Editor's note: Post 2/30 for Inkhaven

Why did the philosophers fail to anticipate the industrial revolution? I often find myself wondering. On the one hand, you could argue that they weren’t in the business of predicting the future. But on the other hand, I’m sure if you plucked Plato and his students from The Academy and dropped them off in 1910, they’d probably have a few things to say about it. The most transformative event of the past ten thousand years is surely interesting to curious observers of the human condition. But then again maybe it's not so surprising. Predicting the future is hard. Predicting an exponential at the start of said exponential is even harder.

So did anyone do it? And if so, who was the earliest? Could anyone possibly predict industrialization in antiquity? The middle ages? The age of the printing press? When did the first mind dare to pull back the veil of agriculturalism and sneak a glimpse at the dazzling, terrifying spectacle of the industrial age? We’ll never know for sure of course. But I present two candidates:

Christiaan Huygens

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Outline:

(01:28) Christiaan Huygens

(05:07) Roger Bacon

(08:33) Are there other Candidates?

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There's a lot even our best neuroscientists don't know about the human brain. How can we have any reasonable hope for preservation given those unknowns? What if there are crucial memory mechanisms that are so poorly understood, we don't even know to check whether our methods preserve them? As it turns out, there's some interesting empirical evidence about the general shape, and limits, of those unknowns.

In Ted Chiang's short story Exhalation, a race of aliens have brains which run on compressed air, performing computations and storing information in elaborate arrangements of hinged gold-foil leaves. The leaves are held in position by a constant stream of air flowing through the brain's tubules, encoding alien thoughts and memories. That ephemeral suspension pattern is the whole self—any alien whose supply of compressed air runs out is reduced to a catatonic state, all of their memories erased as the gold-foil leaves hang limply down. Even if air pressure is restored, the original information is lost for good. The person can never be recovered.

If this was how brains worked in our world, I'd be working on a very different kind of preservation, like longevity researchers or some kind of relativistic time-dilation bubble. [...]

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Outline:

(01:40) The lady in the lake

(03:33) Using cold to save lives: DHCA

(08:00) Electrocerebral silence

(10:27) Known unknowns

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This post reflects my personal opinion and not necessarily that of other members of Apollo Research.

TLDR: I think funders should heavily incentivize AI safety work that enables spending $100M+ in compute or API budgets on automated AI labor that directly and differentially translates to safety.

Motivation

I think we are in a short timeline world (and we should take the possibility seriously even if we don't have full confidence yet). This means that I think funders should aim to allocate large amounts of money (e.g. $1-50B per year across the ecosystem) on AI safety in the next 2-3 years.

I think that the AI safety funders have been allocating way too little funding and their spending has been far too conservative in the past 5 years. So, in my opinion, we should definitely continue ramping up “normal” spending, e.g. pay more competitive salaries, allow AI safety organizations to grow faster, and other things in that vein.

However, these “normal” spending patterns are not sufficient under short timeline assumptions and the obvious way to spend more money quickly is to aggressively encourage finding ways to use automated labor for AI safety.

What is an “automated AI [...]

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Outline:

(00:31) Motivation

(01:25) What is an automated AI safety scaling grant?

(05:48) Other considerations

(05:51) Who should be able to receive such a grant?

(06:30) Why make this an explicit grant program?

(07:17) Arent we just gonna goodhart all of these metrics?

(07:36) Concrete examples of potential grant areas

(07:41) Monitoring & Control

(09:31) Automated black box auditing

(10:36) White box auditing agents

(11:00) Propensity evals

(12:25) Automated conceptual alignment research

(14:58) Addressing various concerns I heard so far

(15:11) We should first understand the concrete areas for these grants in more detail

(15:40) Why should for-profits be recipients of such grants?

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The Whispering Earring (which you should read first) explores one of the most dystopic-utopic scenarios. Imagine you could achieve all you've ever wanted by just giving up your agency. While theoretically this seems rather undesirable, in practice you get double benefits: that enviable high-status having-done-things reputation, without having to do all that scary failure-prone responsibility-taking. Just don't tell anyone you have the earring, otherwise the status points gained are void. Of course the fact that you're cheating takes away most of the satisfaction of winning too, but it's still better than not winning. Moloch says: sacrifice what you love, and I will grant you victory.

Anyway, I've been using Claude chat as an enhanced diary for the past couple of months. I've been incredibly productive. Things I've been procrastinating for years are getting done, and it's not even feeling difficult. Habits I've been trying to pick up repeatedly stick with ease. Work tasks I've been struggling to do in a timely manner mostly get completed by Claude Code, freeing up hours and hours of brain time to actually think with. Playing videogames eight plus hours every day, like I did most of the winter, feels less and less [...]

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As previously mentioned, research feedback I give to more junior research collaborators tends to fall into one of three categories:

• Doing quick sanity checks
• Saying precisely what you want to say
• Asking why one more time

In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like.

I talked about doing quick sanity checks in a previous piece. Here, I talk about the second piece of advice: saying precisely what you want to say.

Saying precisely what you want to say

The second most common feedback is that you should write down precisely what you want to express.

One of the most common interactions I have with junior researchers goes as follows: I read a draft section of their research writeup. This often consists of many paragraphs detailing various seemingly disconnected ideas, as well as 5-10 different figures. I’m confused about what the point of the section is. I ask them what exactly they’re trying to say in the section. They give me a [...]

