybe let's try making it convolutional or something."

If you could do that and have the system automatically generate a bunch of experimental code, and maybe you look at it and you're like, "Yeah, that looks good, run that." That seems like a nice dream direction to go in.

It seems plausible in the next year or two years that you might make a lot of progress on that.

Dwarkesh Patel 00:38:08

It seems under-hyped because you could have literally millions of extra employees, and you can immediately check their output, the employees can check each other's output, hey immediately stream tokens.

Jeff Dean 00:38:21

Sorry, I didn't mean to underhype it. I think it's super exciting. I just don't like to hype things that aren't done yet.

Dwarkesh Patel 00:38:34

I do want to play with this idea more because it seems like a big deal if you have something kind of like an autonomous software engineer, especially from the perspective of a researcher who's like, "I want to build the system." Okay, so let's just play with this idea. As somebody who has worked on developing transformative systems through your careers, the idea that instead of having to code something like whatever today's equivalent of MapReduce is or Tensorflow is, just like, "Here's how I want a distributed AI library to look. Write it up for me."

Do you imagine you could be 10x more productive? 100x more productive?

Jeff Dean 00:39:13

I was pretty impressed. I think it was on Reddit that I saw we have a new experimental coding model that's much better at coding and math and so on. Someone external tried it, and they basically prompted it and said, "I'd like you to implement a SQL processing database system with no external dependencies, and please do that in C."

From what the person said, it actually did a quite good job. It generated a SQL parser and a tokenizer and a query planning system and some storage format for the data on disk and actually was able to handle simple queries. From that prompt, which is like a paragraph of text or something, to get even an initial cut at that seems like a big boost in productivity for software developers.

I think you might end up with other kinds of systems that maybe don't try to do that in a single semi-interactive, "respond in 40 seconds" kind of thing but might go off for 10 minutes and might interrupt you after five minutes saying, "I've done a lot of this, but now I need to get some input. Do you care about handling video or just images or something?" That seems like you'll need ways of managing the workflow if you have a lot of these background activities happening.

Dwarkesh Patel 00:40:44

Can you talk more about that? What interface do you imagine we might need if you could literally have millions of employees you could spin up, hundreds of thousands of employees you could spin up on command, who are able to type incredibly fast, and who- It's almost like you go from 1930s trading of tickets or something to now modern Jane Street or something. You need some interface to keep track of all this that's going on, for the AIs to integrate into this big monorepo and leverage their own strengths, for humans to keep track of what's happening. Basically what is it like to be Jeff or Noam in three years working day-to-day?

Noam Shazeer 00:42:26

It might be kind of similar to what we have now because we already have sort of parallelization as a major issue. We have lots and lots of really, really brilliant machine learning researchers, and we want them to all work together and build AI.

So actually, the parallelization among people might be similar to parallelization among machines. I think definitely it should be good for things that require a lot of exploration, like, "Come up with the next breakthrough."

If you have a brilliant idea that is just certain to work in the ML domain, then it has a 2% chance of working if you're brilliant. Mostly these things fail, but if you try 100 things or 1,000 things or a million things, then you might hit on something amazing. We have plenty of compute. Like modern top labs these days have probably a million times as much compute as it took to train Transformer.

Dwarkesh Patel 00:43:41

Yeah, actually, so that's a really interesting idea. Suppose in the world today there are on the order of 10,000 AI researchers in this community coming up with a breakthrough-

Jeff Dean 00:43:52

Probably more than that. There were 15,000 at NeurIPS last week.

Noam Shazeer 00:43:55

Wow.

Dwarkesh Patel 00:43:57

100,000, I don't know.

Jeff Dean 00:43:58

Yeah, maybe. Sorry.

Dwarkesh Patel 00:44:00

No, no, it's good to have the correct order of magnitude. The odds that this community every year comes up with a breakthrough on the scale of a Transformer is, let's say, 10%. Now suppose this community is a thousand times bigger, and it is, in some sense, like this sort of parallel search of better architectures, better techniques.

Do we just get like-

Jeff Dean 00:44:22

A breakthrough a day?

Dwarkesh Patel 00:44:23

-breakthroughs every year or every day?

Noam Shazeer 00:44:25

Maybe. Sounds potentially good.

Dwarkesh Patel 00:44:30

But does that feel like what ML research is like? If you are able to try all these experiments…

Noam Shazeer 00:44:37

It's a good question, because I don't know that folks haven't been doing that as much. We definitely have lots of great ideas coming along. Everyone seems to want to run their experiment at maximum scale, but I think that's a human problem.

Jeff Dean 00:44:55

It's very helpful to have a 1/1000th scale problem and then vet 100,000 ideas on that, and then scale up the ones that seem promising.

Dwarkesh Patel 00:44:06

So, one thing the world might not be taking seriously: people are aware that it's exponentially harder to make a model that's 100x bigger. It's 100x more compute, right? So people are worried that it's an exponentially harder problem to go from Gemini 2 to 3, or so forth.

But maybe people aren't aware of this other trend where Gemini 3 is coming up with all these different architectural ideas, trying them out, and you see what works, and you're constantly coming up with algorithmic progress that makes training the next one easier and easier. How far could you take that feedback loop?

Jeff Dean 00:45:43

I think one thing people should be aware of is that the improvements from generation to generation of these models often are partially driven by hardware and larger scale, but equally and perhaps even more so driven by major algorithmic improvements and major changes in the model architecture, the training data mix, and so on, that really makes the model better per flop that is applied to the model, so I think that's a good realization. Then I think if we have automated exploration of ideas, we'll be able to vet a lot more ideas and bring them into the actual production training for next generations of these models.

That's going to be really helpful because that's sort of what we're currently doing with a lot of brilliant machine learning researchers: looking at lots of ideas, winnowing ones that seem to work well at small scale, seeing if they work well at medium scale, bringing them into larger scale experiments, and then settling on adding a whole bunch of new and interesting things to the final model recipe. If we can do that 100 times faster through those machine learning researchers just gently steering a more automated search process, rather than hand-babysitting lots of experiments themselves, that's going to be really, really good.

Noam Shazeer 00:47:03

The one thing that doesn't speed up is experiments at the largest scale. You still end up doing these N = 1 experiments. Really, you just try to put a bunch of brilliant people in the room, have them stare at the thing, and figure out why this is working, why this is not working.

Jeff Dean 00:47:21

For that, more hardware is a good solution. And better hardware.

Noam Shazeer 00:47:25

Yes, we're counting on you.

Dwarkesh Patel 00:47:28

So, naively, there's this software, there's this algorithmic side improvement that future AI can make. There's also the stuff you're working on. I'll let you describe it.

But if you get into a situation where just from a software level, you can be making better and better chips in a matter of weeks and months, and better AIs can presumably do that better, how does this feedback loop not just end up in, Gemini 3 taking two years, then Gemini 4 is- or the equivalent level jump is now six months, then level five is three months, then one month? You get to superhuman intelligence much more rapidly than you might naively think, because of this software, both on the hardware side and from the algorithmic side improvements.

Jeff Dean 00:48:26

I've been pretty excited lately about how we could dramatically speed up the chip design process. As we were talking earlier, the current way in which you design a chip takes you roughly 18 months to go from "we should build a chip" to something that you then hand over to TSMC and then TSMC takes four months to fab it, and then you get it back and you put it in your data centers.

So that's a pretty lengthy cycle, and the fab time in there is a pretty small portion of it today. But if you could make that the dominant portion, so that instead of taking 12 to 18 months to design the chip with 150 people, you could shrink that to a few people with a much more automated search process, exploring the whole design space of chips and getting feedback from all aspects of the chip design process for the kind of choices that the system is trying to explore at the high level, then I think you could get perhaps much more exploration and more rapid design of something that you actually want to give to a fab.

That would be great because you can shrink fab time, you can shrink the deployment time by designing the hardware in the right way, so that you just get the chips back and you just plug them into some system. And that will then enable a lot more specialization, it will enable a shorter timeframe for the hardware design so that you don't have to look out quite as far into what kind of ML algorithms would be interesting. Instead, it's like you're looking at six to nine months from now, what should it be? Rather than two, two and a half years.

That would be pretty cool. I do think that fabrication time, if that's in your inner loop of improvement, you're going to like...

Dwarkesh Patel 00:50:19

How long is it?

Jeff Dean 00:50:20

The leading edge nodes, unfortunately, are taking longer and longer because they have more metal layers than previous, older nodes. So that tends to make it take anywhere from three to five months.

Dwarkesh Patel 00:50:32

Okay, but that's how long training runs take anyways, right? So you could potentially do both at the same time.

Jeff Dean 00:50:38

Potentially.

Dwarkesh Patel 00:50:39

Okay, so I guess you can't get sooner than three to five months. But the idea that you could get- but also, yeah, you're rapidly developing new algorithmic ideas.

Noam Shazeer 00:50:47

That can move fast.

Jeff Dean 00:50:48

That can move fast, that can run on existing chips and explore lots of cool ideas.

Dwarkesh Patel 00:50:54

So, isn't that a situation in which you're... I think people sort of expect like, ah, there's going to be a sigmoid. Again, this is not a sure thing. But just like, is this a possibility? The idea that you have sort of an explosion of capabilities very rapidly towards the tail end of human intelligence that gets smarter and smarter at a more and more rapid rate?

Noam Shazeer 00:51:17

Quite possibly.

Jeff Dean 00:51:19

Yeah. I like to think of it like this. Right now, we have models that can take a pretty complicated problem and can break it down internally in the model into a bunch of steps, can sort of puzzle together the solutions for those steps, and can often give you a solution to the entire problem that you're asking.

But it isn't super reliable, and it's good at breaking things down into five to ten steps, not 100 to 1,000 steps. So if you could go from, yeah, 80% of the time it can give you a perfect answer to something that's ten steps long to something that 90% of the time can give you a perfect answer to something that's 100 to 1,000 steps of sub-problem long, that would be an amazing improvement in the capability of these models. We're not there yet, but I think that's what we're aspirationally trying to get to.

Noam Shazeer 00:52:14

We don't need new hardware for that, but we'll take it.

Jeff Dean 00:52:20

Never look new hardware in the mouth.

Noam Shazeer 00:52:23

One of the big areas of improvement in the near future is inference time compute, applying more compute at inference time. I guess the way I like to describe it is that even a giant language model, even if you’re doing a trillion operations per token, which is more than most people are doing these days, operations cost something like 10 to the negative $18. And so you're getting a million tokens to the dollar.

I mean compare that to a relatively cheap pastime: you go out and buy a paper book and read it, you're paying 10,000 tokens to the dollar. Talking to a language model is like 100 times cheaper than reading a paperback.

So there is a huge amount of headroom there to say, okay, if we can make this thing more expensive but smarter, because we're 100x cheaper than reading a paperback, we're 10,000 times cheaper than talking to a customer support agent, or a million times or more cheaper than hiring a software engineer or talking to your doctor or lawyer. Can we add computation and make it smarter?

I think a lot of the takeoff that we're going to see in the very near future is of this form. We've been exploiting and improving pre-training a lot in the past, and post-training, and those things will continue to improve. But taking advantage of "think harder" at inference time is just going to be an explosion.

Jeff Dean 00:54:21

Yeah, and an aspect of inference time is I think you want the system to be actively exploring a bunch of different potential solutions. Maybe it does some searches on its own, gets some information back, consumes that information, and figures out, oh, now I would really like to know more about this thing. So now it iteratively explores how to best solve the high-level problem you pose to this system.

And I think having a dial where you can make the model give you better answers with more inference time compute seems like we have a bunch of techniques now that can kind of do that. The more you crank up the dial, the more it costs you in terms of compute, but the better the answers get.

That seems like a nice trade-off to have, because sometimes you want to think really hard because it's a super important problem. Sometimes you probably don't want to spend enormous amounts of compute to compute “what's the answer to one plus one”. Maybe the system –

Dwarkesh Patel 00:55:22

Shouldn’t decide to come up with new axioms of set theory or whatever!

Jeff Dean 00:55:25

– should decide to use a calculator tool instead of a very large language model.

Dwarkesh Patel 00:55:31

Interesting. So are there any impediments to taking inference time, like having some way in which you can just linearly scale up inference time compute? Or is this basically a problem that's sort of solved, and we know how to throw 100x compute, 1000x compute, and get correspondingly better results?

Noam Shazeer 00:55:50

We're working out the algorithms as we speak. So I believe we'll see better and better solutions to this as these many more than 10,000 researchers are hacking at it, many of them at Google.

Jeff Dean 00:56:06

I think we do see some examples in our own experimental work of things where if you apply more inference time compute, the answers are better than if you just apply 10x, you can get better answers than x amount of computed inference time. And that seems useful and important.

But I think what we would like is when you apply 10x to get even a bigger improvement in the quality of the answers than we're getting today. And so that's about designing new algorithms, trying new approaches, figuring out how best to spend that 10x instead of x to improve things.

Dwarkesh Patel 00:56:44

Does it look more like search, or does it look more like just keeping going in the linear direction for a longer time?

Jeff Dean 00:56:49

I really like Rich Sutton's paper that he wrote about the Bitter Lesson and the Bitter Lesson effectively is this nice one-page paper but the essence of it is you can try lots of approaches, but the two techniques that are incredibly effective are learning and search.

You can apply and scale those algorithmically or computationally, and you often will then get better results than any other kind of approach you can apply it to a pretty broad variety of problems.

Search has got to be part of the solution to spending more inference time. Maybe you explore a few different ways of solving this problem, and that one didn't work, but this one worked better. I'm going to explore that a bit more.

Dwarkesh Patel 00:57:36

How does this change your plans for future data center planning and so forth? Where can this kind of search be done asynchronously? Does it have to be online, offline? How does that change how big of a campus you need and those kinds of considerations?

Jeff Dean 00:57:55

One general trend is it's clear that inference time compute, you have a model that's pretty much already trained and you want to do inference on, it is going to be a growing and important class of computation. Maybe you want to specialize hardware more around that.

Actually, the first TPU was specialized for inference and wasn't really designed for training. Then subsequent TPUs were really designed more around training and also for inference.

But it may be that when you have something where you really want to crank up the amount of compute you use at inference time, that even more specialized solutions will make a lot of sense.

Dwarkesh Patel 00:58:38

Does that mean you can accommodate more asynchronous training?

Jeff Dean 00:58:41

Training? Or inference?

Dwarkesh Patel 00:58:42

Or just you can have the different data centers don't need to talk to each other, you can just have them do a bunch of...

Jeff Dean 00:58:52

I like to think of it as, is the inference that you're trying to do latency-sensitive? Like a user is actively waiting for it, or is it a background thing? Maybe I have some inference tasks that I'm trying to run over a whole batch of data, but it's not for a particular user. It's just I want to run inference on it and extract some information.

There's probably a bunch of things that we don't really have very much of right now, but you're seeing inklings of it in our deep research tool that we just released, like a week ago. You can give it a pretty complicated, high-level task like, "Hey, can you go off and research the history of renewable energy and all the trends in costs for wind and solar and other kinds of techniques, and put it in a table and give me a full eight-page report?" And it will come back with an eight-page report with like 50 entries in the bibliography.

It's pretty remarkable. But you're not actively waiting for that for one second. It takes like a minute or two to go do that.

And I think there's going to be a fair bit of that kind of compute, and that's the kind of thing where you have some UI questions around. Okay, if you're going to have a user with 20 of these kind of asynchronous tasks in the background happening, and maybe each one of them needs to get more information from the user, like, "I found your flights to Berlin, but there's no non-stop ones. Are you okay with a non-stop one?" How does that flow work when you kind of need a bit more information, and then you want to put it back in the background for it to continue doing, you know, finding the hotels in Berlin or whatever? I think it's going to be pretty interesting, and inference will be useful.

Noam Shazeer 01:00:33

Inference will be useful. There's also a compute efficiency in inference that you don't have in training. In general, transformers can use the sequence length as a batch during training, but they can't really in inference, because when you're generating one token at a time, so there may be different hardware and inference algorithms that we design for the purposes of being efficient at inference.

Jeff Dean 01:01:02

Yeah, as a good example of an algorithmic improvement is the use of drafter models. So you have a really small language model that you do one token at a time when you're decoding, and it predicts four tokens. Then you give that to the big model and you say, "Okay, here are the four tokens the little model came up with. Check which ones you agree with."

If you agree with the first three, then you just advance. Then you've basically been able to do a four-token width parallel computation instead of a one-token width computation in the big model. Those are the kinds of things that people are looking at to improve inference efficiency, so you don't have this single-token decode bottleneck.

Noam Shazeer 01:01:46

Right, basically the big model's being used as a verifier.

Jeff Dean 01:01:48

Right, “can you verify”, yeah.

Noam Shazeer 01:01:50

[inaudible] generator and verification you can do.

Jeff Dean 01:01:52

Right. "Hello, how are you?" That sounds great to me. I'm going to advance past that.

Dwarkesh Patel 01:01:56

So, a big discussion has been about how we're already tapping out nuclear power plants in terms of delivering power into one single campus. Do we have to have just two gigawatts in one place, five gigawatts in one place, or can it be more distributed and still be able to train a model? Does this new regime of inference scaling make different considerations there plausible? How are you thinking about multi-data center training now?

Jeff Dean 01:02:31

We're already doing it. We're pro multi-data center training. I think in the Gemini 1.5 tech report, we said we used multiple metro areas and trained with some of the compute in each place. And then a pretty long latency but high bandwidth connection between those data centers, and that works fine.

Training is kind of interesting because each step in a training process is usually, for a large model, is usually a few seconds or something, at least. So, the latency of it being 50 milliseconds away doesn't matter that much.

Noam Shazeer 01:03:06

Just the bandwidth.

Jeff Dean 01:03:08

Yeah, just bandwidth.

Noam Shazeer 01:03:10

As long as you can sync all of the parameters of the model across the different data centers and then accumulate all the gradients, in the time it takes to do one step, you're pretty good.

Jeff Dean 01:03:21

And then we have a bunch of work, even from early Brain days, when we were using CPU machines and they were really slow. We needed to do asynchronous training to help scale, where each copy of the model would do some local computation, send gradient updates to a centralized system, and then apply them asynchronously. Another copy of the model would be doing the same thing.

It makes your model parameters wiggle around a bit, and it makes people uncomfortable with the theoretical guarantees, but it actually seems to work in practice.

Noam Shazeer 01:03:56

It was so pleasant to go from asynchronous to synchronous because your experiments are now replicable, rather than your results depend on whether there was a web crawler running on the same machine. So, I am so much happier running on TPU pods.

Jeff Dean 01:04:20

I love asynchrony. It just lets you scale so much more.

Noam Shazeer 01:04:22

With these two iPhones and an Xbox or whatever.

Jeff Dean 01:04:25

Yeah, what if we could give you asynchronous but replicable results?

Noam Shazeer 01:04:29

Ooh.

Jeff Dean 01:04:31

So, one way to do that is you effectively record the sequence of operations, like which gradient update happened and when and on which batch of data. You don't necessarily record the actual gradient update in a log or something, but you could replay that log of operations so that you get repeatability. Then I think you'd be happy.

Noam Shazeer 01:04:53

Possibly. At least you could debug what happened, but you wouldn't be able to necessarily compare two training runs. Because, okay, I made one change in the hyperparameter, but also I had a-

Jeff Dean 01:05:08

Web crawler.

Noam Shazeer 01:05:09

-web crawler messing up, and there were a lot of people streaming the Super Bowl at the same time.

Jeff Dean 01:05:19

The thing that led us to go from asynchronous training on CPUs to fully synchronous training is the fact that we have these super fast TPU hardware chips and pods, which have incredible amounts of bandwidth between the chips in a pod. Then, scaling beyond that, we have really good data center networks and even cross-metro area networks that enable us to scale to many, many pods in multiple metro areas for our largest training runs. We can do that fully synchronously.

As Noam said, as long as the gradient accumulation and communication of the parameters across metro areas happens fast enough relative to the step time, you're golden. You don't really care. But I think as you scale up, there may be a push to have a bit more asynchrony in our systems than we have now because we can make it work, our ML researchers have been really happy how far we've been able to push synchronous training because it is an easier mental model to understand. You just have your algorithm sort of fighting you, rather than the asynchrony and the algorithm kind of battling you.

Noam Shazeer 01:06:28

As you scale up, there are more things fighting you. That's the problem with scaling, that you don't always know what it is that's fighting you. Is it the fact that you've pushed quantization a little too far in some place or another? Or is it your data?

Jeff Dean 01:06:46

Maybe it's your adversarial machine MUQQ17 that is setting the seventh bit of your exponent and all your gradients or something.

Noam Shazeer 01:06:56

Right. And all of these things just make the model slightly worse, so you don't even know that the thing is going on.

Jeff Dean 01:07:04

That's actually a bit of a problem with neural nets, is they're so tolerant of noise. You can have things set up kind of wrong in a lot of ways, and they just figure out ways to work around that or learn.

Noam Shazeer 01:07:15

You could have bugs in your code. Most of the time that does nothing. Some of the time it makes your model worse. Some of the time it makes your model better. Then you discover something new because you never tried this bug at scale before because you didn't have the budget for it.

Dwarkesh Patel 01:07:33

What practically does it look like to debug or decode? You've got these things, some of which are making the model better, some of which are making it worse. When you go into work tomorrow, how do you figure out what the most salient inputs are?

Noam Shazeer 01:07:50

At small scale, you do lots of experiments. There's one part of the research that involves, okay, I want to invent these improvements or breakthroughs in isolation. In which case you want a nice simple code base that you can fork and hack, and some baselines.

My dream is I wake up in the morning, come up with an idea, hack it up in a day, run some experiments, get some initial results in a day. Like okay this looks promising, these things worked, and these things didn't work.

I think that is very achievable because-

Jeff Dean 01:08:34

At small scale.

Noam Shazeer 01:08:35

At small scale, as long as you keep a nice experimental code base.

Jeff Dean 01:08:41

Maybe an experiment takes an hour to run or two hours, not two weeks.

Noam Shazeer 01:08:45

It’s great. So there's that part of the research, and then there's some amount of scaling up. Then you have the part which is integrating, where you want to stack all the improvements on top of each other and see if they work at large scale, and see if they work all in conjunction.

Jeff Dean 01:09:02

Right, how do they interact? Right, you think maybe they're independent, but actually maybe there's some funny interaction between improving the way in which we handle video data input and the way in which we update the model parameters. Maybe that interacts more for video data than some other thing.

There are all kinds of interactions that can happen that you maybe don't anticipate. So you want to run these experiments where you're then putting a bunch of things together and then periodically making sure that all the things you think are good are good together. If not, understanding why they're not playing nicely.

Dwarkesh Patel 01:09:41

Two questions. One, how often does it end up being the case that things don't stack up well together? Is it like a rare thing or does it happen all the time?

Noam Shazeer 01:09:52

It happens 50% of the time.

Jeff Dean 01:09:55

Yeah, I mean, I think most things you don't even try to stack because the initial experiment didn't work that well, or it showed results that aren't that promising relative to the baseline. Then you sort of take those things and you try to scale them up individually.

Then you're like, "Oh yeah, these ones seem really promising." So I'm going to now include them in something that I'm going to now bundle together and try to advance and combine with other things that seem promising. Then you run the experiments and then you're like, "Oh, well, they didn't really work that well. Let's try to debug why."

Noam Shazeer 01:10:28

And then there are trade offs, because you want to keep your integrated system as clean as you can, because complexity –

Jeff Dean 01:10:38

Codebase-wise.

Noam Shazeer 01:10:39

– yeah codebase and algorithmically. Complexity hurts, complexity makes things slower, introduces more risk.

And then at the same time you want it to be as good as possible. And of course, every individual researcher wants his inventions to go into it. So there are definitely challenges there, but we've been working together quite well.

Dwarkesh Patel 01:11:05

Okay, so then going back to the whole dynamic “you find better and better algorithmic improvements and the models get better and better over time”, even if you take the hardware part out of it. Should the world be thinking more about, and should you guys be thinking more about this?

There's one world where AI is a thing that takes two decades to slowly get better over time and you can sort of refine things over. If you've kind of messed something up, you fix it, and it's not that big a deal, right? It's like not that much better than the previous version you released.

There's another world where you have this big feedback loop, which means that the two years between Gemini 4 and Gemini 5 are the most important years in human history. Because you go from a pretty good ML researcher to superhuman intelligence because of this feedback loop. To the extent that you think that the second world is plausible, how does that change how you sort of approach these greater and greater levels of intelligence?

Noam Shazeer 01:12:14

I've stopped cleaning my garage because I'm waiting for the robots. So probably I'm more in the second camp of what we're going to see, a lot of acceleration.

Jeff Dean 01:12:24

Yeah, I mean, I think it's super important to understand what's going on and what the trends are. And I think right now the trends are the models are getting substantially better generation over generation. I don't see that slowing down in the next few generations probably.

So that means the models say two to three generations from now are going to be capable of... Let's go back to the example of breaking down a simple task into 10 sub pieces and doing it 80% of the time, to something that can break down a task, a very high level task, into 100 or 1,000 pieces and get that right 90% of the time. That's a major, major step up in what the models are capable of.

So I think it's important for people to understand what is happening in the progress in the field. And then those models are going to be applied in a bunch of different domains. I think it's really good to make sure that we, as a society, get the maximal benefits from what these models can do to improve things. I'm super excited about areas like education and healthcare, making information accessible to all people.

But we also realize that they could be used for misinformation, they could be used for automated hacking of computer systems, and we want to put as many safeguards and mitigations and understand the capabilities of the models in place as we can. I think Google as a whole has a really good view to how we should approach this. Our Responsible AI principles actually are a pretty nice framework for how to think about trade offs of making better and better AI systems available in different contexts and settings, while also sort of making sure that we're doing the right thing in terms of making sure they're safe and not saying toxic things and things like that.

Dwarkesh Patel 01:14:21

I guess the thing that stands out to me, if you were zooming out and looking at this period of human history, if we're in the world where, look, if you do post-training on Gemini 3 badly, it can do some misinformation – but then you fix the post training. It's a bad mistake, but it's a fixable mistake, right?

Noam Shazeer 01:14:40

Right.

Dwarkesh Patel 01:14:40

Whereas if you have this feedback loop dynamic, which is a possibility, then the mistake of the thing that catapults this intelligence explosion is misaligned, is not trying to write the code you think it's trying to write, and [instead] optimizing for some other objective.

And on the other end of this very rapid process that lasts a couple of years, maybe less, you have things that are approaching Jeff Dean or beyond level, or Noam Shazeer or beyond level. And then you have millions of copies of Jeff Dean level programmers, and- anyways, that seems like a harder to recover mistake.

Noam Shazeer 01:15:29

As these systems do get more powerful, you have to be more and more careful.

Jeff Dean 01:15:37

One thing I would say is, there are extreme views on either end. There's, "Oh my goodness, these systems are going to be so much better than humans at all things, and we're going to be kind of overwhelmed." And then there's, "These systems are going to be amazing, and we don't have to worry about them at all."

I think I'm somewhere in the middle. I've been a co-author on a paper called "Shaping AI," which is, you know, those two extreme views often kind of view our role as kind of laissez-faire, like we're just going to have the AI develop in the path that it takes.

And I think there's actually a really good argument to be made that what we're going to do is try to shape and steer the way in which AI is deployed in the world so that it is, you know, maximally beneficial in the areas that we want to capture and benefit from, in education, some of the areas I mentioned, healthcare.

And steer it as much as we can away- maybe with policy-related things, maybe with technical measures and safeguards- away from, you know, the computer will take over and have unlimited control of what it can do. So I think that's an engineering problem: how do you engineer safe systems?

I think it's kind of the modern equivalent of what we've done in older-style software development. Like if you look at, you know, airplane software development, that has a pretty good record of how do you rigorously develop safe and secure systems for doing a pretty risky task?

Dwarkesh Patel 01:17:18

The difficulty there is that there's not some feedback loop where the 737, you put it in a box with a bunch of compute for a couple of years, and it comes out with the version 1000.

Noam Shazeer 01:17:27

I think the good news is that analyzing text seems to be easier than generating text. So I believe that the ability of language models to actually analyze language model output and figure out what is problematic or dangerous will actually be the solution to a lot of these control issues.

We are definitely working on this stuff. We've got a bunch of brilliant folks at Google working on this now. And I think it's just going to be more and more important, both from a “do something good for people” standpoint, but also from a business standpoint, that you are, a lot of the time, limited in what you can deploy based on keeping things safe.

And so it becomes very, very important to be really, really good at that.

Dwarkesh Patel 01:18:48

Yeah, obviously, I know you guys take the potential benefits and costs here seriously, and it's truly remarkable. I know you guys get credit for it, but not enough. I think there's just, there are so many different applications that you have put out for using these models to make the different areas you talked about better.

Um, but I do think that… again, if you have a situation where plausibly there's some feedback loop process, on the other end, you have a model that is as good as Noam Shazeer, as good as Jeff Dean.

If there's an evil version of you running around, and suppose there's a million of them, I think that's really, really bad. That could be much, much worse than any other risk, maybe short of nuclear war or something. Just think about it, like a million evil Jeff Deans or something.

Jeff Dean 01:19:47

Where do we get the training data?

Dwarkesh Patel 01:20:20

But, to the extent that you think that's a plausible output of some quick feedback loop process, what is your plan of okay, we've got Gemini 3 or Gemini 4, and we think it's helping us do a better job of training future versions, it's writing a bunch of the training code for us. From this point forward, we just kind of look over it, verify it.

Even the verifiers you talked about of looking at the output of these models will eventually be trained by, or a lot of the code will be written by the AIs you make. What do you want to know for sure before we have the Gemini 4 help us with the AI research? We really want to make sure, we want to run this test on it before we let it write our AI code for us.

Jeff Dean 01:21:34

I mean, I think having the system explore algorithmic research ideas seems like something where there's still a human in charge of that. Like, it's exploring the space, and then it's going to, like, get a bunch of results, and we're going to make a decision, like, are we going to incorporate this particular, you know, learning algorithm or change to the system into kind of the core code base?

And so I think you can put in safeguards like that that enable us to get the benefits of the system that can sort of improve or kind of self-improve with human oversight, uh, without necessarily letting the system go full-on self-improving without any any notion of a person looking at what it's doing, right? That's the kind of engineering safeguards I'm talking about, where you want to be kind of looking at the characteristics of the systems you're deploying, not deploy ones that are harmful by some measures and some ways, and you have an understanding of what its capabilities are and what it's likely to do in certain scenarios. So, you know, I think it's not an easy problem by any means, but I do think it is possible to make these these systems safe.

Noam Shazeer 01:36:56

Yeah. I mean, I think we are also going to use these systems a lot to check themselves, check other systems. Even as a human, it is easier to recognize something than to generate it.

Jeff Dean 01:37:14

One thing I would say is if you expose the model's capabilities through an API or through a user interface that people interact with, I think then you have a level of control to understand how is it being used and put some boundaries on what it can do. And that I think is one of the tools in the arsenal of how do you make sure that what it's going to do is sort of acceptable by some set of standards you've set out in your mind?

Noam Shazeer 01:37:44

Yeah. I mean, I think the goal is to to empower people, but for the most part we should be mostly letting people do things with these systems that make sense and closing off as few parts of the space as we can. But yeah, if you let somebody take your thing and create a million evil software engineers, then that doesn't empower people because they're going to hurt others with a million evil software engineers.

So I'm against that.

Jeff Dean 01:38:14

Me too. I'll go on.

Dwarkesh Patel 01:38:16

All right, let's talk about a few more fun topics. Make it a little lighter. Over the last 25 years, what was the most fun time? What period of time do you have the most nostalgia over?

Jeff Dean 01:38:30

At work, you mean?

Noam Shazeer 01:38:31

Yeah. At work. Yeah.

Jeff Dean 01:38:32

I think the early sort of four or five years at Google when I was one of a handful of people working on search and crawling and indexing systems, our traffic was growing tremendously fast. We were trying to expand our index size and make it so we updated it every minute instead of every month, or two months if something went wrong.

Seeing the growth in usage of our systems was really just personally satisfying. Building something that is used by two billion people a day is pretty incredible.

But I would also say equally exciting is working with people on the Gemini team today. I think the progress we've been making in what these models can do over the last year and a half is really fun. People are really dedicated, really excited about what we're doing.

I think the models are getting better and better at pretty complex tasks. Like if you showed someone using a computer 20 years ago what these models are capable of, they wouldn't believe it. And even five years ago, they might not believe it. And that's pretty satisfying.

I think we'll see a similar growth in usage of these models and impact in the world.

Noam Shazeer 01:39:48

Yeah, I'm with you. Early days were super fun. Part of that is just knowing everybody and the social aspect, and the fact that you're just building something that millions and millions of people are using.

Same thing today. We got that whole nice micro kitchen area where you get lots of people hanging out. I love being in person, working with a bunch of great people, and building something that's helping millions to billions of people. What could be better?

Dwarkesh Patel 01:40:21

What was this micro kitchen?

Jeff Dean 01:40:23

Oh, we have a micro kitchen area in the building we both sit in. It's the new, so-named Gradient Canopy. It used to be named Charleston East, and we decided we needed a more exciting name because it's a lot of machine learning researchers and AI research happening in there.

There's a micro kitchen area that we've set up with, normally it's just like an espresso machine and a bunch of snacks, but this particular one has a bunch of space in it. So we've set up maybe 50 desks in there, and so people are just hanging out in there. It's a little noisy because people are always grinding beans and brewing espresso, but you also get a lot of face-to-face ideas of connections, like, "Oh, I've tried that. Did you think about trying this in your idea?" Or, "Oh, we're going to launch this thing next week. How's the load test looking?" There's just lots of feedback that happens.

And then we have our Gemini chat room for people who are not in that micro kitchen. We have a team all over the world, and there's probably 120 chat rooms I'm in related to Gemini things. In this particular very focused topic, we have seven people working on this, and there are exciting results being shared by the London colleagues.

When you wake up, you see what's happening in there, or it's a big group of people focused on data, and there are all kinds of issues happening in there. It's just fun.

Dwarkesh Patel 01:41:50

What I find remarkable about some of the calls you guys have made is you're anticipating a level of demand for compute, which at the time wasn't obvious or evident. TPUs being a famous example of this, or the first TPU being an example of this.

That thinking you had in, I guess, 2013 or earlier, if you think about it that way today and you do an estimate of, look, we're going to have these models that are going to be a backbone of our services, and we're going to be doing constant inference for them. We're going to be training future versions. And you think about the amount of compute we'll need by 2030 to accommodate all these use cases, where does the Fermi estimate get you?

Jeff Dean 01:42:30

Yeah, I mean, I think you're going to want a lot of inference. Compute is the rough, highest-level view of these capable models because if one of the techniques for improving their quality is scaling up the amount of inference compute you use, then all of a sudden what's currently like one request to generate some tokens now becomes 50 or 100 or 1000 times as computationally intensive, even though it's producing the same amount of output.

And you're also going to then see tremendous scaling up of the uses of these services as not everyone in the world has discovered these chat-based conversational interfaces where you can get them to do all kinds of amazing things. Probably 10% of the computer users in the world have discovered that today, or 20%. As that pushes towards 100% and people make heavier use of it, that's going to be another order of magnitude or two of scaling.

And so you're now going to have two orders of magnitude from that, two orders of magnitude from that. The models are probably going to be bigger, you'll get another order of magnitude or two from that. And there's a lot of inference compute you want. So you want extremely efficient hardware for inference for models you care about.

Dwarkesh Patel 01:43:52

In flops, global total global inference in 2030?

Noam Shazeer 01:43:58

I think just more is always going to be better. If you just kind of think about, okay, what fraction of world GDP will people decide to spend on AI at that point? And then, like, okay, what do the AI systems look like?

Well, maybe it's some sort of personal assistant-like thing that is in your glasses and can see everything around you and has access to all your digital information and the world's digital information. And maybe it's like you're Joe Biden, and you have the earpiece in the cabinet that can advise you about anything in real-time and solve problems for you and give you helpful pointers. Or you could talk to it, and it wants to analyze anything that it sees around you for any potential useful impact that it has on you.

So I mean, I can imagine, okay, and then say it's like your personal assistant or your personal cabinet or something, and that every time you spend 2x as much money on compute, the thing gets like 5, 10 IQ points smarter or something like that. And, okay, would you rather spend $10 a day and have an assistant or $20 a day and have a smarter assistant? And not only is it an assistant in life but an assistant in getting your job done better because now it makes you from a 10x engineer to a 100x or 10 millionx engineer?

Okay, so let's see: from first principles, right? So people are going to want to spend some fraction of world GDP on this thing. The world GDP is almost certainly going to go way, way up, two orders of magnitude higher than it is today, due to the fact that we have all of these artificial engineers working on improving things.

Probably we'll have solved unlimited energy and carbon issues by that point. So we should be able to have lots of energy. We should be able to have millions to billions of robots building us data centers. Let's see, the sun is what, 10 to the 26 watts or something like that?

I'm guessing that the amount of compute being used for AI to help each person will be astronomical.

Jeff Dean 01:46:48

I would add on to that. I'm not sure I agree completely, but it's a pretty interesting thought experiment to go in that direction. And even if you get partway there, it's definitely going to be a lot of compute.

And this is why it's super important to have as cheap a hardware platform for using these models and applying them to problems that Noam described, so that you can then make it accessible to everyone in some form and have as low a cost for access to these capabilities as you possibly can.

And I think that's achievable by focusing on hardware and model co-design kinds of things, we should be able to make these things much, much more efficient than they are today.

Dwarkesh Patel 01:47:36

Is Google's data center build-out plan over the next few years aggressive enough given this increase in demand you're expecting?

Jeff Dean 01:47:46

I'm not going to comment on our future capital spending because our CEO and CFO would prefer I probably not. But I will say, you can look at our past capital expenditures over the last few years and see that we're definitely investing in this area because we think it's important.

We are continuing to build new and interesting, innovative hardware that we think really helps us have an edge in deploying these systems to more and more people, both training them and also, how do we make them usable by people for inference?

Dwarkesh Patel 01:48:21

One thing I've heard you talk a lot about is continual learning, the idea that you could just have a model which improves over time rather than having to start from scratch. Is there any fundamental impediment to that? Because theoretically, you should just be able to keep fine-tuning a model. What does that future look like to you?

Jeff Dean 01:48:40

Yeah, I've been thinking about this more and more. I've been a big fan of models that are sparse because I think you want different parts of the model to be good at different things. We have our Gemini 1.5 Pro model, and other models are mixture-of-experts style models where you now have parts of the model that are activated for some token and parts that are not activated at all because you've decided this is a math-oriented thing, and this part's good at math, and this part's good at understanding cat images. So, that gives you this ability to have a much more capable model that's still quite efficient at inference time because it has very large capacity, but you activate a small part of it.

But I think the current problem, well, one limitation of what we're doing today is it's still a very regular structure where each of the experts is the same size. The paths merge back together very fast. They don't go off and have lots of different branches for mathy things that don't merge back together with the kind of cat-image thing.

I think we should probably have a more organic structure in these things. I also would like it if the pieces of those model of the model could be developed a little bit independently. Like right now, I think we have this issue where we're going to train a model. So, we do a bunch of preparation work on deciding the most awesome algorithms we can come up with and the most awesome data mix we can come up with.

But there's always trade-offs there, like we'd love to include more multilingual data, but that might come at the expense of including less coding data, and so, the model's less good at coding but better at multilingual, or vice versa. I think it would be really great if we could have a small set of people who care about a particular subset of languages go off and create really good training data, train a modular piece of a model that we can then hook up to a larger model that improves its capability in, say, Southeast Asian languages or in reasoning about Haskell code or something.

Then, you also have a nice software engineering benefit where you've decomposed the problem a bit compared to what we do today, which is we have this kind of a whole bunch of people working. But then, we have this kind of monolithic process of starting to do pre-training on this model.

If we could do that, you could have 100 teams around Google. You could have people all around the world working to improve languages they care about or particular problems they care about and all collectively work on improving the model. And that's kind of a form of continual learning.

Noam Shazeer 01:51:27

That would be so nice. You could just glue models together or rip out pieces of models and shove them into other...

Jeff Dean 01:51:33

Upgrade this piece without throwing out the thing...

Noam Shazeer 01:51:36

...or you just attach a fire hose, and you suck all the information out of this model, shove it into another model. There is, I mean, the countervailing interest there is sort of science, in terms of, okay, we're still in the period of rapid progress, so, if you want to do sort of controlled experiments, and okay, I want to compare this thing to that thing because that then is helping us figure out what to build. In that interest, it's often best to just start from scratch so you can compare one complete training run to another complete training run at the practical level because it helps us figure out what to build in the future. It's less exciting but does lead to rapid progress.

Jeff Dean 01:52:32

Yeah, I think there may be ways to get a lot of the benefits of that with a version system of modularity. I have a frozen version of my model, and then I include a different variant of some particular module, and I want to compare its performance or train it a bit more. Then, I compare it to the baseline of this thing with now version N prime of this particular module that does Haskell interpretation.

Noam Shazeer 01:52:58

Actually, that could lead to faster research progress, right? You've got some system, and you do something to improve it. And if that thing you're doing to improve it is relatively cheap compared to training the system from scratch, then it could actually make research much, much cheaper and faster.

Jeff Dean 01:53:16

Yeah, and also more parallelizable, I think, across people.

Noam Shazeer 01:53:24

Okay, let's figure it out and do that next.

Dwarkesh Patel 01:53:29

So, this idea that is sort of casually laid out there would actually be a big regime shift compared to how things are done today. If you think the way things are headed, this is a sort of very interesting prediction about... You just have this blob where things are getting pipelined back and forth – and if you want to make something better, you can do like a sort of surgical incision almost.

Jeff Dean 01:53:55

Right, or grow the model, add another little bit of it here. Yeah, I've been sort of sketching out this vision for a while in Pathways...

Noam Shazeer 01:54:04

Yeah, you've been building the...

Jeff Dean 01:54:05

...and we've been building the infrastructure for it. So, a lot of what Pathways, the system, can support is this kind of twisty, weird model with asynchronous updates to different pieces. And we're using Pathways to train our Gemini models, but we're not making use of some of its capabilities yet. But maybe we should.

Noam Shazeer 01:54:24

Ooh maybe.

Dwarkesh Patel 01:54:27

This is so interesting, and I don't want to lose this thread, but give me one moment.

Noam Shazeer 01:54:33

There have been times, like the way the TPU pods were set up. I don't know who did that, but they did a pretty brilliant job. The low-level software stack and the hardware stack, okay, you've got your nice regular high-performance hardware, you've got these great torus-shaped interconnects, and then you've got the right low-level collectives, the all-reduces, et cetera, which I guess came from supercomputing, but it turned out to be kind of just the right thing to build distributed deep learning on top of.

Dwarkesh Patel 01:39:15

Okay, so a couple of questions. One, suppose Noam makes another breakthrough, and now we've got a better architecture. Would you just take each compartment and distill it into this better architecture? And that's how it keeps improving over time?

Jeff Dean 01:39:33

I do think distillation is a really useful tool because it enables you to transform a model in its current model architecture form into a different form. Often, you use it to take a really capable but large and unwieldy model and distill it into a smaller one that maybe you want to serve with really good, fast latency inference characteristics.

But I think you can also view this as something that's happening at the module level. Maybe there'd be a continual process where you have each module, and it has a few different representations of itself. It has a really big one. It's got a much smaller one that is continually distilling into the small version.

And then the small version, once that's finished, you sort of delete the big one and you add a bunch more parameter capacity. Now, start to learn all the things that the distilled small one doesn't know by training it on more data, and then you kind of repeat that process. If you have that kind of running a thousand different places in your modular model in the background, that seems like it would work reasonably well.

Dwarkesh Patel 01:40:42

This could be a way of doing inference scaling, like the router decides how much do you want the big one.

Jeff Dean 01:40:47

Yeah, you can have multiple versions. Oh, this is an easy math problem, so I'm going to route it to the really tiny math distilled thing. Oh, this one's really hard, so...

Dwarkesh Patel 01:40:56

One, at least from public research, it seems like it's often hard to decode what each expert is doing in mixture of expert type models. If you have something like this, how would you enforce the kind of modularity that would be visible and understandable to us?

Noam Shazeer 01:41:13

Actually, in the past, I found experts to be relatively easy to understand. I mean, the first Mixture of Experts paper, you could just look at the experts.

Dwarkesh Patel 01:41:24

“I don’t know, I'm only the inventor of Mixture of Experts.”

Noam Shazeer 01:57:25

Yeah – oh, the what?

Jeff Dean 01:41:28

Yeah, yeah.

Noam Shazeer 01:41:30

Yeah, you could just see, okay, this expert, like we did, you know, a thousand, two thousand experts. Okay, and this expert, was getting words referring to cylindrical objects.

Jeff Dean 01:41:42

This one's super good at dates.

Noam Shazeer 01:41:44

Yeah.

Jeff Dean 01:41:45

Talking about times.

Noam Shazeer 01:41:46

Yeah, pretty easy to do.

Not that you would need that human understanding to figure out how to work the thing at runtime because you just have some sort of learned router that's looking at the example.

Jeff Dean 01:42:04

One thing I would say is there is a bunch of work on interpretability of models and what are they doing inside. Sort of expert-level interpretability is a sub-problem of that broader area. I really like some of the work that my former intern, Chris Olah, and others did at Anthropic, where they trained a very sparse autoencoder and were able to deduce what characteristics some particular neuron in a large language model has, so they found a Golden Gate Bridge neuron that's activated when you're talking about the Golden Gate Bridge. And I think you could do that at the expert level, you could do that at a variety of different levels and get pretty interpretable results, and it's a little unclear if you necessarily need that. If the model is just really good at stuff, we don't necessarily care what every neuron in the Gemini model is doing, as long as the collective output and characteristics of the overall system are good. That's one of the beauties of deep learning, is you don't need to understand or hand-engineer every last feature.

Dwarkesh Patel 01:43:13

Man, there are so many interesting implications of this that I could just keep asking you about this- I would regret not asking you more about this, so I'll keep going. One implication is, currently, if you have a model that has some tens or hundreds of billions of parameters, you can serve it on a handful of GPUs.

In this system, where any one query might only make its way through a small fraction of the total parameters, but you need the whole thing loaded into memory, the specific kind of infrastructure that Google has invested in with these TPUs that exist in pods of hundreds or thousands would be immensely valuable, right?

Noam Shazeer 01:44:02

For any sort of even existing mixtures of experts, you want the whole thing in memory. I guess there's kind of this misconception running around with Mixture of Experts that, okay, the benefit is that you don't even have to go through those weights in the model.

If some expert is unused, it doesn't mean that you don't have to retrieve that memory because, really, in order to be efficient, you're serving at very large batch sizes.

Jeff Dean 01:44:36

Of independent requests.

Noam Shazeer 01:44:38

Right, of independent requests. So it's not really the case that, okay, at this step, you're either looking at this expert or you're not looking at this expert.

Because if that were the case, then when you did look at the expert, you would be running it at batch size one, which is massively inefficient. Like you've got modern hardware, the operational intensities are whatever, hundreds. So that's not what's happening. It's that you are looking at all the experts, but you only have to send a small fraction of the batch through each one.

Jeff Dean 01:45:17

Right, but you still have a smaller batch at each expert that then goes through. And in order to get kind of reasonable balance, one of the things that the current models typically do is they have all the experts be roughly the same compute cost, and then you run roughly the same size batches through them in order to propagate the very large batch you're doing at inference time and have good efficiency.

But I think you often in the future might want experts that vary in computational cost by factors of 100 or 1000. Or maybe paths that go for many layers on one case, and a single layer or even a skip connection in the other case. And there, I think you're going to want very large batches still, but you're going to want to push things through the model a little bit asynchronously at inference time, which is a little easier than training time.

That's part of one of the things that Pathways was designed to support. You have these components, and the components can be variable cost and you kind of can say, for this particular example, I want to go through this subset of the model, and for this example, I want to go through this subset of the model and have the system kind of orchestrate that.

Dwarkesh Patel 01:46:39

It also would mean that it would take companies of a certain size and sophistication to be able to... Right now, anybody can train a sufficiently small enough model. But if it ends up being the case that this is the best way to train future models, then you would need a company that can basically have a data center serving a single quote, unquote “blob” or model. So it would be an interesting change in paradigms in that way as well.

Noam Shazeer 01:47:10

You definitely want to have at least enough HBM to put your whole model. So depending on the size of your model, most likely that's how much HBM you'd want to have at a minimum.

Jeff Dean 01:47:28

It also means you don't necessarily need to grow your entire model footprint to be the size of a data center. You might want it to be a bit below that.

And then have potentially many replicated copies of one particular expert that is being used a lot, so that you get better load balancing. This one's being used a lot because we get a lot of math questions, and this one is an expert on Tahitian dance, and it is called on really rarely.

That one, maybe you even page out to DRAM rather than putting it in HBM. But you want the system to figure all this stuff out based on load characteristics.

Dwarkesh Patel 01:48:09

Right. Now, language models, obviously, you put in language, you get language out. Obviously, it's multimodal.

But the Pathways blog post talks about so many different use cases that are not obviously of this kind of auto-regressive nature going through the same model. Could you imagine, basically, Google as a company, the product is like Google Search goes through this, Google Images goes through this, Gmail goes through it?

Just like the entire server is just this huge mixture of experts, specialized?

Jeff Dean 01:48:45

You're starting to see some of this by having a lot of uses of Gemini models across Google that are not necessarily fine-tuned. They're just given instructions for this particular use case in this feature in this product setting.

So, I definitely see a lot more sharing of what the underlying models are capable of across more and more services. I do think that's a pretty interesting direction to go, for sure.

Dwarkesh Patel 01:49:14

Yeah, I feel like people listening might not register how interesting a prediction this is about where AI is going. It's like sort of getting Noam on a podcast in 2018 and being like, "Yeah, so I think language models will be a thing."

It's like, if this is where things go, this is actually incredibly interesting.

Jeff Dean 01:49:36

Yeah, and I think you might see that might be a big base model. And then you might want customized versions of that model with different modules that are added onto it for different settings that maybe have access restrictions.

Maybe we have an internal one for Google use, for Google employees, that we've trained some modules on internal data, and we don't allow anyone else to use those modules, but we can make use of it. Maybe other companies, you add on other modules that are useful for that company setting and serve it in our cloud APIs.

Dwarkesh Patel 01:50:09

What is the bottleneck to making this sort of system viable? Is it systems engineering? Is it ML?

Jeff Dean 01:50:17

It's a pretty different way of operating than our current Gemini development. So, I think we will explore these kinds of areas and make some progress on them.

But we need to really see evidence that it's the right way, that it has a lot of benefits. Some of those benefits may be improved quality, some may be less concretely measurable, like this ability to have lots of parallel development of different modules. But that's still a pretty exciting improvement because I think that would enable us to make faster progress on improving the model's capabilities for lots of different distinct areas.

Noam Shazeer 01:51:00

Even the data control modularity stuff seems really cool because then you could have the piece of the model that's just trained for me. It knows all my private data.

Jeff Dean 01:51:09

Like a personal module for you would be useful. Another thing might be you can use certain data in some settings but not in other settings.

Maybe we have some YouTube data that's only usable in a YouTube product surface but not in other settings. So, we could have a module that is trained on that data for that particular purpose.

Dwarkesh Patel 01:51:29

Yeah.

Noam Shazeer 01:51:32

We're going to need a million automated researchers to invent all of this stuff.

Jeff Dean 01:51:39

It's going to be great.

Dwarkesh Patel 01:51:41

Yeah, well the thing itself, you build the blob, and it tells you how to make the blob better.

Jeff Dean 01:51:47

Blob 2.0. Or maybe they're not even versions, it's just like an incrementally growing blob.

Dwarkesh Patel 01:51:56

Yeah, that's super fascinating. Okay, Jeff, motivate for me, big picture: why is this a good idea? Why is this the next direction?

Jeff Dean 01:52:06

Yeah, this notion of an organic, not quite so carefully mathematically constructed machine learning model is one that's been with me for a little while. I feel like in the development of neural nets, the artificial neurons, inspiration from biological neurons is a good one and has served us well in the deep learning field.

We've been able to make a lot of progress with that. But I feel like we're not necessarily looking at other things that real brains do as much as we perhaps could, and that's not to say we should exactly mimic that because silicon and wetware have very different characteristics and strengths. But I do think one thing we could draw more inspiration from is this notion of having different specialized portions, sort of areas of a model of a brain that are good at different things.

We have a little bit of that in Mixture of Experts models, but it's still very structured. I feel like this kind of more organic growth of expertise, and when you want more expertise of that, you add some more capacity to the model there and let it learn a bit more on that kind of thing.

Also this notion of adapting the connectivity of the model to the connectivity of the hardware is a good one. I think you want incredibly dense connections between artificial neurons in the same chip and the same HBM because that doesn't cost you that much. But then you want a smaller number of connections to nearby neurons. So, like a chip away, you should have some amount of connections and then, like many, many chips away, you should have a smaller number of connections where you send over a very limited kind of bottlenecky thing: the most important things that this part of the model is learning for other parts of the model to make use of. And even across multiple TPU pods, you'd like to send even less information but the most salient kind of representations. And then across metro areas, you'd like to send even less.

Dwarkesh Patel 01:54:23

Yeah, and then that emerges organically.

Jeff Dean 01:54:26

Yeah, I'd like that to emerge organically. You could hand-specify these characteristics, but I think you don't know exactly what the right proportions of these kinds of connections are so you should just let the hardware dictate things a little bit. Like if you're communicating over here and this data always shows up really early, you should add some more connections, then it'll take longer and show up at just the right time.

Dwarkesh Patel 01:54:48

Oh here's another interesting implication: Right now, we think about the growth in AI use as a sort of horizontal- so, suppose you're like, how many AI engineers will Google have working for it? You think about how many instances of Gemini 3 will be working at one time.

If you have this, whatever you want to call it, this blob, and it can sort of organically decide how much of itself to activate, then it's more of, if you want 10 engineers worth of output, it just activates a different pattern or a larger pattern. If you want 100 engineers of output, it's not like calling more agents or more instances, it's just calling different sub-patterns.

Jeff Dean 01:55:34

I think there's a notion of how much compute do you want to spend on this particular inference, and that should vary by factors of 10,000 for really easy things and really hard things, maybe even a million. It might be iterative, you might make a pass through the model, get some stuff, and then decide you now need to call on some other parts of the model.

The other thing I would say is this sounds super complicated to deploy because it's this weird, constantly evolving thing with maybe not super optimized ways of communicating between pieces, but you can always distill from that. If you say, "This is the kind of task I really care about, let me distill from this giant kind of organic thing into something that I know can be served really efficiently," you could do that distillation process whenever you want, once a day, once an hour. That seems like it'd be kind of good.

Noam Shazeer 01:56:32

Yeah, we need better distillation.

Jeff Dean 01:56:34

Yeah.

Noam Shazeer 01:56:34

Anyone out there who invents amazing distillation techniques that instantly distill from a giant blob onto your phone, that would be wonderful.

Dwarkesh Patel 01:56:43

How would you characterize what's missing from current distillation techniques?

Noam Shazeer 01:56:46

Well, I just want it to work faster.

Jeff Dean 01:56:49

A related thing is I feel like we need interesting learning techniques during pre-training. I'm not sure we're extracting the maximal value from every token we look at with the current training objective. Maybe we should think a lot harder about some tokens.

When you get to "the answer is," maybe the model should, at training time, do a lot more work than when it gets to "the".

Noam Shazeer 01:57:16

Right. There's got to be some way to get more from the same data, make it learn it forwards and backwards.

Jeff Dean 01:57:24

And every which way. Hide some stuff this way, hide some stuff that way, make it infer from partial information. I think people have been doing this in vision models for a while. You distort the model or you hide parts of it and try to make it guess the bird from half, like that it's a bird from this upper corner of the image or the lower left corner of the image.

That makes the task harder, and I feel like there's an analog for more textual or coding-related data where you want to force the model to work harder. You'll get more interesting observations from it.

Noam Shazeer 01:58:03

Yeah, the image people didn't have enough labeled data so they had to invent all this stuff.

Jeff Dean 01:58:08

And they invented -- I mean, dropout was invented on images, but we're not really using it for text mostly. That's one way you could get a lot more learning in a more large-scale model without overfitting is just make like 100 epochs over the world's text data and use dropout.

But that's pretty computationally expensive, but it does mean we won't run it. Even though people are saying, "Oh no, we're almost out of textual data," I don't really believe that because I think we can get a lot more capable models out of the text data that does exist.

Noam Shazeer 01:58:44

I mean, a person has seen a billion tokens.

Jeff Dean 01:58:47

Yeah, and they're pretty good at a lot of stuff.

Dwarkesh Patel 01:58:54

So obviously human data efficiency sets a lower bound on how, or I guess, upper bound, one of them, maybe not.

Jeff Dean 01:59:04

It's an interesting data point.

Dwarkesh Patel 01:59:05

Yes. So there's a sort of modus ponens, modus tollens thing here. One way to look at it is, look, LLMs have so much further to go, therefore we project orders of magnitude improvement in sample efficiency just if they could match humans. Another is, maybe they're doing something clearly different given the orders of magnitude difference. What's your intuition of what it would take to make these models as sample efficient as humans are?

Jeff Dean 01:59:33

Yeah, I think we should consider changing the training objective a little bit. Just predicting the next token from the previous ones you've seen seems like not how people learn. It's a little bit related to how people learn, I think, but not entirely. A person might read a whole chapter of a book and then try to answer questions at the back, and that's a different kind of thing.

I also think we're not learning from visual data very much. We're training a little bit on video data, but we're definitely not anywhere close to thinking about training on all the visual inputs you could get. So you have visual data that we haven't really begun to train on.

Then I think we could extract a lot more information from every bit of data we do see. I think one of the ways people are so sample efficient is they explore the world and take actions in the world and observe what happens. You see it with very small infants picking things up and dropping them; they learn about gravity from that. And that's a much harder thing to learn when you're not initiating the action.

I think having a model that can take actions as part of its learning process would be just a lot better than just sort of passively observing a giant dataset.

Dwarkesh Patel 02:00:50

Is Gato the future, then?

Jeff Dean 02:00:53

Something where the model can observe and take actions and observe the corresponding results seems pretty useful.

Noam Shazeer 02:01:04

I mean, people can learn a lot from thought experiments that don't even involve extra input. Einstein learned a lot of stuff from thought experiments, or like Newton went into quarantine and got an apple dropped on his head or something and invented gravity. And like mathematicians -- math didn't have any extra input.

Chess, okay, you have the thing play chess against itself and it gets good at chess. That was DeepMind, but also all it needs is the rules of chess. So there's actually probably a lot of learning that you can do even without external data, and then you can make it in exactly the fields that you care about. Of course, there is learning that will require external data, but maybe we can just have this thing talk to itself and make itself smarter.

Dwarkesh Patel 02:02:03

So here's the question I have. What you've just laid out over the last hour is potentially just like the big next paradigm shift in AI. That's a tremendously valuable insight, potentially. Noam, in 2017 you released the Transformer paper on which tens, if not hundreds, of billions of dollars of market value is based in other companies, not to mention all this other research that Google has released over time, which you've been relatively generous with.

In retrospect, when you think about divulging this information that has been helpful to your competitors, in retrospect is it like, "Yeah, we'd still do it," or would you be like, "Ah, we didn't realize how big a deal Transformer was. We should have kept it indoors." How do you think about that?

Noam Shazeer 02:02:51

It's a good question because I think probably we did need to see the size of the opportunity, often reflected in what other companies are doing. And also it's not a fixed pie. The current state of the world is pretty much as far from fixed pie as you can get.

I think we're going to see orders of magnitude of improvements in GDP, health, wealth, and anything else you can think of. So I think it's definitely been nice that Transformer has got around.

Jeff Dean 02:03:39

It’s transformative.

Noam Shazeer 02:03:51

Woo. Thank God Google's doing well as well. So these days we do publish a little less of what we're doing.

Jeff Dean 02:03:54

There's always this trade-off: should we publish exactly what we're doing right away? Should we put it in the next stages of research and then roll it out into production Gemini models and not publish it at all? Or is there some intermediate point?

And for example, in our computational photography work in Pixel cameras, we've often taken the decision to develop interesting new techniques, like the ability to do super good night sight vision for low-light situations or whatever, put that into the product and then published a real research paper about the system that does that after the product is released.

Different techniques and developments have different treatments. Some things we think are super critical we might not publish. Some things we think are really interesting but important for improving our products; we'll get them out into our products and then make a decision: did we publish this or do we give kind of a lightweight discussion of it, but maybe not every last detail?

Other things I think we publish openly and try to advance the field and the community because that's how we all benefit from participating. I think it's great to go to conferences like NeurIPS last week with 15,000 people all sharing lots and lots of great ideas. We publish a lot of papers there as we have in the past, and see the field advance is super exciting.

Dwarkesh Patel 02:05:29

How would you account for... so obviously Google had all these insights internally rather early on, including the top researchers. And now Gemini 2 is out. We didn't get a chance much to talk about it, but people know it's a really great model.

Jeff Dean 02:05:53

Such a good model. As we say around the micro-kitchen, “such a good model, such a good model”.

Dwarkesh Patel 02:05:57

So it's top in LMSYS Chatbot Arena. And so now Google's on top. But how would you account for basically coming up with all the great insights for a couple of years? Other competitors had models that were better for a while despite that.

Jeff Dean 02:06:16

We've been working on language models for a long time. Noam's early work on spelling correction in 2001, the work on translation, very large-scale language models in 2007, and seq2seq and word2vec and more recent Transformers and then BERT.

Things like the internal Meena system that was actually a chatbot-based system designed to kind of engage people in interesting conversations. We actually had an internal chatbot system that Googlers could play with even before ChatGPT came out. And actually, during the pandemic, a lot of Googlers would enjoy spending, you know, everyone was locked down at home, and so they enjoyed spending time chatting with Meena during lunch because it was like a nice, you know, lunch partner.

I think one of the things we were a little, our view of things from a search perspective was these models hallucinate a lot, they don't get things right a lot of the time- or some of the time- and that means that they aren't as useful as they could be and so we’d like to make that better. From a search perspective, you want to get the right answer 100% of the time, ideally and be very high on factuality. These models were not near that bar.

I think what we were a little unsure about is that they were incredibly useful. Oh and they also had all kinds of safety issues, like they might say offensive things and we had to work on that aspect and get that to a point where we were comfortable releasing the model. But I think what we didn’t quite appreciate was how useful they could be for things you wouldn't ask a search engine, right? Like, help me write a note to my veterinarian, or like, can you take this text and give me a quick summary of it? I think that's the kind of thing we've seen people really flock to in terms of using chatbots as amazing new capabilities rather than as a pure search engine.

So I think we took our time and got to the point where we actually released quite capable chatbots and have been improving them through Gemini models quite a bit. I think that's actually not a bad path to have taken. Would we like to have released the chatbot earlier? Maybe. But I think we have a pretty awesome chatbot with awesome Gemini models that are getting better all the time. And that's pretty cool.

Dwarkesh Patel 02:08:54

Okay, final question. So we've discussed some of the things you guys have worked on over the last 25 years, and there are so many different fields, right? You start off with search and indexing to distributed systems, to hardware, to AI algorithms. And genuinely, there are a thousand more, just go on either of their Google Scholar pages or something. What is the trick to having this level of, not only career longevity where you're having many decades of making breakthroughs, but also the breadth of different fields, both of you, in either order, what’s the trick to career longevity and breadth?

Jeff Dean 02:09:46

One thing that I like to do is to find out about a new and interesting area, and one of the best ways to do that is to pay attention to what's going on, talk to colleagues, pay attention to research papers that are being published, and look at the kind of research landscape as it's evolving.

Be willing to say, "Oh, chip design. I wonder if we could use reinforcement learning for some aspect of that." Be able to dive into a new area, work with people who know a lot about a different domain or AI for healthcare or something. I've done a bit of working with clinicians about what are the real problems, how could AI help? It wouldn't be that useful for this thing, but it would be super useful for this.

Getting those insights, and often working with a set of five or six colleagues who have different expertise than you do. It enables you to collectively do something that none of you could do individually. Then some of their expertise rubs off on you and some of your expertise rubs off on them, and now you have this bigger set of tools in your tool belt as an engineering researcher to go tackle the next thing.

I think that's one of the beauties of continuing to learn on the job. It's something I treasure. I really enjoy diving into new things and seeing what we can do.

Noam Shazeer 02:11:10

I'd say probably a big thing is humility, like I’d say I’m the most humble. But seriously, to say what I just did is nothing compared to what I can do or what can be done. And to be able to drop an idea as soon as you see something better, like you or somebody with some better idea, and you see how maybe what you're thinking about, what they're thinking about or something totally different can conceivably work better.

I think there is a drive in some sense to say, "Hey, the thing I just invented is awesome, give me more chips." Particularly if there's a lot of top-down resource assignment. But I think we also need to incentivize people to say, "Hey, this thing I am doing is not working at all. Let me just drop it completely and try something else."

Which I think Google Brain did quite well. We had the very kind of bottoms-up UBI kind of chip allocation.

Dwarkesh Patel 02:12:39

You had a UBI?

Noam Shazeer 02:12:41

Yeah, it was like basically everyone had one credit and you could pool them.

Gemini has been mostly top-down, which has been very good in some sense because it has led to a lot more collaboration and people working together. You less often have five groups of people all building the same thing or building interchangeable things.

But on the other hand, it does lead to some incentive to say, "Hey, what I'm doing is working great." And then, as a lead, you hear hundreds of groups, and everything is, "So you should give them more chips." There's less of an incentive to say, "Hey, what I'm doing is not actually working that well. Let me try something different."

So I think going forward, we're going to have some amount of top-down, some amount of bottom-up, so as to incentivize both of these behaviors: collaboration and flexibility. I think both those things lead to a lot of innovation.

Jeff Dean 02:13:49

I think it's also good to articulate interesting directions you think we should go. I have an internal slide deck called "Go, Jeff, Wacky Ideas." I think those are a little bit more product-oriented things, like, "Hey, I think now that we have these capabilities, we could do these 17 things."

I think that's a good thing because sometimes people get excited about that and want to start working with you on one or more of them. And I think that's a good way to bootstrap where we should go without necessarily ordering people, "We must go here."

Dwarkesh Patel 02:14:32

Alright, this was great.

Jeff Dean 02:14:34

Yeah.

Dwarkesh Patel 02:14:34

Thank you, guys.

Jeff Dean 02:14:35

Appreciate you taking the time, it was great chatting. That was awesome.

Developed in collaboration with Ege Erdil and Tamay Besiroglu. Thanks to Rebecca Hiscott and Gavin Leech for editing and comments.

Epistemic status: Shooting-the-shit; 25% sure this roughly describes how firms of AGIs will actually work.

Even people who expect human-level AI soon are still seriously underestimating how different the world will look when we have it. Most people are anchoring on how smart they expect individual models to be. (i.e. they’re asking themselves “What would the world be like if everyone had a very smart assistant who could work 24/7?”.)

Everyone is sleeping on the collective advantages AIs will have, which have nothing to do with raw IQ but rather with the fact that they are digital—they can be copied, distilled, merged, scaled, and evolved in ways human simply can’t.

What would a fully automated company look like - with all the workers, all the managers as AIs? I claim that such AI firms will grow, coordinate, improve, and be selected-for at unprecedented speed.

This essay is not a prediction of what GPT-5 will be doing, nor about emulations of existing humans. Rather, I'm trying to imagine what the world will look like once we actually have AGIs - the descendants of LLMs that have gotten so good that they can do basically anything any human can do.

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Currently, firms are extremely bottlenecked in hiring and training talent. But if your talent is an AI, you can copy it a stupid number of times. What if Google had a million AI software engineers? Not untrained amorphous "workers," but the AGI equivalents of Jeff Dean and Noam Shazeer, with all their skills, judgment, and tacit knowledge intact.

This ability to turn capital into compute and compute into equivalents of your top talent is a fundamental transformation. Since you can amortize the training cost across thousands of copies, you could sensibly give these AIs ever-deeper expertise - PhDs in every relevant field, decades of business case studies, intimate knowledge of every system and codebase the company relies on.

The power of copying extends beyond individuals to entire teams. Small previously successful teams (think PayPal Mafia, early SpaceX, the Traitorous Eight) can be replicated to tackle a thousand different projects simultaneously. It's not just about replicating star individuals, but entire configurations of complementary skills that are known to work well together. The unit of replication becomes whatever collection of talent has proven most effective.

Copying will transform management even more radically than labor. It will enable a level of micromanagement that makes founder mode look quaint. Human Sundar simply doesn't have the bandwidth to directly oversee 200,000 employees, hundreds of products, and millions of customers. But AI Sundar’s bandwidth is capped only by the number of TPUs you give him to run on. All of Google’s 30,000 middle managers can be replaced with AI Sundar copies. Copies of AI Sundar can craft every product’s strategy, review every pull request, answer every customer service message, and handle all negotiations - everything flowing from a single coherent vision.

There is no principal-agent problem wherein employees are optimizing for something other than Google’s bottom line, or simply lack the judgment needed to decide what matters most.1 A company of Google's scale can run much more as the product of a single mind—the articulation of one thesis—than is possible now.2

Think about how limited a CEO's knowledge is today. How much does Sundar Pichai really know about what's happening across Google's vast empire? He gets filtered reports and dashboards, attends key meetings, and reads strategic summaries. But he can't possibly absorb the full context of every product launch, every customer interaction, every technical decision made across hundreds of teams. His mental model of Google is necessarily incomplete.

Now imagine mega-Sundar – the central AI that will direct our future AI firm. Just as Tesla's Full Self-Driving model can learn from the driving records of millions of drivers, mega-Sundar might learn from everything seen by the distilled Sundars - every customer conversation, every engineering decision, every market response.

Unlike Tesla’s FSD, this doesn’t have to be a naive process of gradient updating and averaging. Mega-Sundar will absorb knowledge far more efficiently – through explicit summaries, shared latent representations, or even surgical modification of the weights to encode specific insights.

The boundary between different AI instances starts to blur. Mega-Sundar will constantly be spawning specialized distilled copies and reabsorbing what they’ve learned on their own. Models will communicate directly through latent representations, similar to how the hundreds of different layers in a neural network like GPT-4 already interact.3 So, approximately no miscommunication, ever again. The relationship between mega-Sundar and its specialized copies will mirror what we're already seeing with techniques like speculative decoding – where a smaller model makes initial predictions that a larger model verifies and refines.

Merging will be a step change in how organizations can accumulate and apply knowledge. Humanity's great advantage has been social learning – our ability to pass knowledge across generations and build upon it. But human social learning has a terrible handicap: biological brains don't allow information to be copy-pasted. So you need to spend years (and in many cases decades) teaching people what they need to know in order to do their job. Look at how top achievers in field after field are getting older and older, maybe because it takes longer to reach the frontier of accumulated knowledge. Or consider how clustering talent in cities and top firms produces such outsized benefits, simply because it enables slightly better knowledge flow between smart people.

Future AI firms will accelerate this cultural evolution through two key advantages: massive population size and perfect knowledge transfer. With millions of AGIs, automated firms get so many more opportunities to produce innovations and improvements, whether from lucky mistakes, deliberate experiments, de-novo inventions, or some combination.

As Joseph Henrich explains in The WEIRDest People in the World,

cumulative cultural evolution—including innovation—is fundamentally a social and cultural process that turns societies into collective brains. Human societies vary in their innovativeness due in large part to the differences in the fluidity with which information diffuses through a population of engaged minds and across generations

Historical data going back thousands of years suggest that population size is the key input for how fast your society comes up with more ideas. AI firms will have population sizes that are orders of magnitude larger than today's biggest companies - and each AI will be able to perfectly mind meld with every other, from the bottom to the top of the org chart.

AI firms will look from the outside like a unified intelligence that can instantly propagate ideas across the organization, preserving their full fidelity and context. Every bit of tacit knowledge from millions of copies gets perfectly preserved, shared, and given due consideration.

The cost to have an AI take a given role will become just the amount of compute the AI consumes. This will change our understanding of which roles are scarce.

Future AI firms won’t be constrained by what's scarce or abundant in human skill distributions – they can optimize for whatever abilities are most valuable. Want Jeff Dean-level engineering talent? Cool: once you’ve got one, the marginal copy costs pennies. Need a thousand world-class researchers? Just spin them up. The limiting factor isn't finding or training rare talent – it's just compute.

So what becomes expensive in this world? Roles which justify massive amounts of test- time compute. The CEO function is perhaps the clearest example. Would it be worth it for Google to spend $100 billion annually on inference compute for mega-Sundar? Sure! Just consider what this buys you: millions of subjective hours of strategic planning, Monte Carlo simulations of different five-year trajectories, deep analysis of every line of code and technical system, and exhaustive scenario planning.

Imagine mega-Sundar contemplating: "How would the FTC respond if we acquired eBay to challenge Amazon? Let me simulate the next three years of market dynamics... Ah, I see the likely outcome. I have five minutes of datacenter time left – let me evaluate 1,000 alternative strategies."

The more valuable the decisions, the more compute you'll want to throw at them. A single strategic insight from mega-Sundar could be worth billions. An overlooked risk could cost tens of billions. However many billions Google should optimally spend on inference for mega-Sundar, it's certainly more than one.

What might distilled copies of AI Sundar (or AI Jeff) be like? Obviously, it makes sense for them to be highly specialized, especially when you can amortize the cost of that domain specific knowledge across all copies. You can give each distilled data center operator a deep technical understanding of every component in the cluster, for example.

I suspect you’ll see a lot of specialization in function, tacit knowledge, and complex skills, because they seem expensive to sustain in terms of parameter count. But I think the different models might share a lot more factual knowledge than you might expect. It’s true that plumber-GPT doesn’t need to know much about the standard model in physics, nor does physicist-GPT need to know why the drain is leaking. But the cost of storing raw information is so unbelievably cheap (and it’s only decreasing) that Llama-7B already knows more about the standard model and leaky drains than any non-expert. If human-level intelligence is more than 1 trillion parameters, is it so much of an imposition to keep around what will, at the limit, be much less than 7 billion parameters to have most known facts right in your model? (Another helpful data point here is that “Good and Featured” Wikitext is less than 5 MB. I don’t see why all future models—except the esoteric ones, the digital equivalent of tardigrades—wouldn’t at least have Wikitext down.4

The most profound difference between AI firms and human firms will be their evolvability. As Gwern Branwen observes:

Why do we not see exceptional corporations clone themselves and take over all market segments? Why don’t corporations evolve such that all corporations or businesses are now the hyper-efficient descendants of a single ur-corporation 50 years ago, all other corporations having gone extinct in bankruptcy or been acquired? Why is it so hard for corporations to keep their “culture” intact and retain their youthful lean efficiency, or, if avoiding “aging” is impossible, why [not] copy themselves or otherwise reproduce to create new corporations like themselves?

His answer:

Corporations certainly undergo selection for kinds of fitness, and do vary a lot. The problem seems to be that corporations cannot replicate themselves … Corporations are made of people, not interchangeable, easily copied widgets or strands of DNA .. The corporation may not even be able to “replicate” itself over time, leading to scleroticism and aging.

The scale of difference between currently existing human firms and fully automated firms will be like the gulf in complexity between prokaryotes and eukaryotes. Prokaryotes like bacteria are not only remarkably simple, but have barely changed over their 3 billion year history5. Whereas eukaryotes rapidly scaled up in complexity, and gave rise to all the other astonishing organisms with trillions of cells working together tightknit6

This evolvability is also the key difference between AI and human firms. As Gwern points out, human firms simply cannot replicate themselves effectively - they're made of people, not code that can be copied. They can't clone their culture, their institutional knowledge, or their operational excellence. AI firms can7.

If you think human Elon is especially gifted at creating hardware companies, you simply can’t spin up 100 Elons, have them each take on a different vertical, and give them each $100 million in seed money. As much of a micromanager as Elon might be, he’s still limited by his single human form. But AI Elon can have copies of himself design the batteries, be the car mechanic at the dealership, and so on. And if Elon isn’t the best person for the job, the person who is can also be replicated, to create the template for a new descendant organization.

So then the question becomes: If you can create Mr. Meeseeks for any task you need, why would you ever pay some markup for another firm, when you can just replicate them internally instead? Why would there even be other firms? Would the first firm that can figure out how to automate everything will just form a conglomerate that takes over the entire economy?

Ronald Coase’s theory of the firm tells us that companies exist to reduce transaction costs (so that you don’t have to go rehire all your employees and rent a new office every morning on the free market). His theory states that the lower the intra-firm transaction costs, the larger the firms will grow. Five hundred years ago, it was practically impossible to coordinate knowledge work across thousands of people and dozens of offices. So you didn’t get very big firms. Now you can spin up an arbitrarily large Slack channel or HR database, so firms can get much bigger.

AI firms will lower transaction costs so much relative to human firms. It’s hard to beat shooting lossless latent representations to an exact copy of you for communication efficiency! So firms probably will become much larger than they are now.

But it’s not inevitable that this ends with one gigafirm which consumes the entire economy. As Gwern explains in his essay, any internal planning system needs to be grounded in some kind of outer "loss function" - a ground truth measure of success. In a market economy, this comes from profits and losses.

Internal planning can be much more efficient than market competition in the short run, but it needs to be constrained by some slower but unbiased outer feedback loop. A company that grows too large risks having its internal optimization diverge from market realities.

That said, the balance may shift as AI systems improve. As corporations become more "software-like" - with perfect replication of successful components and faster feedback loops - we may see much larger and more efficient firms than were previously possible.

The market continues to serve as the grounding outer loop. How does the firm convert trillions of tokens of data from customers, markets, news, etc every day into future plans, new products, and the like? Does the board make all the decisions politburo-style and use $10 billion dollars of inference to run Monte Carlo tree search on different one-year plans? Or do you run some kind of evolutionary process on different departments, giving them more capital, and compute/labor based on their performance?

These are all what we would today call “culture.” Markets facilitate an evolutionary process which selects not only goods and services, but the institutions that are best at turning the world into valuable goods and services. I think this will continue.

…except for the biggie: the intensified principal-agent problem between the CEO-workers (who suddenly know everything) and the shareholders on the outside (who know as much as they know now, i.e. roughly nothing).

Since labor is trivial to copy and spin up, the value of intellectual property will go up. The essence of the firm basically becomes intellectual property. GM can poach as many Tesla engineers as it wants (or, in our hypothetical, clone someone with equivalent skills). But without intellectual theft, they can’t get the FSD model or the millions of hours of driving it was trained on. If firms no longer have a moat in labor, their moat will be this kind of industry-specific knowledge and data.

An intriguing thought, in response to things like Meta’s Continuous Chain of Thought models, is that actually we will want to keep communication happening in discrete tokens, since this is a kind of autoencoding - a bottleneck which makes you usefully compress information into actual insight when you communicate. (Leaders have to force people to keep things simple, since in general people want to sound smart instead.) But these tokens don’t need to relate to natural language necessarily.

There’s a biological analogy here, too. Every cell in your body stores each base pair of your 3GB DNA, despite the fact that only 10 to 20 percent of the protein coding regions are expressed in any particular cell, and only 1 percent of your DNA is protein coding in the first place, with much of the rest long thought of as “junk”. Information is apparently just so cheap to store that there’s been little selective pressure against this redundancy and waste.

The biologist Niko McCarty corrects me here! I think the general point still stands that endosymbiosis was a regime change in evolutionary complexity.

Big changes in evolvability don’t have to involve too much complexity or mutation. Two reasons bacteria didn’t evolve much compared to eukaryotes is that they performed energy production on their surface which scales poorly with cell volume (compared to eukaryotes which solved this with mitochondria); and because they had a constraint on their genome size (because they compete with each other by replicating faster, and replication is mainly bottlenecked on DNA length).

A study by Bloom et al. showed that giving firms' management training increases their productivity by 17 percent. With AI firms, this could be a simple immediate upgrade closer to horizontal gene transfer - i.e. available to all copies, not just one particular treatment group.

Third and final episode in the Paine trilogy!

Chinese history is full of warlords constantly challenging the capital. How could Mao not only stay in power for decades, but not even face any insurgency?

And how did Mao go from military genius to peacetime disaster - the patriotic hero who inflicted history’s worst human catastrophe on China? How can someone shrewd enough to win a civil war outnumbered 5 to 1 decide "let's have peasants make iron in their backyards" and "let's kill all the birds"?

In her lecture and our Q&A, we cover the first nationwide famine in Chinese history; Mao's lasting influence on other insurgents; broken promises to minorities and peasantry; and what Taiwan means.

Thanks so much to @Substack for running this in-person event!

Note that Sarah is doing an AMA over the next couple days on Youtube; see the pinned comment.

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.

Today’s episode is brought to you by Scale AI. Scale partners with the U.S. government to fuel America’s AI advantage through their data foundry. Scale recently introduced Defense Llama, Scale's latest solution available for military personnel. With Defense Llama, military personnel can harness the power of AI to plan military or intelligence operations and understand adversary vulnerabilities.

If you’re interested in learning more on how Scale powers frontier AI capabilities, go to https://scale.com/dwarkesh.

I highly, highly recommend both "The Wars for Asia, 1911–1949" and "The Japanese Empire: Grand Strategy from the Meiji Restoration to the Pacific War".

(0:00:00) - Intro and disclaimer

(0:02:26) - Clausewitz, Sun Tzu, Thayer Mahan, Corbett

(0:08:44) - The 1911 Revolution and the Civil War

(0:13:40) - Clausewitz on Mao

(0:15:00) - Mao the propagandist

(0:23:48) - Mao the social scientist

(0:29:04) - Mao the military leader

(0:39:30) - Mao the feminist

(0:48:47) - Mao the grand strategist

(0:57:25) - Yin and Yang analysis

(1:01:35) - Q&A begins

(1:04:42) - Why was Mao worse than Stalin?

(1:09:53) - Yalta satisfied no-one

(1:13:01) - Corrupt allies, ideologue enemies

(1:14:40) - US indifference to the Nationalists

(1:23:46) - Imagining a Nationalist mainland

(1:26:32) - Communists cling to power

(1:30:13) - Xi and Mao

(1:36:24) - Making victims victimize themselves

(1:40:26) - Journalists’ naivete

(1:45:38) - Visiting China then and now

Sarah Paine 00:00:00

What I'm about to say are my ideas, they don't necessarily represent those of the US government, the US Navy Department, the US Department of Defense, or the Naval War College, you got that clear? Complain to me if you got problems.

All right, I'm going to talk about Mao. He’s an incredibly consequential figure. For the 20th century, he's one of the most consequential political or military figures, and he's also one of the most important figures in Chinese history of any century. And he's also a terribly significant military-political theorist.

And this is not an endorsement of Mao. It is rather just an accurate description of his global and enduring importance.

00:00:43

Think about China historically: it's represented- I don’t know- a third of the world's population, a third of the world's trade. That's a big slice of humanity. Moreover, Mao's theories have been used by many enemies of the United States to take over failing states from within in order to assert dictatorial rule. He is also probably the most brilliant and most famous psychopath in human history, and that is saying a lot.

So here we go. This presentation is based on the first eight volumes of Stuart Schram's Collected Works of Mao. What Schram did is he compared Mao's complete works, as published in the 1950s, to whatever he could find as the earliest version of whatever it was. Then he reinserted whatever had been cut in italics.

So tonight, watch the italics. Mao didn't put all of his best ideas in one place; he scattered them all over the place.

00:01:45

And so what I've done for you all is prepared a jigsaw puzzle of all of these different ideas. In order to make it comprehensible to you, of all these random little tidbits, you have to have like a coat rack to hang all the hangers, and that's called a simple framework, and I'll get there, but in your own lives, when you've got all kinds of complicated things to transmit to others, you can look at what I'm doing tonight, and you can do it for other things as well. So here we go with good old Mao.

And oh, by the way, a lot of those 8,000, 7,000 pages weren't that interesting, so in a way, you owe me.

00:02:26

All right, these are major military theorists, just to run you through them. Clausewitz is the West's major military theorist of bilateral conventional land warfare. Sun Tzu is Han civilization's great theorist of how you maintain power in a continental empire, multilateral world using coercion and deception.

The two fellows on the right are maritime theorists. In a way, they're writing the missing chapters of Clausewitz that doesn't talk about naval warfare at all.

The top one is Alfred Thayer Mahan, the Naval War College's finest. And what he's writing about are the prerequisites for and strategic possibilities for maritime power.

00:03:16

And the Briton underneath him there, Sir Julian Corbett, is writing about how a maritime power, i.e. Britain, can defeat a continental power, i.e. Germany or France. But all of them are writing about warfare between states, and Mao is a different event.

Mao has to do with triangle building. The term "triangle building" comes from Clausewitz. Clausewitz has this nice little passage here where he's talking about these abstractions: passion, creativity, and rationality as being mainly but not exclusively associated respectively with the people, military, and government.

A state has full-up military and civil institutions that have some connection to their people, but an insurgent is going to be building these things from the ground up. So that's what Mao is doing, is he's actually taking over the host from within by building a shadow government and eventually taking power.

Many of the decolonizing world after World War II were really sick of the West. They'd been colonized, didn't want to hear anything about them. But it seemed as if the Soviets or the Chinese perhaps offered a better model, the Communists, and many thought that the Chinese, Mao, offered the better model.

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Why? Because the decolonizing parts of the world were also agricultural and underdeveloped, unlike Russia, which had quite a military—excuse me, an industrial base. They thought Mao was the more relevant guy. All right, here's Mao at his iconic moment. He's proclaiming the victory of the Communists in the Chinese Civil War.

China had been a broken state basically since 1911, when the last dynasty had fallen and the country had broken out into a multilateral civil war that he eventually wins. I’m going to be talking tonight about Mao’s theories from the 1920s and 30s when he had the time to write, but there’s a lot more to Mao than just that. He had quite a track record.

00:05:15

Once he won the civil war, he imposed a social revolution. What's that? It's more than a political revolution. You're not just replacing the government; you're going to wipe out entire social classes. And I don't mean then, "Hey, here's your one-way ticket out of here" kind of way. No, no, a social revolution is, "Here's a mass grave, dig it, and then you're in it" kind of way.

So, if you look at these statistics of Chinese deaths in many of their wars—this is from much of the Maoist period, I think it's '45 to '75—what you'll notice, the figures in white, I believe, are civilian deaths, not military deaths. And it gets really quite ugly. There are more Chinese civilian deaths here than all deaths in World War II.

00:06:03

And then for those of you who think the Chinese are all great long-term strategists, you need to ponder these numbers. How is it possible to kill so many of your own? That's generally not a mark of good strategy.

Moreover, most of them died during the Great Famine, which was the only nationwide famine in Chinese history. Why? Because it's not caused by the weather. It's caused by policies set in Beijing. During the Great Leap Forward, Mao put all the peasants on communes.

That meant the party was in control of the food supply: i.e, who lives and who dies. You don't get a meal, you're very dead. In addition, he decentralized industry, and you can see these backyard furnaces pictured here. As a result of this, production collapses—agricultural and industrial. But Mao keeps exporting food. Why?

00:06:55

Because that's his pocket change. That is a major source of government income if he wants to be able to do anything, so they keep exporting food. As a result, 40 million Chinese starved to death, primarily in rural areas, and disproportionately peasant girls, the least valued members of society.

The statistic of 40 million deaths comes from this book by Yang Jisheng, who has written the definitive work. The English translation is but one volume; the Chinese original is three. Yang worked as a journalist for many years, which gave him access to provincial archives, where he surreptitiously investigated the statistics of people who were starving to death, including his father, for whom he wrote this book to serve as an eternal tombstone.

00:07:42

So, on the one hand, Mao is the military genius who puts Humpty Dumpty back together again when nobody else could, and they tried for the previous 40 years. On the other hand, he is the psychopath incapable of running an economy in peacetime.

Yet many Chinese revere him as a national hero. Why? Because in their minds, certainly of the Han, the preponderant group in China, one of the key things that their country should and must be is a great power. And Mao, by reunifying China under the banner of communism and then fighting the coalition of all the major capitalist powers to a stalemate in the Korean War, or in their mind a victory, that constitutes ending what they consider the era of humiliations that started in the mid-19th century and ended with the Communist Revolution.

So he's a hero at home. All right, to understand Mao's theories, I need to put it in the context of the wars that he fought.

00:08:44

So in 1911, Qing Dynasty collapses. The country shatters into a multilateral warfare among warlords, these provincial leaders. On this map, you can see the different colors and shadings; those are different warlord areas.

But the Nationalist Party and the Communist Party form a united front in 1923 in order to eliminate these warlords. And so Chiang Kai-shek, who is the head of the Nationalist Party, a generalissimo, not just a general, has a nice artwork on his portrait.

He is the man who's leading the northern expedition to fight off all the warlords. Except he stops midway near his power base in Shanghai, and he turns on the communists, massacres them in droves. This is the White Terror.

Sarah Paine 00:09:35

Why? Because he thinks that while he's away fighting, they're trying to take over his government while he's away. He's correct. So he keeps on moving there.

There's a nominal unification of China under Nationalist rule when this takes place. In addition, once he's done with that, then he wants to eliminate the Communists for good. And so he runs a series of five encirclement campaigns around their base areas that are scattered in South China.

The primary base area- a base area is also called a Soviet, is the Jiangxi Soviet. And on the fifth encirclement campaign, Chiang Kai Shek is finally successful. He sends them off on the long march up to, way up north in desolate Yenan.

Long march is a real misnomer. It's the Long Rout. The Communists lose 95% of their forces.