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Outline:

(01:41) Language Models Offer Mundane Utility

(03:00) Heads In The Sand

(07:05) Huh, Upgrades

(08:10) Mythos

(12:07) Whats In A Name

(14:59) On Your Marks

(16:10) Choose Your Fighter

(16:53) Get My Agent On The Line

(17:31) Deepfaketown and Botpocalypse Soon

(24:33) Cyber Lack Of Security

(29:08) Fun With Media Generation

(29:50) A Young Ladys Illustrated Primer

(30:53) They Took Our Jobs

(37:45) After They Take Our Jobs

(39:16) Gell-Mann Amnesia

(41:33) Get Involved

(43:25) In Other AI News

(46:41) Show Me the Money

(51:08) Quiet Speculations

(51:59) Explaining Persistent Model Parity

(55:37) Take a Moment

(01:00:54) OpenAI: The Histories

(01:06:04) The Department of AI War

(01:12:38) Department of AI Solidarity

(01:13:46) Writing For The AIs

(01:16:42) Quickly, Theres No Time

(01:16:46) The Quest for Sane Regulations

(01:18:10) Chip City

(01:20:07) You Received The Federal Framework

(01:21:02) The Week in Audio

(01:24:22) Rhetorical Innovation

(01:27:48) I Am The Very Human Of A Frontier Language Model

(01:38:01) Aligning a Smarter Than Human Intelligence is Difficult

(01:41:22) Aligning Fake Graphs Can Also Be Difficult

(01:49:32) The Lighter Side

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I have bipolar disorder. I was diagnosed in late 2012 following my one and only severe manic episode. Most psychiatrists would regard me as a resounding success case – I never even remotely come close to suicidal depression, manic delusions of grandeur, impulsive spending, or irresponsible sexual behavior. By standard measures, I am well-adjusted, functional, and successful.

Part of this relative success is adherence to appropriate medication, and another part is maintaining good insight[1] into my mental state. Years ago, I defined a personal bipolar index scale to communicate to myself and close ones my mental state.

My bipolar index ranges from -10 to +10 and is a subjective self-report. -10 would be a state of extreme suicidal depression. +10 would be extreme mania with complete loss of insight, delusions of grandeur, pressured speech, psychosis, etc. 0 is the perfectly balanced state in the middle, neither up nor down.

In the last decade and a half, I don't think I've ever broken out of the -3 to +3 range. -2 to +1 is standard, and more so between -1 to 0.5 most of the time. Really, an extreme success case by typical psychiatric standards.

Yet the disease burden [...]

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We’re mostly focused on research and writing for our next big scenario, but we’re also continuing to think about AI timelines and takeoff speeds, monitoring the evidence as it comes in, and adjusting our expectations accordingly. We’re tentatively planning on making quarterly updates to our timelines and takeoff forecasts. Since we published the AI Futures Model 3 months ago, we’ve updated towards shorter timelines.

Daniel's Automated Coder (AC) median has moved from late 2029 to mid 2028, and Eli's forecast has moved a similar amount. The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering.

The reasons behind this change include:1

1. We switched to METR Time Horizon version 1.1.
2. We included data from newly evaluated models (Gemini 3, GPT-5.2, and Claude Opus 4.6).
3. Daniel and Eli revised their estimates for the present doubling time of the METR time horizon to be faster, from a 5.5 month median previously to 4 months for Daniel and 4.5 months for Eli. We revised it due to: (a) METR's new v1.1 trend being faster than their previous v1.0, (b) new [...]

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Linkpost URL:
https://open.substack.com/pub/aifutures1/p/q1-2026-timelines-update

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I’ve noticed that sometimes there is an idea or framework that seems great to me, and I also know plenty of people who use it in a great and sensible way.

Then I run into people online who say that “this idea is terrible and people use it in horrible ways”.

When I ask why, they point to people applying the idea in ways that do indeed seem terrible - and in fact, applying it in ways that seem to me like the opposite of what the idea is actually saying.

Of course, some people might think that I’m the one with the wrong and terrible version of the idea. I’m not making the claim that my interpretation is necessarily always the correct one.

But I do think that there's a principle like “every ~social idea[1] acquires a corrupted version”, and that the corruption tends to serve specific purposes rather than being random.

Here are a couple of examples:

Attachment theory. People with insecure attachment read about attachment theory, and then what they imagine secure attachment looking like is actually an idealized version of their own insecure attachment pattern.

Someone with anxious attachment might think that a secure relationship [...]

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Outline:

(03:12) Emotionally selective reading

(05:42) The effects of vibe and my own corruption-complicity

(09:52) Conflicted authors

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Doctor: Mr. Burns, I'm afraid you are the sickest man in the United States. You have everything! [...]

Burns: You're sure you just haven't made thousands of mistakes?

Doctor: Uh, no. No, I'm afraid not.

Burns: This sounds like bad news!

Doctor: Well, you'd think so, but all of your diseases are in perfect balance, [...] we call it "Three Stooges Syndrome"

Burns: So, what you're saying is...I'm indestructible!

Doctor: Oh, no, no! In fact, even a slight breeze could—

Burns: Indestructible...

In the transition to ASI, humanity's survival ended up depending on a global "Three Stooges Syndrome."

As demographers predicted, fertility rates collapsed. Aging populations were going to hollow out workforces, crush pension systems, and leave a skeleton crew of exhausted 50-year-olds trying to maintain civilization for an enormous retired population whose lives kept extending one year per year. Then AI automated...everything. The worker shortage met the automation wave head-on, and the babies that weren't born didn't grow up to need jobs that no longer existed.

Metabolic disorders, attention fragmentation, and other health effects of increasingly artificial lives were all real. Biomedical AI, however, iteratively compressed the drug development cycle and diagnostic systems such that the treatment curves [...]

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