00:10:30

I believe in English “decimate” means to lose 10%. Losing 95%, I think you need a whole new verb for what's happened to you. But Chiang Kai Shek doesn't wipe them out, because he's suffering from divided attentions.

When the West did the original- well it was both the United States and Europe, the United States does its original America first thing with a Hawley-Smoot tariff, putting tariffs up to historic highs. And then everybody, of course, retaliates. And now everybody's got high tariffs.

Well, here's trade dependent Japan, that's always cooperated with everybody and suddenly they're toast. And so their solution is autarky and they need an empire large enough to be autarchic. And so then that's when they invade Manchuria in 1931.

So Chiang Kai Shek has all of a sudden lost this area from China that's greater than Germany and France combined. It's a mess. And so he has to, he's trying to balance what to do about Japanese versus Communists.

00:11:26

The Japanese don't quit with Manchuria. They stabilize the place, they make massive infrastructure developments, they transform it into the most developed part of Asia outside of the home islands, but they keep on going. And it gets so bad that the Communists and the Nationalists form a second united front because they're facing this lethal threat called Japan.

And they organized that in December 1936 in what's known as the Xi'an Incident. And the Japanese react viscerally because they look at it, and the Nationalists have gone over to the dark side because they've joined up with the Communists. And this is when the Japanese escalate in 1937, go down the Chinese coast up the Yangtze River.

Uh, and well, but then they wind up stalemating. Once they get beyond the Chinese railway system, which isn't that great in this period, the Japanese can't stabilize the place and the Soviets start adding more aid, and we add more aid. It's a mess.

So the Japanese decide they're going to cut Western aid to the Chinese. And that's where Pearl Harbor comes in. That's what the attack of Pearl Harbor is all about, is telling Americans to stay out of Asia, which of course, you know, we did just the opposite.

00:12:36

And then the Nazis interpret their alliance with the Japanese broadly to declare war on the United States. So when that happens, you have a regional war that had already been going on over Poland in Europe and this other war that had been going on since `31 in Asia, they unify into a global World War. Mao understood that he was dealing with three layers of warfare, nested wars, that he was fighting a civil war against the Nationalists within a regional war against Japan.

And then after Pearl Harbor, there's going to be a global war that will eventually morph into a global Cold War. Most of his writings are written before Pearl Harbor. So he's going to focus on the first two layers of what's going on here.

So after the World War II is over, Mao goes after the Nationalists full bore, and the Japanese have already very much weakened the Nationalists and Mao wins the civil war. Okay, these are the wars.

00:13:40

Now, I promised you a simple framework. Here's the simple framework. Simple framework should have three to five things because that's all about any of us could really handle on short notice. And so I got four here.

And I'm going to use Clausewitz's definition of great leadership to analyze Mao. According to Clausewitz, in a general, two qualities are indispensable. First is an intellect that even in the darkest hour – and Mao had many of those – retains some glimmering of inner light, which leads to truth.

And second, the courage to follow that faint light wherever it may lead. The first of these qualities is described by the French term, coup d'oeil. Coup is a glance, d'oeil is an eye, taking in a situation with a glance of an eye. And the second is determination.

00:14:23

Well, Mao had these things in numerous areas. I'm going to first discuss Mao the propagandist, that's how he starts out. And then I've got what I say here is Mao the social scientist.

But what he was really good is data analysis, data collection and analysis. He truly understood the countryside because he collected all sorts of data about it and analyzed it. And then I will go on to Mao, the operational military leader, winning and fighting battles.

And then at the end, I'll talk about Mao the grand strategist, integrating all elements of national power. So that's my game plan. That's the simple framework. And away we go.

00:15:00

All right. Mao began his public service career as a propagandist. And if you look at his early biography, he's born in 1893 to a prosperous but not particularly well-educated father who tilled his own land.

Mao hated his father and he hated farming, so he left as soon as possible. After the 1911 revolution for a little while he worked as a soldier, didn't like that.

He joined and then dropped out of a series of vocational schools. He tried being a, what was it? A merchant, a lawyer, I'm missing something else, a soap maker. Imagine Mao, three stages of personal hygiene, whatever.

00:15:39

It was not to be. But he eventually gets an Ed degree, so he can go off and be a primary school principal. Okay, imagine setting your child off to the psychopath doing show and tell.

And then he joins the Communist Party. And it's during the First United Front so he also joins the Nationalist Party. And he has very important positions. If you look at, he's in the National Party, he's at their central headquarters, and he's the minute taker. So he's the fly on the wall listening to everything.

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And then he's a stand in for the head of the propaganda department, which is probably where he learned a great deal about the importance of propaganda. And here's what he says early on, “the Communist Party can overthrow the enemy only by holding propaganda pamphlets in one hand and bullets in the other”.

And if you look at the original organization chart of the shadow Communist government, you'll see there are only what, six departments there? One of them is a propaganda department. If you have no power, words are, is, your initial way into gaining power. I'm now going to use a framework from my wonderful colleagues, Mark Genest and Andrea Dew, this is theirs, about analyzing strategic communication in terms of messenger, message and medium. And I'll go through each three, all three.

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What you see here is a propaganda poster. It's a woodblock print. That's the medium. And it's a very easy way to reproduce pictures back in the day.

The message is about a model laborer, this “always emulate Wu Man Yo”, lucky us. And to do all the nice things he does with whatever is going on there. So that's what that is.

Now, messengers were the delivery system, the broadcasting system for the Communist Party. So you've got the Communist Party, but you've got to reach an audience. And that's what these messengers are doing.

And so they go into local areas and they identify local grievances for attention by the Communist Party, which when it fixes them or fixes somebody, that will generate loyalties and allegiance. So these propaganda personnel would be identifying local bullies to come in and deal with them, organize mass rallies. During battles they're going to double as medics, after battles, they're going to propagandize POWs, between battles, they are helping on troop morale. But what they're really doing is reporting back to Communist Central exactly what's going on.

00:18:10

And civil and military messengers differ. For civil messengers, they would be activists, maybe in the local government, labor unions, peasant organizations, women organizations, any number of these things. And it's there, it's your broadcasting system to reach a population and mobilize it.

Military messengers are a little different. Every single military unit had about a 20-member propaganda team. That's a lot of people. According to Mao, the propaganda work of the Red Army is therefore first-priority work of the Red Army. This is very different from soldiering in the West. This is not how it would work.

Also, Mao had his international broadcasting system. These would be foreign journalists. While Mao was holding court up in Yenan, he invited many of these journalists up there. Edgar Snow was by far the most famous. Why? Because he was the first one in and the last one out. He had really long interviews with Mao, and Edgar Snow, this is when he was an old man, but when he was a young man he never asked, "Why does this A-list political leader spending so much time with me?" That never occurred to Edgar. But it's hours.

00:19:24

And he was a very fine writer, Edgar Snow. What Edgar Snow writes, Red Star Over China, you can probably go to Barnes and Noble and pick up a copy there. It's been in print ever since, and it's the original footnote in Chinese history because no one knew anything about Mao.

And so then everybody starts citing Edgar Snow, and then we cite everybody and everybody and everybody. But actually, it only goes back to Edgar Snow. So, Mao got his word out.

Mao thought, "You want to keep the message simple. You want to make it epigrammatic so that people can understand it rapidly." In his day, this meant having matching slogans, the equivalent of newspaper headlines, to provide a lens for people to understand events, rather like tweets in our own day. So, when the White Terror occurred, when Chiang Kai-shek is turning on the Communists in the first United Front, the slogan was, "Arm the peasants."

00:20:16

And then, when the Japanese invaded Manchuria in 1931, the new slogan is, "Down with imperialism and the Nationalist Party too," because you want to smear your enemy in the civil war while you're at it there. But there are a whole series of these slogans. And here, Mao is one of the most popular poets in China, certainly, of the 20th century.

He could write really simple couplets. If you look here, I think it's a total of eight characters so that someone who's semiliterate can make their way through this poem.

00:20:46

On the other hand, he wrote really complicated things because he needed to garner the support of intellectuals initially before he'd educated enough peasants and workers to take over. Intellectuals prize poetry, and they also prize what's called grass writing, which is that unintelligible Chinese stuff that’s under there. But if Mao set these poems to tunes that everybody knew, people could sing them on the Long March and elsewhere and learn them that way.

So, he's an incredibly accomplished man. He also understood you have to manage the message, and the way he did that is through political mobilization. Part of that is you've got to tell people what the policy objective is, which for him was abolishing imperialism, feudalism, and the landlord class, and then presenting a strategy for how to get there.

00:21:36

And here are the media that he used: not only the written and spoken word but also the dramatic arts in order to get the message out. He also used an institutional medium of education. And here is Mao, the primary school teacher, in his element.

Most of the people in his armies were illiterate, but Mao knew all about how to reach them. And if you can see in this slide, there are a lot of political commissars. What are they?

Political and military commissars come in a pair. Military commissar is the military professional who actually knows about the fighting. The political commissar is the one with the direct line to the secret police who will cap the military commissar if there are any problems whatsoever.

00:22:21

So, Mao's got an elaborate network to get the message out, offering all kinds of social services to people, not only medical but also education for peasant children. And he also educated their parents. This is for the first time in Chinese history.

He did it during the winter slack season. Now, the Nationalists had also tried to improve education, but once the Japanese invaded full bore, they had to drop it because the Nationalist conventional armies are the ones that are fighting off the Japanese conventional armies. The Communists are a guerilla movement, and they're operating behind enemy lines.

So, as the Nationalists are dragooning people into their armies, the Communists are busy offering social services, and I'll get to land reform. And so for the peasants, before too long, it becomes a no-brainer whom they're going to support. Mao also emphasized professional military education because he needs to turn peasants to cadres, to guerillas, to conventional soldiers.

And there's got to be an educational pipeline to do this. And if you look at this Northwest Counter-Japan Red Army University, of the first original departments, political work is one of them. This is not professional military education the way it's done in the West, it's a separate thing.

00:23:48

Okay, part one over: Mao the propagandist, I've covered that. Now I'm going to go about Mao the social scientist.

And here, he says, "The peasant problem is the central problem of the national revolution. If the peasants do not rise up and join and support the national revolution, the national revolution cannot succeed." And if you look at his, further along in his biography, while the first United Front was still operative, he's heading the Nationalist Party's Peasant Institute in Guangzhou and also their Central Commission on the Peasant Movement, learning a great deal about it.

But once the White Terror hits, he needs to get out of Dodge fast, or they'll kill him. And that's where he flees to Jiangxi Province, to the Jiangxi Soviet, where he is going to become the political commissar of the 4th Army. He's also going to be in charge of land reform as he figures out how to calibrate that to make it work.

00:24:46

All right, for Mao, he's doing data-driven survey after data-driven survey, does a whole series of them between 1926 and 1933. And he's trying to figure who owns what, who works for whom, who tills where, and inventories down to the last pitchfork and last chicken, as he's trying to establish what is really going on on the countryside, and he does. What he concludes is that 6% of the rural population owns 80% of the land, and 80% of the population owns only 20% and his solution is going to be revolution.

And he goes further into the statistics and he identifies 70% as poor peasants, 20% who are like his father—they till their own land, they're middle peasants—and then there are the exploitative 10% who don't get their hands dirty with anything. And what Mao is trying to figure out is how you can incentivize 80% of those people into actively taking part in the revolution. This is the key.

00:25:53

And what he wants to do is take the bottom of the social pyramid and mobilize it to crush the top of the pyramid. The way he's going to do this is by determining class status through a land investigation movement, which he says is a violent and ruthless thing. We're going to talk about class, approval of class status, confiscation of land, and redistribution of land in order to invert the social pyramid.

He's got a real plan for doing it. He argues that land reform is just essential for peasant allegiance. This is how you're going to get it, to draw these hundreds of millions into supporting the communists. But you got to do it sequentially: you’ve got to propagandize first, and then you’re going to distribute land later. A little bit later. He had a very bureaucratic way of redistributing land, the approval of class status, he said, it’s a life-and-death decision for the person in question.

00:27:02

It starts out with a vote at the local level, and then it goes through many layers of party approval before being sent back to the local level to announce who's going to get the land and who's going to take a bullet. And then Mao leverages the enthusiasm of this movement for the people who are going to get the land, the other people, not so much. He's going to leverage this enthusiasm to get people to join the party and also to join the army.

Mao is planning to collectivize all land. That's what the Communists are going to do. But he says, "Look, the system of landlords and tenants cannot be completely destroyed yet."

00:27:45

Because he needs the peasants to join him, and the peasants desperately want land. So, Mao gives it to them, and he gets a great deal of support for doing this. But he also keeps the rich peasants around, too.

This is a deleted portion in the collected works: “because rich peasant production is indispensable”. Until he wins the civil war and can then turn the guns on them. And he's also got a duplicitous program for the middle peasants. It's a big bait and switch.

It looks like you're going to get -- see, you got the land? Well, now you do, and now you don't, because at the end, they're all going to lose their land.

00:28:26

In order to reform, to get the land, Mao is talking about a red terror to get it. And while he was still with the Nationalists, he wrote a report on the peasant movement in Hunan, where he's talking about taking all the land from the landlord class and shooting them. And that won't cut it with the Nationalist army because their officers are landlords. So, as part of his program, it's not just land reform and educating people, warm and cuddly. It's also coercion.

00:29:04

Okay, that's Mao the social scientist. Enough of him. Now, we're going to do Mao the military leader.

You've probably heard this chestnut from Mao: "Political power grows out of the barrel of a gun." Mao spent his- it’s still part of his early career- being right but being a minority view. That he had certain views about military operations that were not shared by Communist Central. And Mao kept following that dim light wherever it may lead, and events eventually vindicated him. He survived a variety of encirclement campaigns, but then he had some troubles. And here are his critics.

Li Lisan was a labor organizer, he was the de facto head of the Communist Party from 1928 to 1930. And after the white terror on the Northern Expedition, Moscow had told its communist buddies in China that the next thing to do was to take the cities.

00:30:10

And so Li Lisan tries to, with the Nanchang uprising in 1927. It was a total disaster. And he tries it again in 1930 with the Changsha uprising, another disaster.

That got him into exile in Russia and according to Mao, Comrade Li Lisan did not understand the protracted nature of the Chinese Civil War. Li Lisan is trying to fight a decisive, war-winning battle far too early. You try to do that, and you can get yourself ruined.

Here's another critic, Xiang Ying. Mao is in the Jiangxi Soviet, and he thinks a smart strategy is to lure the enemy into your own terrain, which is favorable to you, let them get exhausted, then you spring the trap and you annihilate them.

Communist Central in Shanghai thought this was nuts, that you shouldn't be ceding territory at all. So Mao, for the longest time, he’s off in Jiangxi, they were off in Shanghai, they were a long way apart, and so Communist Central can’t do anything about it- Mao does his own thing.

00:31:08

So the communist Central sent Xiang Ying to Jiangxi Soviet to fire Mao personally, and you can imagine how this works for his later career- not well. He fires Mao and this is where his strategy winds up producing the Long March, the Long Retreat, in which they lose 95% of their people by trying to defend territory, so people began to get it, that Mao may have known what he was doing.

Then, on the Long Retreat, Mao chose as his terminal point of retreat, like where you’re going to wind up, as up in Yenon, way up north, deep in Muslim and Mongol lands, but near the Soviet border.

Mao thought that was essential because they're the big benefactor. Whereas this gentleman, Zhang Guotao, who was the military commissar of the Fourth Army, thought, "Nonsense, we're Han Chinese, we want to be in Han lands." So he wanted to go into western Sichuan, which he did.

00:32:10

And he suffered a series of defeats over 1935, and as a result, he was never as important ever again, and eventually defected to the Nationalists. So, Mao had proven himself prescient and right and determined. He had kudoi and determination, and people eventually recognized that.

All right, Mao and Clausewitz define war somewhat differently. Clausewitz has this famous line, "War is thus an act of force to compel our enemy to do our will." Mao says, "No, no, war is politics by other means. It is something that is used to achieve political ends." So far that's not incompatible.

But then here's Mao's twist: "A revolution is an uprising, an act of violence whereby one class overthrows the power of another." Clausewitz is not about class warfare at all. In fact, his wife is always trying to wine and dine the aristocrats, so completely different in that department.

00:33:07

Mao is looking at the world, and he believes the linchpin of the social order are landlords. He's going to detonate them and try to destroy them.

He talks about the violence of all of it, that you're going to get the peasant masses to overthrow these landlords, and that this will require terror in rural areas, but this is absolutely necessary and of course this is what the Nationalists absolutely would not tolerate. Mao also understood he was operating in a period of nested wars, and that the ones that were ongoing were the civil war with the Nationalists and then the regional war with Japan. Pearl Harbor comes a little later. And he talked about defeating Japan in three stages.

00:34:02

He said “defeating Japan requires three conditions: first is progress by China”, i.e, the civil war, “which is the basic and primary thing; the second is difficulties for Japan”, i.e, the regional war, “and the third is international support”, the big friend. I'm going to talk about each of these three things in turn.

So, in order to win the civil war, Mao believes you need base areas, these Soviets, where are they located? Often on the boundaries of provinces, in very difficult terrain, where provincial authority, let alone national authority, simply does not extend.

Mao thought that there were certain prerequisites for a good base area. One is strategic terrain. It's got to be defensible so that the weaker communist forces can defend it against conventional nationalist or conventional Japanese armies. So that’s key, pick your terrain carefully.

00:34:54

Also, you need to have a strong Red Army presence there to make it work. You need numerous organized workers and peasants. You've got to have some local support there. And then you need a good party organization.

This is Mao's idea of what you need for a base area. He believed that you needed to match your military unit, the type of military unit, to the territory. He said there are three kinds of territory: there's base areas, there is enemy-controlled areas, and then there is the interface in between, which is where guerilla forces are going to be roaming.

So he was all about deploying the Red Army to the comparatively safe base area, they'll protect that. You might send guerilla detachments to some of the guerilla areas, but really only really small things would you ever send into enemy territory. Moreover, he has prerequisites to fight. There are six possible prerequisites; you've got to have at least two before you fight. The most important one is that people have to actively support you. You probably need a base area to pull this off.

00:36:00

He said that the last three things about enemy weak points, enemy exhaustion, enemy mistakes, those things could appear quite rapidly. But you better choose your terrain very carefully; terrain is immutable.

He also said that if you're weak the way the Communists were, you had to follow a strategy of annihilation. What you do is you annihilate one small enemy unit at a time, and the cumulative effects will eventually change the balance of power. Only someone who is really strong can tough out an attrition strategy.

He's also about triangle building in these areas. So, little guerilla detachments go out into the interface. If it works out well and it looks like they can start either a new base area or expand an existing one, that's what they're going to be up to.

So, these guerilla forces are either a disposal force, which you could send them out to do risky things, and if they get wiped out, it doesn't endanger base area defense, or they can become a nucleus of a new base area. So in small guerilla groups, party members are toughened, cadres trained, the party government, mass organizations are consolidated. If they're successful, then you bring in the Red Army to do higher-level institution building and either greatly expand an existing base area, or you're forming a new one, is what’s going on.

00:37:30

Mao had two military services, we always think of army, navy, air force—that's not what it was for him. It was guerilla forces versus conventional forces.

And guerilla forces are operating in the rear of the enemy so that there's no stability or security. In fact, there isn't even a front line; it's just so amorphous. And so what guerilla forces are supposed to be doing is exterminating small enemy forces, weaken larger ones, attack enemy lines of communications, establish bases, and force the enemy to disperse, but they're doing all this in combination with conventional forces. Because here's the thing: you think about Mao and his guerillas, well, actually, here's what Mao really says: "Regular forces are of primary importance because it is they who alone are capable of producing the decision," like winning the war.

00:38:25

“There is in guerilla warfare no such thing as a war-winning battle”. The relationship of the two is really important. Mao also thought that you needed to establish a fire escape if you had a base area, like, if it all goes south, where do you go? And his terminal point of retreat for the Long March was up in Yenan. He thought it was important to figure those things out in advance.

Mao also cultivated an unprecedented group of allies, never before assembled in Chinese history: not only peasants, but women, minorities, youth, intellectuals, and the enemy army, most creatively. For cultivating the allegiance of peasants, it wasn't just education and land reform, it was also army discipline. This is where the three rules, six points for attention, and a couple of additional points were enforced through 1949, when the Communists win the civil war. Why? You don't want to alienate the peasantry.

These are the people that are forming your cadres, your guerrillas, everything. So, maintain army discipline. Don't mess with it.

00:39:30

Mao also took an incredibly forward-looking view about women. Here he is with his fourth wife, the actress. The other three had suffered, respectively, abandonment, execution by the nationalists, and commitment to a Soviet psychiatric ward. Not fates for the faint-hearted.

But Mao calculated that women are about half the population. They're miserably treated, so they're naturals for wanting a revolution. They're a force that will decide the success or failure of the revolution.

He calculated correctly, and he was way ahead of his times. He also understood that in a guerrilla war, you're sending all the guys off to be fighting, and you've got to be building base areas and things. This is where women came in to do those activities.

As a result, he offered women the unthinkable, which is “men and women are absolutely equal. Women have the right to vote, be elected, and participate in the work of the government”. He's just way ahead of his times.

00:40:37

Mao also offered minorities the previously unofferable, which was self-determination. What the minority people didn't get is that a promise made in a really desperate civil war, with a regional war overlaid, once you win those things and you can turn your guns on those trying to secede, that promise may be unenforceable. You can ask the Tibetans and Uyghurs how it all worked out.

All right, so Mao's strategy: he had a strategy of disintegrating the enemy army and let me tell you how that one worked.

In every county, you select a large number of workers and peasant comrades, people below the radar, and then you insinuate them into the enemy army to become soldiers, porters, and cooks. You can use women to do this as well. Talk about people who are below the radar.

00:41:34

You're creating a nucleus of a Communist Party to erode them from within, and eventually, it'll have a shattering effect. He said, also, part of this disintegrating the enemy has to do with leniency.

Sun Tzu advocates: never put your enemy on death ground. Death ground means that you just have no hope, that you're a dead person if you don't fight. So, your only hope is to fight.

If you put someone on death ground, they tend to fight with incredible willpower. And Mao is: don't do that.

So, what he did when you capture people, propagandize a little, recruit the willing, but release the unwilling, so that the comparison of communist leniency and nationalist brutality becomes absolutely stark in this otherwise pitiless war.

00:42:29

Okay, that's the civil war. Now, we're going to go to the second problem, which was Japan, the regional war. Mao made a really thoughtful assessment of what were the key characteristics of China that would determine what kind of military strategy would use. And this is his assessment.

He said, "Okay, China is a large, semi-colonial country. It's an undeveloped country." Point one. "Second, its enemy is really strong." Point two. "Thirdly, the Red Army is weak. And fourthly, there's an agrarian revolution going on." And from this, he concluded that revolution was definitely possible, but it's going to take a long time. So he didn't kid himself about quick wins.

He's going to come up with a strategy for protracted warfare. And he thought that Japan had certain weaknesses that the communists could leverage. For instance, the Japanese had inadequate manpower to garrison a country the size of China.

This meant that guerrillas could roam far and wide behind Japanese lines. Also, the Japanese were brutal, just gratuitously brutal, and they're outsiders. This means that the peasantry are naturally going to gravitate towards the communists, just simply regardless of what the communists do, but based on what the Japanese are doing, they're going to gravitate towards the communists.

00:43:47

Also, the Japanese had grossly underestimated the Chinese. And as a result of underestimating the Chinese, they made errors. When they made errors, they started quarreling among themselves and making more errors and the communists could leverage these things.

All right, Mao's most famous paradigm theory is his three stages of people's war. The first stage is the strategic defensive. It's the "prevent defeat" phase.

The last phase, phase three, is the strategic offensive, the "delivery victory" phase. In the first phase, you're focusing on the peasantry. In the last phase, you're annihilating the enemy army.

And if you look at activities that go on in each phase, the activities of phase one and two never cease. Rather, you add additional activities as competence increases. So, in phase one, you're doing popular mobilization, base area building, triangle building, guerrilla warfare.

And then, as you get more of these things, then you can start engaging in mobile warfare, try your hand at a little conventional warfare, reach out with diplomacy. And then if you go further in stage three, then you're talking positional warfare, and you're going to have the war-winning battle. And how do you get from the phases?

00:46:41

Well, the transition from phase one to two is basically you have a critical mass of base areas, cadres, armed forces that you can move into phase two. But the problem of being in phase two is what had looked like isolated acts of banditry in phase one to the incumbent government, now the incumbent government gets it, that they're facing an insurgency bent on regime change, and the regime changes strategy.

So the communists are no longer under the radar, but they're in the crosshairs. And it's dangerous because they're weak and the enemy is strong. So when you transition to phase two, initially it is quite dangerous.

And here's Mao writing about these problems and saying, "Look, in these stages one and two, the enemy is trying to have us concentrate our main forces for a decisive engagement," i.e. decisive in their favor, they'll win the war because they'll annihilate us. And of course, this is what General Westmoreland was trying to do in the Vietnam War, getting the North Vietnamese to concentrate so he could blow them off the map. And of course, they'd read their Mao and didn’t do that nonsense.

00:47:50

Mao was saying you only fight when you're sure of victory. Also, in order to get to phase three, you need a big friend. Why? Because phase three is conventional warfare, which requires infinite supplies of conventional armaments that requires an industrial base to produce it, and somewhere like China lacks this industrial base.

So, good old Soviet Union played this role the world over. And so this is the secret sauce of people's war: if you want to get to phase three, you need a big buddy. That's where the Soviet Union came in.

And so this is why Mao determines that Yan’an is going to be his terminal point of retreat. He's fighting his way through to the Soviet Union. No kidding, you've got to have the conventional arms. To fight this stuff.

What's interesting about Mao's description of people's war is it actually applied not so much to the war with Japan, which he claimed it applied to, but rather to the civil war with the Nationalists. And here is the key. Mao didn't actually fight the regional war against Japan. The Nationalists did.

The Nationalists did every bit of the conventional fighting, except one, and that's the Hundred Regiments campaign that Mao fought in North China in 1940, and he was smeared. The Japanese responded with a "three alls" campaign, which is "kill all, burn all, loot all," which is what they did. And it wiped out loads of communist base areas in North China.

So, Mao never tried that ever again, and he certainly didn't write about it in his collected works. Don't talk about failures there. So, it's interesting. What he's talking about really applies to the civil war.

00:45:01

And Mao understands these different layers. So, as the Nationalists are busy fighting the Japanese and actually being destroyed by them, the communists are pretending that they're fighting the Japanese. They're later going to take credit for it and say, "We won against the Japanese," which is nonsense.

There was also the United States in that as well. Because he's using that to strengthen the communists during all of this rural mobilization. So, when Japan's defeated and the communists, when the civil war resumes full bore, he's in a good situation.

00:48:47

Okay, that's it on Mao, the operational military leader at the operational level. Now let's put it all together as Mao the grand strategist, of linking all elements of national power into a coherent strategy. These are Mao's instruments of national power: the peasantry, propaganda, land reform, base areas, institution building, warfare, and diplomacy.

The US military, when they're thinking about elements of national power, love this little framework: DIME. D is for diplomacy, I is intelligence, M for military, E for economics, as being critical elements of national power.

It's better than only looking at the military elements, at least you got three more things. But if you look at Mao, this is not a cookie-cutter event. This is a different society, different national elements of national power are available. You've got to get to the other side of the tennis court net to see what the other team is doing.

Mao is famous for all these reasons, but also for his sinification of Marxism, where he makes all the things that I’ve told you about, makes his version of Marxism much more applicable to these countries, the newly independent countries after World War II, of how they put things together. He positions himself to replace Stalin, who dies in 1953, as the leader of communism.

00:50:15

Mao was prescient on numerous levels. He was certainly prescient about the centrality of the peasantry. He was way ahead of his times on the importance of women.

He was calculating and cunning on how he was going to use minorities and POWs. He had proven his kudoi and determination with his military strategy.

He also anticipated when the Japanese war in China would stalemate, and he also anticipated, more or less, when the United States was going to get into the war in Asia. He's the great sinifier of Marxism.

Mao produced all kinds of concepts and paradigms that are useful for insurgents who are trying to take over the host from within. I've listed a variety of them here, and I'm going to go through them in turn. These are the things that the counterinsurgent then has to counter.

All right, rural mobilization. This is obviously a big deal in Mao. If you compare—and I'm going to be doing a lot of comparisons with the Vietnam War and the Korean War because they're communists and all these things—you can see Mao's rural mobilization was very successful in China. The North Vietnamese rural mobilization was also really good. South Korea, not so much.

00:51:34

Why? The leader of North Korea was trying to mobilize the peasantry in the south, that wasn't so successful. And why? Syngman Rhee, the leader of South Korea, immediately did land reform, and this glues loyalty of soldiers to the leadership doing this. Maybe that is not the only factor, but an important factor for why the Korean War turns out differently.

Base areas. Mao says those are really important. The North Vietnamese used them to great effect. They had all kinds of areas in the south and then on the borders of South Vietnam. North Korea, not so much. It couldn't form base areas in the South.

Why? It's a peninsula which the US Navy cut off. It's also cold. So where are you going to flee if you want to do a base area? I think it's up a mountain in South Korea, and that will get cold in the winter, and you'll probably freeze to death.

I believe “Al Qaeda” means "the base." I believe that's the correct translation. So if you're thinking about ISIS or whatever's left of it, you can go back to Mao's ideas about base areas, that you need a particular kind of geography that's good for the defensive. You've got to have a big party organization, a lot of local support, and you have to have military forces there.

Does Al Qaeda—well, it's going to be ISIS or something—do they have all four of these things or can you remove any one of them?

00:53:03

Another idea from Mao is luring the enemy in deep. Mao had done that very successfully in the first three encirclement campaigns, and then he was removed from command, so he wasn't doing that anymore. Again, in the final phases of the Chinese Civil War, the `45 to `49 event, Mao lures the Nationalists deep into Manchuria.

The Nationalists are a South China phenomenon. I showed you the map. Chiang Kai-shek starts in the south and he goes way up north, so he's weakest in the north. Mao lures him way up there in Manchuria, and then he springs a trap and destroys Chiang Kai-shek's armies up there. Then the entire civil war wraps up within a year of that.

Mao also lured good old General MacArthur, who fancied himself a great Asianist in the Korean War. MacArthur goes all the way up to the Yalu River, right on the Chinese border in the Korean War, and Mao springs a trap. MacArthur didn't realize that 350,000 Chinese troops had been infiltrated around him. Oops, missed that.

00:54:12

It did not work out well. But for the US Navy, now, it needs to think about, what about being lured into the South and East China Seas and then the Chinese pulling the trap. There are places you don't need to go. The Chinese may have to go there, but maybe you don't have to.

Another one is terminal point of retreat. I've talked about Yan'an being a really good one, and that worked. Then when the Manchurian campaign initially wasn't going well for Mao, he retreated up to Siping, which is a little bit north, and that worked well enough. But when Chiang Kai-shek tried to pick these Manchurian cities as a place to retreat in Manchuria, bad news.

There's only one railway system that gets you south out of Manchuria. You suppose the Communists don't know about it? They encircled the Nationalists in these cities and destroyed them there.

So when you're thinking about insurgents and things, think about, well, if you knock them out of one area, where might they go next? Another concept from Mao is disintegrating enemy forces, which is what happens to the Nationalists. Think about it: Chiang Kai-shek had been fighting since the 1920s, forever and ever, and he fought the Japanese, they're brutal. The United States had trouble fighting the Japanese, and Chiang Kai-shek fought them alone for a long time before we joined the war.

And yet, he loses a battle in '48 in Manchuria, and that's it. The rest of the country wraps up. So what was going on there? Or the South Vietnamese: they'd been fighting forever.

00:55:55

And then the whole place just wraps up. And the same thing happened with Japan in World War II. They'd been fighting all over the place forever, fighting us brutally. And then in 1945, we don't even have to invade the home islands.

Think how unusual that is. The Germans fought every street on the way to Berlin; the Japanese quit. This is about disintegrating the enemy and why it happens.

But what you can say in all those three cases is the warfare had been going on for an incredibly long time, and it was ruinous. The places in question were ruined, so don't expect that to happen too fast.

Of course, Mao's big contribution was his three stages of people's war. Mao presents them as sequential: you go from one to two to three, and tada, you win. And they're cumulative, right?

You do certain things, and then you get to phase two, and you've had this cumulative effect of destroying enemy forces. You get even more accumulating casualties on the other side, and finally, you win.

A student of mine said that's actually not a great way to look at it, or there's an even better way to use it. It's like a metric of how an insurgency goes up and down, so that ISIS may be on the cusp of going into stage three. I don't know that they ever really—well, possibly, with all the equipment they got initially—or whether then they get knocked back to stage one, where you wonder whether they still exist anymore, and come back and forth. Anyway, that was that person's take. I thought I'd pass it along to you.

00:57:25

All right. I have one last thing to talk about. Mao, when you read these 7,000 pages—and I don't recommend it—one is struck by all these dualities. I think it goes back to Yin and Yang analysis, which is very prominent in traditional Chinese thinking.

I got a little abbreviated Yin and Yang list of opposites here of these coexisting opposites constantly changing. So, if you look at Mao discussing triangle building, it's in terms of the presence or absence of factors, presence and absence being opposites.

So you're going from the absence of political power to the seizure of political power, from the absence of the Red Army to the creation of the Red Army. It goes on and on. Studying the differences and connections between dualities is a task of studying strategy, and it's really everything.

To defend in order to attack, to retreat in order to advance. It goes on and on, and it's all about correctly orienting yourself between these opposites.

00:58:32

So, “oppose protracted campaigns and the strategy of a short war. Uphold the strategy of a protracted war in a short campaign” and I'm starting to lose it.

You've got to put everything in the context of each other: losses, replacements, fighting, resting, concentration, and dispersion and I'm thinking, "I don't get this. Mao's bipolar disorder."

So I turned, and then you look at the diagram. Okay, they're opposites, and it's either one or the other, but even- they're not even true to themselves.

Each opposite is a dab of the other. I was having trouble figuring it out, so I went to this gentleman, Brigadier General Samuel B. Griffith.

He is the only translator into English of Sun Tzu who has a distinguished military career, and also, he went on to get a DPhil—it's like a PhD—from Oxford in military history.

00:59:32

If you look at his career, he's in China during the Japanese escalation of the Sino-Japanese War. He's back in China, does another tour at the end of the Chinese Civil War. He gets top marks in the military's Chinese language exam.

Oh, look at these details: Navy Cross, Purple Heart, Distinguished Service Cross. He's a distinguished man, and for retirement, he decides he's going to go get the Oxford degree, and he writes the translation of Sun Tzu. He is the only translator of Sun Tzu who translates "死地" as "death ground." Other people, it's like, oh, I don't know, I can't remember the words, like, I don't know, "contested ground," something else.

But I'm guessing that when he chose those words, "death ground," it's because when he was thinking about it, it might well have conjured up his memories of what exactly it was like to be on Guadalcanal or New Georgia. He has provided insights- oh also the other thing to mention about him is he's really modest.

01:00:35

I had to dig around to find these biographical details. They're not on the cover of his book which is where they should be. And his service to his country continues to this day.

Because it's his translation that continues to educate officers over 40 years after his death. But here's his take: "In every apparent disadvantage, some advantage is to be found. The yin is not wholly yin, and the yang is not wholly yang. It is only the wise general," said Sun Tzu, "who is able to recognize this fact and turn it to good account." And of course, Mao could, and he did. But in peacetime, choices are not binary, they're graduated, and evolution is much more conducive to economic development than revolution.

So that's what I have to say about Mao's bipolar disorder and other things about him, and thank you very much for bearing with me.

Dwarkesh Patel 01:01:35

Okay, so here's something I'm confused by. You were talking about Mao as a shrewd commander, somebody who had studied not only the military but had also compiled these rigorous records of farm life and agriculture. And then you fast forward to when he's in power, and you go to the Great Leap Forward, and some of the things that he was doing, you just can't imagine that somebody thought this was a good idea: that peasants would take off the harvest, wouldn't attend to the crops, but they're going to make iron in their backyards. They're going to shoot the sparrows and have a locust infestation.

How do we square this shrewdness in the beginning and these idiocracy-level ideas in the Great Leap Forward?

Sarah Paine 01:02:17

Well, first of all, you need to think about what his objective is, which number one is to stay in power, right? And number two is probably Communist Party in power and pursue his visions of revolution. So that's one thing.

Then you're worried about the welfare of people as if that's the primary objective. It most certainly is not. And then he may have assumed that it came along with these things, and that's a whole problem with communist ideology is people don't believe it because they know it's wrong, they believe it because it's right. There's a whole problem with that about labor theories of value and things. It turns out there are things called services that are also valuable.

So the basic theory that he's implementing is incorrect. It just takes the communists a little while—some of them haven't figured it out—but it takes them a little while to figure out there are parts that don't work. But there's a whole other piece to him, if you think about our own world, how much expertise does any individual have? It's amazing the man reunited a continent, and he did all those things. Then to expect him to run a peacetime economy is crazy.

01:03:29

But of course he wants to do it so he stays there. Or if you think about like in Britain, Winston Churchill: a great wartime leader, but he's booted out of office right after that war. If you think about different capabilities of people, don't expect one individual to do everything.

So that's a whole other problem. When Mao is running stuff from afar—this is a country with how many people? It hadn't hit a billion then, but it's hundreds of millions and it's been shattered by warlord rule forever. And so how are you going to extend central control to the countryside? Really tricky. I suspect that’s why he puts them on communes, because it lines up with communism, but it also lines up with party control.

Because if you put all this vast population—because most people are peasants—on communes, then you control whether they eat or don't. You truly control them. So it's those factors. It's so big when you start putting a policy in place from Beijing. And then how that actually fans out over the whole country, it's got to be a mess. Different localities and things.

Dwarkesh Patel 01:04:37

But when I compare him to other, even other communist leaders.

Sarah Paine 01:04:41

Which ones?

Dwarkesh Patel 01:04:42

So if you look at Stalin, for example, yes, he causes the Holodomor, right? Three million people die, but-

Sarah Paine 01:04:48

Uh, details, yeah.

Dwarkesh Patel 01:04:48

But that was an intentional famine where he was trying to root out the kulaks. The Great Leap Forward was- he wasn't trying to kill tens of millions of Chinese. And there’s instances in the 20s where Stalin threatens to resign, and people close to him, Molotov and others, say, "You can't do it because nobody else can run the government like you can."

Sarah Paine 01:05:09

Yeah, right. [gun gesture]

Dwarkesh Patel 01:05:11

True, but the level of…

Sarah Paine 01:05:16

[noose gesture] Yeah?

Dwarkesh Patel 01:05:19

Stalin was a devoted communist, and that led to many deaths. But he didn't seem deluded in the same kind of way that Mao was.

Sarah Paine 01:05:26

Well, first of all, Russia is quite a developed country compared to China. And if you think about it, Russia had been industrializing since the late 19th century, and China's a much later event. Russia also has all sorts of institutions and things that China was lacking.

So already, Stalin has many more tools at his disposal than what the Chinese communists are going to have. Also, the kind of warfare. Think about the warfare in China. You got the Taiping Rebellion, only the biggest of the peasant rebellions of the 19th century, and I can't remember if it was 20 or 30 million people.

That's a large number, right? Because I think World War II is supposed to be like 55 million. So there are a whole series of these peasant rebellions in the 19th century that go on for, I don't know, 25, 50 years, one's here, one's there. By the end of it, they've basically devastated parts of every province. Then you overlay this with the Warlords coming in and all of that. Overlay that with the communist-nationalist thing. Overlay that with the Japanese.

01:06:41

You're talking about a massively trashed country. And think about it. I'll give you another example. For Americans in the room. And for others who aren't Americans, you'll have to, based on your dealings with Americans.

This country had a civil war. It ran, what, four or five years? It was mostly in the South. Northerners came, joined armies, and then went to the South. The institutions of government didn't change in Washington. They certainly did in the Confederacy. But those were new things, right? Because they were trying to secede.

And the losing side of that war still hasn't gotten over it. Right? So if that's what's going on here, and that thing wasn't nearly as brutal as the civil wars... At the end of the Civil War, who gets… Lee's army is allowed to go home. They aren't shot on sight.

Which would be the kind of thing that went on in the Russian Civil War, Chinese Civil War. So, if that kind of bitterness and unsettledness is still present in this highly institutionalized, wealthy country, this one, you better believe China's a mess. So then when you're wondering why Mao can't surf that wave, no one can.

Dwarkesh Patel 01:07:57

Just to linger on this...

Sarah Paine 01:07:59

Uh-oh. New exit strategy.

Dwarkesh Patel 01:08:05

Even if he's not a specialist on agriculture, and even if China is a hard country to govern, he was somebody who was shrewd in the sense of, when these battles were happening, I imagine if somebody came up to him and said, "We're winning these battles," and it's a total fabrication, they're in fact losing these battles, he would have been quick enough to realize, "Listen, this is just wishful thinking. I realize you're just trying to make me feel good, but this is a lie and I'm not going to stand for it."

Whereas, if you go to the Great Leap Forward, millions of Chinese are dying, and they come to him and say, "The grain harvests have never been higher." And he's like, "Great, let's export our grain." And there's this ability to have this basic discernment of what is actually going on. That situational awareness is gone.

Sarah Paine 01:08:44

Okay, there's, no. Mao doesn't become canonized as Mao, or- I'm mixing metaphors- the Emperor of China until victory in the Korean War. So in the early period...

Oh, Mao is supposed to have written this book called On Guerrilla Warfare. It is not written by Mao. It took time to figure it out, for outsiders. It's written by Peng Dehuai and others, his generals. And so there were many people's ideas that went into winning the civil war. It is not just Mao. And then he winds up purging these people later.

And so it's not until after the… During the Korean War, he's busy purging everybody massively because you can use it as a big excuse, right? We got this war, we don't want to hear from these people. And so he has purged more and more and more and more people, so that there are fewer and fewer counterarguments. It's a real case against dictatorship. For all the chaos of party politics, you're at least forced to confront the counterargument. It is a healthier situation to be in.

Dwarkesh Patel 01:09:53

Let's go back to the end of World War II.

Sarah Paine 01:09:55

Okay.

Dwarkesh Patel 01:09:57

So if you look at basically all the main actors, none of them get even most of what they want. Some of them get the exact opposite of what they want.

Germany and Japan initiate to expand their territory. Both of them end up with the smallest territory they've had in a long time. Britain starts the war- or not starts, but enjoins the war- in order to defend Poland. Poland ends up in totalitarian occupation afterwards, and the British Empire disintegrates.

But Stalin ends up with the borders of the Soviet Union vastly expanded. China becomes Communist. How do we make sense? Is Stalin just a master strategist here?

Sarah Paine 01:10:35

I believe there's a detail of how many tens of millions of Russians died on that thing.

So great, it's a personal success for him. The numbers of Russians who were destroyed in that are incredible. So, okay, for dictators, yeah, they can have personal successes, I think more, it's in these horrible wars that dictators can rise. Also, Great Depressions provide hothouse conditions for dictators.

I think the lesson is the last thing you want to do is fight a world war, but as Britain discovered in World War II, if Hitler's insistent, you're stuck. It might be a lesson for our own day. Isn't this Ukraine's problem? They didn't want to fight a war. What are you going to do? Putin launched.

We don't exactly want to fight a war. Well, what are you going to do? Let Putin do whatever he wants forever, however long he wants? The reason you all are prosperous is there's a global maritime order in which people obey rules because it is so much cheaper to obey rules. Because what do you do when people break the rules? You hire a lawyer.

It's not protection money or starting to blow up each other's buildings and destroying wealth at an incredible clip, which is what you're seeing going on in Ukraine. So these things are consequential, none of us makes all the choices, and when other people make bad choices, you're stuck responding to them.

Dwarkesh Patel 01:11:58

Before Mao, before the Communists completely won the civil war, did people anticipate truly how terrible the communist power would end up being in China?

Sarah Paine 01:12:08

I doubt it. I think Communism was a new thing, right? So you've got it going on with the Russians. I mean the Nationalists were telling us that it would be like this, and we looked at them and go, "What could be worse than the Nationalists?"

Because they were in a desperate situation with all that the Japanese were doing, and then they get blamed for it all. Yeah there can be something worse than that, which we call communists.

But I don't know that anyone could have predicted... Can anyone do a crystal ball what the world's going to be like in 10 years? It seems when you read the history books that it had to be that way, and yet in our own lives, we know it's contingency, of why things turn out as the aggregate of all of our choices. How would you ever calculate that?

Dwarkesh Patel 01:13:01

I wonder if there's a lesson in there that America should be more open about supporting corrupt, somewhat autocratic regimes because, especially when they're facing fanatical ideologues, because things really can get much, much, much worse.

Sarah Paine 01:13:15

I think Americans need to worry about overextension. Any country has to worry about overextension. We have finite resources. Also, you're talking about sending your fellow Americans to go get themselves killed, and that's quite something to ask someone to make that kind of sacrifice. It had better be worth it.

There are 300 million Americans, while the world's got 8 billion. Uh, be cautious. What's key on this maritime order, the big insurance policy of it all, is our allies and institutions. This is the great gift of the Greatest Generation, of having created the UN, which is how many millions of lives have been saved from polio vaccines and other things that come through the UN? Do not dismiss these organizations, they've done a lot. Or the EU.

Work through these things, and listen to your allies. They will have insights, and there is power in allies. Tell me, who China's allies are? The crazy man in Korea who can't even feed people in the 21st century. Although he certainly feeds himself, but that’s a whole-

It's incredible. Who are China's friends? I mean, Iran, a theocracy? I mean, talk about passé. Who does theocracies anymore? Okay, the Iranians. Good on them.

Dwarkesh Patel 01:14:40

After World War II, the Soviets are giving the communists in China tremendous amounts of leftover weapons from the Japanese and a bunch of other goods, supporting them tremendously. At the same time, Truman is wishy-washy and does an actual arms embargo on Chiang Kai-shek in 1946. The Marshall Plan for Europe is $13 billion to help build up defenses against communist appeal.

At the same time, Truman has to be forced in 1948 by Congress to give a couple hundred million to China, literally one-hundredth of what was given to Europe. And by that time, it's too late for Chiang Kai-shek.

If you look at that record, it seems like we abysmally messed up after World War II in helping the Nationalists stay in power, right?

Sarah Paine 01:15:36

Do not exaggerate the capabilities of any one country for openers. But I think it's really important to distinguish between nation-building and nation rebuilding. If you're rebuilding, which is what happens in Japan and Germany, they already had full-up institutions, modern economies before the war.

They had no problems with educational institutions going all the way up, judicial institutions, they had competent police forces, competent… They had parliaments and other things. So that when you give Germans some cash, and Japanese as well, they know exactly how to recreate things and rapidly produce modern institutions. You're talking about China. They never had these institutions. There is no indigenous expertise.

Oh, and by the way, what's the illiteracy rate in China compared to Japan? Whoa. No one reads in China, and everyone reads in Japan. It's not quite that bad.

01:16:35

And we've had this problem in Iraq and Afghanistan. So we decide we're going to do the de-Ba'athification thing, and then we think the police are going to still show up and work. Except no, that's not how it works, they haven't got these institutions. And so it's not feasible. A Marshall Plan in China would not have worked.

And also, we had really competent foreign service officers in China in this period. Why? They're the children of missionaries. And so they spoke fluent Chinese and had a deep understanding of China.

And they were saying it's hopeless, that there is no way Chiang Kai-shek's going to win this thing because he's hated by the peasantry, which he was, because for the reasons I've told you. If he's busy dragooning them into his armies because he feels he has no choices, whereas the Communists are giving them land and educating them, you better believe who the peasants are supporting.

And the missionaries, they were then caught up in the McCarthy purges and were just about ruined. Lost their jobs in the State Department and elsewhere, only to be exonerated 10, 15, 20 years later when they've already lost their careers and who knows how they raised their families?

Dwarkesh Patel 01:17:52

I thought it was the case that George Marshall, who was the envoy to China, the diplomat. I thought it was a case that later on they realized it was hopeless and so then they stopped supporting Chiang, but, but-

Sarah Paine 01:18:01

It's at the time, different people realized it at different times.

Dwarkesh Patel 01:18:04

But the reason they didn't support Chiang as much as they should have was because they thought he was- I mean, the Communists almost seemed like hopeless underdogs.

It was just thought that Chiang is going to win and therefore we don't have to support him.

Sarah Paine 01:18:18

No no no no.

Dwarkesh Patel 01:18:19

They were constantly goading the Nationalists to form some sort of ceasefire, do some sort of coalition government, when in fact what it should have done is like, no, you have to make sure that you keep China.

Sarah Paine 01:18:28

No, it was considered hopeless. This is called making a net assessment of not what you want it to be, but an accurate one. They believed it was not feasible.

Dwarkesh Patel 01:18:37

Even in 1945, 46?

Sarah Paine 01:18:40

You're talking hundreds of millions of people. We can't even deal with Afghanistan today. With what, 20 million people? It's not feasible. It's at the end of World War II. American GIs are sick of it, and as are their parents, of fighting more wars.

Dwarkesh Patel 01:18:58

We didn't have to send GIs, we just had to not cut off support.

Sarah Paine 01:19:01

Europe has been leveled and there's this absolute fear that the Communists are going to move into Europe, which actually counts for Western economies in those days. The Italian and French Communist parties were incredibly strong, so all the focus of limited resources is going to make sure that Europe settles out. We don't have infinite resources.

Dwarkesh Patel 01:19:24

I feel like you could do better than 1/100th of the Marshall Plan to keep China from turning Communist. And look what the consequences were: Vietnam, we had to fight, Korea, we had to fight, Cambodia, the genocide there.

Sarah Paine 01:19:34

But you can't solve all these things. There are things that are not feasible.

Dwarkesh Patel 01:19:37

I'm going to linger on this because...

Sarah Paine 01:19:40

You're an optimist. But anyway, maybe you're right, but anyway you've got my take on it and I can't prove I'm right. That would be my take.

Dwarkesh Patel 01:19:50

I remember in your book, which is to your right, The War for Asia, there's a passage where you say there are so many sort of individual, contingent things that led to the Communists taking power.

Sarah Paine 01:19:59

Yes.

Dwarkesh Patel 01:20:00

If any of these factors was off…

Sarah Paine 01:20:01

Yes.

Dwarkesh Patel 01:20:02

The outcome might have been different.

Sarah Paine 01:20:03

Yes.

Dwarkesh Patel 01:20:04

Of all these factors, the fact that American support, or lack thereof, was not one of them, it just seems like it was a super contingent thing. But also, America not being as strong as it should have just didn't matter.

Sarah Paine 01:20:13

I don't know- it's just not politically feasible. I mean, talk about it?

Put yourself back in those days. You've already done a three-year tour in World War II because, in those days, you started serving whenever it was in the war, and you weren't coming home until the war was over. It was none of this nine-month tour, a year tour here and there. You were there for the duration.

So you get home. And then you're told, "Go make nice to the Chinese and go get yourself killed there." How is that going to fly in your family? Probably poorly.

Dwarkesh Patel 01:20:47

I mean, again, you don't have to send the GIs there. You can just not do an arms embargo on them.

Sarah Paine 01:20:53

I think it was so minor, by then it's too late. The great question is, some would argue that in 1946 when Marshall tells Chiang Kai-shek to halt his advance—this is when he's doing quite well—and when I mentioned that the terminal point of retreat for the Communists was up in Siping, Manchuria, some would argue that Marshall should never have done that, he should have let the Nationalists go all the way up, and that would have changed the outcome of the Chinese Civil War. You could make an argument that that might be true. Here's the counterargument, and I don't know the answer.

If you look at a map of China, or imagine one, Manchuria is way up, it's like a salient into Communist territory because it's bordering all the Soviet Union. And then it's got quite a coastline, but the Soviets had blockaded that, so nothing's getting in that way. The only way, given the Chinese railway system, is that literally one train line connects Manchuria to South China.

01:21:59

So, it means Chiang Kai-shek's movements are incredibly predictable. So one argument you could make, and people have, and I don't know the answer, none of us does, is that, hey, that was the big error. So if that's the big error, the mistake- and this is a common one that Americans make so this is worth talking about- is Americans often don't look at warring parties to understand if they are primary adversaries there is no way you're going to make them make nice. So the United States had trouble for years trying to get Pakistanis and Indians to cooperate, and it would want to give aid to both of them and just didn't get it.

As long as they're primary adversaries, you aid one, it infuriates the other, and they're never going to cooperate the two of them. Or I suspect what was going on in Iraq and Afghanistan, so you want to have a democracy, and you want to have all the parties represented.

01:22:53

Well, if they all want to obliterate each other, the last thing they want to do is have representation of the other side, right? So, if you have parties that want to exterminate each other, the idea of getting them to cooperate is impossible. So don't try it.

That would be the lesson from this thing. We kept trying to do a coalition government with the communists and the nationalists. It's a non-starter.

But the United States was a very isolationist country and didn't have the attitude of a great power until after World War II. In World War I, we felt dragged into it, and these horrible wars, and being quite irresponsible during the Great Depression, and just ignoring everybody else's problems, didn't want to hear about it. And then we get a World War II out of that, and then we rethink that whole proposition.

Dwarkesh Patel 01:23:46

If you had to speculate, suppose the Nationalists win. In Taiwan, because they're forced to, they have a very pro-American policy. But if they had won in the mainland China, would it have been similar where mainland China just turns into Taiwan, or would it have been different?

Sarah Paine 01:23:59

No, I'm sure it would be different. One of the reasons Taiwan is Taiwan is after they lost the civil war and they are on the island of Taiwan, they did a big after-action analysis of what went wrong and they decided it was corruption and that it was land reform that they needed to fix those things. But they couldn't do land reform on the mainland because that's their officer corps. But when they come to Taiwan, they can more than redistribute Taiwanese land, which they do and it's bloody. Because the Taiwanese don't want their land redistributed. The Taiwanese were given bonds, and they thought those bonds would be like the pieces of paper that were issued in the mainland, i.e worth nothing, but actually after time they became quite valuable.

Land reform was bloody in Taiwan, but today Taiwan has a very equal income distribution.

Dwarkesh Patel 01:24:55

And what is the explanation for -- you just had these tallies of numbers up of how many deaths through different events. The famine obviously makes sense, a lot of people would die.

But you just go down the list, and there are so many things where a couple million people die, and it's like one out of ten items that happen within a span of a couple of decades.

Sarah Paine 01:25:13

It's famine. If you have warfare, you have famine. It's not mostly bang, bang, you're dead. That goes on too, but it's starvation when you disrupt things in this way.

Dwarkesh Patel 01:25:25

Why didn't the Communists have the same troubles that the Kuomintang had in terms of not being able to pay its soldiers? I mean, it was the same China, right? It wasn't a developed economy. So why didn't they have trouble paying their soldiers and retaining them?

Sarah Paine 01:25:39

Oh, I think it's because everyone's on board. The peasants have got their land, the only way they're ever going to keep it, they think, is by supporting the Communists and Mao isn't living any kind of luxurious life, there's no -- that was another thing. You have these rich officer corps and the Nationalists flaunting their wealth among absolutely starving people. And that's another reason why they're hated.

Whereas the Communists never had those people, so no one's flaunting their wealth. You saw the pictures of Mao and his beloved there in the incredibly baggy clothing. No one's got luxury wear or anything.

Dwarkesh Patel 01:26:18

Although after they won, Mao is famously a fan of luxury and has -- right? In a way that I think Stalin would sleep on his couch in his office.

Sarah Paine 01:26:27

Yeah, I don't know the details on Mao. Mao had other things that had to do with little girls, but anyway.

Dwarkesh Patel 01:26:32

Okay, you were just mentioning China, this period of turmoil, constantly there’s insurgencies and chaos, and then we go to all the turmoil that Mao causes, all the people, when he gets into power, tens of millions of people are dying. And yet, there's never a major insurgency after he takes power. There's never a coup that works.

Sarah Paine 01:26:54

Oh communists are good at this. When the North Vietnamese won, what do they promptly get? Famine. And in Vietnam, I think you have kind of three harvests, like how do you ever have famine in that place? But yeah, they did.

This is the “brilliance” of communism is this commissar system and the party system, and how they set up their government, it’s incredibly effective about maintaining power. It's very effective about seizing power during warfare, maintaining it thereafter, but it does not deliver prosperity, it delivers compounding poverty, take a look at North Korea. It's a mess, but the man's in power.

Dwarkesh Patel 01:27:34

Why don't the high-level officials in the CCP, after they see the impact of the Great Leap Forward, the Liu Shaoqis and the Deng Xiaopings, why don't they-

Sarah Paine 01:27:43

They did.

Dwarkesh Patel 01:27:34

Well they- he wasn’t running day-to-day affairs. But in terms of making sure he's never in a position to, in the future, do something like the Cultural Revolution?

Sarah Paine 01:27:51

They try. This is when you get Peng Dehuai, who- he was the general in charge of forces during the Korean War, and also Long March generation. He had lost several of his siblings to starvation over the course of these civil wars. And he and Liu Shaoqi pulled the plug on Mao, and then Mao is demoted from one of his positions, and that's when you get the Cultural Revolution because Mao is on his way out of power from these guys.

So what does Mao do? He gets out of Beijing, and he rallies the youth, the Red Guards. These are the children who've been educated in communist education about how wonderful the communists are and how beloved Chairman Mao is. So Chairman Mao tells the teenagers that they should be in charge.

01:28:44

Think about teenagers: not much life experience, but telling them they're in charge. And then they start out by killing off their teachers, right? They have teachers they probably don't like, and it's incredibly empowering. Then they start working their way up the educational system and fan outward.

And it's like a semi-civil war. And then this is when Lin Biao comes in, who is a military leader, and Mao is going to use him to restore order, but after he's ousted all the people who are going to get rid of him, that's what the Cultural Revolution is about.

And it is ruinous for production, right? Doing all this. And you're gutting your educational system. All the people who gave the hard grades, who may have actually known something, they're gone.

Dwarkesh Patel 01:29:33

From Mao's perspective, didn't the Cultural Revolution work extremely well? Because-

Sarah Paine 01:29:38

For him, yeah. Not for China.

Dwarkesh Patel 01:29:40

He launches it because he sees Khrushchev's speech in 1956 denouncing Stalin and what he had done, and didn't want the same thing to happen to him after he dies. And today, look, the portrait of Mao hangs in the square, and he's revered in a way that Hitler or Stalin aren't revered in their countries, despite killing more people.

Sarah Paine 01:29:57

Well, though I think Stalin's having a bit of a comeback with Vladimir Putin. And Vladimir Putin is starting to mimic more of the centralized... He's going to recentralize his economy in ways that start looking like the old one.

Dwarkesh Patel 01:30:13

When people say things like, "Xi Jinping is acting like Mao," how do you react knowing how Mao actually behaved?

Sarah Paine 01:30:19

Oh, Xi Jinping apparently reveres Mao. My understanding about Xi Jinping's education is his dad was a very high-level Communist leader who was purged but not killed, and sent to sort of interior north somewhere in China.

And so when Xi was on the, what is it, the "down to the countryside" movement, where a lot of kids were sent to really horrible places, he was sent, I think, where his dad was. Or where protectors who liked his dad were. So it wasn't as bad as it could be.

But he was not well-educated because he couldn't be in that period, right? It's during the... He may have degrees from places, but the institution in question during the Cultural Revolution wasn't delivering an education.

So he's a believer in communism. Don't ever kid yourself that the Communist Party of China doesn't believe in communism now. They think they've modified some of the economic stuff under Deng Xiaoping, but now they're recentralizing it right back the way it was.

Dwarkesh Patel 01:31:20

Given the fact that he personally suffered during the Cultural Revolution... I think there's a story that at some point, because his dad is announced, he was announced and he tried to come back to their home to get fed by his mother, and his mother had to turn him away. This happens to him during the Cultural Revolution.

How do you explain these CCP officials who personally suffered during the Cultural Revolution being pro-Mao?

Sarah Paine 01:31:42

I mean, Stalin was abused as a child, and then he's incredibly abusive. Hitler was abused as a child. Why would you expect… I use the word sociopath or psychopath, both apply to him. Don’t think he’s going to have any tender mercies for anyone.

I mean his own son is… Oh, he abandoned various sets of kids who starved to death and died and one son, he put him right up in Korea where he could get killed, and he promptly did, so.

Oh and Stalin, that was another great one- the Nazis captured Stalin's son and wanted to trade him for something. Stalin said, "Nah nah nah."

Dwarkesh Patel 01:32:25

How do people in China today think about Mao? Why aren't they more pissed off, you know, you look at these numbers?

Sarah Paine 01:32:32

I'm guessing, I'm not Chinese, but I think all of us need to be proud of things. Of course, one thing to be very proud of if you're Chinese is Chinese civilization. These enormous achievements in philosophy, in the sciences, all kinds of arts, it's incredible.

But many Chinese also want to feel proud of their leaders, and so Mao is incredibly consequential. I think it's also really hard to look at the dark side of your own country. This country has slavery; it is our original sin. We have not gotten over it.

And the part of the United States where slavery was most deeply embedded to this day has the deepest problems dealing with that. So if Americans who are rich and have everything have trouble with these things, why would we think- and this country has so much that's just obvious to be proud of, the wealth and innovation and things, a recent innovation and then we claim we won all these wars all by our lonesome, and of course we had allies, come on.

01:33:46

But I think that's part of it, is understanding the… Chinese history has been so sad. If you ever go to a Chinese movie you hear the erhu, whatever the instrument is, it's just so sad and melancholy. Whereas American movies are all happy. Millions of people die, but the good guys live, right?

It's always a happy ending, and what is it, in Arnold Schwarzenegger in True Lies, he says, "Oh honey, they were all bad.".

Dwarkesh Patel 01:34:20

I want to try out an idea on you.

Sarah Paine 01:34:23

You've tried many, what's the latest?

Dwarkesh Patel 01:34:29

One could claim that Mao, being so terrible and being this strong counterexample, is what the reformers needed in order to create China's post-Mao growth. In the sense that, if you were to really abandon communism and pursue pro-growth policies, you needed, viscerally, the cultural revolution as a counterexample, the Great Leap Forward, to say, "No, we've got to try something else."

Sarah Paine 01:34:55

Possible. Definitely possible, because of all the people who were ruined by it.

But I think there's another one. You should give the Taiwanese great credit. Taiwan did an after-action report. They're one of the four Asian tigers that went from a very poor country to an absolutely wealthy country. I think it's around 1990-ish where Taiwan is—I think this statistic is right, I'll try this—it's got 1/50th the population of the mainland, it's got 1/250th of the land mass of the mainland, and it has about 40% of the GNP.

And if you're PRC mainland looking at this, how embarrassing. The losers of the war have won the peace and put you to shame for how incompetently brutal you are.

And that's Taiwan's problem to this day, is that they are a rebuke to everything the Communist Party is. There used to be this racist notion that "Oh, Han Chinese and democracy, incompatible, can't do that." Well, China—Taiwan has proven that wrong. And look at Taiwan. They have, what is it, their chip foundry? Incredible. No one can duplicate it. Right?

Impressive. Tell me something that China has that can't—that no one can duplicate—except for crazy stuff.

Dwarkesh Patel 01:36:24

What I find disturbing about communism in comparison to—I mean, maybe even especially Chinese communism— is.. when Hitler is killing the Jews, he doesn't make them first say, "Listen, you know, we were responsible for World War I," which he falsely claimed, "and we renounce our Semitic ways," or something.

And with the Cultural Revolution, with Chinese communism, there's a very important aspect where the victim must participate in their own victimization. They must be the one to say, "I'm guilty," in a way that didn't seem necessary in other kinds of regimes.

Sarah Paine 01:36:56

Oh, well, Hitler's just trying to annihilate people, so he didn't care what they were going to tell him. He's just going to kill them all. So that's just complete—he's into destroying every last Jew there is, right? Plus Gypsies and some other people. So that's a separate thing. China has a long tradition of education.

I mean, what's Confucianism about? It's the scholars at the top, right? It's—in Japan, it's the general, the shoguns at the top. In China, the soldiers at the bottom of the social pyramid, and it's all about educating people. And then the entire imperial system, how did you get mobility? There was a way to mobility: take the imperial exams and if you pass them and do well, you're off and running. So, and there's also a tradition of re-educating people. You could shoot everybody, you could, but that's not the Chinese tradition.

The loads of people dying is, a lot of it’s starvation. So, people like Deng Xiaoping, who, if you'd been in Russia, they’d just-- Stalin killed all the old Bolsheviks, right? There was no chance to come back, whereas the Chinese will put people in prison and try to re-educate them, and then you let them out afterwards. It's a tradition of educating people, different culture, different approaches to these things.

Dwarkesh Patel 01:38:10

If you look at, you were mentioning Chiang Kai-shek's purge of the Communists, where he just massacred thousands of them.

Sarah Paine 01:38:16

Oh yes.

Dwarkesh Patel 01:38:18

Whereas-

Sarah Paine 01:38:19

He’s a soldier.

Dwarkesh Patel 01:38:20

Whereas other people, even other nationalists, wanted to accommodate the Communists more, especially early on, and he has one quote that “the Japanese are a disease of the flesh, but the Communists are a disease of the soul”. So, what was the nature of his anti-communism? Because in retrospect, obviously the means we don't endorse, but he was right about that understanding of what would happen with the Communists in power.

Sarah Paine 01:38:45

He was interested in communism, and he sent his son, Chiang Ching-kuo, to Russia to study. Actually, his son was a hostage up there. He married a Russian.

That's one of the reasons you don't have a Chiang dynasty because these children are not 100% Han, right? Because their mom's Russian. However, as he learned more about it, because initially he's interested in that, he's interested in playing fascism as well, and how you organize society.

He studied in Japan, knew a great deal about how the Japanese had done things. I think he had military education there as well. But then he starts realizing how dangerous they are. He was accurate. He did not want to fight the Japanese at all.

And he was forced to by Chinese students, among others, and a lot of the urban elite that he depended on, who were horrified with what Japan was doing. But Chiang Kai-shek knew you only take on one enemy at a time. You'll become overwhelmed if you do, and he believed that really he needed to kind of accommodate or put the Japanese aside, fight the Communists, get rid of them, and then he's hoping the United States will get in or something that then he could deal with the Japanese separately.

But he faced so much popular pressure that that's when the Xi'an incident takes place at the end of '36, where he agrees to form the second United Front. He realizes he'll be overthrown by popular hatred of the Japanese, thinking they have to fight the Japanese. So he didn't have his choices on military strategies at the end there.

Dwarkesh Patel 01:40:26

When you read Western journalists who are covering the communists in China sympathetically, people like Edgar Snow, and this is not just true of China, Walter Duranty covering up the Holodomor in Russia, what's going on psychologically? How do you understand their naivety here?

Sarah Paine 01:40:45

Oh I don't know about the other guy, but I do know about Edgar Snow. So Edgar Snow is in the Depression. He's from the Midwest. He was a bored young man. He wants an adventure, so he goes off to China. This is typical.

And he's in these cities where he's seeing nationalist corruption, and it's evident there. Mao is looking for a foreign journalist. There had been a guy, Jack Reed, who'd written however many days of the Russian Revolution, back during the Russian revolution he’d really popularised it and I suspect, but I don't know, that Mao probably needed his own Jack Reed, and Edgar Snow seemed the perfect guy because he's a very good writer. You want someone who could write well, and he's young and naive.

So what was it? Sun Yat-sen, the founding father of China, is married to one of the Soong sisters. Another one of the Soong sisters is married to Chiang Kai-shek. And there's a third one who's married to a banker. But anyway, it's the communist one who puts Mao on, I think, to Edgar Snow. So someone goes to Edgar and says “Hey, you want to come up to Yenan to interview the big man?” And Edgar goes, "Sure." They infiltrate him through Japanese lines and all the rest of it. And there's Mao, the busy man, who's spending hours with the 30-year-old, or I don't even know if he was 20-year-old. I can't remember how old he was.

01:42:13

And Edgar Snow doesn't get it. Like, why is he spending all this time with me? And he's taken by it. And Mao's, of course, showing him all the good stuff, right? So Edgar Snow's seeing all the happy peasants who've got the land, and they're cooperating in the guerilla this and that. And he is the last person to get out of Ye’nan. And then the Japanese in the war just shuts it down so nobody's got any more information about Mao ever.

So Edgar Snow writes his book, Red Star Over China. He gives the drafts to Mao- oh yeah, he does the Mao interviews and then he has Mao correct them. And his translator corrected them back and forth, which isn't the normal thing. And then over the different editions of Red Star Over China, because I've actually read them and followed the changes, more and more of it gets eliminated. The original version has all kinds of pictures of people that Mao subsequently purges, so those pictures go away. All references to those people go away, and they're no longer in the index.

And Edgar Snow goes along with this. And for the rest of his life, he is publishing books that are a variation on a theme on this book because it's the only thing that makes him important.

Dwarkesh Patel 01:43:28

Before he dies, is he discredited?

Sarah Paine 01:43:31

No. Mao wanted him- it was right when Nixon was reopening China- Mao desperately wanted Edgar Snow to cover that, and Edgar Snow got pancreatic cancer, and he was living in Switzerland. He'd run into trouble with the McCarthy era, as you can well imagine, for good reasons, actually.

And so Mao sent his personal physician to attend to Edgar Snow. But pancreatic cancer still kills, and it killed him. So, no, and this was even though people Edgar Snow knew had been purged during these various campaigns and also the great famine. He denied all of it till the very end because this is what made him important and so that's what he was to the end of his life: me, me, me, me, me.

Dwarkesh Patel 01:44:22

When you had the slogans up about what Mao is telling the peasants or the different slogans he would come up with, it's not tremendously sophisticated stuff. It's like one line that's catchy or something.

Sarah Paine 01:44:34

Like a tweet.

Dwarkesh Patel 01:44:35

Yeah.

Sarah Paine 01:44:36

Still works.

Dwarkesh Patel 01:44:40

Why was this enough? You can just write one line and that's-

Sarah Paine 01:44:42

It's not enough. I think if you're talking about someone who has no power, the one thing they've got are words. And so this is how it turns out “strategic communication”, or whatever the jargon is for it, is terribly important. And we're finding it now, right? We're wondering on what is actually fake news, what's disinformation, what's accurate, what's not. What's deliberately inaccurate? Who's posting it? Where? It's powerful.

You can see in the current election how important information is. Mao and the Communists play it well, and the Russians absolutely understand how to do this, this is their- they're really good at understanding both sides fighting, and then you feed information both ways so that they're just at each other's throats, and being more and more dysfunctional. Oh, yeah.

Dwarkesh Patel 01:45:39

When was the last time you were allowed to go into the archives in China?

Sarah Paine 01:45:42

Oh, it wasn't a question of being allowed. I got a fellowship right- it was supposed to happen right after Tiananmen, and that got closed down. That was, Tiananmen was in June of '89. So the fellowship was delayed for half a year, and then we went for all of 1990. And so I used different archives, but then I did other things, like I had two children and I stayed at home with them.

I was the spouse that didn't travel so that there would be stability in little people's lives. And now I would no more visit China you've got to be kidding. The archives have shut down now. And then the Russian archives it was, again, it was early Gorbachev when the archives were opening. They're slammed shut now.

Dwarkesh Patel 01:46:32

It's funny to hear you say I wouldn't visit China now. So in two weeks, I'm going to go to China. My next question-

Sarah Paine 01:46:38

Have fun.

Dwarkesh Patel 01:46:39

-my next question was going to be, what is your advice?

Sarah Paine 01:46:41

Keep your mouth shut. Be a good guest. And don't bring any computer equipment that you ever intend to use ever again and link with anything.

Dwarkesh Patel 01:46:52

Yeah. When you're in these archives and you're studying all these atrocities that happen, if you look at the numbers themselves, it might be fair to call it genuinely the worst thing that ever happened in human history in terms of pure number of deaths.

That's a hard record to match if you look at everything else that's happened in human history. Viscerally, what's the reaction just going through record after record of this kind of...

Sarah Paine 01:47:20

Oh, it's just tragedy. It's the human condition.

And don't think that China is unique in this. The West has butchered itself, westerners have butchered each other. In fact, it's the West that industrializes warfare. So instead of- before you had to go at it with spears, and that slowed people down. With industrial equipment...

So it's human, sadly. Humans have great capacity for creativity and doing wonderful things, and then they have great capacity to do awful things. So let's do one and not the other.

Dwarkesh Patel 01:48:00

That seems like a pretty good place to close. Sarah, this was, this was… I mean, I don’t want to say “fun” because we’re talking about Mao here. But it…

Sarah Paine 01:48:12

Great topic.

Dwarkesh Patel 01:48:13

It was interesting.

Sarah Paine 01:48:14

You want to save your reputation.

Dwarkesh Patel 01:48:16

Great, thank you.

Sarah Paine 01:48:17

You're welcome. Thank you for coming.

This is the second in the trilogy, a lecture series by Professor Sarah Paine of the Naval War College.

In this episode, Prof Paine dissects the ideas and economics behind Japanese imperialism before and during WWII. We get into the oil shortage which caused the war; the unique culture of honor and death; the surprisingly chaotic chain of command. This is followed by a Q&A with me.

Huge thanks to Substack for hosting this event!

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.

Today’s episode is brought to you by Scale AI. Scale partners with the U.S. government to fuel America’s AI advantage through their data foundry. Scale recently introduced Defense Llama, Scale's latest solution available for military personnel. With Defense Llama, military personnel can harness the power of AI to plan military or intelligence operations and understand adversary vulnerabilities.

If you’re interested in learning more on how Scale powers frontier AI capabilities, go to scale.com/dwarkesh.

If you want to sponsor the podcast, take a look here.

I highly, highly recommend both "The Wars for Asia, 1911–1949" and "The Japanese Empire: Grand Strategy from the Meiji Restoration to the Pacific War".

(0:00:00) - Lecture begins

(0:06:58) - The code of the samurai

(0:10:45) - Buddhism, Shinto, Confucianism

(0:16:52) - Bushido as bad strategy

(0:23:34) - Military theorists

(0:33:42) - Strategic sins of omission

(0:38:10) - Crippled logistics

(0:40:58) - the Kwantung Army

(0:43:31) - Inter-service communication

(0:51:15) - Shattering Japanese morale

(0:57:35) - Q&A begins

(01:05:02) - Unusual brutality of WWII

(01:11:30) - Embargo caused the war

(01:16:48) - The liberation of China

(01:22:02) - Could US have prevented war?

(01:25:30) - Counterfactuals in history

(01:27:46) - Japanese optimism

(01:30:46) - Tech change and social change

(01:38:22) - Hamming questions

(01:44:31) - Do sanctions work?

(01:50:07) - Backloaded mass death

(01:54:09) - demilitarizing Japan

(01:57:30) - Post-war alliances

(02:03:46) - Inter-service rivalry

Sarah Paine 0:00:00

Before I get going, I've got to make a disclaimer. You can see it written up there: What I'm saying are my ideas. They don't necessarily represent the US government, the US Navy department, the US Department of Defense, let alone where I work, the Naval War College. You got it? This is just me here. Nobody else.

Alright: Americans have a penchant for what I call half-court tennis, which is: they like to analyze international affairs and wars by focusing on Team America – what Americans did or didn't do – and then that explains causation in the world.

And Americans, on the other hand, their beloved sport, I believe, is football. And those people who love football, many Americans, my understanding of it – I'm just someone who reads books, I don't follow football – that's disqualifying, I suppose –

0:00:53

But anyhow, Americans who follow football, they study both sides, right? They look at their home team, but then they also look at not just one opposing team but many down to the individual player and they would no more follow the football game by looking at one half of the football field. And yet Americans when we do foreign policy, that's often what we do and it gets us into all kinds of trouble. For instance in the Iraq war, Americans thought that the Republican guard was going to be really tough. And it turns out it wasn't so tough. But then there was this post-conventional phase, insurgency that went on and on and on that surprised Americans.

Well, the problem isn't actually a new one. In World War II, Americans were terribly surprised by the things that Japanese did. Starting with Pearl Harbor, right? That was a surprise. But also it was the entire way the Japanese fought the war. The way they fought to the last man. The suicides. The brutality. Not only to the POWs, civilians, but into their own wounded.

0:02:03

And the question is, is there any way to anticipate in advance how other people are gonna behave? Is there any way to get a sense of the other side of the tennis court net? Now, here are the two gurus of warfare. One is Sun Tzu for Asia. And the other one, Clausewitz, is the big guru of warfare in the West. And both of them would say, hey, you want to understand the other side. You've got to make a net assessment. What's that? You would look at political, military, geographic, economic factors, the strengths and weaknesses of all sides to get a sense of things.

And today I'm going to make a case for culture. You need to look at that as well. And it's often said that mirror imaging is not what you're supposed to do. What's mirror imaging? It's… we get into a situation and then I decide what I think you're going to do based on what I would do. I project me and mirror [it] on you. And that doesn't work so well. Okay. If I'm not supposed to generalize on the basis of my experience, what am I supposed to do instead?

0:03:12

I'm going to get at this problem today. How do you analyze the other side of the tennis-court net by looking at Japanese behavior in the thirties and forties. But the method of analysis I'm using, you could apply to anyone you want, you wanna think about Russians today or whatever, you can apply it that way.

So culture, it's important, but it's as amorphous as it is important. For instance, if I'm going to try to figure out the defining characteristics of another culture, it would be difficult to figure out what the list is of all the different things I would need to look at. And even if I could come up with that list, still, how would I figure out how that would work in something like warfare? Hard to know. But the difficulty of the problem doesn't make it go away.

0:04:01

And so we're going to look at it today, and we're going to look at Japanese theorists and belief systems, and that if you believe these things, how this influences your practice. Alright, before I get going here, or actually, I am going to get going here. Tojo Hideki said on December 1st, 1941, that our country stands on the moment, the threshold of glory or oblivion. He got that right, and he's in an imperial conference where he is confirming with Hirohito that Pearl Harbor is going to be a go, but he felt that Japan really needed to do something rather than being ground down, being passive.

And here is Admiral Yamamoto Isoroku, who was the man who came up with the operational plan for Pearl Harbor. He thought it had really long odds of being successful. General Tojo gave it a 50-50 chance. Admiral Yamamoto wasn't even sure that it was that good. But he felt it was the best possible plan for Japan to get out of its predicament.

0:05:05

Now, from a Western point of view, this makes no sense. You're talking about getting the United States potentially into a war with Japan that's already overextended in China. Who does this? Either you need to ratchet back the policy objective and/or you need to downgrade your strategy to something a little [less] costly or risky. And I suppose we can go, "Oh, they're stupid." Okay, I guess if I call you stupid that makes me so smart because I can denigrate you. Explains nothing. So, rather than do that, they’re very intelligent men. And why are they doing these things? Why do they consider their actions rational? And rational in what context?

So this is what I'm going to be up to. And I can start with a little story to illustrate my point. In the summer of 1943, this is after the battles of the Solomons, New Guinea, Guadalcanal, they're all over with. The Imperial Japanese Army had a war college, and an instructor comes into class one day, and he says, from now on the curriculum's changed. The main emphasis is going to be countering U.S. tactics instead of, what they had been teaching, was Soviet tactics. And it will become the A course. If anyone can teach this, go ahead, because I don't know a damn thing about it.

0:06:27

Talk about being unprepared for seminar. And then think about it. Where are the Japanese actually fighting most of the time? What is the country that most matters to them at the end of the day? It would be China. And that's not what their war colleges are studying. Something's up here.

Now, they're clearly making a really bad net assessment about the United States. Okay, this country also is known for lousy net assessments. I don't believe ours about Vietnam was particularly good either. And that's one part of the problem.

0:06:58

So here's my game plan for this evening. I'm first going to talk about traditional Japanese theorists and then it’s going to be talking about Japanese practice - how if you believe, if you have this belief system, how explanatory it is for practice. This is my game plan right now.

So the Japanese don't have just the one book like Sun Tzu's "The Art of War," or Clausewitz's "On War." What they have is Bushido, Code of the Samurai. It's a whole literature, and it was written in the Tokugawa period, which, quite ironically, was a period known for peace, not warfare. Never mind. And what's interesting about this literature from a western perspective, it's really not about military strategy. It's about deportment. It's how a samurai should conduct himself. And this reflects Japanese values and the things that they emphasize..

0:07:55

And so I'm going to go through it with you. And starting, here's the game plan on the theorists: first I'm going to talk about the philosophical origins of bushido, then the values that underpin it, and then the operational preferences that grow out of it.

That's the game plan for first half. And I'm going to use as my cultural bridge this man, Nitobe Inazō, who wrote a book much later in 1900, "Bushido: Soul of Japan." Why am I doing this? Because he provides concise definition of Bushido, and you can see he's an important figure in Japan. Not everybody gets their mug on the 5,000 yen note, so he's an important figure in Japan.

0:08:36

He had spent 18 years abroad, and he had received higher education in Japan and a variety of Western institutions. He married, believe it or not, I don't make up these things, a Philadelphia Quaker, and he converted to Christianity. And he spent his life trying to serve as a cultural bridge, and that's how I'm going to use him today.

And what he said is, unlike in the West, where notions of morality come from religion, in Japan they come from Bushido. And what is it? It's a code of, chivalrous code of honor for the warrior class, these precepts of knighthood. And from this, there are three pillars of Bushido according to Nitobe.

0:09:21

They're Buddhism, Shinto, and Confucianism. From Buddhism, it's where you see Japanese fatalism, the origin of it. And here you have Nitobe saying, it's this “calm trust in fate, a quiet submission to the inevitable”, a “friendliness with death”. And it strikes Westerners reading this Bushido literature, is a preoccupation with death. That, for instance, Clausewitz will talk about violence in warfare, but he's not interested in what constitutes an honorable death, let alone choreographing a soldier's final moments. Different culture.

And from Buddhism, there are four noble truths of Buddhism. One is that existence is suffering, pessimistic view of this life. Second, it's caused by craving and attachment, so don't cling to this life or the things in it. It's all ephemeral. It's like a cherry blossom, blooms for a day and then it's gone. And, but there's a good ending to it all, which is nirvana. And how do you get there? The fourth noble truth is through forms of right conduct. So, the emphasis isn't on what you achieve in your life. It's how you lead it. It's a focus on deportment. It's different from the West.

0:10:45

Second pillar, Shinto, is this extreme patronism, reverence for the emperor and the third pillar is Confucianism, these imported ideas from China. And what Confucianism is at heart, of how it’s organizing a society and regulating it through interlocking social obligations, hierarchical, and through ritual and etiquette.

So in the West, there's much talk about equality. Right? And in the east, it's duty. It's what you owe other people. In the east, there is no such thing as social- in China, in Japan- of equality. Even twins have a birth order. And it's not about freedom either. It's about what you owe others.

0:11:35

So, if you think that these value systems seem really different, yeah, no kidding. It has nothing to do with the Greco-Roman Judeo-Christian West. Completely different value system. And so, Alice, welcome to Wonderland. Buckle up, we're off for a ride.

And I'm going to start, here's my first piece of the Tokugawa literature, Yamamoto Tsunetomo's "The Hagakure" that he wrote in the early 18th century. It translates variously as “Hidden Leaves”, “Hidden by the Leaves”. And in it, he is describing, I'm going to read you some short passages from it all. What was he? He was a retainer for a daimyo, a feudal lord in Japan. He hadn't actually done any fighting, even though he's writing all about it. So if you don't do, what do you do? You publish.

0:12:32

And I will tell you what the man had to say. One of the first things is this preoccupation with death. And here is Yamamoto: "The way of the samurai is imagining the most sightly way of dying. Merit lies more in dying for one's master than in striking down the enemy. The way of the samurai is found in death. It is not necessary to gain one's aim, but if you live on without achieving it, it's cowardice. However, if you don't gain your aim and die, that's okay."

This is really different from Clausewitz, where it's all about achieving the policy objective. It's not about how the soldier's leading his life. And here you can see the consequences of this, right? If you're focusing on no fears of death, and if you can't succeed, living on is a disaster. So, think of the banzai charges. When Japanese remnants would go headlong into oncoming machine gun fire, knowing full well what was going to happen. This is not the way other armies have behaved. Different value system.

0:13:45

In addition to death, the Bushido literature's emphasis is on honor. Back to Yamamoto. "The way of avoiding shame is different. It's simply death. Even if it seems certain that you will lose, retaliate." Think of General Tojo and Admiral Yamamoto Isoroku. And here's the, if you suffer a catastrophic defeat, here's the solution. In the event of a mortifying failure, you're gonna wind up committing suicide. 'Cause the alternative, if you live on in shame, you're bringing everyone you're associated with shame.

So how does the suicide work? It's seppuku, hara-kiri. The Samurai who's doing it kneels down with short sword, he plunges it into his belly tries to do a full revolution. And then his second, usually a close associate, takes the long sword, if he does it right, one sweep, head in lap, upright corpse, blood everywhere.

0:14:47

Diplomats in the 19th century, Western diplomats, were told to witness this. If a samurai had murdered a Westerner, okay, the diplomats had to come in and watch the proceedings. They just about lost their lunch. So the Japanese think that they are showing, and what the Westerners think they're seeing, aren't aligning.

And I'm going to use Nitobe to be the cultural bridge. He said, "in our minds, this mode of death is associated with the instances of nobles, deeds and most touching pathos. So this form of death assumes a sublimity and becomes a symbol of new life." It's a way to escape from disgrace. And in Japanese literature, there is the tragic hero who is pursuing noble but unattainable aims, and rather than making disagreeable compromises, goes down in flames.

0:15:42

This is what seppuku is all about and Nitobe is saying look, death involving a question of honor was accepted in bushido as a key solution to many complex problems. And you can think in World War II, yeah, it was. Complex problems like battle plans not working out and a war that was truly not working out. And you can see the suicide going on both individual and group. And I remember going in the caves in Okinawa and understanding how that works because you could see a lot of the damage. If your commanding officer decides to commit suicide and tosses a grenade in the right direction, the entire room goes with him.

All right, so in addition to death and suicide and honor, we've got loyalty. It's another key value. I'm back to Yamamoto: "Being a good retainer is nothing other than being a supporter of one's lord. A man is a good retainer to the extent that he earnestly places importance in his master. Having only wisdom and talent is the lowest tier of usefulness." So much for Silicon Valley. "For a warrior, there is nothing other than thinking of his master."

0:16:52

And so back in the day it's thinking of your feudal Lord and more recent times it's prioritizing your company over family. In China, it's the reverse - priority is family over company. It’s different cultures, different priorities.

Alright, there are strategic implications if this is your value system. This is what arises from it. First of all, you're looking at damage limitation, damage control, not in terms of the physical cost of losing lives, having property blown up, but in terms of honor. Also there's a tendency to equate operational with strategic success. Operational success is "I win this battle here and now." Strategic success is, “okay, we're in a war for some reason”.

0:17:42

What is the reason you're in the war? Japan's reasons for being in China had to do with containing communist expansion and also stabilizing the place so that they could make money out of business. So that's your strategic objective. It's not your operational one, but the Japanese Samurai are equating the two saying if I take this hill somehow it's automatically going to deliver the strategic objective. And in fact, they won most of the battles in China, but they lost that war.

Also, there's this focus on what constitutes an honorable death. The western focus of literature is all about preparing the field of battle in advance for success, whereas this literature is all focusing on what to do after disaster.

0:18:28

Here are some more implications. Once the Japanese are failing in battle, operational failure, they are on death ground. What does that mean? It means that… death ground means the only way you survive is if you fight harder. This is what's going on in Ukraine right now, is that when you decide you're going to annihilate an entire culture, you put people on death ground and then they have very few choices on what they do next.

It means for the Japanese to feel that they're on death ground when they're failing means they're not going to give up. They're going to fight brutally against overwhelming odds. So in the West, when we like to mirror image, we want to think of the rational actor with some kind of mathematically based cost benefit of when you should give up, when the costs are so high above whatever your value of the object is you ought to call it quits. Well that kind of calculation does not translate well across the divides between civilizations.

0:19:32

I've got a nice picture here of Lieutenant Onoda who had been hanging out in Philippine jungles for what, 30 years after the end of the war? Carrying on the war in isolation. I don't believe this is how most other armies work or soldiers in them. Different culture, different things you do with your life.

And here is Sir William Slim, Field Marshal, British 14th Army, that he led in Burma, commenting on his experiences: "If 500 Japanese were ordered to hold a position, we had to kill 495, the last five committed suicide before we could take the place." And “it was this combination of obedience and ferocity that made the Japanese so formidable”.

0:20:12

Okay, this brings me to another value that's emphasized in Bushido: willpower. Back to Yamamoto: "There is nothing that cannot be done. The way of the Samurai is in desperateness. Simply become insane and desperate, and it'll somehow work out for you. One can accomplish any feat." Think of Pearl Harbor. And this emphasis on willpower and just trying harder, it denigrates strategy.

And here you see a picture of the supreme example of honor and loyalty and willpower - the kamikaze pilots. But if this is what you're doing, you're denigrating strategy. And here Yamamoto's talking about tactics, but it has operational and strategic implications. He says, "Learning such things as military tactics is useless. The way of the samurai is one of immediacy and it is best to dash in headlong. If one were informed of military tactics, he would have many doubts."

0:21:14

So the idea is if you think about these things in peacetime, you'll start hesitating in wartime. It won't work out for you if you do this. "During times of peace, when listening to the stories of battle, we should never say 'In facing such a situation, what would a person do?'" Well, so much for my job at the Naval War College. So much for the case studies. "No matter what the circumstances might be, once you be of the mind to win, once you'd be holding the first spear to strike."

So, here's the implications, if you believe this. What you're doing, it's a very unanalytical way to approach wars. It's all about, whatever it is you want, you just steel the will and go for it, and somehow you'll get it if you want. There's a lack of grand strategy. What's grand strategy? It's integrating all the instruments of national power, not just the army, or the army and the navy, which is what the Japanese are trying to do. But all instruments of national power in pursuit of - if the bigger aim is to stabilize China and keep the communists out, there ought to be some diplomacy and some other things going on. But that's not what's happening in the samurai literature. It's a focus on the military instrument exclusively.

0:22:31

So, I'll give you an example of how this works out. Before the Imperial Japanese Army and the Imperial Japanese Navy invaded French Indochina, neither one of them did a little study saying, "Hey, if we do that, let's check the other side of the tennis court net and what other people, how they might react." They just steel the will and march right on in. Okay, that triggered the U.S. 100% oil embargo. That's a problem. Strategy operational success, strategic mess.

Focusing on just the operational level is the basis for this ill-founded optimism with which the Japanese just took territory after territory without saying, "Hey, what about the cost of actually occupying these places?" Oh, we're going for these places for resources. So maybe we ought to check it out with the finance ministry and others about how we’re ever going to get these resources back home? None of that's going on. It's a disaster for them.

0:23:34

Okay. I'm going to talk about a couple of secondary theorists. One of them is Taira Shigesuke, who is a contemporary of Yamamoto because he provides a really concise definition of the operative values of samurai culture. Only three things are considered essential: loyalty, duty, and valor. So steadfastly loyal as to disregard his own life.

What he's actually talking about is group loyalty. In the West, the basic unit composing society is the individual. Well, in the East, it's the group. And group interests take primacy over individual interests. And for the Japanese, society is divided by in-groups and out-groups. The most basic in-group, biggest overarching one, is the Japanese people vis-a-vis everybody else. But within Japan everybody comes from a different province, a different locality. They go to different educational institutes. They graduate from different kindergarten classes, I kid you not, and college classes. If they work for different companies or they work for the military, they're in different branches.

0:24:49

And you also have family loyalties and you owe each of these nested and overlapping groups different obligations, and sometimes these obligations conflict. And if they're really - if the conflicts are really awful, that's another reason for committing suicide.

And then if you look at the Japanese language - the moment a person opens their mouth to speak to another Japanese, you can immediately listen to the grammatical forms that are being used, the specific word choices to know what's the degree of hierarchy, like where do they sit in this unequal hierarchy and whether it's in-group, out-group.

0:25:27

So everybody feels, or most people feel, some level of group loyalty. This is human. But in Japan, the levels of membership are much more finely calibrated and they're re-emphasized by these social, cultural and linguistic reasons. So this group membership and stove-piping ultimately is going to be a much stronger feature of Japanese culture than some other places.

All right. Last theorist is Miyamoto Musashi who unlike the other two actually did a little fighting. He was born a little earlier and he was a master samurai who taught people martial arts. And from him you get a sense of some of the operational preferences deriving from these values and I'm going to go through all of these in turn.

0:26:15

First is risk intolerance because remember at the beginning I started with the two flag officers saying “well, we're going to do this war in the Pacific when it's unlikely we're going to succeed but we're going to do it anyway”. And here is Miyamoto: "Furthermore, to fight even five or ten people single-handedly in duels, that's what my military science is all about. So what's the difference between the logic of one person beating up ten people and a thousand people beating ten thousand?” Logistics, my friend, but never mind.

And then another thing that he emphasizes, and in addition, don't expect long odds to deter the Japanese back in the day. Surprise is another one - a situation that has stalemated and is going nowhere, which is what the China theater was for the Japanese. And how do you get out of it? And the answer that Miyamoto has is: not come up with a new policy objective, but come up with a tactic that'll somehow put your enemy off balance and then get what you want that way.

0:27:22

And the way the Japanese did this was often by opening a new theater in a war, by surprising people by the new places that you were going to start engaging in military operations. And here's how it worked: China had been a failed state since 1911. And it had had an escalating series of warlords fighting each other in this multilateral civil war and the Japanese were appalled particularly after the United States passed the Hawley-Smoot Tariff of 1930 that cut them off from international trade.

So then they're thinking, now what? Well we're going to need an empire big enough to survive since no one's going to trade with us. And so they invade all of Manchuria in 1931 and they have it pretty much stabilized by 1933. So okay, that was surprise number one, but the rest of China's a mess and the Japanese - it's coalescing into a bilateral communist-nationalist, nationalists under Chiang Kai-shek, communists under Mao Zedong fight with increasing dosages of Soviet aid and the Japanese are appalled with all this.

0:28:31

And so it's time to surprise everybody again in 1937. And that's when they invade all the way down the Chinese coast and up the Yangtze river. And it works: they take a lot of territory really fast, but then they get to the end of the railway system. Oh, and by the way, China's not pacified. It's just churning and so now Japan is even more overextended. As a result of doing that, Russian aid goes up and then you're going to get US aid in there.

So the problem's actually getting worse. Time for another surprise - really big one on that infamous day in December '41. It wasn't just Pearl Harbor. That's Team America focusing only on Team America. The Japanese attacked all across the Pacific that day.

0:29:16

Uhh, okay. Now what? Here, China had never been able to threaten the Japanese home islands. Well the United States - here, the United States was totally isolationist. Most Americans couldn't find Japan on the map. Well, after Pearl Harbor, they sure could. And suddenly, the United States isn't isolationist anymore, and they're coming to get the Japanese. So, you can see the samurai values in operation here; “Just try harder”. “More dosages of willpower”. “Eventually you'll win or you'll die trying”. Okay.

Another operational preference that you can see, which is these, [inaudible] surprise or preemptive attacks. And this is how Japan began all of its wars. The First Sino-Japanese War and the Second Sino-Japanese War, Russo-Japanese War, and the Pacific War. This is how all of them begin.

0:30:07

And finally, Miyamoto offers some advice on how you break the enemy willpower. And in this case, you've already won conventionally. But they're waging an insurgency against you. I'm modernizing the terminology. And the idea is you want a psychological victory. You want them just to quit and somehow you're gonna break their will to resist. And I suspect this is what the Japanese thought they were doing in the Rape of Nanjing and other atrocities. That they were going to do these horrifying things and that would break the will of the other side.

Okay, be careful whom you put on death ground. The Japanese were repeating a mistake done by the Nazis, which is if you're dealing with even a failing state, which Russia was - Stalin had shot so many of his officers in the thirties and then he inflicted a famine on Ukraine - but when the Nazis came in and they were going to wipe out not only the Russian government, but also the Russian people, you will superglue people, government, and military, and you will transform a failing state into a lethal adversary. And this is what Nazi brutality does to Russians, what Japanese brutality does to Chinese, and what Russian brutality today is doing to Ukraine. Don't do it. Bad strategy.

0:31:34

All right, there are strategic implications from these values. One is this emphasis on the offensive preemption. It's emphasis on military action to solve all your problems. And you have a fixed policy objective, whatever it is. And if you're in a given battle, you have to win that battle. It's not, "Oh, I have an overarching objective. It's too costly here. I'm going to call off this battle and I'm going to try again somewhere else." Uh-uh.

This is your field, the moment your plan has failed, you're a failure. So they're not thinking of planning in terms of branches of sequels and there'll be unexpected events that take place you'll adapt to - none of that. You're a failure if any of that stuff happens to you. So there's a real insensitivity to risk and there's no grand strategy.

0:32:26

But if you believe these things you will be lethal in warfare. You're not going to give up easily at all. And so you look at the Japanese at the end of the war and go why don't they quit a lot earlier? Well, it's because in a way, they're already dead men. They suffered social death. And so they're going to keep on until the very, very end of all of this.

And it's a great sin of omission, this absence of grand strategy. The Japanese aren't the only ones to have done this. The belligerents on all sides in World War I were thinking all in terms of using the military instrument, got themselves into trouble. So if you look at what the Japanese are doing, they had some vague ambitions and wanted to take advantage of opportunities. But there's no definition of what "win" in this war is. How much territory should Japan take? And then call it a day. Say, done, we've been successful here. Rather, their territorial acquisitions are really a function of what they were able to take and what, in anger, they did take, but also a function of strategic failure. No matter what they did, it never pacified the China theater. Problem for them.

0:33:42

Okay. Alice, that was Wonderland. Now we're going to get to how it works, how other people live - of if you believe these things, how does it help explain what actually happened in World War II? And I'm going to start with two sins of omission, the Japanese neglect of paying more careful consideration to logistics. And then another sin of omission is a neglect of protecting their sea lines of communication. And then I'm going to - the last two are about these in-group, out-group divisions and the problems it caused for within each military service, intra-service rivalries, and then between the two services, the Navy and the Army in the war that caused them such difficulties.

Okay. The Japanese, if you start at the beginning of the war, Japan never produced more than 1/13th U.S. steel and coal production. It never did more than 10 percent of what U.S. munitions productions were. I believe if you do the math and take all the battleships and divide people into… everything, that each U.S. soldier had four tons of equipment per, whereas each Japanese soldier had about two pounds of equipment. Japanese main weapons in this war were the grenade and the bayonet. Their artillery and machine guns were totally, were very obsolete, what they had going in the Pacific War.

0:35:07

And then you flip it around and look at the United States. The United States had about 18 men in- or men and women, but mostly men- in supply services supporting each rifleman at the front. Other militaries in this period had about an 8 to 1 ratio, Japan had about a 1 to 1. So Japan's already suffering food shortages before Pearl Harbor. And then when you get to the winter of 1942-43, the Japanese are having critical shortages of oil, so they no longer can deploy the fleet at will.

That means forget about convoying anything because you just haven't got the oil to do it. And yet when you get to '45, when they're predicting they're going to have absolutely zero aviation fuel and other fuel by the end of '45, you have the government saying still we're going to fight on for this honorable whatever it's going to be. These bushido ideas that you just persevere - loyalty, honor, duty. Keep going.

0:36:08

I'm going to be quoting this gentleman's diary, Admiral Ugaki Matome. He was the chief of the staff of the combined fleet until his plane and Admiral Yamamoto Isoroku's plane were shot down. US code breaking was quite good. We figured out where they were and we killed Admiral Yamamoto, but this man survived.

And by the time he wrote this entry, he was the head of an air fleet in the home islands sending kamikaze flights out, because Japan simply lacked the ability to do too much else this late in the day. And here is his last diary entry written on August 15th, 1945. This is after two atomic bombs had been dropped and after the Russians had deployed into Manchuria.

0:37:01

And he said, okay, “there are various causes for today's tragedy. And I feel that my own responsibility is not light, but more fundamentally it was due to the great differences in national resources between the two countries." It's too late to come to that conclusion. U.S. production statistics had been on the books forever. But when Japanese read these numbers, they thought they were ludicrously high and discounted them as propaganda. And those who knew better, who'd done tours of duty in Britain or the United States, they weren’t promoted because they were defeatists.

I believe that Admiral Ugaki kept and maintained his diary is because he was an honorable samurai. He had believed in Bushido, the ability of material, of willpower, to more than compensate for inferior resources, but the war's outcome had proved him incorrect. And so, as an honorable samurai, he paid with his life, and he kept his diary - nothing to be ashamed of, he’d done what he was going to do.

0:38:10

Here I have the last Prime Minister of Imperial Japan, Prince Higashikuni Naruhiko, talking about his take on the war. He said, "I think the basic cause of defeat was the loss of transport shipping." Okay, by the end of the war Japan was down to one ninth of its transport shipping. It meant the empire was paralyzed. What's the point of taking all these territories if you can't get the resources back?

The navy had always focused on the mission by Alfred Thayer Mahan – who's from where I work, back in the day. It was all about fleet on fleet engagements and things. But it turns out that the Japanese navy hadn't focused on convoy duty. Mahan had called that a promising secondary operation. Actually, it turned out to be primary in the Pacific that U.S. submarine services paralyzed their sea lines of communication. Go submarines!

0:39:14

But so here's Admiral Ugaki Matome who is talking- he eventually comes around to recommending a more defensive strategy of not having this fleet on fleet because they don't have - they've lost a lot of the fleet and then they don't have the fuel to run it. But by the time he's recommending a more defensive strategy, they don't have the fuel or the assets to do that either.

Earlier in the war, here's his take before all that bad stuff had happened: "It's too bad for the officers and men of the submarine service that they have not yet sunk any important men of war, only merchant men." Well, his disdain for the target would cost him. And he noted, later on, when he's trying to - it's the anniversary of Pearl Harbor, when he's trying to account for why the Battle of Guadalcanal is going so badly for Japan, he said, "The aim and transport to the front has not even been half fulfilled each time. It led those on the verge of death”- i.e. the army- “to be extremely skeptical about the Navy and thinking that the Navy is just sacrificing the army."

0:40:19

Well, no kidding that's what the army thought. Because for an expeditionary force, you absolutely need the Navy to deliver you there, to maintain your supplies there and they're thinking, the army's thinking, you navy are being irresponsible, not doing any of these things.

So there are tremendous inter-service rivalries between the army and navy in Japan. And it goes back to the pre-war budget wars where Japan's a resource poor country, and both services have what they consider absolutely essential things to be funded. Japan didn't have the money to fund both. And then when you get in war and you start expending these things, you need even more money.

0:40:58

And so the disagreements were brutal. But before I get there, the in-group, out-group differences that stovepipe things and cause problems aren't simply between Army and Navy. They're within each service. So I'm going to start there, and I'm going to be, to be fair, I'm going to provide one example for each service.

I'll start with the Army. It was the Kwantung Army, or Japan's Army in Manchuria, that decided to invade all of Manchuria back in '31. It was not the home office back in Tokyo, but it's this branch that turns out kicks off a 15-year war. So these folks think that they know what's best for Japan and how best to defend the empire and they're just off and running.

0:41:44

Meanwhile, there are a series of coup attempts. Some of them were Navy as part of it. A lot more of them were the army that's dealing with it, going back and forth. And at the very end when Emperor Hirohito is capitulating, there was one last coup attempt which, amen, it failed, because the war might not have turned out quite the way it did if it had succeeded.

So the point is, if you've got coups running on, that is not called unified command. It's a mess. And the Navy wasn't any better. I have a different sort of example here. During the war, the U.S. Air Service, people who were flying planes, they would alternate combat and training missions so that you would bring back someone who had survived and learned something from combat to tell new people the things to avoid, how not to get yourself killed and some other things.

0:42:40

Well in Japan, in-groups out-groups - you sign up together, you train together, you fight together and you die together. It doesn't mean the Japanese couldn't have grafted people between groups. It's just culturally it's not the natural thing that comes to mind. Moreover, and this apparently applies to the present, that in the U.S. military, they have what are called hot washes after different operations, where you come back and you're very self-critical about all the things that went wrong, to figure out how to do it better the next time.

Well, there are cultural reasons why you would not want to do that in Japan, just, it's different. So, if these in-group, out-group things are causing problems within services, and it gets toxic between the services and I've got four examples and I'm going to start with organizational issues.

0:43:31

So it's only in 1944 that the Army and Navy finally get it together to have regular liaison meetings in Tokyo. Just in time to, you know, figure out how the capitulation is going to work. And then, the army wanted to unify the two high commands. The navy wanted nothing to do with that one because they knew they'd just become the box lunch delivery service for the army. Didn't want to do that.

So, by 1945, they did unify their information department. Great. They can spew the same propaganda and maybe share Tokyo Rose on a good day, who knows? But there was no planning even under the imminent threat of invasion to how they're going to coordinate their assets to protect the home islands. They aren't even coordinating their air assets. Disaster.

0:44:22

And this disaster goes back way in time. They had very- far back in time. They had a very successful war against Russia that ends in 1905. But afterwards, in 1906, immediately afterwards, the Army and Navy are allowed to have completely separate war plans. The Army plan is all about fighting Russia for the big land grab in Eurasia. The navy plan has a completely different set of enemies. It'd be the United States and Britain for the big gambit - you're not gonna use ships in Siberia - the big gambit for empire in the Pacific.

And each of these plans, A, they're secret from the other service and B, each plan assumes the other service is going to do all kinds of important things for them. Okay, great.

So, I guess the idea of secrecy and surprise, normally you apply that to your enemies, not your sister service, but that's how it works in this setup. Now, the army does come around to the Navy plan. Why? Because they get walloped by the Russians on the Mongolian border at the Battle of Nomonhan. The Russians just decimate them in 1939.

0:45:35

So now the army says, okay, okay, maybe that southern advance thing wasn't such a bad idea. And so the Navy thinks this is great. And they do their Southern advance. Let me see where I am here. They go zooming down and the Japanese mind over matter stuff seems to be going really well.

Because, in 1942, the Army takes more land than, over a more dispersed theater, than any country on the planet. The Navy hasn't lost a single ship. I mean, it's looking really good. Except there are a few little details here that are a problem. What the Navy hadn't told the Army is that actually they weren't ready for this whole thing - that they needed this outer perimeter reinforced by airfields in order to make the thing work. And actually, that wasn't complete.

0:46:39

And the army learned about this on August 17th, 1942, because one of these airfields was being built in this tropical nightmare called Guadalcanal that the United States knew about, even though the army didn't. And all of a sudden the Navy is in deep, dark trouble and needs the Army to help them out of Guadalcanal.

So now think Samurai. The Japanese 17th Army had been ordered to take Port Moresby in New Guinea, that's what they were up to. But with Guadalcanal, they are told, "Ah, you need to tack on Guadalcanal to that Port Moresby event." Okay, enter logistics. They're a thousand kilometers apart.

0:47:14

So now the army is gonna be lying to the navy about how many people they've got at Guadalcanal because they're scared the navy won't provide enough rations and things. The navy doesn't provide enough rations, people starve anyway. And then the navy that got the army into this mess wants to call it off and move out. But the army, good samurais, want to fight on and they just expend all kinds of resources.

And this thing has enormous strategic effects. Prior to Guadalcanal, the Japanese army wanted to continue their strategy of chasing the nationalists out of China. Back in 1937, the Japanese had conquered Nanjing, which is the original nationalist capital, and the nationalists had fled up the Yangtze River to Chongqing beyond some gorges and some other things, and beyond the rail network.

0:48:07

And in 1943, the Japanese were planning to attack Chongqing and then at that point, I think if you're a nationalist you're fleeing into Burma. And if that had happened, then the Japanese could have probably pulled hundreds of thousands of people out of the China theater and put them elsewhere and that would have caused all kinds of problems. Also, the Japanese had to call off their plans to invade Australia. So Guadalcanal has enormous strategic implications. So if you're focusing samurai on one battle, Guadalcanal, well, it has implications in places called China and Australia that are a long way off.

Okay, the United States also had inter-service rivalries. Right between our army and navy and that's why you have two separate campaigns for Admiral Nimitz and General MacArthur - each big ego is one campaign for each ego and apparently that wasn't even big enough for MacArthur. Okay but even so I don't believe the inter-service rivalries in the United States were remotely on the scale that they were in Japan.

0:49:10

I have one final example to prove that one. So, after Pearl Harbor, that had been tremendously successful for Admiral Yamamoto, he wanted to do, the next thing was to attack Midway, because U.S. basing there. And the Army said, I don't want, we're not going to do this. And Yamamoto goes, “I'm going to resign”. And the Army, “we don't care”. “I'll commit suicide”. “We'll buy popcorn”.

And here's what changes this. So after Pearl Harbor, Americans wanted to let the Japanese know that we were thinking about them. And so this is where Lieutenant Colonel James Doolittle, named for the Doolittle raids named after him. In April 1942, it was a one way trip off an aircraft carrier because they had so much fuel in order to get to Japan that the idea was they were going to go bomb Japan and then ditch in China whoever survives. Very brave people who did this.

0:50:10

Are they going to cause massive damage in Japan? Well, yeah, if you're directly underneath you won't appreciate it, but in general it causes minor damage. But it has a major unanticipated strategic benefit - think samurai. The Army, all of a sudden, is backing the Navy, that they're gonna now do Midway with them. And it's, right, don't think, retaliate, avenge your honor. The Army was appalled that anyone had been able to bomb Japanese skies, so now they're all over it.

Okay, so how does Midway work out? Really poorly for the Japanese. They lose four aircraft carriers. They've only got 12. They've lost a third. Oops. And- here we go, in-group, out-group- the Navy doesn't tell anyone for three or four months. Incredible. In a war. Right? So, they're thinking about their little stovepipe, and they're ignoring Japanese interests when this is going on. Okay?

0:51:15

Different story. So, they do get their operational end to the whole thing. It's called the firebombing of Tokyo. The whole place went up in flames. In fact, it got so hot, the canals boiled. It's an operational solution. It's unconditional surrender after a protracted war of annihilation that destroys just about every single Japanese city, minus a couple that survived.

What broke the stalemate? And here's what happened. It's three really bad things that happened in four days. Talk about a concentration of really bad events from a Japanese point of view happening all at once. When you want to talk about- this is the psychological shattering that actually happens to the Japanese.

0:51:59

First the United States drops an atomic bomb on Hiroshima. Two days later the Russians pour 1.3 million people into Manchuria- the nightmare scenario of the Japanese army. And they know if this war protracts, the Russians are going to come down through Manchuria, down the Korean peninsula, onto Hokkaido, and down the home islands. It'll yield a divided Japan if it goes on for a long time.

And then the next day the United States drops the second atomic bomb with a bluff - the idea being we're going to keep doing this daily or every other day. Except we don't have any more atomic bombs and we cannot build them quickly for a long time. So that's big bluff.

0:52:40

But the emperor then has had enough and he breaks the deadlock in the cabinet and the cabinet allows the deadlock to be broken the next day and then he makes an unprecedented radio broadcast - never had that happen before - to his subjects telling them “game over”. And then the next day he sends three imperial princes to the Manchurian Chinese and southern theaters conveying his orders at game over.

And from that moment on his samurai obeyed him and they absolutely cooperated with the occupation. There's no insurgency, no nothing going on after this. And at the end of the war, the United States came to understand the Japanese. At the beginning, totally misread the situation with the oil embargo that's meant to deter and instead it precipitates the war that we didn't want.

0:53:34

But at the end of the war, the United States realizes you're going to need some level of Japanese cooperation if you're going to occupy the place. I'm going to use Emperor Hirohito for this. Hirohito is scared to death that it's not so much that he'll be hanged, but that the United States will extinguish his dynasty, kill him and his son, and then it's over.

And so he's willing to sign any piece of paper that MacArthur puts under his pen. And one of those is the Constitution of Japan, that it's going to change their civil and military institutions, demilitarize the place, and try to get a democracy going there. The Constitution was written in one week by MacArthur's staff. They're running around raiding bombed out libraries for examples of Western constitutions, and they cobbled this thing together. And this is the unamended Constitution of Japan still in effect to this very day - MacArthur's gift to Japan.

0:54:32

All right. I've been incredibly critical of the Japanese but to sum up here, there are cultural explanations for their neglect of grand strategy, inability to cut their losses, inability to coordinate, and the ferocity with which they fought. So if you look at their values, they're explanatory of what may well happen when things get set off.

But I've been really critical of Japan. I want to even out the story by ending on the United States a little bit because the United States played a good game or bad game of half court tennis and mirror imaged at the beginning of this war.

0:55:10

So when the Japanese go into Manchuria in 1931, we want them out. We don't ask, well, why are you doing this? And their answer would be, well, hey, you passed the Hawley-Smoot tariff. That means we're trade dependent. Whom are we going to trade with? And once you did the tariff, everyone retaliated. So you've now shut down international trade. So we need an empire that's big enough to survive. So that's why we're in Manchuria. And by the way, there are way too many communists here. We got to get rid of those.

And then in 1937 when they up the ante, going into the rest of China, we didn't inquire what's going on. And from what the Japanese want to do is wall off communism, don't want that, and then they want to stabilize China so that you can have some productive economic growth. And if you go, well, what were U.S. post war objectives for China? Ooh, sounds remarkably similar, familiar. Communists out, stabilize the place.

0:56:09

Okay, well, how does the war affect all this? Well, actually the warfare that went on wiped out the two barriers to communist expansion in Asia. What are the barriers? Well, one is Chiang Kai-shek and the nationalists in China. The Japanese wipe him out. They don't totally defeat him, but they have so weakened him and so discredited him that by the end of World War II, he is really poorly positioned to win the Chinese Civil War, which he promptly loses.

And then what does the United States do? What's the other barrier to communism? Well, it's the Japanese! We wipe them out. So what do you get? A unified communist China, which makes really complicated wars in the Korean War and the Vietnam War. And the problem is the gift that keeps on giving. We're still dealing with this problem today.

0:56:57

So take a little word from Sun Tzu - know your enemy or the other side. Know the person you're talking to. Don't play half court tennis. It's a really dangerous game. Rather, try to analyze why - ask yourself, why is someone doing whatever they're doing? And just because you're trying to understand it doesn't mean you're condoning it. It's just trying to figure out the logic of the other person. It'll set you up for more informed choices.

Anyway, that was my Code of the Samurai event for you. And thank you very much for showing up and listening to all this. Appreciate you being here.

Dwarkesh Patel 00:57:37

I'm a little confused about some of the Bushido stuff and how it explains Japan's actions in the war. This Zen Buddhism stuff, the cherry orchards that are blossoming, and "you must act with the generosity of a samurai" — all this Bushido moral stuff. How does that square with the conduct of Japan during the war? The Rape of Nanking, the killing of millions of Chinese, the treatment of prisoners of war, which rivaled the fatality rates of the Nazi extermination camps?

It’s like, where's the Buddhism there?

Sarah Paine 00:58:11

Well, I'm not an expert on Buddhism, but you've got a lot of things conflated in there. If you're asking about the part of the brutality of the war is, Japan was totally out of resources. You're thinking it's going through a massive area of territory.

They actually had no ability to take POWs. Or if they took POWs, they'd have to halt the military operation and then put these people somewhere. So they just slaughtered them instead.

00:58:50

There were POWs and there were cases of hostile civilians who also got slaughtered. I don't know the "because," but they had very limited numbers of people to deal with this. On the one hand, you've got absolute desperation. I don't think any people behave well when they're desperate.

The war had been going on for years by the time we get interested in it, right? It starts in '31. So you have desperate people.

There's another piece. I can just add little pieces. I can't explain a whole people. In the prison camps in Japan, if I'm going to be — let's say you were Japanese. You and I are looking each other in the eye. That's how you do it in the West, showing that you're paying attention.

That's not how you do it in Japan. In Japan, if you're Japanese, I'm looking at your shoulder. It's rude to look people in the eye. It's just too intrusive; you're getting too much information probably from that person's face.

00:59:50

So you can imagine a Westerner in a prison camp looks his guard in the eye, and the guard's going "Oh, who is this arrogant person? You’re a POW-" You can imagine bad things are then going to be happening. These are guesses on what's going on.

There are certain values that I've talked about. There's certain desperation that's going on. And then there's the dehumanization of what wartime's all about, right?

Initially, conscripts go in of all armies having trouble killing people, and then they get better at it over time. This is the tragedy of human beings. I don't know if I answered your question. I don't know that I know the answer.

Dwarkesh Patel 01:00:31

Here’s another thing that I want to clarify: If you were trying to understand Britain's conduct in World War I, why they initiated it and why they conducted it in the way they did, and you tried to understand it using cultural explanations — what some British guy wrote in the 17th century — I don't know how far you'd get. Maybe the more illuminating thing is just to look at what was happening in the 1910s. What were the proximal strategic objectives?

So with Bushido, why are we looking back at what people were writing in the 1700s?

Sarah Paine 01:01:02

I'm going to break up the British thing into two parts. One analytical framework is that you can look at wars in terms of underlying causes and proximate causes. The underlying causes are like the tinder of grievances on both sides, and there can be cultural components to that, or other components.

So there's this accumulating tinder where you've got two different sides, at least across purposes. But then there's the match, the proximate cause, which is a whole series of matches. Finally, the last one is Pearl Harbor, and you are off and running to a place you might not want to go to.

01:01:41

So there's that. And then there's culture. Let's look at Britain's strategic culture. I'm no expert on British strategic culture, but these are some basics.

They are an island, and they want to be able to trade with the world, but they don't want any one power dominating the continent. This is their strategic thinking from way back. So if there is a power that's on the verge of dominating the continent, you want to back the other side to prevent that outcome.

That's very much a part of British thinking, it goes back a long time and you can read things going back a long time describing that situation. There's another piece that goes back a long time in the British: navies are rarely decisive in warfare.

What I mean by "decisive" is that you actually get the goal that you're after from fighting, whereas armies can be. If your goal is, "I want to occupy all of France," or better yet, the Holland, something smaller, an army might be able to do that for you, one instrument of national power. But Britain's reliant on a navy and doesn't like to have a big standing army.

01:02:50

So they're thinking in terms of diplomacy and allies, working economics, and making money from trade. They are the ones who coined the term "grand strategy." It is their gift to us, and it absolutely informs their thinking at a very macro level.

No one thing is entirely explanatory. Also, we human beings game the system. The moment I tell you you're Japanese and you think this way is the moment you go, "Oh, that's what she thinks? We're going to do something different."

Right? So this is the problem with human beings.

Dwarkesh Patel 01:03:23

The loyalty precept: Wasn't one of the problems with the Japanese military that they weren't loyal, that they were trying to do these coups all the time, and the young officers were insubordinate?

Sarah Paine 01:03:37

That's an excellent question. And what you're doing is feeding me back the Greek principle of logic, which is the law of non-contradiction. You cannot simultaneously believe mutually exclusive things. So what's going on?

You're telling me it's all hierarchical, and now you're telling me junior officers are doing, or mid-level officers are doing things. What's going on here? Ah, but that's a fundamental principle of logic that the West puts great credence on.

Not the case in the East, necessarily. Now, people have educations that are different. So we're going back in the day where people are not looking in terms of, "Okay, we're going to have a logically consistent argument."

01:04:20

Rather, there are these social values that we are gonna- if it's all about group loyalties, that's what we're going to prioritize. And if my subgroup is going to be my unit or whatever, that's how that's going to go.

So you're doing a wonderful piece of Western logic. It's excellent. And this is why other cultures find dealing with Westerners like battery acid because they have these different belief systems.

And you go like, "Okay, you have women, and we got women. Our women drive cars, and yours are like, where? Is there something wrong with your women?" It's battery acid on other cultures.

Dwarkesh Patel 01:05:02

I was struck when you were describing that the Nazis were putting their enemies on death ground, the Japanese are putting their enemies on death ground, and in both cases, it was detrimental because you're preventing the other side from surrendering.

That seems even worse than what was happening before that period in history where you can think over time that our norms about civility and war crimes are improving, but it seems like in World War II, the way people were acting was even worse than they were acting in World War I. The way Germany and Russia were fighting in World War I was probably more civil than how they were fighting in World War II. And then obviously what Japan was doing in China at the time.

What was going on around the world that people just got so demonic during this period?

Sarah Paine 01:05:47

Warfare is not civil, you're killing people. I love people who talk about just wars. It's rather a horrible piece of human existence.

There are a number of things that have gone on with the Industrial Revolution. You can now kill people on an industrial scale. When you're doing it with bows and arrows, it takes a lot more time to create the mayhem. So that's one thing, the ability just to wipe out people.

World War I on the Western Front was all entrenched. On the Eastern Front, there was a great deal of movement, but on the Western Front it was entrenched, which meant civilian populations weren't really touched by it. Where the initial fighting, yeah, they're leveled, but once you get a trench, you're not. And then we in the West don't actually study too much what happened to the civilians on the Eastern Front where it's moving around.

This is back to my half-court tennis, so we're not paying attention to those civilians. For the West, very few civilian casualties. Whereas when you get to World War II, you're bombing people. Now, with new technologies, you can get at people.

01:06:51

And invading also, it's the lesson of World War I, the feeling that the Germans really hadn't felt their defeat, and that allowed them to make up this story about how it was they weren't defeated. "The Jews did it," or whoever. They were betrayed. And Churchill and Roosevelt decided there would be a march to Berlin to disabuse them of that.

And that involved killing a lot of civilians to get to Berlin. And of course, the Russians were determined to pay back for what the Nazis had done to them. And we had no sympathy for what- we were going to turn a blind eye to what the Russians were up to because the Nazis had been so heinous.

Dwarkesh Patel 01:07:32

This is probably wrong, I want you to correct me, but maybe one way you can explain why the Japanese were so brutal in their campaign around this time is, if you think that when you lose, you have this idea that you have social death, it's better to kill yourself than go back to your family and say, "I surrendered". Maybe they just applied, this is their failure to empathize with, or think it from, the perspective of their enemy, but they were just thinking, "Listen, if we lost, we would commit seppuku. When they lose, they forfeit human rights." And in some sense, it was just applying the principle of social death to their enemy.

Sarah Paine 01:08:09

The whole war is brutal. They're doing a lot of hand-to-hand brutality, and part of it has to do with lack of equipment. That firebombing of Tokyo happened in one night. I think it's 80,000 Japanese are incinerated.

Okay, let's talk about brutality. Now, the reason why Americans did that is because they knew the alternative was sending American kids onto Japan who would die doing that. And so the decision was it was better to kill a lot of Japanese civilians than it was to kill American soldiers.

And that's also the reason that went into the atomic bombing. That's controversial, right? The Americans, why did they drop atomic bombs on the Japanese? And there was no disagreement about that in the United States at the time because it was a question of: are you going to send American young men your age- and millions of them would have died hitting the home islands- or are you going to do the bombing? And of course, the Americans did the bombing.

So there's brutality all around in this war. Wars don't come up with clean hands.

Dwarkesh Patel 01:09:25

Was there any way for the West to, or for America to, win the Pacific War without the firebombing?

Sarah Paine 01:09:30

Well, okay, let's, this is a whole other topic. Win in wars. What does win mean? For us, it was put Japan back in its box, right?

But this is a whole problem for Japan. What's win? Or this country in Afghanistan, what's win? Is it booting Osama bin Laden out of Afghanistan? Once that happens, it's a day. Is it overthrowing the Taliban at a particular period, or is it trying to turn the whole place into a democracy?

Okay, those are all radically different things, but you need to make up your mind what it's going to be. I think it's a miracle. Well, A, okay.

01:10:10

The win, if you're going to have the win be that the United States transforms Japan into a functional democracy, or sets them on the road so that they will become one, if that's what the win is- no. Because if you don't, I showed you the three horrible events in four days. That's quite incredible to have that much bad news happening in a half a week.

And that absolutely shattered the Japanese, and it also opened the door for those who thought they were in crazy land to capitulate. If you don't do that, okay, we invade the home islands. Americans were sick of the war, and you start losing lots of American kids in Japan.

I think at some stage, we decide to pull them out of Japan and blockade them eternally. And then you've got, I don't know, Japan is like a new North Korea, just this inevitable, these eternal, non-functioning society. So, no.

Wars are tragic, and also, don't think that you have all the cards, that you're going to make the decisions about what's going to happen. The other side is going to put you into corners where you're going to choose from very unpleasant alternatives.

Dwarkesh Patel 01:11:30

I want to ask you about how the war starts. So, there's obviously, you go 10 years before, and you've got the tariffs, and that creates the incentive to build an empire. But even months before, when there's negotiations between Japan and America to get rid of the embargo, it's striking to me how much miscommunication and the ability for both sides to just understand that there was a compromise here was such a big factor here.

I feel like if Prince Konoe and FDR could get on a Zoom call, I feel like the war...

Sarah Paine 01:12:08

You're an optimist. Let's talk about sunk costs. I'm going to talk about sunk casualties. By the time you're there, the Japanese have suffered 600,000 casualties in China.

There is no easy out of that one. The United States' minimum program is "you get out of China". Not happening if you're Japanese.

If you look at the government, the government's definitely on death ground with that one because there's no way they stay in power if they get out of China. Particularly, this is why Hirohito is Mr. Silent for most of the war. Initially, he's all for it until it goes sour, and then he's less so.

He knows that he'll be, if not assassinated, declared insane. Then his perfectly serviceable adolescent son, or however old his son was, would be the token emperor. So, I don't think the Zoom call is going to change the fundamentally high stakes that are involved for both sides.

Dwarkesh Patel 01:13:18

You really think if Hirohito had stepped in and said, "No, we're not doing this," that he would have been usurped as emperor?

Sarah Paine 01:13:25

Yeah, early on. Oh, and there's another piece. Let's look at the United States in 1941: Great Depression, isolationists. This is where the first "America Firsters" are. They're the ones who created the idea. They didn't want to know about all these foreign places.

Totally isolationist. Hawaii wasn't even a state. It doesn't become a state until 1959. If you're Japanese, you look at a place, "Oh, it's a colony."

So, it'll be like the dog that barks. You take a newspaper and flap them a few times, and maybe the dog will start barking. Because what the Japanese want is the United States to just mind its own business, stay out of Asia. "It's our backyard. It's not your backyard."

And looks at the United States. The United States doesn't have that much trade with Asia compared to the rest of the world. Sure, it sells Japan most of its oil, but that's not most of US trade.

Dwarkesh Patel 01:14:15

I was reading about this before preparing to interview you, the particular cases where diplomacy broke down. There are examples where the translators between Japan and America are, and I can't believe this is true, you can tell me if this is not, but they're exaggerating what each side is saying to make them more vivid to read. Like if Tojo says something conciliatory, it's exaggerated.

That's obviously not the role of a diplomatic translator. And there are many cases where, after the war, Tojo says, "If we had gotten the modus vivendi that FDR apparently was contemplating sending to us, if we saw those agreements, we would have agreed." Or apparently, they misunderstood that the final agreement they got from Secretary of State Hull, where it said that you must return China, they thought it included Manchuria.

Hull didn't intend it to include Manchuria. They might have said yes to that. It seems like the work really could have been averted if a couple of mistranslations were avoided.

Sarah Paine 01:15:07

I wouldn't take Tojo as my source for anything. He's a guy who, he was before a graduating class of cadets or something, and he was talking about how people had said that he was mediocre, but look where he'd risen to be. He's this great man.

And then at the end, when he's supposed to commit suicide, well, the way you do it is as I've described. He's an army officer, so what does he do? He takes a gun and he shoots himself.

Buddy, it's point-blank range with a gun. How hard can it be? And he survives that one.

01:15:45

We glue him back together. So here's the honorable samurai who sent so many children, or young people, to their deaths, and at the end, when he knows full well what he's supposed to be doing- can't do it. So I wouldn't take him as my source.

Also, at the end, he's got this whiny answer of, "Oh, it wasn't my fault. It's all your translators."

And, "Oh, the peace on Manchuria." No, I don't know that that would have been a compromise. The League of Nations had sent in something called the Lytton Commission, which had told the Japanese they had to get out.

Oh, by the way, it is a fiction that Manchuria is not a part of China. It is an integral part of China, has been for the longest time. What the Japanese did is they kidnapped the last Qing emperor- Manchu, Manchuria- whose ancestors came from Manchuria, popped him in, Henry Puyi, and made him the emperor to try to come up with this fiction that oh, Manchuria is a separate place.

Excuse me, it's not. It's part of China. It's the internationally recognized territory of China.

Dwarkesh Patel 01:16:48

So I wonder if one of the problems here is, look, it was not in the vital strategic interest of America to secure or liberate China. In fact, the outcome of the war obviously is a national seizure of communist state power. And as we'll talk about in the next lecture, that was the very opposite of liberating China.

Basically, America puts in this oil embargo and knows that the outcome of failed negotiations on getting rid of that embargo is a total world war of this kind. And it's not even for the main strategic objective, which is, you know, you got to get Hitler, you got to beat Germany. Wasn't this just our failure of grand strategy to realize, why are we doing this? What's our strategic objective here?

Sarah Paine 01:17:31

Hold on. We weren't fighting Germany. The only reason we fought Germany is because Hitler made a major blunder.

He had an alliance with Japan that said if Japan was attacked, he would come in. Not if Japan attacked someone else that he would come in. He interprets it broadly, and he declares war on this country.

That is how the United States got into World War II, Europe. If Hitler had not made that blunder, FDR would have been in a world of trouble trying to explain why he's suddenly going to be fighting Nazis over an attack on Pearl Harbor by Japanese. So that's a separate issue.

01:18:06

The main thing that Americans who thought deeply cared about is the international system, that we should deal with each other through laws, through freedom of navigation. So this is how you run your commercial transactions, that big countries don't get to overrun small countries because if they do, the entire international system goes down.

And the logic that you're describing is excellent logic, and this is what the Japanese are saying. It's like, why would the Americans care about this? Americans care deeply about the international system, or people who are thoughtful about it.

And it's also like, why not let the Russians eat all of Ukraine? Why are the Europeans suddenly all over this problem, right? And they've unified very recently on this.

It's because the whole system is at stake. So it is really high stakes at a strategic level. The Japanese are looking at the operational level and going, why do you care about these countries?

We care about the entire system because our prosperity is based on it.

Dwarkesh Patel 01:19:15

So I wonder if the problem here is that America isn’t, at least at the time, wasn't willing to give enough concessions to the factions within Japan that cared about peace so that they can save face and actually argue for ending war? Just the idea that they're going to give up Manchuria as well, obviously that's not going to happen.

And when Secretary of State Hull just sees these vacillating telegrams from the Japanese, he assumes it's because they're fickle or something. It's like, no, it's the civilian part of the government trying its best to prevent the military from taking over. You got to give them something to save face.

Sarah Paine 01:19:52

That faction had already lost. They lost in 1936. Takahashi Korekiyo was Japan's longest-serving finance minister, and he was a very distinguished man.

In fact, he brought the Japanese economy out of the Great Depression before anyone in the West was doing it, through government spending and trying to get people to spend at home. He told the army, he said, "Fellas, if you go on this bent for empire, you're not gonna actually get resources because you're gonna spend a lot of money fighting with people, resources doing that. And then it's gonna take you years of investments to access these resources. So you gotta be able to cover these investments for years and years and years. Cooperate within the international system. That is the way to prosperity for Japan."

Well, that was the February young officers revolt, where he and I can't remember how many others were murdered. They came to his home in the middle of the night, and as he stood up to talk to them, they literally hacked him apart. They lost. That whole game is over.

Dwarkesh Patel 01:20:57

But Prince Konoe doesn’t, like he knows he going to- the person who’s Prime Minister in 1941, realizes that they're going to lose a war against America and he doesn't want to do that. It's just that Tojo, the war minister at the time, doesn't answer to him. The Prime Minister doesn't want war, and we don't give him enough to save face.

Sarah Paine 01:21:14

Wait a minute. They have a huge army there that's more than capable of assassinating people when they get in the way. So that ship has sailed. This is what a pivotal decision is. Once you've made it, there's no going back to the way the world was.

Japan lost too many people, and there are too many figures in the army. We think that it's an inevitable outcome of having the world the way it is now, with Japan this wonderful country now, just at the lead of so many different areas of human endeavor. We think, oh, that's the inevitable outcome. It's not.

Dwarkesh Patel 01:22:02

Is there anything America could have done in the immediate years leading up to the war that could have prevented this outcome? Because the fact that there is a world war, prima facie, you should assume that if there is a world war, things weren't done optimally, right? That doesn't mean everybody's equally at fault. But is there anything that could have been done differently?

Sarah Paine 01:22:22

Yeah, Hawley-Smoott.

Dwarkesh Patel 01:22:23

But like in the years directly before.

Sarah Paine 01:22:24

No no no, but this is serious. That Hawley-Smoot tariff is a game of half-court tennis. You're looking at the United States, it’s in a terrible depression, you want to protect jobs here, and so you raise these big tariff walls.

Okay, what's the other side going to do? They're going to raise their tariff walls and pay you back. What's that going to do? It's going to cost a lot of American jobs if you play that game.

You need to be talking with other people. So once you have set the conditions, hothouse conditions, for fascists to take over in Germany and in Japan, you are in a world of hurt. The easy solutions are no longer there anymore.

Dwarkesh Patel 01:23:05

Maybe I’ll ask the question this way: the oil embargo, was it a mistake? Because if the idea is to protect the international system, prevent empire- well we got more empire, we got a world war, we got communists taking over in China. Whatever would have happened if we got rid of the oil embargo, it can't have been worse than that, right?

Sarah Paine 01:23:20

Well yeah, it was. What Roosevelt was really scared of was that the Japanese would attack Russia, because he thought Russia would fall. And if Russia fell, he thought the Nazis would win. So at least when the Japanese attacked, they attacked us. That was actually better than attacking Russia.

So the oil embargo… so let’s say you don’t do it and Japan never attacks us, and then the Russians are down and you've got Nazis in control of the world, somehow that’s better?

Dwarkesh Patel 01:24:02

But you don't have the world war with America. It doesn't seem insane to think that, and we're still going to fight the Nazis.

Sarah Paine 01:24:06

You're not going to have an international system. Oh, and if that's the case, the Nazis were gearing up, because eventually they're going to fight us.

So, this is another problem. Another concept that I think is useful is limited versus unlimited objectives. An unlimited objective is, "I want to do regime change on your country." The most unlimited variety is that not only do I want to do regime change, but I want to kill all your people while I'm at it. So if that is what your opposite number is planning, if you compromise with them, you are simply setting them up and putting them in a stronger position when they come at you for the final kill.

If they have limited objectives, then by all means compromise with them, and negotiate away on what it is you want, just this little sliver of territory, or you want some preferential treatment? We can do that for you. But you're talking about a world order here, whether it's going to be based on laws increasingly, or these opposing spheres of influence.

Dwarkesh Patel 01:25:16

To keep pushing back on this...

Sarah Paine 01:25:18

I don't have an answer. I've given you what little I can think of. But hey, I'm not the grand puppeteer in this world. These questions you're asking me are way above my pay grade.

Dwarkesh Patel 01:25:30

People in the YouTube comment section get mad at me when I keep asking about counterfactuals. And I understand. Obviously, I don't understand what was happening at the time. I'm naive about history or whatever. But I do think it's important.

What are we trying to do when we try to understand history? We're trying to understand if we had done things differently, what would have happened. What are the lessons we take? And counterfactuals are the main way we can do that.

Sarah Paine 01:25:50

Yeah, I'm all for it. We teach by counterfactuals. Replay it. Can you come up with different options?

I think you're in a series of really awful options.

Dwarkesh Patel 01:26:00

The difference between the Japanese and the Nazis is that the Nazis had the ideology that we got to kill millions of people. That is what they believed. The Japanese didn't, in the same way, have that ideology.

Naturally, they had, they're a continental [maritime] empire. They also want to trade. And they don't like Communists. They don't want the Communists to take over in China.

It just seems like, naturally, if we didn't go to war with them, we might have been allies, as we ended up being later on.

Sarah Paine 01:26:27

Well, they attacked us…

Dwarkesh Patel 00:26:30

After the oil embargo.

Sarah Paine 01:26:31

Yeah yeah. But-

Dwarkesh Patel 01:26:32

But the question is, should we have gotten rid of the oil embargo? So the attack wouldn’t have happened.

Sarah Paine 01:26:35

But wait a second, the Japanese are saying, "We have the rights to your oil." Excuse me, we have the right to sell it or not sell it to anyone we feel like.

Since when do they have the rights to it? Okay, great. So you're going to do all this crazy stuff and then still think we have to sell you oil? And, oh, by the way, this oil is being used to kill Chinese all over the place. This is what their Japanese bombers are running on.

Dwarkesh Patel 01:27:02

Morally, I agree, we don't want to like, they don't own our oil. The question is, it's not about are they are entitled to it. It's like, should we have given it to them? Would the outcome have been better if we did?

Sarah Paine 01:27:12

I'm telling you, I suspect the outcome wouldn't have been better, but I don't know. And you're asking me things where my experience is, I'm just a professor. I just show up at seminar on time, and I try to do reading and prep.

And my experience has not been in government, let alone at the highest echelons of government, of what's feasible and what's not feasible. And the answers I've given you are where I'm at, but I can't tell you more.

Dwarkesh Patel 01:27:44

All right.

Sarah Paine 01:27:45

Sorry.

Dwarkesh Patel 01:27:46

I'll ask about other things, too. The sort of delusional optimism of the Japanese to think that they could beat America, how much of that is motivated by the idea that they actually do think they're led by a living deity?

Is that related to why they thought they could win?

Sarah Paine 01:28:06

No, but you're looking at the United States as isolationist. If you're Japanese, and you're looking at these absolutely isolationist Americans who are letting Britain, their closest ally, potentially go down to Nazis, right?

Because think about Britain. How did it all work? Fall of Norway, fall of France, then they lost Crete. We aren't even in the war. They're about losing everything, and we're not doing anything.

So… wait a second, now I'm losing my track of my train of thought. Revisit the question again, please.

Dwarkesh Patel 01:28:47

Can somebody in the audience remind me of the question?

Sarah Paine 01:28:53

Yeah, the optimism of the Japanese. So they're looking at it and going, "The United States is not bailing out its key ally. Hawaii’s a colony, right? It's a bunch of white people dominating Hawaiians."

"So we're just going to do a little, set an example that, hey, if you mess with us, it's going to get costly. And we know you don't have much trade in Asia. Sure, we want to buy your oil, but overall, most trade is elsewhere." And so they're looking at it. Isolationists, won't the answer be the isolationists go, "Woo, this is expensive, let's get out of here?"

01:29:27

And of course, that's not understanding Americans. I remember I was in seminar the day that 9/11 happened and there was a TV in the seminar rooms, and there was a break, and after the end of the break, the students had the TV on, and we’re watching. Tower One had gone down, and then while we're in seminar, the next one, we're watching it, goes down. And I thought, "Oh my, there's going to be hell to pay for this one."

Because this is how Americans are, that if you mess with us, boy, does it get ugly. And boy, this is like, don't think, retaliate. So the Japanese didn't understand that part of us. In fact, a lot of people don't. They look at Americans, we look like a bunch of hedonists. But if you mess with us, it's ugly.

Dwarkesh Patel 01:30:14

If you are in the Japanese government a couple of years before, like 1939 or something, it sounds like from your earlier answer that you think it was a sort of hopeless situation. If you're the finance minister or if you're the prime minister, to- is there anything they could have done to prevent an inevitable conflict with America?

Sarah Paine 01:30:32

They brought it up, and they say, "Well, we can't afford this. The resources aren't here." The army wasn't interested, and they shut up because the last guy had been killed in his house in his pajamas. So, they weren't up for that.

Dwarkesh Patel 01:30:46

How much stock do you put in the idea that if you have a society that rapidly industrializes, that goes from a feudal state to advanced industrial nation in a generation, it's just not enough time for the culture to evolve? And so, the way that the Japanese behaved in the lead-up to the war and during the war, the feudal values just didn't have a chance to evolve. The Meiji Restoration happened too fast.

Sarah Paine 01:31:12

No, it didn't happen too fast. It’s that institutions take a long time to take root. Look at this country; it's been an evolving democracy for hundreds of years. We no longer have slavery- amen- but look at really consequential things about our own institutions.

So, now we're going to go pick on the Japanese because they managed in one generation to westernize their political, judicial, legal, educational, you name it, they westernized them, but there wasn't enough time for these things to grow deep roots.

And then if you think about who's doing this, they're called the Genrō, this very distinguished generation of Japanese who all knew each other. Their career paths were very broad, covering both civil and military areas. And when they died, they couldn't transfer their prestige to anyone else, nor had they institutionalized it in some kind of cabinet or something that would force all these different groups to discuss things without giving primacy to the military.

01:32:27

Then, understand what is the tradition in Japan: Shogunate. That's what Japan had before. What's a shogun? Shogun is a Japanese word for "general." So the long history of Japan is military rule so when it comes back in World War II, that is a kind of normalcy.

It would have taken a long time. So, this comes back to the United States and fooling around with tariff walls, thinking that was such a clever idea. Maybe if the Great Depression had been managed better, it would have given time for these institutions to take deeper roots.

No one knows, but I would not criticize the Meiji generation. They're brilliant. They did so much, but you're talking just a number of years.

Look at this country. We're having major problems with our institutions, wondering whether we've got a stalemated legislature, whether we've got a skewed court system, or whatever it is and we're sorting these things out. And then to criticize the Japanese because they couldn't do it all in 25 years?

Dwarkesh Patel 01:33:30

One thing I found really interesting in your book is that you were arguing that not only was the military not in charge of the officers, but nobody was even in charge of the military. I think you call it a "system of irresponsibility," that it was basically government by committee. Tell me more about that. Why does that lead to mistakes?

Sarah Paine 01:33:53

I'm going to flip it around and look at how the West has done it. It's all about- it’s supposed to be and of course there are exceptions- that it’s not that it’s about you if you have a particular job, it’s that your job gives you certain authority by law to do things, and then we have courts to adjudicate when you in that position, other people think that you've exceeded your authority, and they start suing about different things.

And that's how it goes in the West, it's very legalistic, going all the way back to the Romans. When I think about what is the West, it's Greek logic, it's Roman law, and then it's these Judeo-Christian moral values. Those are essential pillars of what the West is all about.

So in Japan, yeah they get laws and they westernize, but they had their own indigenous way of dealing with things and it's very much about different in-groups and out-groups handling things in whatever the committee is. And so we're going, "Well, who actually did that? Whose fault is it?" Because we have this very legalistic way and fault and law.

01:35:07

We're going to either put you in jail or whatever. It's just different ways of organizing ourselves. But we in the West assume, because going back to Roman times, that institutions are going to be a really big thing, that that's how things are going to work.

So then when we get into somewhere like Iraq and we think the police is going to still be functioning after we blow the government, it's like, whoa, whoa, whoa, it's not an institutionalized- I’m no expert, but you're projecting the kind of institutional setup that Western countries typically have to other people.

They may name these things the same thing, police or whatever, but they may function in very different ways.

Dwarkesh Patel 01:35:51

When you look at the soldiers who fought these hopeless battles, where tens of thousands of them on a single island might starve or be forced into a banzai charge that they knew was hopeless and they were all going to die, and they knew that they were put in this hopeless situation because of these destructive petty fights between the Navy and the Army, where one of them isn't willing to supply the other one, why didn't that break their morale more? It's just like, "Look, we're supposed to die and commit seppuku because you guys didn't give us the right supplies, because you guys aren't willing to share information or something."

Sarah Paine 01:36:22

I don't know the answer, because I'm not a social historian where you would really be doing- I've always done more diplomatic and military, so you're looking top-down. But what you're asking is a very important question: how do individuals react to all this? And that I do not know the answer.

And you'd have to--and there's another piece that makes it hard in Japan since people don't want to talk about failure because it's considered a loss of face. Whereas in the West, one of the fundamental assumptions of Christianity is we're all sinners, right? Original sin, we're all defective goods from the very beginning.

And so there isn't this expectation for perfection because it's known you're kind of a mess to begin with, and so you can talk about these things. So that's a whole other problem of getting people to open up about truly horrible events, so I don't know the answer.

01:37:16

I know that the World War II generation, they came home, they just didn't talk about it with their children. It's just not a matter for discussion. So I can't answer that, of why they followed.

But you look in the West in World War I, soldiers would go up and over the trenches, and they knew exactly what was going to happen to them. And yeah, there were people who didn't, and then they were court-martialed and shot. A lot of people were shot in World War I.

But there's been a change in society about what young men are willing to do when their officers start telling them. The way the Russians solved this problem in Ukraine, but Stalin would do this too, is you send people up ahead, and then you've got the KGB, or whatever the killer unit, so that anyone who tries to go the wrong way in the battlefield, they get machine-gunned by their own side. That's one way to get an army to go forward, and that's what Putin is doing right now.

But anyway, in more democratic places, people aren't willing to go along with this now, but in the West, we did it too.

Dwarkesh Patel 01:38:22

When you're having lunch with your colleagues, with the people who are also experts in this field, and you're discussing, "Look, what are still the big unresolved questions? We don't understand why this person did X, or why events transpired in a certain way," what are the big things where you guys have to hassle it out?

Sarah Paine 01:38:39

It's more that someone has decided what the curriculum is going to be, and then you teach it. Oh and by the way, as of like six months ago, I've moved to the Maritime History Center, so I'm no longer teaching. I'm just doing research at this stage.

Well, I think the whole point of the curriculum at the War College is to get people out of the operational level. So, the junior course is about the strategic effects of operations. It's like, okay, Pearl Harbor.

To me as an example I’ll use, Pearl Harbor it’s an A+ military operation. They sink everything. Of course, it's shallow water and we raise most of it afterwards. They lose nothing.
I mean boy, how can you do a more perfectly choreographed military operation than that one? Except it turns an absolutely isolationist country into one hell-bent on coming after Japan, and that would be called a strategic disaster.

01:39:38

So, you've got to be cautious about your military actions and think about what are the strategic consequences. And it's very difficult to gauge those accurately. So that's one big issue that you need to get at.

And then for the seniors, it's: Okay, great, you're an officer and you understand if you're in the Army, you understand how to use armies, in the Navy, about how to use navies. But how does this integrate with all the other instruments of national power that actually account for how wars turn out? And economics is a huge one.

There is no definitive answer in these wars, but rather you can give analytical concepts. And this is my reason for doing these lectures: to say, hey, don't play half-court tennis. Wouldn't that be a basic thing?

Is it going to provide you the answers if you look at the other side? No, it won't, but it'll position you to be in a better shape. Or, tossing out limited versus unlimited wars, and unlimited wars are putting people on death ground.

01:40:37

Does that answer the question? No, but it goes, okay, if Vladimir Putin has an unlimited objective vis-a-vis the world order- and he's basically said that he does- then compromising with him would be a mistake on that subject. And you're going to have to deal with him one way or another.

So, it's the... What is it? History isn't over. The great, long-standing struggle between continental powers that want to divide the world up into mutually exclusive spheres of influence and butcher each other over territory -- very negative sum, they tend to be very authoritarian -- or this maritime order that says, hey, surely, join the party, follow the rules, you'll make money, right?

And this is, Angela Merkel's thinking this, of “hey, we’re gonna”- she was the German chancellor for many years- and she's thinking Putin will sell a lot of oil, he'll make money. It's a win-win, surely.

Well, he's back to his continental stuff, destroying wealth and lives. That's what the alternate world order looks like, is killing people to get things. It's more promising to spread the other one, and this disagreement has been going on for a long, long time.

Dwarkesh Patel 01:41:56

I feel like we had a mini version of this debate in the podcast we did a year ago.

Sarah Paine 01:42:00

I have a limited number of ideas.

Dwarkesh Patel 01:42:03

But if you just said we can't compromise with people who will have these unlimited goals, like Putin, who have these territorial goals... If we're using the analogy, as you're explicitly making it, to Japan during World War II, look what it took. If we wanted to say we're not going to compromise with your territorial goals, we will stop you in every way, what it took was a world war that led to an unconditional surrender. So, if we're using that analogy, does that imply that no compromise requires some unconditional surrender-type event?

Sarah Paine 01:42:35

No, no, no, no, no.

Dwarkesh Patel 01:42:37

Doesn’t this counter the death ground thing?

Sarah Paine 01:42:38

There's something called precision nuclear strike, and it has changed things. During World War II, the United States had sanctuary, which means other people couldn't touch us at home, and our productive base was not touched and that was essential.

There are many things that were essential for Allied victory. Take any one of them away, and the outcome is different. One of the essential things is this productive base.

This time around, all kinds of people can come get the United States. A lot of times in foreign affairs, you can't solve things; and I don't know who originally said this, but you manage things.

And so, this is where the sanctions and things come in. You give people a timeout from the world order. You don't solve it.

01:43:25

You look at North Korea, that is not solved by a long shot. But at least South Korea can go become prosperous, and the North is just sitting with its timeout and being more and more malign at home. But it's more like that it’s- of not recognizing, for instance, conquest.

This is what went on, actually, when Stalin took over the Baltic states. We never recognized that. In fact, they ran governments in exile based on the funds that, I think, the Baltic states had their funds in US banks. So when Stalin overran them, I think those funds became what funded their embassies.

And so, N generations later, we recognize their independence. These things are not solved on anyone's watch. Basically, if you want the things… the way the last Cold War ended, it's because the Chinese and Russians changed their minds. You have to wait for others to come to their own decisions.

Dwarkesh Patel 01:44:31

What is the track record of sanctions actually changing other people's minds? Because if you look at the world today —

Sarah Paine 01:44:37

Unknown.

Dwarkesh Patel 01:44:39

Just to finish the question, because you're advocating that as the way we should proceed, so it's worth asking. If we look at the world today, North Korea, we've sanctioned them for decades. If the idea is we're going to deprive them of resources to do these bad things, well, they're just going to divert resources from their civilians to the military.

And you're just making the people poor. You're not changing the government's decision. Same thing in Iran, same thing in Venezuela.

Sarah Paine 01:45:04

Correct

Dwarkesh Patel 01:45:05

So what are we doing? We're just making the people poor. We're not even changing the government's decision. What's the idea here?

Sarah Paine 01:45:10

You know, I don't know the answer, and I'm working on an edited book with a wonderful colleague who knows much more about economics than I do, and we have all kinds of chapters. This book will not be a good read.

Well, because you want to learn things, and so I've done a lot of edited books to learn things, different kinds of naval operations, and then they feed into the books that are more overarching. But this one's on sanctions, and we're in the process of writing the concluding chapter now.

And the things that you’re bringing up. Sanctions don't seem to make people do what you want them to do. And so a lot of people go, "Well, they don't work," but boy do they inflict pain.

01:45:52

And then of course you're doing it on a, usually an authoritarian regime, that diverts as much of that pain as possible onto the civilian population, and then uses that as an excuse to justify its rather brutal rule. And I don't have the answer.

And the other piece is, so you're dealing with these expansionist powers with a limited toolkit, and the question is which tool to use? And I truly don't have the answer. You've mentioned a really good counterargument, and I've given you an argument, and I don't know which rebuttal is the better one, honestly.

This is another thing about what do you- you were asking about what you’re worried about, but here's something to help you with what you're worried about: is thinking in terms of arguments, counterarguments, and rebuttal.

So, you have an argument, I have an argument, and on this one, I'm not sure what the rebuttal is. But it's a good way of thinking about things because it allows you to change your mind. You may well be right, or I may be right, or maybe we're right in one country but not another. I honestly don't know the answer.

Dwarkesh Patel 01:47:03

It does seem like one of the biggest things you want to try to figure out in strategy in general, because if you try to do sanctions, it doesn't seem to work that well. Well if you try to replace the regime, for example, people said look at Saddam's regime after his invasion in Kuwait, this is somebody who has territorial goals, so we can't have somebody who's going to keep trying to do this in power. You try to replace that, and that doesn't work.

Sarah Paine 01:47:27

That worked well, you’re right.

Dwarkesh Patel 01:47:28

Yeah. So, just the idea of what do you do with irascible regimes? That's a big open question.

Sarah Paine 01:47:33

In the Gulf War, Bush Sr. fought a limited war for a limited objective. The limited objective was: get Iraqi troops out of Kuwait and restore the Kuwaiti government. Those were the two really big things, and then call it a day when you've done those things.

That's what he did. It was an incredibly successful war, and it went to our heads. Right? Because when you run a successful limited war, it's over very rapidly. You get stuff with not too much cost, and it's done.

So then we try to do the total makeover of another country and excuse me, what right does one country have to do a total makeover on another country? And then, of course, the Russians and Chinese are apoplectic watching us do this, and that's a problem.

Or you think about Afghanistan, maybe you go in there and at least boot Osama bin Laden out of the country, and then proclaim it victory and leave.

Dwarkesh Patel 01:48:33

All right, going back to Japan.

Sarah Paine 01:48:34

Yeah, okay. I tried to divert you.

Dwarkesh Patel 01:48:36

No, no, I did the diverting. It was interesting. Do we have some sense of what Japanese public opinion was like through the 30s and 40s?

Sarah Paine 01:48:44

I don't know. It's also because it’s becoming more and more authoritarian. Yeah, I have no idea on that subject. Again, it's a social historian, that would require really detailed knowledge that I don't have.

Dwarkesh Patel 01:49:02

I had Daniel Yergin, who wrote The Prize, which is the Pulitzer Prize-winning history of oil. He has a big section in the book about World War II because, in his view, oil was a big component of why the war happened the way it did. One of the interesting points he made was that he thinks that the kamikaze missions, a big part of the motivation behind them, was that Japan lacked the fuel to have the pilots fly back. Is that accurate?

Sarah Paine 01:49:29

Oh, it's worse than that. They didn't have the fuel to actually train the pilots. And as we learned in 9/11- sorry to bring it back- it's really much easier to learn how to take off a plane and crash it into something, right? That's what those guys were doing. Than it is to teach them how to make safe landings under weird conditions.

So, the kamikaze was just an act of—it's a guided missile. And it was an act of having very few assets at the end of the war and not enough fuel to fly anyone anywhere.

So it's going to be a guided missile into an aircraft carrier or a battleship, destroyer, or something.

Dwarkesh Patel 01:50:07

One of the things I learned from your book is that the overwhelming fraction of deaths on the Japanese side during the war happened after it was known that they were going to lose. I think in the last 14 months—and this is all from your book—1.8 million of the total 2.1 million Japanese deaths, that's 85% of the total, happened in the last 14 months of the war. A similar thing is true with Germany, where I think 43% of the deaths happened in the last year of the war, I think 2.3 million out of the 5.3 million.

Walk me through what is going on inside government – when the higher-level people must know that they're losing, but they're going on to make bigger and bigger sacrifices.

Sarah Paine 01:50:48

Well, you're conflating, I think, Western values about what the purpose of the government is. Well, in common wealth, common weal—common weal is common good—this notion that governments are about the well-being of individuals. Okay, well, we're not in societies where we're talking about individuals for openers, certainly not in Japan.

Then there's another: why the deaths so huge? It's at the end of these wars, you've broken the transportation system that gets produce to hungry mouths. And you've also removed so much manpower, literally from the fields, you aren't producing anything.

So that you're talking about mass starvation as a result of having done a number of previous years of warfare. And also, this mass starvation helps account for why one side gets shattered and quits.

01:51:46

And then the thing that ought to give everyone pause is, okay, if Japan's economy was, I don't know, a tenth of ours in World War II, and these are the kind of costs that they could inflict, watch it on getting into wars with countries that you think you're vastly superior to. It may not work out.

The costs are horrific. You're illustrating the problems with this continental order of taking territories. This is how it goes.

But sometimes other people visit wars on you, and then you're into their world of hurt. I mean, I think it's like Angela Merkel or Neville Chamberlain back in World War II, who's going, surely these people don't want to do this. It's going to wind up being a bloodbath. Surely they don't want to.

So Neville Chamberlain makes the compromise at Munich, thinking, surely this will be it. We'll compromise them, and they'll go. Wrong. They're going to come in for the whole thing.

Dwarkesh Patel 01:52:45

Yeah, I still feel like I don't understand. So there are a couple of options, and maybe the correct answer isn't one of these.

One is that there's just denialism. They genuinely do not understand that they are going to lose the war. Another is that they know they're going to lose, but they would prefer to just go out dying.

Sarah Paine 01:53:07

Well they're dead anyway, so who cares, right? Well, this is the problem with nuclear weapons. Push someone into a large corner, or a tight enough corner, maybe they think, "Well, it's my last day, so guess what? It's going to be your last day, too."

Dwarkesh Patel 01:53:18

How much should we question the demand for unconditional surrender, knowing that most of the deaths in World War II happened because, if there wasn't unconditional surrender, maybe we could have reached a peace earlier, and most of the deaths could have been spared?

Sarah Paine 01:53:29

I think by the time, those deaths are Japanese deaths for us. We're looking at these things and we've lost a lot of people. Our idea is, "Hey, we're almost there." We're not worried about Japanese civilian deaths or Japanese military deaths.

We're looking at, these are the people that have killed how many Chinese? And I don't believe China was ever part of Japan. What were they doing there?

So then you're asking for tender mercies out of Americans for the Japanese in those days? No. After all that had happened. It's ugly by the time you've killed this many people, the kind of bitterness on both sides.

Dwarkesh Patel 01:54:09

Was it a mistake to demilitarize Japan after the war? A couple of things: One is, look, they did become an ally afterwards, and if they did have a military, they could have helped us in the Korean War. The outcome might have been different with military help.

Sarah Paine 01:54:26

Yeah, it was the secret part. They were the ones who knew about de-mining. So we had a big military drawdown after the end of World War II, and the Navy really got cut back because it was going to be all nuclear weapons, and the Air Force was going to deliver, so the Navy didn't get a whole bunch of things that it cared about, including minesweepers.

So when the North Koreans are throwing a lot of mines around, it's Japanese that are helping. It was secret; that's why you don't know about it. It's not your fault. It came out in research that's recent.

Another thing, when MacArthur does his Incheon landings, it's very tricky because the tides are, I don't know how many feet, like 30-foot tides. It's enormous. So you can really get stuck on mud flats if you don't time that right and know where you’re going. Who are the pilots to bring it all in? It's Japanese.

And also, they did- they’ve always had a self-defense force, and they have a very competent navy. It's called a Self-Defense Force, but anybody else would call it a navy. But that then fits in with what the Constitution says we're supposed to have.

But why is it a mistake to demilitarize Japan?

Dwarkesh Patel 01:55:32

Another reason is they had a vested interest in making sure the Communists don't take over in China. They could have provided some amount of support. If the Nationalists, it's obvious the Nationalists are going to lose, at that point maybe they would have decided to support the Nationalists against the Communists.

Sarah Paine 01:55:47

It's too late. They've gutted the Nationalist armies. That was a massive strategic error on the-

Part of it comes from arrogance of don't denigrate the other side. And so the Japanese thought they could… China’s a failed state. These people are hopeless. They can't even decide what they're doing. They got warlords everywhere. We’ll just push them around and we’ll get what we want.

And they do really well moving into Manchuria. Manchuria is, it’s bigger than Germany and France combined. The Japanese actually stabilize it, do lots of investment, that's why it becomes the most industrialized part of Asia outside the home islands, more so than Shanghai, and this is in the 30s. And so they're looking at it like, "Things look pretty good, and they'll get away with it." And they didn’t.

Dwarkesh Patel 01:56:38

Let's say in the couple of decades before the war, if you looked at somebody who's young and ambitious in Japan, the kind of person who might come to Silicon Valley today if they're in America, what do they want to do? Do they want to join zaibatsus? Do they want to become a general of the military?

Sarah Paine 01:56:54

This is social history. I just don't know. You don't see a lot. I just truly don’t know.

It has to do with- there is so much to know in this world that all of us, all we can do is know a corner of it. So my corner has been a top-down. Actually, in my expertise, I started out with a major field in Russian history and a minor field in Chinese history. And then I realized to deal with those two, you had to understand Japan.

So you're asking me in my weak suit about the social history of that country, and I can't answer it.

Dwarkesh Patel 01:57:30

My friend, Ege Erdil, has this theory that, look, you have… many of the powers that we fought in the 20th century -- Japan, Korea, Vietnam, not fought directly, but at least fought over -- we ended up becoming allies with them later on, and in fact, they're some of our best allies.

There’s two ways to think about it. One is that we had especially good diplomacy with them after the war. The second is that despite having the same interests, despite being in a position where we should naturally have been friends, we basically screwed up the diplomacy beforehand and we didn't ally ourselves with the peaceful factions within these countries. Basically illustrates a failure of diplomacy on our part, that the natural allies we ended up going to war with.

Sarah Paine 01:58:18

There's no manner of diplomacy that's going to solve Hitler. One of the lessons is, don't let the world global economy melt down. When that happens, people who are, not only Americans will get desperate, but people who are poorer than Americans, which is lots of the world, will be truly desperate. So you don’t want to do that.

As for why the Japanese and the Germans are such wonderful allies as they are, is a- what’s the word? It's a testament to having a generous peace. So the peace after that war was not to get even, which is what had been the peace in World War I. It was “the Germans, they'd done all this terrible stuff, we're going to make them pay all kinds of money and we’re going to tell them it was all their fault and blah blah blah”. That doesn't work well.

01:59:12

That was of course what the Russians did to East Germany. But for West Germany, it's how do you reintegrate them back into Europe and get them back on in? And the Japanese as well. And also a serendipitous fact of the Korean War, the Japanese economy was a total mess at the end of this war.

And the United States, of course, is overtaxed because there's Europe to rebuild and there isn't enough money to do everything. But when the Korean War hits, which is immediately in 1950, tons of the supplies were bought in Japan.

That's how the Japanese economy initially is restored, is through the Korean War and then the Vietnam War. Because it's the local place to buy, or more local than the United States, to buy a lot of supplies. So that's another piece of why things work out.

02:00:04

And then there's also the Germans and Japanese who lived- you were asking about what do conscripts think? There are two brilliant generations in Japan's modern history: one's the Meiji generation, and the other is the post-World War II generation.

They did this incredible makeover, and I think of all these wonderful, high-quality products they built. Like, I love my Toyota. I want to rent an American car, and “I can't find anything.

I'm going to kill everyone as I'm trying to figure out how to adjust something”. Whereas Toyota is like [makes noise], and we're good.

Dwarkesh Patel 02:00:45

When you were showing the things we learned from the diary entries of different admirals or generals on the Japanese side, as you were going through the archives and looking at these things, I'm curious, were they writing knowing that they were writing for future historians? Or was that actually just a personal record-keeping thing? What was the motivation behind it?

Sarah Paine 02:01:08

I did not read that stuff in archives. Japanese Archives, I was doing strictly diplomatic stuff. The Admiral Ugaki Matome's diary, which is the one thing I cited on these subjects, it was translated. Someone ran into it, translated it into English, and so I just read the whole thing.

And then the other stuff, the quotations, I think I was ripping off secondary sources where someone else had done the archival research. Then I pulled those, but those are where the quotations came from.

So again, I haven't done this deep read into all of the Japanese thinking about all of these… of what motivated them in their careers. That I have not done.

Dwarkesh Patel 02:01:54

Was there some big “aha” moment when you were going through all this Japanese stuff where like, "I get why they did certain-" like, "Why certain things transpired in a certain way?"

Sarah Paine 02:02:05

Oh, well, this lecture. When I came to the Naval War College in 2000, they assigned me a couple of lectures because it's a group-talk class, and everyone only gives two, three, or four lectures. And so I was junior; I think I got one or two.

And so I was told to lecture on World War II Pacific, and I'm like, "Oh, great, U.S. Navy. This will be a joke, me telling them about World War II Pacific." Because I'm a historian, major field Russia, minor field China, and I've dabbled in Japan. And now I'm supposed to talk about this.

02:02:42

And so my husband said, "Well, hey, I've taught this survey of Japan, read this Bushido stuff." He had some of the books hanging around the house. So I read that, and then I had read some basic things about World War II.

And then I… This is why I'm telling you, I think it's a useful way to approach other cultures: read what they read. What are the key books? Apparently, these were key books.

Then you go, okay, if I believe this, then I'm going to take Western analysis, of going [makes noise] rather than mirror imaging. I'm going to basically take their software, put it in here in my brain, and then say, "Okay, does this inform me of what they're doing in the banzai charges?" I was like, "Oh, yeah, that actually makes sense now," instead of saying, "What are these nut jobs doing?" And it's everyone.

No, no, actually, if you believe these things, then this would explain how you're doing things. So that's how that all came about.

Dwarkesh Patel 02:03:46

One thing I was wondering about when you were talking about the inter-service rivalries is, what were the allies doing that they avoided this? Not only between the branches of the military, but I mean, allies, the word literally says it. Japan wasn't coordinating with Germany in the way that America was coordinating with Britain.

How are the institutions set up, or the culture set up, that made this possible?

Sarah Paine 02:04:09

Well A, this institutional rivalry is terrible all over the place. Alright, more concepts that are useful: It's really helpful in looking at people when you have disagreements with them, to ask: who is their primary adversary or rival? Not their secondary - they probably have a whole list of people who annoy them - but who's the number one?

And then, where is the primary theater of wherever that disagreement is? Look at that. And then, on the disagreement, how big a deal is it? Is it existential?

Japan thought- the reason it was undeterrable, that we didn’t figure out- they thought their entire existence was at stake, that they absolutely had to have territory in China to survive. Now, whether that's correct or not is a completely different story. If they believe it, that's what's motivating them.

02:05:00

So, on these, when you're looking at the war on the Allied side, who's the primary enemy? Hitler. Where's the primary theater? Germany. Even though there's Japan, it was Germany first.

And what about the ultimate goals? Well, actually, that doesn't align at all because the Russians want a communist wonderland, we want everyone decolonized, and the British want the empire on which the sun never sets. But they all shared an intermediate objective that Hitler has to go, and that's a superglue of an alliance.

Now, let's flip it to the Axis. Okay, Italy, what does Italy want? Roman Empire, whatever it is. So, who's the primary enemy? Well, Britain, because that's where British interests are.

Okay, Hitler, what's he all about? He wants to do his Lebensraum, Nazi wonderland all over Eurasia. So, who's his primary enemy? That would be Russia.

02:06:04

Japan, what do they want? They want the big Japanese empire. Who's their primary enemy? It's China. And then when they get us in, they got a whole new problem in their hands. None of this aligns, and so the Axis aren't going to be trading many resources with each other, whereas the Allies... and it really goes back to your original question about the British.

Part of their way of running wars is you absolutely coordinate with allies. You give them serious resources, which is what they finally figured out at the end of the Napoleonic Wars, where they're paying big money to support different allies, and then you've got to put skin in the game, actually send your armies.

So the British, and we take this from them, we're students of the British on this, do massive sharing with Lend-Lease. And Stalin isn't sharing too much with us.

And then we've got the unified commands with the British because they're all about doing that. The Russians won't even let us on their territory to do things. And there are only little bits with the Arctic convoys going up to Murmansk, and it's hardly anyone who's up there.

This is a different cultural tradition on how to deal with things.

Dwarkesh Patel 02:07:18

All right, we've come full circle. I think that's a great place to close things. This was excellent. Thank you so much, Sarah.

Sarah Paine 02:07:25

Thank you for coming.

I’m thrilled to launch a new trilogy of double episodes: a lecture series by Professor Sarah Paine of the Naval War College, each followed by a deep Q&A.

In this first episode, Prof Paine talks about key decisions by Khrushchev, Mao, Nehru, Bhutto, & Lyndon Johnson that shaped the whole dynamic of South Asia today. This is followed by a Q&A.

Come for the spy bases, shoestring nukes, and insight about how great power politics impacts every region.

Huge thanks to Substack for hosting this!

Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.

Today’s episode is brought to you by Scale AI. Scale partners with the U.S. government to fuel America’s AI advantage through their data foundry. The Air Force, Army, Defense Innovation Unit, and Chief Digital and Artificial Intelligence Office all trust Scale to equip their teams with AI-ready data and the technology to build powerful applications.

Scale recently introduced Defense Llama, Scale's latest solution available for military personnel. With Defense Llama, military personnel can harness the power of AI to plan military or intelligence operations and understand adversary vulnerabilities.

If you’re interested in learning more on how Scale powers frontier AI capabilities, go to scale.com/dwarkesh.

I highly, highly recommend both "The Wars for Asia, 1911–1949" and "The Japanese Empire: Grand Strategy from the Meiji Restoration to the Pacific War".

(00:00) - Intro

(02:11) - Mao at war, 1949-51

(05:40) - Pactomania and Sino-Soviet conflicts

(14:42) - The Sino-Indian War

(20:00) - Soviet peace in India-Pakistan

(22:00) - US Aid and Alliances

(26:14) - The difference with WWII

(30:09) - The geopolitical map in 1904

(35:10) - The US alienates Indira Gandhi

(42:58) - Instruments of US power

(53:41) - Carrier battle groups

(1:02:41) - Q&A begins

(1:04:31) - The appeal of the USSR

(1:09:36) - The last communist premier

(1:15:42) - India and China's lost opportunity

(1:58:04) - Bismark's cunning

(2:03:05) - Training US officers

(2:07:03) - Cruelty in Russian history

Sarah Paine 00:00
I need to start with a disclaimer, because I work for the US Government, and they require you to do a disclaimer. So: the ideas that you're about to hear are my ideas. They don't necessarily represent those of the US Government, the US Navy Department, the US Department of Defense, let alone the Naval War College where I work. Are we all good on this?

All right, so today I'm going to tell you a story of three protagonists, Russia, the United States and China, that all wanted to work their magic on India and Pakistan, which didn't exactly appreciate it.

So two big topics. One is intervening in someone else's problems, a cottage industry for the United States. And also before you do that, you really ought to check out the alignments. Who's the primary adversary of whom? How long has it been that way? And also ask these questions about all the neighbors and anyone who might want to crash the party along with you.

It's also a story of a series of limited wars. What's a limited war? It means it's for something less than regime change. So however it turns out the governments that started that war are still in place. And two of them resulted in quick victories, the ideal in warfare. The first one was the Sino-Indian War of 1962. And the other one was the Bangladesh War of Independence in 1971. And these wars change things in many short term expected ways and then in many long term, highly unexpected ways.

So I'm going to go into all of this with you all. So here's my game plan, and it's literally a game plan. I'm going to start out with the pivotal decisions made by different players. Then, once they're made, certain things are foreclosed and certain things are possible. And this is the playing field delimited by these pivotal decisions. And then I'm going to look at the teams. Some allies were prime allies, others were subprime, and they mixed and matched over time. So I'll do teams, and then I'll do the game, the interaction, and then at the end, I'm going to do the plays, some of the techniques, things that you can do to play this game.

02:11

Pivotal decision number one. When Mao won the Chinese Civil War in 1949, it didn't end. He also spent the next two years not only eliminating Nationalist remnants, but also conquering Xinjiang and Tibet. Tibet had been autonomous since 1911, when the last dynasty had collapsed. And Mao decides that he is going to reconquer Tibet. Tibet's an interesting place it contains, I think, about 40% of China's mineral resources. So there's a lot of money being made in Tibet for those with the capital to invest in big mines. If you look at this map, the Han Chinese, the preponderant group of China, they inhabit, they dominate as far west as the Chongqing Basin and Sichuan. And then you go further west, in-towards Tibet. China has put large armies into Tibet exactly twice. Once under the Qianlong emperor in the late 18th century, they didn't stay for very long. And then under Mao in 1950. And they have stayed forever and built roads so they could keep on sending more in. Between 1950 and 1957, China built a series of road systems through Tibet. And the western route there is the only one that provides year round traffic. The problem with the other two is, well, check it out. They go through 14 or 15 mountain ranges. It means you go vertical up, vertical down – do that 14, 15 times. And then between monsoon rains and snow and mudslides, they're very difficult to maintain. And then the eastern one crosses the major river systems of South Asia. So that's difficult. So only the western route is the really good one. And it's really important for the Chinese if you want to conquer Tibet, you truly want that one.

All right, so if you look at this, that western route provides not only the ability to control Tibet, but it also provides a pincer onto Xinjiang. If China wants to come in one way and the other way, it's a good way to get in. If you look at those two circles there, those are the disputed areas between China and India. The northern one is the Aksai Chin Plateau, which China has taken from India and India still claims. And in the south is Arunachal Pradesh, which India still owns, but China claims. And so these are the areas that they're fighting over. But once China took Tibet…Before, there had been a big buffer zone between China and India, right? There's all this Tibet, and no one could really get in there. Now China's built roads so it can get into places where India cannot deploy troops until it gets into the road races with the Chinese. And so it reduces the buffer zone between China and India to these small Himalayan kingdoms of Nepal, Bhutan and Sikkim. So it changes things. So that's pivotal decision number one, deciding to conquer Tibet.

05:40

Pivotal decision number two is the United States, in order to deal with the Soviet Unions under Eisenhower, did what the wits back in the day called pactomania. What is that? It's forming all sorts of bilateral relations and also regional groupings in order to counter the Soviets institutionally and wall them in that way. And part of this was what was called the Northern tier strategy, as seen in the Baghdad Pact, where you get Turkey, Iraq, Iran and Pakistan to form this thing, and it's to wall off the Soviet Union from the oil fields of the Middle East. East. And the other thing, you should look at this map before it goes away. Look where Pakistan's located, where you think it is, and then go to the east, and that's East Pakistan. In the 1971 war, there's going to be a civil war, and Pakistan's going to lose East Pakistan, which is Bangladesh today. So just keep that in mind.

So as part of the sweetener for Pakistan to join the Baghdad Pact, the United States allied with Pakistan and gave them a big military aid treaty. And here's Nehru, the Prime Minister of India, and he is horrified. A military pact between Pakistan and the United States changes the whole balance of power in this part of the world, affects us most especially. The United States must realize that the reaction of India is going to be, you're arming the Pakistanis. Whom do you think they're going to shoot? It'll be us. And the Indians were just appalled that we did this. And afterwards, Eisenhower admitted it was “perhaps the worst kind of plan and decision we could ever have made.” It was a terrible era, but now we're stuck with it. Because what the United States is slowly discovering is that if you arm either India or Pakistan in this period, it's going to aim it at the other one. And so that pact poisoned US Relations with India for the duration of the Cold War and set up things in ways the United States ultimately wasn't happy with.

06:52

Okay, those are two pivotal decisions. Now for a pivotal situation, it's really the devolving situation between Russia and China. Until Mao got atomic weapons in 1964, he really had to shut up. And because he needed Russian technological aid, he's been totally cut off from the West. After the Korean War, he's being isolated. So he truly needs Soviet aid. And he also, if he wants nuclear weapons, he needs some of their aid to do that as well. So he has to keep his mouth shut. But once he detonates an atomic weapon, here's what he tells the Russians, and they just about lose it. There are too many places occupied by the Soviet Union. The Russians took everything they could. We have not yet presented an account of this list. Under the czars, the Russians took from the Chinese sphere of influence territory exceeding US east of the Mississippi. Think the Chinese didn't notice? Yes, they noticed. So Mao all of a sudden is calling that. And the Russians are appalled.

But Mao has other gripes against the Russians. Stalin, in the lead up to World War II, had made sure to set up the Chinese to fight Japan so that he wouldn't have to. So that leaves him just fighting Nazis, not Nazis and Japanese. And then Stalin takes Mongolia, which had formerly been a part of the Chinese sphere of influence. And in the Korean War, he's more than happy to fight to the last Chinese. And then during the Chinese Civil War, the Russians tell Mao, oh, stop, Yangtze, you need to take a little breather here. Because he wants a divided China like the divided Germany he has, and then the divided Korea he's going to get. You want to be surrounded by these little broken states around you if you're a continental power. And then when Stalin dies and Mao wants to be senior statesman of communism, Khrushchev is appalled by that. Then Mao is appalled when Khrushchev does de-Stalinization, because Mao has his own cult of personality. And then Khrushchev wants to do peaceful coexistence with the west, while Mao is ramping it up in the Cultural Revolution, so there's no meeting of the minds. And then all this becomes very public when it hits the propaganda press of the Communists, of the Sino-Soviet split in 1960.

09:02

All right, the Russians have their own gripes about the Chinese, and here's how they go. The Russians look around at the west and particularly the United States and go, wow, they got bases everywhere. The British have got bases everywhere. How come our allies won't give us bases? I mean, well, if you occupy Eastern Europe, the whole place is a base, but that's a different matter. So the Russians want the Chinese to let them keep a couple of remaining czarist treaty ports, essentially, and want to expand them. And the Chinese say, forget it. And in fact, after the Korean War, when the Chinese have troops all up in Manchuria, which is where these bases were located, and there's a succession struggle going on because Stalin's just died, the Russians have to return the bases because there's just too much bad stuff happening where they live, live. And then what Mao does in ‘54 and ‘58, which just appalls the Russians, are these two Taiwanese Strait crises. What's going on? Mao is lobbing all kinds of ordnance on these islands that are owned by Taiwan, that are very close to the People's Republic's shores. And the Russians are appalled they are not consulted, and yet they have a friendship treaty that obligates them to provide to join a war under certain circumstances. And the Russians are going, whoa, whoa, whoa, whoa, there could be nuclear follow-on from this stuff. So the Russians then asked the Chinese if it's okay if they have a combined naval base on China's shores. And China says, forget it. The Russians are thinking, okay, well then we're not going to give you any of the plans for the atomic weapon. And it all devolves. So there's no love lost on either side. And then what exacerbates these tensions is the Vietnam War where China wants influence over neighbor Vietnam. That is pretty typical. But Russia wants influence over Vietnam to do a pincer on China, which China doesn't like at all. Meanwhile, both of them want to prove their revolutionary credentials by aiding the Vietnamese, North Vietnamese. So Russia's aid needs to come by train over China, lest the United States sink at the good stuff if it goes by sea. So the Chinese feel obliged to let it go through, but they're just hassling Russians the whole time through. They take it apart, tear it apart, say it was from China. And the Russians are just apoplectic. So their relations are getting worse and worse and worse and the squabbling is just incessant.

So it's not surprising that the Sino-Soviet border conflict of 1969 breaks out - during the Vietnam War. And while all this is going on, this is one of the river islands, the Amur river forms much of their border, and this is one of the islands there. And there's much fighting over it. And the Russians come to us, the Americans, and say, “is it okay if we nuke these people?” And the United States says, “no, there's no way it's okay to nuke these people”. And Mao figures it out. The one that wants to nuke you, that's the primary adversary. So prior to that moment, the United States is the primary adversary of both Russia and China. Now with this, they're primary adversaries of each other. It causes a reshuffling of the allies, and I'll get to that later. So, okay, I've done the playing field of these decisions that delimited it. But now I'm going to get to the allies, and some allies are better than others. And here we got Mao and Khrushchev. Look at these lovebirds. Boy, when that divorce took place, boy did it mess up the extended family. Nevermind.

12:24

And the point, for my purposes tonight, I'm going to use the word "alliance" really loosely. If you sign a mutual defense pact - for my purposes tonight that makes you an alliance - allies. And if you're a political scientist, you've got something that's much more complicated, but forget it, I just can't handle it. So we're going to do it this way. All right?

So Stalin didn't think much of Nehru at all. He thought he was a lackey of British colonialism. But Khrushchev thought India was really important to counterbalance China. And here's Nehru thinking about it. Well, look, we have to be on friendly terms with both Russia and America. But actually he felt much more in common with Russia. Why? Because he favored Fabian socialist economic policies that were much more akin to what's going on in Russia than it was in the United States. Moreover, the United States was segregated, which appalled Nehru. And in addition, the United States was cozying up to all the colonial powers. So Nehru thought the Russians were the better bet. While all this is going on, the Indians were non-aligned and they treated the Chinese really generously. And I've got a whole list of generosity. So India immediately recognizes China in 1950. Countries like the United States didn't, for forever. And when the San Francisco Treaty, I think, is signed in 1951 in the United States, ending the war with Japan, India refuses to sign because China and Russia aren't there to sign as well. And then to help China break out of its diplomatic isolation at the end of the Korean War, India signs a friendship treaty with China. And as part of that friendship treaty, it recognizes Chinese sovereignty over Tibet. Under international law, contrary to what Vladimir Putin is doing lately, under international law, if you recognize someone's sovereignty over territory that is permanent, you cannot back out of it legally under international law. So the Chinese promise, I don't know, there's some like, peaceful coexistence or whatever they're promising the Indians, but that has no permanence under international law, whereas this thing does. And then from 1960 on, the Indians are voting to seat the People's Republic of China, not Taiwan, on the UN.

14:42

Meanwhile, in the background, all this road-building is going on. Those roads are being built between 1950 and 1957. And the Indians aren't going to figure out until 1958 that the roads are there. Meanwhile, road's completed. The Chinese want to complete their control over Tibet, and so they're going to send big armies up there. And Tibetan culture is much more…It's of Indian origin, it's not Chinese origin. So this repression of Tibetan culture just appalls the Indians. And then two days before the People's Liberation of Army is going to make it into Lhasa, which is the capital of Tibet, the Dalai Lama flees-he's the spiritual leader of Tibet-he flees to India where he's remained ever since to the absolute horror and anger of China. So at about this time, the Chinese come to the Indians and say, “look, why don't we do a swap on sovereignty?” You recognize our sovereignty over that Aksai Chin Plateau where nobody lives, but it's really good for the roads, China's western route. And then we'll recognize your sovereignty over this much more densely populated Arunachal Pradesh. And Nehru doesn't want to hear anything about it.

So during the Cuban Missile Crisis, when Russia is much too busy worrying about who's going to be lobbying nukes at home, this is when China launches the 1960s Sino-Indian War and China just takes the Aksai Chin Plateau. The Indians are appalled because they don't have any roads to be able to deploy up there, whereas the Chinese do. Their defeat is just total. And they can't believe the Chinese did this to them. Here's Nehru afterwards. There are not many instances in history where one country, that is India, has gone out of her way to be friendly and cooperative with the Chinese government and people and plead their cause in the councils of the world.

And then for the Chinese government to return evil for good — and even to go to the extent of committing aggression and “invade our sacred land”. Who does this? So I get it. The Chinese get the territory they want. That was the goal of that war. But what they have done is taken a country, India, which had its leadership terribly idealistic, not interested in becoming militarized at all and making them angry forever. India immediately doubles the size of its army within the next 10 years to up to 750,000 people. Creates 10 mountain divisions useful against China. And they've never ceased being so angry. And then if you think about this, what if instead of playing this game this way, China and India had teamed up? I would suspect we would be in a completely very different world order now, if that is what they'd done instead. But this is China's decision, not India's fault on this one.

17:38

All right, so that wasn't great. So let's check out other possibilities once that happens to India. India is all of a sudden looking for Russia to counterbalance China. And you also have Pakistan wondering what to do and what the Pakistani notice after all this: well, the Chinese are not going to be teaming up with India, right? They've just invaded the place. And so this is when Pakistan sees that China might have real possibilities as an ally. And Bhutto is going to play the China card for the nuclear chip, trying to get Chinese help for all of that. And here's what happens. So you have the '62 war, and then in 1963, Pakistan really inexplicably is ceding territory to China. Who does that? And there are various possibilities down here, but I'm surmising it's because it's going to help on nuclear development. That would explain why you would give a lot of territory. But we don't know. There was supposed to be some mutual defense pact maybe, and there's some other things going on. Anyway, you can imagine what it may or may not have been. Okay, in the case of Pakistan and China and India and Russia, they had quite a good relationship because the Pakistanis and the Chinese shared India as their problem, and the Russians and the Indians shared China as their problem. And that worked pretty well. But the United States was just a disaster from both Indian and Pakistani point of view, and vice versa, because the United States wanted to befriend both of them. But if you befriend one, the other is appalled. And so the United States wound up appalling everybody. And so what the United States wanted to have happen is for India and Pakistan to put aside their differences and then combine against China and stop communism from spreading. India and Pakistan want to use the United States for maximum aid to use against the other, which is a non-starter for the United States. And then Pakistan really would like it if the United States would be nice with China as well, because Pakistan wants to have good relations with the United States and China. And that's a non-starter for the United States until 1971, when there are secret visits and things going on. It's later on.

20:00

Okay, so in 1962, India gets trounced in this war with China. They look like they're militarily feckless. And then in 1964, Nehru dies, right? He'd been the head of India since independence in 1947. He'd been there a long time, so he's dead.

So 1965, if you're Pakistani, it looks like a good year to settle border problems. And so what they do is first they invade through the south, if you look way down at the bottom there, the Rana Kuch, and that seems to go pretty well. And then they decide they want to go for the thing they really care about, which is Kashmir, and they do that well, the enemy gets the vote. And the Indians invaded straight through Lahore, which isn't remotely what the Pakistanis had in mind. And then the United States does a double arms embargo on both of them for doing this. And the problem is the Pakistanis are much more dependent on U.S. military aid. The Indians were more diversified, so they just didn't have enough spare parts to continue this thing. So it's a very unhappy event for them. They lose it, and what happens, neither the United States nor Russia wants either one of them fighting that war.

The Russians are thinking, “we want the military aid to go to India in order to counterbalance China, not to decimate the Pakistanis”. And the United States doesn't want it either. So the United States is very happy that the Soviets broker the Tashkent Declaration that ends this war. But Pakistan is worse off after this thing. And India has restored its reputation for knowing what it's doing on the battlefield.

So for Pakistan, the United States is really problematic because we're interested in being nice to them when we want something out of them, and then we're not so interested when we don't want something out of them because we don't share a primary enemy. So what we really wanted were listening bases. The technology of the day was such that if you want to surveil the Soviet Union, you want to send these big U2 planes over and given their ranges, and you're not supposed to be doing it. And so we had U2 bases, I think Norway, West Germany, Turkey, Pakistan, and then Japan. And in addition, we had a listening base at Badaber. And these are really important things for us during this period. So we're paying the Pakistanis a lot of money to get it. And except there was one of these U2 planes, gets shot down over the Soviet Union. They finally get it so they can. Because they fly at really high altitudes, [but] they shoot it down. And Khrushchev is furious. He hauls in the Pakistani ambassador in Moscow and he goes, where is this place Peshawar? We've circled it on the map, and we're going to blow it off the map if you all don't wise up. And the Pakistani is like, whoa, whoa, whoa, whoa, whoa.

22:56

And so between these sort of threats in 1960 about the U2 and then the United States freezing arms in the 1965 war, which the Pakistanis believe they lost it over that. Oh, yeah, and by the way, in that 1965 war the United States had, when we provided arms to everybody, we said, oh, we will guarantee that no one uses it, that Pakistanis and Indians don't use it against each other. And of course, we could do nothing about that. And in the 1965 war, the Pakistanis are using US tanks to go after Indians in the largest tank war battle since World War II. So there are a lot of upset people in South Asia. But here is Ayub Khan, leader of Pakistan, telling the United States that the United States forgets that our security hazards and political liabilities have increased to a dangerous level due to this U2 stuff. And we kept our part of the contract whilst the Americans betrayed us at every turn. They built up India against us, they failed to help us in the ‘65 war and finally stopped military aid. They think that we exist for their convenience and that our freedom is negotiable. Dream on. So when the lease came up for the listening post at Badaber in 1968, the Pakistanis canceled it. They're sick of it.

Meanwhile, the Indians weren't too thrilled about the United States either. This is earlier when Franklin Delano Roosevelt was president: here's Mahatma Gandhi telling him allied support for freedom and democracy seems hollow so long as America has the Negro problem in her own home. Indians were appalled by segregation. They knew exactly which end of the bus they'd be sitting on. So there are issues both ways. And in fact, Nehru and his daughter Indira Gandhi found the United States really impossible to work with. And they looked at capitalism as the way station to imperialism and fascism, whereas Americans looked at socialism as the way station to communism. So there's no meeting of minds on all of this. And so if you look at the alignments of primary adversaries India and Pakistan, from most of the time our primary adversaries: India is always Pakistan's primary enemy. But you could argue that with the ‘62 war, is it Pakistan or is it China who is the primary adversary of India? And then when you get to the 1971 war, which I'll discuss a little more in a second, where Bangladesh is broken off and then Pakistan is left, has less than half the population, then you could argue that for India, China is the primary adversary. And then if you look at that reshuffling, if you also look at the 1969 war, that reshuffles the nuclear powers. So formerly Russia and China had shared the United States as their primary enemy. But after the ‘69 war, they're each other's primary enemy. And this gives the United States the swing position of team up with A or team up with B. And the United States teamed up with China to overextend Russia in the Cold War, because I had always felt that the Soviets were the bigger threat in those days.

26:14

So anyway, as you're looking at alignments, you can apply this kind of framework to any country on the planet to try to figure out what's going on. And think about how alliances work. If I look at The World War II Allies – probably one of the most effective alliances in world history, if you think about what people ultimately want. The British want an empire in which the sun never sets, the United States wants to decolonize everybody, and Joe Stalin wants a communist wonderland. Those are mutually exclusive. But to get there you have to go through the common way station of getting rid of Hitler. So the common existential threat can be a superglue of the most unlikely partners.

But let's look at the Axis. What they want at the end of the war are series of influence in different parts of the world. So for Italy, it's empire in the Mediterranean, Japan in the Pacific, and then Hitler, it's all over Eurasia. That's not mutually exclusive. But if you look who they're the primary enemy, who stands in the way of those plans, it's Britain for Italy, it's Russia, for the Germans and for the Japanese, it's first China and then the United States. None of it aligns. So they fight parallel wars and allow the allies to, to take them out in detail. So when you're thinking about alliances in the world today, when you're wondering what's going on with Iran or whatever, figure out who's their real primary enemy, get it straight. Does that primary enemy, is that an existential threat for them? So if you've got countries that line up on same primary enemy, existential threat for all around, the most unlikely people will cooperate. On the other hand, people who are very likely to cooperate, maybe like the fascists, they all shared this basic ideology. But if they don't have the same primary enemy and the same theater of interest, geographically the same theater, they may not cooperate very well at all. So you can apply this to anything you want to apply to.

So back to my game here. If you're looking at the cards people have to play. The United States has lousy cards because we don't share primary enemies with anybody. So it's a stalemate. You help India, the Pakistanis hate you. You help Pakistanis, the Indians hate you, it's no win. But if you look at India, India and Russia share a China problem. That's good, they can cooperate on that. And then you have Pakistan and China, they share an India problem. They can make things happen over that. So there are cards for them to play and 0 for the United States to play. It's just the way it is. So the name of the game and strategy is to get the outcome that you want to have happen. And it's like, how do you play this game of five person, five country cutthroat billiards to get remotely what you want out of it? So for the English majors among you, I have a metaphor. For the rest of you, you can just bear with us. Imagine a game in which every ball can be a cue ball and players can take turns, come, leave, do whatever at will. Sometimes they'll cooperate some of the time, but they don't necessarily want to put the same ball in the same pocket. And so if that's the case, there's going to be no enduring cooperation. And understand that you want to have your goal is going to be the ultimate shot you want to take. But as you're taking the intervening shots, people are going to try to disrupt it. How on earth do you get through this game? So this is what the next section is all about. So if you look at this map and where Pakistan's located, it's this very strategic location right in the center of, not quite center, but of Eurasia, the center of the Soviet boundary there.

30:09

And I'm going to give you a map. This is from Halford Mackinder. He's one of the finest, most famous people to publish on geopolitics. This is his 1904 map. It's actually quite famous. And he talked about how Russia occupies the heartland. In his day it was all these railway systems. He thought that was the prime piece of real estate in the world. And then it's surrounded by this inner marginal crescent. You look where Pakistan's located, it's right in the center there, right up by Russia. And it's a really crucial location before satellite imagery is available, to put listening posts on Russia. Russia's huge. You gotta have a bunch of listening posts to track their missiles and things. And then if you want access to Afghanistan, which when the Russians go there, we want access, and of course, when we go there, we really want access. So it's a strategic location. For the Pakistanis the United States was so frustrating to deal with because we'd be on-and-off interested in them because we don't align on a primary enemy.

So pre-satellite-imagery, we really wanted to cozy up to the Pakistanis. So we have U2 bases but then there are technology changes. And before we get facilities in Iran, we want this listening post in Badaber. But technology will eventually change. And then for a while, we truly want the Pakistanis to get the mail through to China when we're trying to break China out of diplomatic isolation and then cooperate against the Soviet Union, and Pakistan delivers the mail. But then we set up an alternate setup in Paris to go through our embassies that way, and Pakistan is again irrelevant. And then when Russia's in Afghanistan, Pakistan's essential to get aid to insurgents to cause the Russians trouble. And then, of course, when we're in Afghanistan, we really want to cooperate with the Pakistanis. And that works until we cap Osama bin Laden without telling the Pakistanis in their territory, Abbottabad. And then relations are really not so great. And so it's a very bumpy ride. And in these periods when we really need the Pakistanis, we don't pay attention to human rights or the really big one, nuclear proliferation. And so the proliferation is pretty steady.

So if you look at after the United States having trouble negotiating all this so that whatever you do in the short term doesn't wreck you in the long term. But in order to get to the long term, of course, you've got to go through the short term. So after the U2 crash in Russia, where it gets shot down and the Pakistanis are having a heart attack about that, that's when the Pakistanis look to cultivating more, better relations with China because the US relationship is just too potentially costly and the Americans cut off the military aid. And then when the Pakistanis are being very nice about delivering the mail for Richard Nixon and Henry Kissinger to line up invitations in Beijing, the United States is ignoring a humanitarian nightmare because all of this that coincides with the 1971 Bangladeshi War for independence.

33:00

So let me explain what that is to you. So Pakistan was holding presidential elections. The dominant ethnic group in Pakistan are Punjabis, sometimes the Sindhis, like the Bhutto family. I think they're Sindhi. But anyway, generally speaking, particularly the army, the Punjabis dominate. Bengalis, live in Bangladesh. They won the election and the Punjabis are furious. So they send the army to start butchering people in East Pakistan to overturn the election. So there are refugees pouring into India. So this is the backdrop of what is going on there. And for anyone who wasn't in the know, the United States is saying nothing about this. The United States, there's this massive humanitarian crisis in the United States. Got nothing to say. The United States has Something to say about everything. But it had to do with,this is the moment that Nixon is trying to get himself invited to Beijing so that he can talk to Mao about cooperating. With the Soviets. No, with the Chinese. To overextend the Soviets in the Cold War, which is ultimately what we do. And it's very important to win the Cold War. And this is integral to this. But everyone else is looking and going, what on earth is going on? So you have Nixon's doing the mediation in the background. We got refugees flying all over the place. India comes to the United States and says, look, you need to tell the Chinese not to intervene in this thing. And not only did we not do that. Oh, the Indians also say, you need to bring this up at the UN, the human rights stuff, because India is literally getting millions of refugees trying to flee this, this mess. And the United States won't do any of it.

35:10

It gets even better. The United States has the gall to blame the Indians for the war. Dream on. So Indira Gandhi is just furious at this one. And so the United States had wished that India would cease being non-aligned and align with the West. Well, they cease being non-aligned, all right. They sign a military pact with Russia over this. And then they upgrade their relationship with Vietnam, which totally upsets the United States. And it gets even better. They shut down Indians. They won't give scholars any visas to come to India to study India. So you wonder why US Universities didn't have any Indian studies programs? It's all about this. So that explains what's going on with all of that. Total mess.

Meanwhile. But for Pakistan, as all this is going on, the Shah of Iran falls in, I think it's like February 1979. And then the Russians invade Afghanistan in December 1979. And suddenly Pakistan is totally essential once again. And the Pakistanis are really getting sick of being kicked around. So when outgoing President Jimmy Carter offers them, I don't know, $400 million or something, this is Zia here going peanuts to the peanut farmer. And the incoming Reagan administration then ups it to $3.2 billion and that money gets funneled through the Inter-Services Intelligence directorate. That's like, I don't know – the CIA+++ of Pakistan. And when you put that kind of money into that kind of bureaucracy, you're going to make them incredibly powerful. And then they're the ones who decide how they're going to allocate money to insurgents in Afghanistan. And I get it. There weren't any great choices, but they're arming some really anti-Western folks in there, probably some guy named Osama, last name Bin Laden. But anyway I'm not sure of the details on that one, but it is going to have 9/11 follow-on effects. And also the Pakistani, the ISI is also taking some of that money and putting it into Kashmir which is going to have real problems for India later on. So there are real ramifications for all of this of needing Pakistan. But actually what is happening anyway. And then, throughout, there is the Pakistanis are getting closer and closer to building the bomb. So when the Russians go piling into Afghanistan, here's Zbigniew Brzezinski Carter national security advisor telling him our security policy cannot be dictated by our non proliferation policy. Really? I thought that was our security policy. And the problem with proliferation is it tends to be a one way street whereas Afghanistan has been anything but. And then here Deng Xiaoping was in town and he told Carter, we applaud your decision to basically toss all these proliferation human rights considerations for Pakistan and just arm them. No kidding. Because the Chinese are providing the nuclear spare parts. So the United States isn't the only country to have trouble navigating this cutthroat billiards. The Pakistanis have their share of boomerangs.

37:53

Just look how the wars work. So wars create incredible costs. So the 1965 war, the Pakistanis get exactly nothing. And the United States, Pakistan had been the largest aid recipient of the. I'm not sure if I got that right. But anyway they're a huge aid recipient from the United States. Well after this war were not so interested. So that's a lot of money down the tubes. And then in 1971 war, great guys, you lose Bangladesh which, by the way, has over half your population. So Pakistan is no longer the most populous Muslim country, Indonesia is. And if you look at the Kargil War in 1999, this is when Pakistan tries to again go below the line of control in Kashmir to try to take some more of Kashmir back. Pakistan has to cross right back and then it gets sanctioned for all of this. So none of these wars have actually worked out very well for Pakistan. And then if you think about it, India and Pakistan are natural trade partners.

So if you take all these wars and just add up all the costs and then think of the opportunity costs if Pakistan had been able to take this money and spend it on road systems, on education, and then all the lost trade, it gives you a sense of the real cost of all of this.

Okay, well those are Pakistan's problems. India has its own problems. Here you've got Indira Gandhi and Richard Nixon, they really didn't like each other. I mean look at her, she looks as if she's just been fed bad fish and he looks like he served it up. And they just, they cannot abide by each other. So in the 1940s when Kashmir is erupting, Indira Gandhi thinks – well no, it's Nehru – her dad thinks that the United States should be supporting India because it's secular and it's a democracy. And the United States is appalled during the Korean War when India remains non-aligned instead of supporting the United States because it's secular and democratic. And the Pakistanis are totally outraged because they're looking at this go, okay, these Indians are non-aligned. We're aligned. We're taking these risks for Peshawar and stuff with the U2s and you're helping these people who are about to ally with the Soviet Union, who are you kidding? So it's a total mess.

So the Indians have their own self inflicted blows. Nehru and his very controversial, but devoted advisor Krishna Menon and his daughter Indira Gandhi were really good at making these totally insulting remarks to American VIPs. Okay, it hits the target without a doubt, but the ego that has just been hit is huge and like an elephant is not about to forget. And meanwhile Pakistan in contrast is just being this welcoming host.

So the United States is going “ugh India and Pakistan”. And it makes really bad trend lines for India because in the 1962 war the United States supported India. In the 1965 war, it's neutral. In the 1971 war it supports Pakistan. That's not great. And then India's own very heavy handed treatment of solutions to the insurgency of Kashmir doesn't make that thing go away, it just gets worse. So they have their own problems.

41:10

China also has its problems with the interaction. It's complicated. So on the one hand, on the Sino-Indian war, absolutely, China gets the territory, but at what cost? You've got this permanent enemy forever. And as opposed to teaming up with them, if they teamed up, they actually would have had incredible leverage for what the global order is going to look like. But that's just not to be. And moreover, if you look at the 1971 war, after the United States won't help with China, India's going, okay, I think we need nuclear weapons because then we'll be able to protect ourselves against China. And after that war, when Pakistan's lost over half its population and has to deal with Indian population and territorial, just overwhelming superiority, the Pakistanis go, “I think we need nuclear weapons in order to solve this problem”. So there's proliferation all over the place.

But as a result of the 1971 war, where Pakistan tries to overturn the elections, here you have an Indian defense analyst, Subrahmanyam, saying the Pakistani decision to overturn its elections by deploying the army to East Pakistan gave India an opportunity the like of which will never come again. And what they did is they armed insurgents in East Pakistan then sent the conventional army in and that was it for Pakistan: in East Pakistan over. The interaction for Russia works a little better. For Russia and India, it's really quite a good relationship. What Russia offers to India, not only military and economic aid, but also very useful vetoes on the UN Security Council. India does not want plebiscites in Kashmir that it might lose, so it gets the Soviet Union to veto those things. So there are no plebiscites. And then as India is trouncing Pakistan in the 1970 war, starting one war, and the United States wants them to halt India, no way. India wants to finish the job. So it gets the Soviet Union to veto that one. And India does indeed finish the job. And meanwhile, for Russia, India is really useful. It's a good counterbalance for China. So theirs is rather a beautiful relationship. They have very cordial relations.

43:23

Okay, so I think I've now covered the playing field, right? And I've covered the players and teams and their problems with interacting. That's very difficult. Now for some of the plays and the instruments of national power. And here's the menu of choices. You can start with the light items. Diplomacy, public support and denial of public support. You can move into more expensive things down the menu. One of the things you can do is help negotiate a really useful treaty, which the United States did. It brokered this Indus Waters Treaty of 1960. It's the only time that I know of, maybe you all know of something, where India and Pakistan have signed an agreement to the massive benefit of both of them.

What does this agreement do? You can see it's a really dense river system. Both India and Pakistan need to irrigate. To do that efficiently, you need dams. And both of them were poor and didn't have the dams. They were going to cost a billion dollars. And the United States was willing to kick in half that money if they would both sign the treaty. And no terrorist event or anything derailed it, so they signed it. And this treaty has been, it's been operating some of these dams ever since to the enormous benefit of both countries. Does the United States get any enduring gratitude from either one for doing this? No. Zip. Okay, next one is the United States tried to exercise diplomacy and to convince the Pakistanis and Indians to settle their differences and it was a total flop. And because if you're going to try to befriend both India and Pakistan, you wind up becoming the enemy of one or the other.

And the United States’ diplomacy was based on certain false assumptions, which are one, that India and Pakistan could be cajoled into settling their differences. And their idea is, anyone who's so stupid as to think that is crazy. And if you're going, well, what are the origins of these differences? Partition was brutal. So the British colonized the Indian subcontinent and then they left in 1947 and they left really rapidly so that there was no time to set up any institutional framework. And also, you're talking millions of people. And so Pakistan's going to be one thing and then India's going to be the other. And so Hindus are just fleeing and Sikhs are fleeing out of Pakistan and Muslims are fleeing out of India, going back and forth and millions are killed while this is going on. So this is the origin, at least the modern origin, of why Pakistanis and Indians are so bitter. In addition, the United States thought, well, surely the China threat is going to make the Indians come around and realize this non-aligned stuff's nonsense. Not quite. Yeah, When India aligns, it aligns with Russia, not the United States. So that doesn't remotely work out the way the United States wanted.

46:26

And then the United States thought, well, hey, we in the west were rich. We give Indians and Pakistanis all this aid. This will force them to be nice to us and be less nice to the communists. Wrong. India and Pakistan are really astute and they get lots of aid from everybody. So when the great powers do align, Russia, China, us, or at least two of those align, then you can actually get stuff done. So that's when you get the Tashkent Agreement for the 1965 war. This is: the United States and Russia both want India and Pakistan to cease and desist and stop blowing each other off the map. And also in the Kargil conflict, when Pakistan is yet again trying to resolve Kashmir by invading and then gets itself into trouble. And this guy Nawaz Sharif, who is the head of Pakistan, he all of a sudden ups and gets on a plane with his family. It looks like he's coming into exile and he's trying to fly into the United States. And the United States goes, whoa, Whoa, whoa, whoa, whoa, what do you think you're doing? And says, you're not coming in here until you admit that you crossed the line of control. And then you need to get right back. And so he agrees to sign the Washington agreement to go right back. But it's absolutely humiliating for the Pakistanis to go, oh, yeah, we went south of the line of control. And then, well, that didn't work out. So now we have to go right back. He had gone to China already and pleaded his case to the Chinese, and they told him to get right back because there was a lot of nuclear saber-rattling going on, and the Chinese were not interested in a nuclear war over this. So Pakistan had the choice of, okay, fight India by your lonesome or cross back. So they crossed back.

And there were other cases in the inter-Cold-War period when the great powers cooperated and tamped things down, like terrorist incidents in New Delhi and in Mumbai that didn't go anywhere because the great powers told the Indians and the Pakistanis to just dial it back. All right? Another thing you can do is to publicly support someone. And this is what goes on with Goa, which is a Portuguese colony. The Indians wanted it back. The Portuguese said, no way, you cannot have it back. And the Indians took it back. And the United States supported Portugal. Why? It's a NATO ally and we have very important bases in Portugal. So we kept the bases, but we made the Indians really angry. And there are other areas of public support or not criticizing people publicly. For instance, when the. In the. In the 1971 Bangladesh war, when the United States refuses to support India by telling China, don't enter this place. But of course, it was December, so the road system would have been a little rough to even try that, but it made the Indians mad. Or if you think during the Cold War.

So with Nehru, it's the 1956 Hungarian crisis where the Russians sent tanks into Hungary, the Indians don't say anything about that. There's a Berlin crisis in 1961 where the Russians are pretty rough and the Indians don't say anything about that. And then Indira Gandhi comes in, and when the Russians send tanks into Czechoslovakia in 1968, the Indians say nothing. And then when the Russians invade Afghanistan in '79, again, the Indians say nothing. So this is one way that you make your allies feel better about things. Another thing is if you're one of the five veto holders at the UN Security Council, you can do your public support that way. And I've already mentioned these Russian vetoes on Kashmiri plebiscites that the Indians truly didn't want to have happen, or short circuiting the Indian offensive in Bangladesh. So this is what Russia did for India, and it was a very valuable thing for them. You can also put money where your mouth is. Economic aid. And it's interesting, the United States provided far more economic aid than either the Russians or the Chinese, but still, both Pakistanis and Indians preferred China and Russia respectively. And some of this aid was really important. During the Bihar famine in 1967, the United States sent 20% of its wheat crop to India. It was worth $1.5 billion. That's not something to be sneezed at for, I don't know, was it 90 million people? It's a lot of people who might have starved to death.

50:44

And that didn't work out well at all because Johnson, at that point, President Johnson at that point was so mad at the Indians because, from his point of view, they were cuddling up to the North Vietnamese. And the Vietnam War wasn't going well for Johnson. So he was furious. So he provided the aid, but he did it always at the last minute, ship to mouth.

And Indira Gandhi was furious. She said, “I don't ever want us ever to have to beg for food again”. And she never did. So the United States got no gratitude or enduring anything. Oh, and a whole other piece of it is that India is not subject to famines anymore. And part of it's from the Green Revolution. And who does that? It's the Ford and Rockefeller foundations who figure out the different strains of grains that you want to grow. And does the United States get any credit for that? No, zip. And here is Krishna Menon, who is Nehru's controversial advisor, saying, look, we want to encourage a little competition between the donors. And they did. And India just. Even Indira Gandhi, who hates Nixon, she's racking up the aid.

And back in the Eisenhower administration, the United States had noticed. Secretary of State John Foster Dulles is saying, look, concerning India and Pakistan, it's difficult to help one without making the enemy of the other. And of course, the United States tried to help both and angered both of them. Amazing.

So another instrument of national power is military aid. It is even more difficult to calibrate than the economic aid. So you can see with the pactomania event where Eisenhower is building these bilateral relations and treaty organizations to contain the Russians. Formerly there'd been no Cold War in South Asia, but once Eisenhower allies with Pakistan, all of a sudden the Russians are in there too. So that's a bit of a boomerang. Another one.

So when the United States provides military aid to Pakistan, that just drives India to seeking an alliance with Russia, which isn't exactly what the United States wanted. And then when the United States helps India right after the 1962 war with China, that alienates the Pakistanis and then they try to buddy buddy with China, not remotely what the United States wanted to happen. And then when the United States provides aid to the ISI, the Inter Services Intelligence Directorate, which is the Pakistanis then are funding things to get the Russians out of Afghanistan, they're also diverting it into Kashmir. So in 1989 this insurgency heats up and it's remained heated ever since. And then you wind up with China providing nuclear help to the Pakistanis. So it's difficult with these things. You get a short term thing, but then the long term thing that winds up may not be what you want at all.

53:41

The other instrument of national power, if you got one, is the carrier battle group. You can send one of those around, which is what the United States did. Here's Enterprise. It was the United States’ first nuclear propelled aircraft carrier. And so during the ‘71 war, the United States sent this into the Bay of Bengal, the Russians sent some naval assets, had no effect on that war: Pakistan lost. Right. The Indians were furious. They just regarded this as an absolute threat. And how dare we do this anyway? And maybe it would have been better to have left Enterprise in their home port rather than doing this.

And then of course there's sanctions and embargoes. The United States does this all the time. And if you look at the list of the times we're embargoing stuff. So at partition we're embargoing everybody. And then during the ‘65 war we're embargoing everybody. And then as various people are making nuclear progress and different things, there are these embargoes that come and go. And Pakistan's really mad because India does a test, an atomic test in 1974 and Pakistan doesn't do anything until much later. And it's looking why are you sanctioning us on this nuclear stuff? The Indians have actually done this. We haven't. And so if you look at this chart, you can see where the ups and downs of these sanctions go. And clearly they didn't stop proliferation because in 1998 you have these tit for tat nuclear atomic tests by both and the United States tries sanctioning, but then it's just too late. They've already tested the stuff. And so the United States basically gives up and then after 9/11, of course, we desperately need Pakistan again to deal with Afghanistan. So it's a complicated world out there.

So another instrument of national power is you can trade off your territory if you really want to. Most people don't, but the Pakistanis clearly did that in 1963. And we can all speculate on what they got. I mean, my hunch would be something to do with nuclear things, but hey, it's not as if this information is out there in the public. It isn't.

Oh, another thing you can do is go fund the insurgency. So you can. And this is done by the United States, Russia, China, India, Pakistan. Think if you've got a country that you don't like that has some minority people that want to secede and so they're fighting there, well, you can go fund that insurgency and then the one you don't like is pinned, because they're going to be paying attention to that insurgency. And while they're pinned there, they can't probably do things elsewhere that you might care about. So this is the logic of what's going on. So the United States belatedly decided, ah, let's help the Tibetans. And so the CIA is helping them between ‘57 and ‘61. But look at the dates we did the road system. The road system in Tibet's completed by 1957. It's too late. So all you do is get these people killed because the Chinese have got the road system all set up in there.

56:44

So that doesn't exactly work. But after the ‘62 war, all the way until 1979, when Deng Xiaoping calls it off, the Chinese are funding insurgencies of, let's see, the Mizos, the Manapuri and the Naga people all don't like different aspects of Indian rule. And the Chinese are more than happy to stir that pot. And the really big pot to stir are the Naxalites. It's huge. And while as long as the Pakistanis have got East Pakistan, they can stir some of that up. And by the way, these Naxalites are still there in India. They have not gone away. It's a serious part of India where they are. And then, of course, Pakistan's location right next to Kashmir means they can stir that forever.

And the tragedy of these, what they become are frozen conflicts. Is the outside power. if they are playing their cards right, their amoral cards right, they're not bearing any of the costs. They're pinning someone they don't particularly like. All the costs are borne by the local population who are suffering horrendous deaths, lack of economic growth. You're just having warfare where you live. What a total disaster. So that's how it works.

… two can play at this game. So the Indians, according to the Pakistanis, have funded the Baloch people. They straddle Iran and Pakistan, don't particularly like being told what to do. So apparently India's supposed to have put its finger on that scale. The other thing the Pakistanis accuse Indians of doing is encouraging a Pashtun insurgency up north. And that would be a way of diverting Pakistani attentions from Kashmir. If they're totally busy with Pashtuns, they can do less in Kashmir. But the result of these things is people are becoming more and more bitter. The hatreds just spike, the economic growth isn't happening, poverty everywhere. And it makes these problems more intractable, not less.

So when I think about these frozen conflicts, there are a number of ones that you know about besides Kashmir, there's Korea, there's Palestine. So if you look at Kashmir, if I've got it right, so you fund that thing. And then what's great from China's point of view, if Pakistan is doing that India is frozen, that it can't do other things because it's constantly paying attention to what's going on in Kashmir as opposed to going “hmmm China, I don't know about this”. Or in the Korean War back in the day, if things are all stirred up in Korea, China has to really pay attention to that. And it delays the rise of China. And in those days that benefited Russia. But these things can change over time as to who the beneficiary is. And then you can play this game and think about how it works in Palestine. And I'm no expert in that part of the world, but I think this frozen conflict veto player works, that there are these veto players who are vetoing peace very easily. All you have to do is send a certain number of package bombs and peace is not going to happen.

0:59:49

So, cutthroat billiards. What can you take away from all this? Well, common enemies cannot be conjured. So check out the alignments of who these common enemies are before you leave the parking lot and figure it out for all possible players who might want to crash the party. Like, what's their primary objective? Who's their primary adversary? What primary theater are they truly interested in? And then you've got your hunches on how you think this is. And then you should reassess early and often to see if your assumptions are correct and don't worry about changing your mind. Some people get really hung up about being wrong. Don't worry. Reassessing is a sign of strength. It's like, I got more information, I've changed my mind. Good thing, don't double down on bad information. And then if you're looking into areas of the world that are ethnically diverse where people have been at odds for a long time, expect veto players and real difficulty in settling that matter out. And part of good strategy is recognizing some problems it's not feasible to solve. And then we all have scarce resources. You can't do everything. Focus on those things where you think you can solve. But if the great powers align, things can happen. And the story is even better than that. That, sure there are a few big powers, but the small and medium powers, if you add them all up, are by far the aggregate. Their aggregate wealth exceeds any one great power. So if the smaller powers agree on what they're up to, then the big powers have to pay attention. And that's a positive thing. So that is what I had to say to you this evening and thank you for listening.

Dwarkesh Patel 1:02:41
So it seems like the lesson from this lecture, the big lesson, is we couldn't be allies with both Pakistan and India at the same time. We had to choose one. So which one should we have chosen?

Sarah Paine 1:02:51
Oh, well, our primary enemy was pretty much the Soviet Union for the duration of the Cold War. I mean, China was a problem, but the Soviets were the big problem. And so India and Pakistan never fit that bill. And so it had to do with whatever the problem of the day was. And Pakistan had a very critical location, and so we needed it for a whole bunch of things, and you can see how that works. While you need them, you pay a lot of money to them, and presumably the money's worth it for them for the hassles of dealing with a difficult tenant. So that's how it worked. But nowadays, the relationship with India is changing. In fact, it has changed because their big buddy Russia is. Well, after the end of the Cold War, instead of being the big friend, it was the big nuisance, not so much for India, but for everybody else. And now for India, its primary adversary is China, without a doubt. And the United States, I think, is looking at China poses the primary threat. And to the extent that both of them see it that way, that will make them align and Pakistan less so.

Dwarkesh Patel 1:04:03
It sounds like at the time it was right to choose Pakistan as the main ally for the United States.

Sarah Paine 1:04:08
Well, we tried to choose India too, but yeah, we tried to do them simultaneously, but India's further south, right. And so for a long time before we got satellite imagery, you want to have listening posts and things up in Pakistan. So India just simply doesn't have the location for that. So that was the original reason for interest in Pakistan.

Dwarkesh Patel 1:04:31
I want to better understand why the Soviet Union was such a charismatic example for countries that were decolonizing like India.

Sarah Paine 1:04:39
Well, I think go back and put yourself as a generation of what your life experience was. If your life experience was World War I, the total butchery of it. So you're telling young men your age “go” – you're in a trench – “I want you to go up and over the edge into oncoming machine gun fire”. We know what's going to happen when that happens, right? And they're literally in battles like the Somme, they're losing hundreds of thousands of people. I don't mean casualties, I mean dead, dead. And so, and then you have the Great Depression afterwards and then add to it, India's been colonized by a bunch of really opinionated white people, right? And transferring a lot of wealth out of India to England.

And so you look at Marxism and boy, it sounds plausible, right? It sounds like these evil capitalists, they're just wasting lives. And surely this is the explanation. Now the technicalities of Marx's labor theory of value don't actually turn out to be accurate. It's not just labor that produces value like Silicon Valley, you understand, it's what comes up here that produces a tremendous amount of value. And Marxist theory doesn't recognize that. And so there are massive flaws in theory. And then when you put communism into practice, if you want to have equality, which is what they wanted, if you can have equality, you're not going to have any liberty, right? Because in order to make it equal, you just expropriate and then you divvy it out to everybody and there's no liberty in that, right? And another piece about democracies and even flawed ones, even if they have a semi-free press, a lot of the icky news bubbles up, but in dictatorships it doesn't, right?

So you compare the seamy sides of some democracies that aren't doing that well. And then to the public, the PR thing that the dictatorships are presenting. So you can picture Nehru of his generation, he's sick of the British. Right? And so Fabian socialism. He's not for killing lots of people to impose socialism. He has a much lighter touch than Stalin.

Dwarkesh Patel 1:06:57
One thing I was thinking about while you were mentioning the Gandhi and Nehru had a big problem with American segregation is at the same time in the ‘50s, by that point over 10% of the Soviet population had at some point been in the gulags or are currently in the gulags. And segregation is very bad, Gulags are even worse. And it's the same proportion of the population that's been in the gulags. Maybe people just didn't know about the gulags back then.

Sarah Paine 1:07:23
I don't think communists let you know those things. Right. This is one for a lot of Russians because my husband and I were in the Soviet Union in the 1988/89 academic year and that's early Gorbachev before everything totally falls apart and there's no food. So there was, there wasn't much food, but there was some food. And it was just as the Russians were suddenly being able to read in fairly free press about the things that had happened. And they were appalled about what had gone on under Stalin and they just hadn't realized all of it.

Dwarkesh Patel 1:08:00
One of the reasons that socialism was appealing to these decolonizing countries was because in the preceding decades, from the ‘20s to ‘40s, the Soviet Union actually had pretty good growth rates. In those 20 years, their GDP went from 8% to 18% of world production. Was that just catch-up growth? What was going on by the time Russia did?

Sarah Paine 1:08:16
Well, there was tremendous rebuilding after World War II. There's another piece. It was so weird when we were in Russia in 88, 89, anything that was sold at the markets that came from somewhere else, they would market means imported. So the idea, and I remember thinking this is really weird because normally you would say it's, I don't know, Hungarian this and that or whatever. I thought, ooh, this means that anything from somewhere else is better than what we got, that it's a really bad sign. And they had what I would call a doughnut empire. Normally when you think of an empire, mother Central is the most developed part. And then there's the periphery where all the natural resources are taken away. That it was an inverted empire. Russia is the donut. The rich places are places like Czechoslovakia and Poland had been much richer places. And so the Russians – serf owners – are sucking in all the wealth from these places. And I think that's another reason why the shattering of the Soviet Union when they lost all of their enserfed Eastern Europe, why it was such a mess for Russia, they did not realize, and they still don't realize the degree to which they were living off the wealth, product produced in the west, the European portions of Russia that since became independent.

Dwarkesh Patel 1:09:36
Who was the last Soviet leader who truly believed in communism?

Sarah Paine 1:09:40
Gorbachev was a true believer. He did not believe it was possible to take a U turn on the road to communism, so that when he was doing his reforms, it was to improve communism, make it better. So he absolutely believed in it.

Dwarkesh Patel 1:09:57
When you started your career in Soviet studies and you're trying to figure out like what's going on in the Kremlin all the time, what is it? What is the key to figuring out Kremlinology? What is the key to figuring out what's happening in the Politburo?

Sarah Paine 1:10:09
Well, what I did for my doctoral dissertation, so I wrote a history of the Sino. Well, the Russo-Chinese border, when it was figured out, which is between 1858 and 1924, this is when all this territory went. And what I did is. So most of this isn't Soviet, right? It's 17 to 24 is the Soviet part. But you go to archives in Russia, you see what they'll let you see. This is when you could go to those archives. They're closed now. And I went to Beijing, saw the archives you were allowed to see. And then in Nanjing, there were some other ones there. And then in Taiwan, the ones they would allow you to see, and then Japan, the ones they would allow you to see. And they're all lying about different things or covering up different things. So it's a jigsaw puzzle. You put it together that way. So that was how I did it. And also for the whole Kremlinology is pay attention to what dictators say because they've got to communicate at some level to their own populations. They quite often tell you exactly what they want to do and you think that's insane. Who would do that? Well, hello. Hello. Right, so read what they have to say and read what others have to say. Then you need to read things in the Russian language. So I just, I read a lot of things. Just read broadly, read more broadly, than whatever your topic is, because it's the unknown unknowns is what you're after. What are the things that are truly important and you have no idea about? And the only way you get to those is archives or reading broadly where you bump into things. Ooh, that turns out to be important.

Dwarkesh Patel 1:11:44
When you were giving the lecture, it seemed like these primary adversaries, whoever it is, is like very permanent. And there's some, like, deep reason why it has to be this way. But if you look at the Sino Soviet split, it's just like they're both Communist, they're both Leninist parties, and they have this falling out over. There were a couple of things you mentioned, but it's not clear why somebody has to be somebody else's primary adversary or why you can't turn that around.

Sarah Paine 1:12:06
Well, you can, but Russia and China have these territorial differences and the issues I listed there of fighting to the last Chinese in the Korean War, Mao didn't really like that. There are a lot of Chinese who die in the Korean War where the Russians sort of get them in there and then you're stuck. You have to, for prestige reasons, stay there. And the really big one I didn't discuss in any detail. So Stalin's really worried about a two front war. Nazis in the west and Japanese in the east. And so he brokers the second United Front between the Chinese Nationalist under Chiang Kai Shek and the Chinese Communists under Mao at a place called Xi'an in late 1930. Wait, is it 36? And so it's called the Xi'an Incident where Chiang Kai Shek was reputedly kidnapped and up there. But really what's going on is it's setting up the second United Front. And then the Japanese go ballistic and that's when they invade all the way down the coast and up the Yangtze River. And that means the Chinese are fighting the Japanese for you. The Japanese are not going to launch into Siberia. So that works for Stalin. But the Chinese realize this and it's not appreciated.

Dwarkesh Patel 1:13:35
Yeah, but then they helped the Communists get into power in China, right? You think they'd be grateful for that?

Sarah Paine
No, no, he wanted to divide China. He's telling Mao, oh, stop at the Yangzi stock at the Yang. You need a breathing space there. And Mao's and keeps on going.

Dwarkesh Patel
If you're the president, if you're the Secretary of State and you're looking at this, where there's these interweaving systems of alliances and you give some guy aid because you need something and pisses everybody else off, how the Hell are you supposed to keep track of this? Do they have these matrices that you got up there? And they're…

Sarah Paine 1:13:52
We have so many wonderful people working in the State Department. I've met a number of them because they come, they're students at the War College. You should be proud of all the people who work for the Department of State. They're all of these different experts, different parts of the world. They get sent around hither and yon, very thoughtful people. Also I've read in the archives many CIA reports because I've gone to these presidential archives starting with the Truman administration all the way up to Bush Senior. As you get to the more recent stuff, less of it's been declassified. So the…I've read most of the CIA reports I've read are for earlier periods where they've been declassified. It's impressive the level of analysis. And I know that the CIA has several…there's the analytical part of it and then there's the send people out to do, we aren't even going to talk about what they're, which is completely different. But they're very fine analysts. And then we have like the Department of Agriculture has very fine analysts about agriculture, the whole world. So we have a tremendous amount of expertise. And the other thing that presidents get and also the high level bureaucrats get are these daily reports where they're very short because they have to be about what's going on everywhere in the world that people think is important for them to know. So there are a tremendous number of smart, well educated people trying to get information to presidents, secretaries of states and things. And then you have all these regional desks where there's I don't know, I can't remember exactly. I don't know the organization chart of the State Department. But you can look at it by region of the world. People work there and, and provide this. So there are wonderful experts that are working on this.

Dwarkesh Patel
You mentioned in the lecture that if China and India had figured out a way to work together, the world order could have looked completely different. Spell out the counterfactual for us. How would it have been different?

Sarah Paine 1:15:42
So I spent the grand total of a week in India. It was a trip on the way back from Australia and that was the amount of time we had. But we went into the Mahatma Gandhi Museum in New Delhi and there's a whole piece of it where Chiang Kai-shek is giving him things. And it became really clear that Chiang Kai-shek had a really good relationship with India. Deep respect for India. This shared desire of we want to be independent, we don't want Japanese running around our country, we don't want a bunch of Westerners running around. We share this with India. And so I think if you get Chiang Kai-shek winning the Chinese civil war and things, you might have had them team up and had a much more productive relationship.

Dwarkesh Patel
That's interesting because often you think of like when you hear Nationalists, you think they're the ones that are interested in territorial disputes, but in fact, you know, they wouldn't have cared about Tibet or something.

Sarah Paine 1:16:34
Well, next time you're in New Delhi, go check out the museum. But when I was there it was like 20 years ago, but I remember being impressed, wow, there's all. And then when Gandhi was assassinated, this is Mahatma, not the Indira who's also assassinated. No relations. That the Chinese, the nationalists sense all sorts of condolences and different things. So yeah, there could have been a different outcome.

Dwarkesh Patel
I wonder if the worst thing about the Non-Aligned Movement was that because they wouldn't fall under anyone else's nuclear umbrella, they wouldn't align with the great power that had nukes. In order to keep up deterrence, they had to build nukes themselves. And so then you have this nuclear proliferation and that was like it's really important to get people allied with one great power, even if it's not you, just to prevent the nukes from going wide.

Sarah Paine
I think the 1962 war made Nehru realize that non-alignment was a non-starter. And that's for the reasons you're talking about. It's like, okay, we just got eaten alive by the Chinese. Okay, we need a big friend, and we probably need some big missiles eventually. And it's not immediately. The Indians don't come around that “we absolutely need nuclear weapons” until the Bangladesh war when China's become more of a threat.

Dwarkesh Patel
By 1971 like did Nehru really think that the Soviet Union might still win the Cold War?

Sarah Paine
Nehru's dead in like ‘64, so it's his daughter.

Dwarkesh Patel
When did they become allies with the Soviet Union?

Sarah Paine
Well, the official alliance, I'm just defining, I made up what an alliance is. So I would say it is the security pact that Indira Gandhi signs in 1971.

Dwarkesh Patel
Right. So at that point, did they really thought the Soviet Union could have won the Cold War? Like, why didn't they just choose the winner in retrospect?

Sarah Paine
Oh, well, I remember the Cold War vividly. We felt we were losing it most of the way through.

Dwarkesh Patel
And why is that?

Sarah Paine 1:18:33
Well, we always felt – because of the propaganda, right? – that “they're all living beautifully”. And then they're quite good, they're very good at propaganda. So the United States had massive racial problems. Think about the 1960s. Martin Luther King are pointing out hideous problems with this country. Civil rights demonstrations everywhere, anti Vietnam War demonstrations everywhere. The women's movement, the environmental movement. There are loads of unhappy people pointing out all of these problems, which is a good thing because then the United States starts working on fixing some of these problems. Of course they're not fixed, but there's improvement on them. Whereas in Russia, no one can travel, so you can't see how unbelievably poor they are. And so you go to Moscow and you think, well, this is a plausible city. Well, there is only one plausible city in the place. Or maybe it's two. Saying Petersburg. Well, it's Leningrad in those days. So people didn't realize. But yeah, we felt we were losing the Cold War. In fact, at the very end of the Cold War, because I was in the Bush archives. So Gorbachev's in and Bush, Reagan, and then Bush are thinking, well, he just looks like another one of these people and we're not going to take the propaganda seriously. And then they sort of go, whoa, whoa, whoa, whoa. And then it's in 88 where Bush is going, whoa, maybe it is going to end. And then the United States teams up with Helmut Kohl of Germany to do the fastest get the Russians out of Eastern Europe ever. But Gorbachev is the one who wants to cooperate with the West. One thing you should know about warfare, you think the winners win it. No, actually, it's when the losers decide they've had enough. That's when a war ends. Otherwise it doesn't end. So the Russians got sick of it and they quit.

Dwarkesh Patel
I wonder if there's a lesson in here for what people call the “Cold War with China” today, where people think it's really competitive, China might have a bigger economy than us soon. And really, if you actually got to see what's happening day to day in China, maybe you might decide, like the Soviet Union, there's a bunch of things broken with it. They're not really competitive in the long run economically. And so we're going to win.

Sarah Paine 1:20:45
Well, I don't know, win, lose. But they have a very low per capita standard of living. Do the math. You go, yeah, they've got a big economy because they're, they're not a third of the world's population now, but it's, it's huge. And divide the 1.3 billion, whatever it is, you come up with a low number for per capita standard of living. And I think for human beings, that's the number that matters, right. Of what is your family income look like? So they got a long way to go on that one.

Dwarkesh Patel
I want to go back to Kissinger in 1971. So because they want somebody to like, send the message along to China to open up, they shut up about Pakistan's genocide. In Bangladesh, where numbers vary, but somewhere between 300,000 and 3 million people died.

Sarah Paine
It's awful.

Dwarkesh Patel
Hundreds of thousands of people were raped. I know they didn't have email back when there was no other way to get the message across. You have to abet a genocide in order to get a message across to Mao.

Sarah Paine 1:21:47
Well, first of all, the Cold War is a misnomer because the bloodshed in the third world was horrendous. And I will eventually compile the stats on this. Not that they're great stats, but it's obscene numbers. I think Congo had a civil war where literally over 10 million people died. And that's only just one of these things. So the United States cannot deal with all of these. And if you think about it, the time when the United States was really quite idealistic, the Iraq war, goes in, they create democracy in this place. It's a massive civil war, and then we're blamed for it. So be careful about where you intervene because you may not be able to stop a thing and then you'll get blamed for what's going on. So Bangladesh is horrible, but it is not our fault. You will get overextended if you intervene in everything under the planet. Also, there's a question of why the United States or anyone should be doing all of it. Shouldn't it be through the UN and other things? The really key thing was winning the Cold War. So Nixon absolutely did prioritize getting the mail through to Mao. He did. So 1971, good old Henry, “Hack”, as he's known in the archives, Henry A. Kissinger. He gets his secret trip in there, lines up Nixon's trip in ‘72, and then it's going to overextend Russia in the Cold War. Why? Because with this Sino, Soviet split and all the rest of it, Russia has to militarize its entire border with China. It's already militarized its entire border with Europe. Right. Think about this. And you're talking about mechanized nuclear armed forces with all the tanks and everything else that goes with them. It is not cheap. Imagine if the United States had to do this on our Canadian and Mexican borders. Bankrupting, right? And we're a lot richer than they are. So we team up with China, we provide them all kinds of naval technology, which is not going to bother us. They give us listening posts again, we love listening posts right up on the Russian border. And the Russians are scared stiff of the Chinese. The Chinese with good military equipment, that wouldn't be a good thing for them.

Dwarkesh Patel
When I asked how was President and he built these bases in Pakistan to have the U2 planes go to Russia. We had bases in Turkey, we had bases in Japan, we had bases in a lot of other places in Europe. This marginal increase in coverage, was it really worth breaking off the alliance with India or like pissing off India?

Sarah Paine
Oh, yeah, yeah, yeah, sorry, India. I think at the time people felt we absolutely have to be able to cover everything. And I don't know the details of where they had their nuclear this and that stored and things and all their testing areas. But I'm sure you needed to have the entire place covered. And so, yeah, it was very serious on these nuclear weapons. They can reach the United States. So we had better know what's going on.

Dwarkesh Patel
So new books about the Cold War are coming out all the time. New books about World War II are coming out all the time. People are writing like thousands of papers about this stuff all the time. How much new stuff are we learning every year? All these scholars are still setting this up. What kinds of new things do we keep unearthing?

Sarah Paine 1:25:08
Well, I'm personally sick of writing about World War II. I've done enough of it and so I'm personally not going in that direction. Yeah, it's endless. I think for a lot of the books of these narrative feel good histories. Like I'll tell you about some Regimen and all the heroic stuff they did. And it's nice, light, airplane read. And a lot of people like that stuff. I personally like things where I learn things that are conceptual that I can then apply to other problems which those books wouldn't help me, but that's just me.

Dwarkesh Patel
How do you decide which are the good history books and which aren't? You're just on Amazon. You can't like, read it yet. You're just like, looking out on Amazon.

Sarah Paine 1:25:47
It's hard. What I would say is give it a book. The 30 second test. And what's that? If the book's about another part of the world, flip through the bibliography, which you can do on Amazon, and zing through there for about 30 seconds and see is there anything in the language of the country in question in the bibliography, like, how much? And if there is zip, I would toss it. I'm curious how many of you would be interested in reading a book that says it knows everything about the United States and there's not a single English source cited? I think you'd regard the book as garbage, and that Americans do this routinely. And it's a problem with political science degrees where it would be very easy to require anyone who's going to do a specialty in comparative politics or IR international relations that, hey, you need to pass a language test. And don't try Spanish if you think you're going to talk about the Middle east. Right? If it's going to be Middle east, it better be something like Arabic, Farsi, Turkic, Hebrew. And better yet, if you're really going to be a serious Middle Eastern expert, try all four. Right? I mean, that would be true expertise and where you can read books in the language and get what other people are thinking about things. So that would be my basic is find people who aren't playing half court tennis all about Team America. Get to the other side of the net and try and figure out what other people are thinking. Doesn't mean you have to agree with them, but it means you are making the effort to see where they’re coming from.

Dwarkesh Patel
During the Cold War, all these proxy wars, was it good that we were just like letting off steam in these smaller ways and therefore we could avoid the bigger great War?

Sarah Paine 1:27:30
It wasn't letting off steam. It was horrendous. I don't know the answer. In fact, my current project is a history of the Cold War and I've read a lot of things and I will, I'm going to compile a lot of tables on things like this. Just putting the data together, it'll take a long time, but then when you get the table, it'll be a really quick, really quick skim. So if you've got a civil war turning somewhere in Latin America or somewhere in Africa, so no one's aiding it, fine. Then if you have Russia put its finger on the scale, then of course we're going to put our finger on the scale. So that means the Russians are providing industrial age weapons, we're providing Industrial age weapons. What happens? Casualties just go through the roof. And that's what went on through the Cold War. And then there was this magical inner Cold War period when all that stopped. And that's when you get this tremendous growth across the third World because there aren't these insurgencies just tearing up wealth.

And then you could see recently, look at North Africa – it's a mess right now, right? It's both ISIS or whatever it is in there doing its stuff, and then you have Wagner group or whatever they are in there, and then you have the Chinese further south and everybody's feeling everything. You got civil war all over the place. And then that means military age males want to get out because people kill you, right? Or they'll either dragoon you into fighting where you don't want to fight, or they'll just kill you outright. So they're all trying to get into boats to Europe, which is destabilizing. It's a mess. We're in the second Cold War and this is part of it.

Dwarkesh Patel
What was the biggest, when you're doing this project on the Cold War, what were the biggest miscalculations that people were making? Were they too suspicious of the other side? Were they not suspicious enough? Were they too concerned about bases or what were they missing?

Sarah Paine 1:29:18
I need to think more about that because I think there are probably all sorts of things. And also it's impossible to know the right thing to do. And then afterwards, when it pans, you go, well, that was a mess. It's hard to know, but I think it was probably a terrible mistake when Eisenhower thought, here's the Supreme Command allied commander of World War II, who knows all about hot wars, doesn't want to do that. When the United States deposes Mosaddegh in Iran in 1954, I could see Eisenhower's view as, hey, we're just deposing one guy and we're going to avoid the Russians taking oil fields here, etc. And he thinks that's a better trade off. Well, the Iranians have never forgotten that they're furious to this day. Wouldn't you be if you're Iranian? Mosaddegh was democratically elected. Or another time, actually, in Vietnam, we didn't want to hold, we didn't push the South Vietnamese to hold elections because we knew Ho Chi Minh would win. He was the only national figure in Vietnam. So the United States is all about free elections. The Vietnamese could have run a free election and Ho Chi Minh would have won. That was our assessment, that he would win. Okay, well, if you're for free elections, it means you don't get the outcome you want. And I wonder if that wasn't a terrible mistake, because the Vietnamese actually can't stand the Chinese. So they were always going to be a counterbalance to the Chinese. But due to the McCarthy era, we purged all of our China experts. All these children of missionaries who knew a great deal about China, less so about Vietnam, but at least it's a related cultural group. So we had gotten rid of all that expertise in the McCarthy era. So we're running blind in Asia.

Dwarkesh Patel
What you just mentioned about the fact that we would curtail what kinds of elections you can hold in these countries after Germany and Japan surrendered, if they tried to elect another fascist party, we wouldn't have been like, all right, well, that's what you guys voted for. They're in charge now. We would have, you know. And so why is the principle different in Vietnam?

Sarah Paine 1:31:25
Because the South Vietnamese would have voted for them as well. We've just been fighting a massive war with Japan and Germany. There's no way we're going to let the Germans and Japanese decide anything initially. But what we did do for both Germany, our half, West Germany, and Japan is we did our best to set up democratic institutions that within a cycle or two, they're making their decisions, whom they're electing, we are not choosing their leaders. And it was a very generous peace. It was realizing In World War I, ostracizing Germans, ostracizing Russians, it probably doesn't. You need to bring them back into the family of nations, because they're on the planet. They're not leaving the planet. And the generous peace with Germany, look at them. They're the most wonderful allies, as are the Japanese. They're terrific.

Dwarkesh Patel
During the Cold War, the USSR is constantly funding these communist parties around the world, and these parties are doing, like, exactly their bidding. Whatever Stalin says they'll do.

Sarah Paine
I don't know about that.

Dwarkesh Patel
Yeah, Feel free to clarify, but it doesn't feel like any other country managed to pull this off. There's not like an American party in the French parliament that does exactly what the president would tell them to do. How did they pull this off?

Sarah Paine 1:32:42
I don't think it's that they did exactly what anyone wanted. What the Communists have is a really powerful doctrine for how you go from nothing to something in a failing state. How you start out with a bunch of people on some campus. Mao literally starts out as a junior librarian in Beijing University. So I don't know, he's there with the librarians, I don't know the ladies. Who knows what it is? It's probably guys in those days. And then he's turning these people into cadres and eventually to guerrillas and eventually conventional army. But they have a really good program that works the world over, and it's very effective for taking power within a broken state. It's lousy at producing prosperity thereafter. Right. And you look at the Communist Party today, they're really good at staying in power. For a while there, under Deng Xiaoping, it ran in parallel what the economic reforms needed to be to produce wealth and what the Party needed in order to be more powerful. But now that's running at cross purposes, because for the party to maintain its monopoly, well, all these much more educated Chinese aren't going to be interested in that anymore. And so it's time to lock those people up. And then you're going to have trouble with keeping your economic growth rate going.

Dwarkesh Patel 1:34:06
Mao dies, and immediately things improve. China experiences its growth trajectory. Stalin dies, and you know Khrushchev is not a teddy bear, but at least you cut down the Gulag system. And so it does seem like if you have an especially bad dictator, you do have this sort of regression to the mean, where the next guy's probably not as bad as the worst guy who's ever been in charge of the country. And I wonder if that implies that, you know, if we got rid of Kim Jong Un or the Ayatollah or something, the next guy probably won't be as bad.

Sarah Paine 1:34:34
I don't know. But I think iconic leaders like Mao and Stalin, they were canonized by winning their wars, Stalin by winning World War II. The Russians revered him for doing that. And they had experienced. Every Russian experienced World War II. It was central to their lives to win that thing. So Stalin is canonized for doing that. And now the man who puts Humpty Dumpty back together again when no one else could and then fights the great powers, capitalist powers, to a stalemate in Korea, he's again been canonized. Whoever comes after them is not going to have that level of prestige. It's just going to devolve. That's period. And then for the successors of Stalin, they were sick of being terrified about whether they were going to be purged. He kept putting bullets in people's heads, like Vladimir Putin. You do this long enough that people are horrified and don't want to have it. They don't want to live that way anymore. So there's that piece of it as well.

Dwarkesh Patel
I guess I'm not sure what the answer is today. If you found out tomorrow that Putin is dead, is your estimation that things are going to improve or get worse?

Sarah Paine 1:35:46
My estimation in that kind of system is there's going to be a leadership struggle. That's the problem with dictatorships. They're absolutely brittle. And that's what's wonderful about having elections. You just every four years or whatever it is, you have an election. Whoever it is, it's going to be whatever it is. But in these dictatorships, no one knows. And so everyone. So Stalin, literally, I think he didn't show up at breakfast. No one wanted to open the door. And then probably they decided, let's not open the door. Let's just let that door stay shut for a few days. And then when it starts stinking, well, no, perfect. So that's how he dies. So you're going to be guaranteed a free for all. So if there's a free for all for something like Ukraine, they will probably have to call that off because they got to focus on Moscow. That's what happened with the Korean War. Why is the Korean War and Stalin dies? Russians want it over because they got all kinds of unrest. Various prisons in their own gulags,they're having riots. They're having, I think, some Bulgaria, maybe East Germany as well. They got problems. So they don't want to deal with the Korean War anymore. It’s like, who cares? Forget about it. So you would be guaranteed that, which might be highly beneficial for Ukraine. But knowing Putin, he's an evil man. He'll probably stick around forever.

Dwarkesh Patel
While you're starting this Cold War history. How close did we get to a nuclear bomb? Some people argue that, like, you know, there were all these “close miss incidents’. It like, basically should have happened almost in the sense of like, the odds. And some people say, like, it wasn't going…, MAD had worked, it wasn't going to happen, why would they risk a first strike?

Sarah Paine 1:37:30
Oh, it's miracle world here. I think in the Cuban Missile Crisis, it came down to a Soviet sub Captain. They had like the political commissar, the military commissar, I have to reread this, but I don't know whether you needed a vote of 2 out of 3 to launch a nuclear weapon or whether one guy was senior and you have to get him to come along. I have to read the data. But basically, someone put their foot down in the Soviet sub not to launch a nuke. Okay, we lucked out of that one. And then I think under Reagan there was an able Archer missile test, or we were doing a war game or something, and the Russians looked at it and thought it was the real thing for a while. And that just scared the bejesus out of them. And I think it was after that that Ronald Reagan started moderating his rhetoric. It's like, whoa, you don't want to have them on the edge like this. This is not going to be beneficial. So it was quite close. The advantage in the Cold War is all our leaders all the way through Bush Sr. were all veterans of World War I. In one way or another they'd served, some of them on the battle line like Bush senior had, and others near enough so that they understood how dangerous it was. And like all the Soviet leaders, Brezhnev and all those people had seen all sorts of people be butchered. So they were cautious in a way I don't think Vladimir Putin is.

Dwarkesh Patel
Going back to the lecture if it's true that you can't get primary adversaries to be friends with each other. And if you want to be friends with one of them, you're going to make enemies of the other one. The world is full of people who are enemies with each other. Does that mean that at once America can only be friends with half the world? And it's got to be anyways with the half that this half hates.

Sarah Paine 1:39:13
With India and Pakistan, it was so intense because of partition, of so many people who died. And it was recent. Right. ‘47. And so you wouldn't expect those animosities to die down. And also there are new states trying to establish their legitimacy. So that was particularly difficult. I don't know that most countries in the world necessarily want to kill all of their neighbors. And then you can look at things: forever, Britain and France were going at it, and then Germany became the primary problem between World War I and World War II. So then Britain is teaming up with France, its former primary enemy, and that whole problem goes away. And then you have the generous peace after World War II. Not one, World War I was not a generous peace. And also we have the mismanaged Great Depression that allows fascists to rise all over the place. But after World War II, generosity to the Germans, it's over. Look at Europe. If you study European history, they're all trying to butcher each other for centuries. And then they realize, what a waste. Let's just trade, play by the rules.

Dwarkesh Patel
Should we have cut off Lend-Lease aid to Stalin after 1943, when it's obvious that–

Sarah Paine
–Bad idea–

Dwarkesh Patel
…when it's obvious that they're winning against the Germans? If you'd done that, then they would have stopped advancing as fast in Eastern Europe.

Sarah Paine
Is that what you're thinking? No, no.

Dwarkesh Patel
More of Europe could have been free earlier on.

Sarah Paine 1:40:40
No, because you're assuming that World War II was not a closely run thing. I think it's a much more closely run thing than you think. And there's no. No one's sure of how it's going to turn out. Everyone knows if it goes longer, it's more of my kids who are dying. So there's no way you are going to cut aid to the army that's fighting World War II. All right, here's a concept for you. There's a main theater in the war. How do I know what the main theater is? That's where most of the people fighting are. That's the main front. Main theater. Russians fought between 2/3 and 3/4 of the Nazi army, always. That makes the eastern front the main theater. So you all think, Pearl, that you all think that the Normandy invasion was a big deal. No, that is a peripheral operation. It is peripheral to the main theater. And if the Russians hadn't been doing Bagration, this big operation that coincides with Normandy, no way would we ever have gotten onto the continent. And that's in 1944. So cutting lend-lease aid? Insane. But you're eliminating the problem of fascism until people have forgotten what fascism meant. But you're left with the problem of communism.

Dwarkesh Patel 1:42:07
By that same logic, look, the main theater in the Pacific War was in China, where most of the fighting was happening. Our Lend Lease aid to Chiang kai Shek was 1 to 2% of what we were sending to Stalin. In today's dollars we send Stalin $1 trillion worth of aid. Just tremendous amounts of supplies he used to then instil “install” meant a communist totalitarianism on Eastern Europe. If he had sent a fraction of that aid to Chiang Kai Shek, and help us with the Japanese, maybe we somewhat unclear if Dwarkesh says “we” or “he” here would still be in power. We wouldn't have to fight as much.

Sarah Paine
We couldn't get it in. It's because if you want to deliver Lend-Lease, this is a problem in World War I. It's why did Russia fall in World War I? Why would you ever let that happen? There's no way to get the aid in because Russia hadn't built its Murmansk Railway, which goes way up north. And it hasn't completed the Trans-Siberian Railway. It doesn't do those things until, Murmansk is early in 1917 and the Trans-Siberian’s in 1916. That's like right before the Tsar falls. And so couldn't aid Russia in World War I. It falls for lack of arms. And then the blockade that the Japanese did was really effective. So to get aid in, you need railways and you need ports. The Japanese had covered all that and they owned all the railway systems. So the only way we could get stuff in is we're flying over the hump, the Himalayas. Okay, so you're going to fly in aviation fuel plus the airplanes and then you're going to try to bomb Japan from China. Imagine flying in aviation fuel. It's like a non-starter. And I think the best argument you can make is, on that, is that rather than getting Chiang Kai Shek to do the Burma campaign, ‘43–44, and he was dead against it, is let him keep all that equipment, whatever you got over the hump, instead of sending it over to Burma, which is strategically irrelevant, turns out in this war, let him keep everything. Because when the Japanese try to do Ichigo, it's their biggest offense of the war. They're going right down through central China because the United States is cutting off so much of their maritime stuff, they want to be able to have railways all the way through central China.

Anyway. Chiang Kai-shek gets wrecked in that. Maybe if more of the equipment had been available to him, maybe, maybe he would have been in better shape to then put on a better showing in the civil war. Also not look to the Chinese people as like a serial loser. And it's not his fault. It's because he's an unindustrialized country trying to deal with the Japanese. Whom we found very difficult to fight. So why wouldn't he find them difficult.

Dwarkesh Patel
But we kept Lend-Lease going even after Berlin had fallen and Stalin used that aid in order to. Even after the first atomic bomb is dropped, only then does Stalin break the non aggression pact with Japan and invade Manchuria. So he basically had no moral right to Manchuria. It was the Chinese who had been doing all the fighting. And he gets Manchuria. Should have stopped the Lend Lease by then. Right? Just to make sure like Chiang Kai Shek…

Sarah Paine
I think we stopped it very rapidly at the end of the war. So, yeah, the British were appalled how rapidly the Lend Lease aid stopped because they desperately needed it because they'd been wrecked by the war. So yeah, we did cut it off, but I don't, you definitely want Nazi Germany gone and you want to keep support Russia until that happens.

Dwarkesh Patel
Can I try another idea on you?

Sarah Paine
Yeah.

Dwarkesh Patel 1:45:42
So it seems like one of the big achievements of modernity is that we get countries to stop caring about history. The idea is like, look, if you hear people talk about the Israeli-Palestinian conflict on both sides, they'll go back like two, three thousand years about who has rights. On the Tucker Carlson interview, Putin was talking about things that happened in like 500 BC. And then so the big achievement is just like, look, the past is the past. Whatever the borders are right now, we're going to enforce those. We don't care who claims to have rights to it because of what happened in the 1700s or something.

Sarah Paine 1:46:12
That is modern international law. And I don't know what. You'd have to be a lawyer. That's probably a lawyer who actually knows about when you decide modern international law begins. But certainly by the time you're getting through World Wars, we're in modern international law. And that's the problem with what Putin's, his magic he's working on Ukraine, is they signed multiple times at the breakup of the Soviet Union and then afterwards what Ukraine's borders were and they're violating all of that. Well, have fun with it, because the Chinese can play that game in reverse on Siberia and we'll see how happily that one ends. But I think there is a realization that these disputes go back forever. And it's like musical chairs. We're now freezing whatever chair you're in. That's it. And that's modern international law. And it turns out that those countries that focus on territory of ripping it off from someone else or… it's negative sum, you're going to fight over it, you're going to destroy it in the process, whatever. And the Europeans are a great example to us all of just call it a day. Let everybody travel, go get a rental property somewhere. Right. And run your businesses. You really don't need to own Luxembourg or whatever it is. What a hassle. You want to offload their crazy domestic problems anyway and just run your transactions. This is the miracle of the rules based order. It is the only way to have a compounded, rapid, compounded growth. And you go, well, how's it possible that China had this rapid growth for a while? And their dictatorship, how'd that work? It's because they had the growth when they cooperated with the international order. When they rejoined the family of nations. Yeah. That's when they, they prospered. When now Xi Jinping is going to do a U turn on that. Have fun.

Dwarkesh Patel
The real motivation for the CCP is to keep itself in power. It's not a democratic system. And if keeping yourself in power doesn't align with making your country rich and therefore staying in the international order, if that is a correct mental model, then like what's the pitch to Xi? Why should he abide by the international order? Because we want him to abide by the international order, right?. And if the idea is like, well, it's not going to work out for you…

Sarah Paine
I don’t think it will work out for him or for the Chinese people.

Dwarkesh Patel
That's not a great sales pitch.

Sarah Paine 1:48:36
Yeah, but there are things that you just cannot convince other people. Another key thing in strategy is you can have a very worthy goal, but you need to look at it carefully and go, is it feasible? I don't think it's feasible to make Xi Jinping change his mind. Because he's of a particular background and this is what he believes. And he has a whole bunch of other Chinese people around him who believe the same thing. And we're going to second Cold War. It's going to be a long wait ‘til they change their mind. If you think about Gorbachev's generation, they decided Gorbachev said over and over again, we cannot live like this anymore. The realization is that their standard of living was lousy compared to the west. So clearly they needed to reform and do things. But that was his decision and his generation's decision to reassess and do something else. It's going to be a long wait until the Chinese reassess or the Russians reassess. So then you have to think about protecting yourself and working with allies, your friends.

Dwarkesh Patel
In the first Cold War, the Sino Soviet split, getting China to oppose the Soviet Union was a big deal in terms of winning that war. Who plays the role that China played in the first Cold War against China?

Sarah Paine 1:49:57
Oh, they still played. It's interesting. So Putin has Russia lost. The Soviet Union fractured at precisely the time that China's rising. So during the Cold War, China's really weak and Russia's in the dominant position. Well, I guess that's reversed. So Putin is dumping all his ordnance on Ukraine, his Cold War stockpile, and he is leaving Siberia open, wide open to Chinese predations. And I believe Siberia has precisely the resources that China covets on lands the Tsars stole. And I think there's a big body of water out there called Lake Baikal. It's 20% of the world's surface fresh water. China is chronically short of fresh water in North China. Baikal is the only fix, and I think it's flat enough that you could pipe it around. So I don't think the question is when. It's not a question of whether the Xi-Putin bromance is going to end, it's when. And I assure you, the Chinese will pick the moment that is least convenient for Putin.

Dwarkesh Patel
You think they'd actually go to war about it, or would they just negotiate?

Sarah Paine
Oh, I bet it'll be negotiation of just you get them in a corner and say, okay, buddy, this is what we want. This, this, this, this. And then he'll want some things from them and…

Dwarkesh Patel
Sounds like what Zhou Enlai tried to do to Nehru, it didn't work out.

Sarah Paine
What do you mean? Zhou Enlai absolutely got the Aksai Chin Plateau. Yeah, he offered the deal to Nehru. Nehru wouldn't take it. It's like, okay. I don't think, since they both have nuclear weapons, I don't think that kind of play is probably the play to make. But as long as Putin's desperate for whatever, for ordnance and other things, Putin's getting himself into a grand old corner.

Dwarkesh Patel 1:51:58
You wrote a textbook about Chinese history. From 1644 onwards. In Silicon Valley, there's this community called Progress Studies, and they're very interested in why the Industrial Revolution happened, how to increase growth, what has the history of progress been like. And one big question is, why didn't China have the Industrial revolution first in 1750? That much higher fraction of the population, much higher fraction of world output during this period. Why didn't they have higher growth rates and come up with all these technologies?

Sarah Paine 1:52:26
Well, there are whole books on the subject that I haven't read that look into this. But I would flip it around and go, well, the Industrial Revolution starts in Europe. That's the unusual thing. So the question isn't why China didn't, but why Europe did. And I have some, some guesses, but they're really…There are very thoughtful books on this subject and you can go find them and have fun reading them. But if you look at Europe, it's a peninsula and then it's got the Mediterranean there. And so the Europeans, and they've got quite a very good river system, the Danube and the Rhine and the Ruhr, all these different rivers. And so they have…have always been a lot of trade going on. And then it was… No one's ever been able to make a big empire in Europe stick. The Romans did quite well for quite a bit of time. Napoleon tries not so good. Nazis, not so good at all. And I guess the French tried on and off and it didn't succeed. So you have all of these competing countries. And I think a lot of that was essential to the Industrial Revolution of having, you're focusing on commerce, and you've got a lot of competing places. So you don't have one big empire that can just sit there, be fat, dumb and happy that you have to innovate in order to survive against all these other little states.

Dwarkesh Patel 1:53:47
Earlier, talking about how in North Africa or other regions there's been a bunch of chaos because people are constantly... There's these insurgencies, there's these civil wars, there's these smaller wars happening all the time and that causes chaos. But if you look at Europe throughout this period, these countries are constantly at war with each other. And the fact that because of that no one big empire forms apparently helps explain the Industrial Revolution. So it seems to imply that we should see a bunch of growth in North Africa or something.

Sarah Paine 1:54:15
It's a tremendous innovation. And look at things long prior, like Michelangelo and things Leonardo da Vinci. It's just a lot of these really bright people. Oh, yeah, it's my guess it's related to all of this. And I think the Industrial Revolution doesn't begin in England. And the Industrial Revolution isn't strictly industry. Yeah, it's about textiles and steam engines and railroads and things, but it's also about institutions. One of the big things for the British was insurance. So that when you do your trading vessel and the whole thing sinks, you don't bankrupt all your investors, because you've got that boat insured. And then banking, banking rules. So you can't just rip off everybody's cash. And so Britain, because it's surrounded with the moat, becomes a banking center. Because it's unlikely in a war to have some alien army come and just open all the vaults and take everyone's money. So there are these institutional things. And then the British trading countries like the Dutch Empire want to have to be able to trade in peace. So they want people just follow laws. So it's not surprising the founding father of international law is a member of the Dutch Republic, Hugo Grotius. And so it's all a piece. It's this gravitation. the Romans are gravitating towards law and institution and then these competing places so no one controls. I think the question is why do these. What's special about Europe rather than why it should have been somewhere else?

Dwarkesh Patel 1:55:56
What's mysterious about China is not only that it doesn't have the Industrial Revolution first, but its development starts so slow. So Japan copies Western institutions during the Meiji Reformation and industrializes almost a century before China is able to do the same thing. And look, they're communists for a while. So it makes sense that during the Mao period or during the civil war that would impede development. But even during the Qing dynasty or something, it takes till 1980 for them to start development. That just seems awfully slow.

Sarah Paine 1:56:25
Well, communism is a really lousy system. So they did that. In the 19th century they have all these peasant rebellions where they're literally losing tens of millions of people that are just devastating the country. So that's why the 19th century is a lost century of all of that going on. So it's hard. And then there's another piece that's hard for the Chinese. If you've been such a successful civilization in Asia the way they had been, they were the center of everything culturally, science, governmental institutions, you name the area of human achievement and the Chinese were at the top of it all in Asia. With that kind of success, it's pretty hard to reform yourself. And so they also fed themselves the Kool-aid that the Chinese never absorbed foreign ideas. It was always foreigners absorbed their ideas. And of course that's nonsense because they were conquered by Manchus and actually absorbed a lot of Manchuans’ institutions. But that's not the story they're telling. Whereas the Japanese are well aware they're absorbing Chinese institutions originally, Western institutions later. And so that isn't antithetical to who the Japanese are.

Dwarkesh Patel
That's interesting. The past civilizational greatness may be an impediment to development.

Sarah Paine
Yeah, it's hard. Well, also because the way they define civilization, there's only one everybody else are barbarians. People come and kowtow to China. Okay, great. But this doesn't set you up to learn from others.

Dwarkesh Patel
One of the big dichotomies you talk about is the difference between limited and total objectives. How does Bismarck illustrate the value of having limited objectives?

Sarah Paine 1:58:14
Limited and unlimited objectives? Yeah, Bismarck does illustrate that an unlimited objective is. The simplest way to look at it is regime change. So if I have an unlimited objective against you, I win the war, you're going to be out of power. Versus I win the war, and yeah, you lose a province, but you'll be in power. So what Bismarck does, he's very clever. Well, he's operating in an age of kings. And actually his achievements illustrate why you truly don't need royal families running the show. It's breathtakingly incompetent. So initially he fights the Danish War, so that's way up north and it's over these two provinces. And he wants to set his fingers on the scale of the more northerly province because then that'll give him the rights to march troops, I think through the more southerly one to cause problems later. So war number one's the Danish war. No one thinks too much about it. Prussia gets an extra province as part of Denmark. Was part of Denmark, no big deal. But then he fights the Austro-Prussian War and then what he's after is there are all these little Germanic states and Austria had dominated a bunch of them and in that war, a whole bunch of them as a result of that war, he collects a whole bunch of these other states and no one is really paying attention that he's actually upending the balance of power in Europe. They're just looking at it. It's limited. He's not toppling the Austrian government, he's not toppling the Danish government. And then he fights the Franco-Prussian War, where he takes Alsace-Lorraine, which are these provinces, if you look at it's like a salient into one country or the other. He takes that and the French never forgive him, and that leads into World War I later. But by the time it's over, he's overthrown the balance of power in Europe that all of a sudden Germany, Prussia, which was the weakest of five great powers, suddenly has a central position, the Germanic states, and it owns the whole thing. And Britain and others who'd been asleep at the switch like ooh. Cause they could have weighed in on the scale at any point in these things to altered outcomes, and they didn't. There's a real reason why. And the people who are losing out here are all. They're all monarchies. There's a reason why you don't want to have people inherit offices. They don't tend to be very good at it.

Dwarkesh Patel
Actually, so World War I. I'm not sure how you explain World War I using limited and sorry, was it ultimate objectives?

Sarah Paine 2:00:50
It's limited or. Well, they're different. There are. It's useful in warfare to think about limited versus unlimited objectives. So if you look at Ukraine today, Putin has one of the most unlimited kinds of objectives from Ukraine. He not only wants to topple the Ukrainian government, but he wants to eliminate Ukrainian culture. And if you look up genocide in the dictionary, genocide can mean kill everybody, but it can also mean destroy their culture. That also applies to genocide. So Putin has a very unlimited objective. Kyiv, on the other hand, does not have an unlimited objective. I mean, as much as they'd like Putin to go, they just, they want Russia to get out of Ukraine. Right. Just get out of Ukrainian territory, stay in Russia. That's their objective. So the Ukrainians are a limited objective. Putin has an unlimited objective.

Dwarkesh Patel
Yeah. In World War I, I mean, maybe other than like the Austro Hungarian Empire, there's like. Germany doesn't have a unlimited objective with respect to France or Russia.

Sarah Paine
No, it doesn't.

Dwarkesh Patel
But it still devolves into this. 10 million people die.

Sarah Paine
It doesn't. Although the effect on Russia is to overthrow the government. In fact, it winds up overthrowing one government after another. I think as that war goes on and as you lose a lot of people, then you start think, then the objectives become more unlimited. Whereas, like originally it was just get back into German territory. After a while it's no, we want to overthrow your government for doing this.

Dwarkesh Patel
On a different topic. So your name is Sarah Paine?

Sarah Paine
Yeah.

Dwarkesh Patel
When I go on Amazon and I find your books, they're not listed as Sarah Paine, they're listed under the author SCM Paine.

Sarah Paine
That's correct.

Dwarkesh Patel
Why is that?

Sarah Paine 2:02:25
Well, it's interesting. When I first started out, I just wanted my books to be read by what the content of the book was. I didn't want it to be read whether I was male or female. Just read it for being a book. And then when the first reviews came out, it was “he, he, he, he, he, he, he”. And this is by Americans doing this. And I thought, wow. I guess the idea, because I wasn't doing women's history and this was a serious topic that, you know, women don't do serious topics. So I guess I'm glad I did it. So it looks like I'm a Briton, right? Because Britons love all the initials.

And then why do I have all the initials? It's because my dad named all – from his side of the family — my three older brothers, and there's me. And my mom felt that some names from her family ought to be dumped on me. And so since there were three older brothers anyway, she had a lot of extra names going around. So she gave them to me and I had no say in this.

Dwarkesh Patel
When you're training these Officers in the U.S. Naval War College, you've done it over many decades. Has the quality of the people you're training, has it improved, decreased over time? What's your class been like?

Sarah Paine
They're wonderful people. And also I don't look at people like linear, I just. It's a classroom, it has wonderful people in there. They're the next generation and I will do everything I can for every single one of them to catapult them as far forward as I possibly can. So that's what it is.

Dwarkesh Patel
Do other countries have some equivalent of the Naval War College where they have this professional cadre of historians and strategists who are going through the archives trying to figure out insights for current leaders?

Sarah Paine 2:04:05
Oh, not all my colleagues publish much of anything. So it varies. The Naval War College has always had a few very A-list people. And the most famous person by far ever to work there is Alfred Thayer Mahan, and he's 19th century (well, he dies early 20th century). But he's the guy who, when the United States had been doing Manifest Destiny, a continental, continental paradigm because we're expanding, taking territory all the way to the Pacific. And he's the guy who says, hmm, you know, power and position in the world really is about trade. And he writes these enduring books that have lasted to the present. So there are these occasional people like Alfred Thayer Mahan who are very special. And as for some cadre of anybody's: we're just professors, we teach mid level and senior officers. And then most of them never become flag-rank with the kind of leaders you're thinking about. So I mean, I've talked to the occasional flag officer, but I'm just a minion.

Dwarkesh Patel
So you started in Soviet studies and you found yourself in the Soviet Union during its fall in ‘88, ‘89. Why was that the field you started in?

Sarah Paine 2:05:32
Oh. So I grew up during the height of the Cold War and listened to family dinnertime conversations all about it. And then you listened people describing the Russians as being totally evil. And I thought, well, I'll learn more about this. I'm curious. And then I realized, whoa, it's just unbelievable how cruel the Russians have been to each other and others. It's like they're a class by themselves. I don't make it up. So. And then the question is, why? And learning more about it.

And then in history, you do a minor field as well. And I think the typical minor field for Russian history would be French or German. And I thought, “no, I want to learn about China” and – just curious. And then learn about China and then realize that Russia and China have relations. But then Japan always weighs in. So then I had to learn more about Japan. So that's where all of that led. And then I also thought, I don't want to be studying the United States. That's where I'm from. And I want to learn about other countries. Also, my presumption is my bias would be pretty bad about the United States or things I wouldn't see. I thought, I'm going to study other places because I don't have any family ties. I mean, I'm not a descendant of Russian immigrants or Chinese or Japanese immigrants, obviously. So these are places that I'm not related to. And just curious.

Dwarkesh Patel
What is the answer to that? You said you were trying to figure out why the Russians have been so cruel to each other through history and then especially, I guess, after the communist revolution, why is it?

Sarah Paine 2:07:03
I don't know the answer, but I know…I was, I've just recently read Aleksandr Solzhenitsyn. I should have read it years ago when it came out, but I didn't. So I finally read it. And if you read that, look at the level of cruelty. It's mind blowing. It isn't simply that the prisoners are mean to each other, or that the prison guards are mean to the prisoners, or the guards are mean to each other. All that's true. And it's not simply that the civilians outside of the Gulag are mean to everybody. It's family members who are mean to people who are in the Gulag, the neighbors are mean to the family members who have anyone who's in the Gulag. And you start looking, going, who's not being mean in this place? They're all being mean.

And then you look at, oh, then I haven't got the list of all the words. But then I was reading a lot of Russian stuff last winter and then maintaining a list of words in Russian: “sila”, it means strength or coercion in Russia. In English, strength doesn't mean coercion, it just means having power. Right? But in Russian, it also means coercion. Or detente. It comes from the French, which means a relaxation of pressure. The Russian word, what it means is unloading a gun. So if you look at their language, it's so coercive. And I think probably if you're living in a really dangerous piece of real estate, which they are, no defensible boundaries. So you're always fighting people. And their answer to surviving that is to be meaner than anybody else. And they just do scorched-earth. You mess with them, they kill everybody, and they just don't care. And okay, you want to. We have the modern world now where we can deal with each other, with international law, so none of that stuff is necessary. But the Russians have not pulled from that tradition. And you can read in the newspaper how absolutely cruel they are to their men in service, right? They don't allow any of them to retreat. They've got the execution battalions behind them. So any kid who turns around, they're immediately murdered by their compatriots. Who treats their own people like this? Russians do, but other people don't.

Dwarkesh Patel
I mean, Solzhenitsyn talks in that part about that this is the direct result of the communist regime's policy where if somebody's an orphan because their parents went to the gulags, if you try to help the orphan, you must be a sympathizer for the criminals, and therefore you must also be sent to the Gulags. And so, like, you have these orphans running around who's like parents have been sent away.

Sarah Paine 2:09:44
The orphans – a lot of them were put into orphanages, and then they wound up being set up to join the KGB in other places. So they were probably some of the execution battalion people, I don't know the details, but there's some of that going on. And the cruelty in Russia was remarked by Europeans who were traveling there in the, I don't know, 17th century. So it goes back a long time, about the level of cruelty. Peter the Great, back in the day, he tortured his son to death. Who does that? He didn't have a replacement son either.

Dwarkesh Patel
Unlike Stalin. Final question. This new project you're doing on the Cold War, is it going to be a book? How will it come out?

Sarah Paine 2:10:28
Slowly. Oh, I've been working on it for years. It'll be a culmination of previous research. Starts in 1917. It will end in 1991ish. Why 1917?: We may not have noticed that these crazy communists in Russia were serious, but they did declare war on the capitalist order. It is really clear. The documentation is just there. And they were trying to fund people to overthrow governments in Europe, all around the world, immediately. This is in the teens, and then certainly in the 20s. So just because they weren't remotely powerful enough to do it, but they were like, what is it? A Maoist phase-one insurgency where people aren't paying attention. They're, like, below the radar. They're doing stuff.

And it's going to be looking at all the strategies that the Russians played, that we played, what medium powers played and what worked and what didn't. And it's going to be organized around concepts to understand these things. So on the one level, if I can do it right, it'll be a history of the Cold War from one year to another year. So there's that, and then there's another one about strategy. Okay. People are trying to achieve objectives, and they're trying different things and integrating different elements of national power. So it'll be that. And then what I found helpful in my own career are these concepts like limited and unlimited war or primary adversary, primary theater or frozen conflict, or veto player. I find these things helpful. And so I'm going to put as many of those things in there as possible. And I'm of a certain age, so it's passing the baton to the next generation. Say this is what I've managed to figure out as I go putt-putt-putt reading in archives, and then others can skim it and see if there's anything worthwhile. If not – I tried, right?

Dwarkesh Patel
I hope you'll come back on the podcast once the book is ready.

Sarah Paine
Thank you.