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   Number 1: Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI

   There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right. A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren’t traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents: We’ve been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex’s most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March. With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex’s user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team. However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it. From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company’s broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone. We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw. Side note: also don’t miss Abhihek’s sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident. Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress. We discuss: * Why Codex unexpectedly took off among non-developers inside OpenAI * Why employees felt like using Codex gave them a new superpower * The product insight that led OpenAI to build ChatGPT Work * Why Codex and ChatGPT Work share the same underlying agent harness * How their UX, Git visibility, artifacts, and sandboxing defaults differ * Why OpenAI merged its agent experiences instead of building separate products * How AI is blurring the boundaries between engineering, design, strategy, and operations * Why OpenAI wants the default model configuration to work for most users * When power users should use deeper reasoning, Ultra, or multi-agent modes * Artifacts, agentic spreadsheets, and creating high-fidelity work products * Why interactive Sites may replace decks and spreadsheets * The challenge of designing a simple interface for an agent that can build almost anything * Why users should retry tasks that models could not handle three or six months ago * How AI can gather context for performance reviews without replacing human judgment * The OpenAI automation that turns internal Slack and document activity into memes * What reaching ten million ChatGPT Work and Codex users means for the product * How OpenClaw inspired persistent environments, scheduled tasks, and personal agents * Using ChatGPT for financial planning, budgeting, workouts, meals, and household management * The design tradeoffs behind sub-agents and how much of their work users should see * ChatGPT memory, Chronicle, and long-term context * Why AI may make more people generalists with deep specialties * Why ideas and taste become more important when almost anyone can build * Why LLMs still struggle with the instruction “bring me new ideas” * Measuring productivity through quality at-bats instead of commits, tokens, or pull requests * The critical difference between AI-generated motion and meaningful progress Akshay Nathan * LinkedIn: https://www.linkedin.com/in/akshaynathan/ * X: https://x.com/akshaynathan_ Timestamps 00:00:00 Introduction and Bringing the Power of Code to Everyone 00:01:33 Joining OpenAI and Preserving a Startup Culture 00:02:40 What OpenAI Learned from Enterprise AI Adoption 00:05:28 Why OpenAI Built ChatGPT Work 00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness 00:12:07 Why OpenAI Merged Its Agent Experiences 00:16:24 Models, Reasoning Levels, and Choosing the Right Default 00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration 00:24:22 Why Sites Could Replace Decks and Spreadsheets 00:30:08 Designing an Agent That Can Build Almost Anything 00:34:28 From Developer Agents to Knowledge Work—and Everyone 00:36:07 Power-User Advice and AI-Assisted Performance Reviews 00:40:41 OpenAI’s Internal AI Memes and the Ten-Million-User Launch 00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System 00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need 00:54:39 ChatGPT Memory, Personalization, and Chronicle 01:00:19 How AI Is Reshaping Product Development and Tech Roles 01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas 01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. Progress Transcript Introduction: Akshay Nathan, ChatGPT Work, and the No-Code Arc Swyx [00:00:00]: We’re here in the studio with Akshay from OpenAI. Welcome. Akshay Nathan [00:00:07]: Thank you. Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It’s been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt. Akshay Nathan [00:00:32]: Yeah. It’s funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there’s this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It’s funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that’d be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what’s going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we’ve been up to is, like, the manifestation of that. From Walrus and Airtable to OpenAI Vibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we’ve got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed? Joining OpenAI and What Hasn’t Changed Akshay Nathan [00:01:44]: I think the more interesting thing is how things haven’t changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn’t changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we’re probably gonna, like, try different products and have different things that succeed and don’t. But the vision has stayed the same, and the mission has stayed the same, and we’re starting to see the pieces, fall together, and that’s really cool. Enterprise Lessons: No One-Size-Fits-All AI Swyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you’re bringing into your work now? Akshay Nathan [00:02:52]: I think how there’s no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you’d get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it’s interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it’on the flip side, it also means that, like, you don’t know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there. Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering? Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who’s, like, looking at their computer or looking at their phone, like, it’s our job in the product to, like, be enabling them and showing them where to go. So we’re really excited about that. Vibhu [00:04:24]: Do you think there’s been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don’t know what to do with it, or have things changed? Adoption, Agents, and the Next 10x Market Akshay Nathan [00:04:39]: We’re seeing now that, like, there’s this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we’re seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it’s connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there’s, like, this, like, 10x or 100x bigger market where, like, they don’t yet get that, or they don’t yet see that. And so I think that’s the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That’s where we wanna play, especially with ChatGPT Work. ChatGPT Work, Codex, and the Super App Merge Swyx [00:05:27]: Yeah. well, let’s, let’s skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we’re officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing. Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it’s only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they’re all using Codex for, their use cases. That part’s cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, like Swyx [00:06:34]: It’s like, “I’m not supposed to be using it, but I am.” Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it’s, like, a tricky thing, right? There’s many ways you can go about it. And so that’s what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone. Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there’s a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it? Akshay Nathan [00:07:36]: I think we want to get it to position it for if you’re doing work-related things, for lack of a better word, right? Who ChatGPT Work Is For Akshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that’s the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there’s also personal productivity, right? And, like, I think ChatGPT Work is I’ve seen people do things in their personal lives that you wouldn’t classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn’t receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there’s all these things that, like, you, work-related or productivity-related things, I think that’s what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don’t want a user to get stuck in a tab or an experience where they don’t get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you’re in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there’s, like, some opinions that go behind that, but we do want We don’t want the user to need to choose which experience they’re in. Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don’t want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It’s not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences? Shared Harness, Different UX: Codex vs. Work Akshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you’re in. On the UX side, there’s opinionated takes that we have when you’re in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same. Swyx [00:10:16]: I’m just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes? Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you’ll see, like, the diffs of, like, the sheet that it’s creating and stuff like that, and the file edits. But in Work you won’t be able to see that. Swyx [00:10:42]: I think that’s, that’s super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn’t productivity everything? Productivity Teams and Core Chat Akshay Nathan [00:10:55]: So Swyx [00:10:55]: Science? Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there’People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there’s so much more inside to create images. And there’s so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there’s teams focused on enterprise and infrastructure and API and stuff like that, so. Swyx [00:11:33]: I will bring it up. Retirement Calculator Demo and Git-First UX Swyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There’s a Codex version here. I picked “Five Little Ducks” song, so this will take a while. Akshay Nathan [00:11:43]: Huh. Swyx [00:11:43]: I think we’ll just keep it in the background and, as they finish, we’ll look into some of the differences. Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you’ll see that, Swyx [00:11:53]: That it assumes Akshay Nathan [00:11:54]: Like the Swyx [00:11:54]: It assumes Git. Yeah. Yeah. Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you’re in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah. Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let’s not do it?” Like, what’s the thinking behind that? Why Merge the Experiences Akshay Nathan [00:12:14]: In, ChatGPT Work? Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it’s like, why Swyx [00:12:22]: Different apps. Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I’m, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we’re, we’re building, this technology is giving people leverage. Like, the things, maybe it’s the more mundane parts of your job or parts that, like, if you were able to automate, you’d be able to share more ideas faster or whatever, like, you’re able to do now. And because of that, like, that might blur the lines between someone who’s, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn’t box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It’s this thesis that, like, eventually things are gonna come together and we don’t wanna be Like, we wanna be prescriptive about when to be in either experience, but we don’t want to box anyone in. Swyx [00:13:45]: I wonder if there’s users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can’t imagine what that was, but maybe they’re more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you’ve seen it. Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app, Harness Engineering: ChatGPT vs. Codex Swyx [00:14:08]: The classic, right? Akshay Nathan [00:14:09]: The Vibhu [00:14:09]: You just start a new chat, and you don’t go under Work, right? Akshay Nathan [00:14:13]: Yeah. If you start Vibhu [00:14:13]: So Akshay Nathan [00:14:14]: A new chat and go to chat, then you’re, you’re talking to ChatGPT with the instant model. Vibhu [00:14:16]: Oh, we can technically do another. But on instant. Swyx [00:14:21]: Yeah. So this one’s not gonna code or it’s gonna be in line. It’s on a in line in a sandbox. Akshay Nathan [00:14:26]: It’ll Vibhu [00:14:27]: Oh, that’s cool Akshay Nathan [00:14:27]: We try to push you to go to Work if you’re creating a spreadsheet. Yeah, but this is Swyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision? Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you’re able to. or you’re trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness? Swyx [00:14:48]: It’s more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let’s call it the ‘01 era, until now, and now it’s being replaced by the Codex harness effectively. And they’re, they’re overlapping somewhat, but I’m curious what changed if there is. Akshay Nathan [00:15:10]: My perspective on this is, like, there’s, there’s, there’s there’s like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we’re, we’re really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we’ve been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I’m sure there’ll be work down the road in order to get things to be, equivalently capable in all scenarios. But it’s just a question of, like, what we’ve been focusing on the product on historically and what we’re focusing on now. Models, Defaults, and the Reasoning Slider Vibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there’s also the new models you’ve released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all these Akshay Nathan [00:16:44]: There’s 32 options. Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don’t have the breakdown of what all this is what’s, what’s the advice, right? Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That’s happening again. I think it’s like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we’ve we’ve chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we’re, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you’re not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough. Swyx [00:18:09]: I have, I’m just gonna run something by you since you have way more experience than me. I’ve recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns. Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High? Akshay Nathan [00:18:29]: Yeah. It’s hard to say because Swyx [00:18:31]: Yeah. It’s like an interaction effect. Akshay Nathan [00:18:33]: exactly. It’s like there’s a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it’s doing the right thing? Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you’ll be able to make consistent progress in a way that’s verifiable over time. But I think for most tasks, they don’t fall into either of those buckets. And so like at least when they’re starting, and so that’s why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there. Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic. Vibhu [00:19:36]: It’s nice on mobile at least. There’s a nice slider there. Swyx [00:19:38]: It’s nicer. Vibhu [00:19:39]: I haven’t tried it. Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah. Vibhu [00:19:44]: Ooh, it’s just a nice slider. Yeah. Swyx [00:19:46]: Very pretty, very colorful. Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there’s multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side. Artifacts, Spreadsheets, and the Work Launch Swyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lower Vibhu [00:20:07]: No Swyx [00:20:07]: Grounds I would’ve used Vibhu [00:20:08]: I think the slider, if I’m not mistaken, is Swyx [00:20:09]: Terra. Vibhu [00:20:10]: Oh, it is. Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity. Vibhu [00:20:22]: I’m the reason. Here’s ten minutes of our Swyx [00:20:24]: There you go Vibhu [00:20:25]: Retirement calculator. Swyx [00:20:26]: Oh, that’s the Excel thing working for you. Vibhu [00:20:28]: This is, Swyx [00:20:28]: Oh my God. Look at that Vibhu [00:20:28]: This is work, and then Codex is still cooking, so we’ll get back into it. I think it’ll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking. Swyx [00:20:41]: Yeah. And by the way, so I’ve, do Gabriel Chua? He’s part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it’s agentic Excel. Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right? Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you’ll see that there’s been pretty dramatic improvements in the quality of these artifacts and then also on the product side. Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like it Swyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I’ll need to take the visuals here, but we-we’ll, we’ll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day? Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that’s like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you’re seeing, like underneath the hood, there’s a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it’s not necessarily that you wouldn’t need an Excel license. This is stage one, right? Like, this is probably not what you meant when you’re like making a retirement calculator. Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah. Akshay Nathan [00:22:24]: You wanna iterate and like when you’re seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration. Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that? Multiplayer Artifacts and Collaboration Swyx [00:22:46]: You can already share it, right? Akshay Nathan [00:22:48]: Yeah. It’s inter It’s something that, we’re actively thinking about. one thing that, we’ve noticed internally without talking too much about the roadmap is that like there’s many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I’ll ping them back the answer. Akshay Nathan [00:23:04]: And then I’ll be thinking like Vibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted. Akshay Nathan [00:23:07]: Exactly. And I’ll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there’s so much context like in the rollout and stuff that could be interesting. Vibhu [00:23:28]: Yeah, it’s Swyx [00:23:28]: So like the answer was preemptively respond to every inbound request? Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job. Swyx [00:23:36]: I know you copy-paste and then you’re just a message forwarding service Akshay Nathan [00:23:39]: Yeah. Yeah, exactly Swyx [00:23:39]: From AI to AI. Vibhu [00:23:40]: But I think it’s interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don’t realize until they try or someone shows you, and then you’re like, “Oh, okay. Okay, I see.” Swyx [00:23:52]: I think it’s als there’s also like a, light security issue, where like you’re the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I’m not supposed to see it. And that there’s no way I would know because I’m not supposed to know what I don’t know. Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we’re, we’re asking you to connect your plug-ins and, it’s pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that’s something I think we need to preserve, so that’ll be definitely a challenge. Swyx [00:24:22]: There’s Excel, there’s PowerPoint, there’s Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there’s like OpenAI Airtable? Like what does that look like if you ever ended up doing it? Akshay Nathan [00:24:41]: It’s a really good question. I think, Formats of Work: Sites as Knowledge Artifacts Akshay Nathan [00:24:43]: one that you didn’t bring up was Sites, and I think that was Swyx [00:24:46]: Sites Akshay Nathan [00:24:46]: A core part of this launch. There’s one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who’s on like our corporate finance team, and like we were mentioning how like now when they have these reports that they’re, they’re working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they’re just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it’s like, it’s like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn’t support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it’s been really valuable. Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I’m a fan of this game called Strata. It’s, it’s like a little board game that you Sites, Auto Research, and Research Dashboards Swyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thing Akshay Nathan [00:26:32]: Wow Swyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that’s, that Akshay Nathan [00:26:45]: That’s your auto research Swyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they’re, they’re gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn’t gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it’s created this lab, panel. Where is there a, is there a shortcut for a site that is created? Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar. Swyx [00:27:33]: This one? Oh, left? Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top. Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah. Akshay Nathan [00:27:39]: Ooh. Swyx [00:27:40]: So it create, it creates the sites. I don’t, I don’t think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there’s also a huge sprawl. Like look at how long this thing is. There’s so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it’s an interesting transition from Markdown effectively that you’re putting out to, you’re putting out a whole functional site. Akshay Nathan [00:28:41]: I think Markdown just isn’t that optimal for people to read, right? Might as well just write HTML website and I don’t know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they’re quite verbose. I don’t need a lot of this information. Swyx [00:28:57]: It’s very verbose. Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don’t, right? Swyx [00:29:05]: Yeah. I don’t know if, any that triggers any stories for you of how it’s run internally. Am I doing this right? Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we’re seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it’s just HTML, you can like. It’s infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There’s more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they’re very, they’re long and verbose, could be broken up. I’m sure that there’s still something to do there. Swyx [00:29:53]: They’re super long. Yeah. Akshay Nathan [00:29:54]: Yeah. But I think we’re starting to see that like there is this aspect of this is a really interesting, format, for people to use, that’s like much more flexible than what they ever had before. Swyx [00:30:07]: I think your job also comes becomes meta. You’re not designing the products. You’re designing a product to make products, and I’m curious how you manage that. Designing a Product That Makes Products Akshay Nathan [00:30:18]: I think one thing that we’ve been Like when we look at the UX, like that we’ve been thinking a lot about is how can we balance like simplicity with capability? Like if we’re designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can’t put that all in front of you because you’ll get overwhelmed. Vibhu [00:30:41]: Yes. Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there’s so much that can be done, I think the balance that we’re constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it’s using the right tools, it’s pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI. Games, Private Evals, and Show-Don’Tell Vibhu [00:31:19]: Yeah. It’s interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn’t take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I’ve got a similar version. not with all the auto research and whatnot, but Akshay Nathan [00:31:39]: You gotta do all the latest trends. Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it’s interesting, right? Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. Like Vibhu [00:31:51]: And Akshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets. Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It’s, it’s a pretty niche game. It couldn’t find how to do this on its own. Akshay Nathan [00:32:15]: Oh, yeah. Vibhu [00:32:15]: GPT-5.6 Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability. Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right? Akshay Nathan [00:32:27]: Yeah. It’s some private eval. That is not this private. Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this. Akshay Nathan [00:32:36]: It’s a hard game. He’s very good. Vibhu [00:32:39]: It’s good to when no one is competing with you. But yes, it’s a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy. Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we’ve we’re not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever. Akshay Nathan [00:33:18]: So meeting them in the moment. Vibhu [00:33:19]: It’s a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you’re like, “What do you mean? You don’t, you don’t need.” your job is to tell. And then. But the product people are like, “Well, we don’t need you if our product is intuitive enough.” So Akshay Nathan [00:33:37]: Yeah. that’s the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they’ve done in the past, exactly where they are on the adoption journey. So I think that’s like gonna be a super big opportunity. Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don’t wanna segment different people into different buckets, right? It’s also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity? From Developers to Knowledge Work to Everyone Akshay Nathan [00:34:12]: Productivity. Vibhu [00:34:12]: Productivity. So how Akshay Nathan [00:34:12]: Which is now work. Vibhu [00:34:14]: Is it work? Is there another distribution that we’re not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn’t targeting? Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that’s where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there’s inherent challenges with like, this show not tell thing that we’re talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they’re doing in their lives. And we’re already seeing that a little bit. Like this game example that you have is, something that’s like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I’m doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there. Vibhu [00:35:51]: ChatGPT life. Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there’s a lot of, there’s a lot of opportunity there, but I see it as like, we’re, we’re built we built a foundation in software engineering, and we’re gonna take the same learnings that we take from software engineering to knowledge work to everyone. Vibhu [00:36:07]: Do you have any power user advice? I feel like, there’s a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there’s a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you’ve found that help bridge that gap? Power User Advice: Push the Frontier of Imagination Akshay Nathan [00:36:30]: I think a couple things that I’ve seen is like, one, that it really helps to broaden your imagination of what’s possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it’s like, wow, it’s like it can. Like, Swyx [00:36:52]: Give an example Akshay Nathan [00:36:52]: We’re going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there’s a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousness Swyx [00:37:09]: And it can evaluate it as well. Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I’ve found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they’ve like the things that they’ve done to make a difference, highlighting like, wins that they’ve had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they’ve caught, reviews, Slack, everything. And so it’s, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn’t even I tried using it, but it was not at all helpful. And this time it’s been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what’s possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you’re in, whether it’s your life or your work, the more valuable it becomes, and it’ll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first. Reviews, Agentic Search, and Context Gathering Swyx [00:38:27]: One thing I just wanna talk about the review stuff because I’m still that’s a very sensitive thing and you’re, you’re a founder, you’ve managed people, you’ve hired people. As manager myself, I’m very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don’t care. Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what’s the etiquette? Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That’s the place where it’s incredibly helpful. Swyx [00:39:08]: So it’s just search. Akshay Nathan [00:39:09]: Yeah, exactly. Swyx [00:39:09]: It’s agentic search. Yeah. Akshay Nathan [00:39:10]: It’s like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it’s all there’s a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you’re able to do so much more, it’s easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful. Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things? Remembering What Humans Miss Akshay Nathan [00:39:50]: Probably, but I think that I also miss things. Swyx [00:39:52]: Like, it doesn’t matter, right? Vibhu [00:39:53]: I think sometimes it’s Swyx [00:39:53]: Like it’s, as it needs to be human-level Akshay Nathan [00:39:54]: It’s all relative, right? Yeah. Vibhu [00:39:56]: Sometimes it’s nice when it finds things you wouldn’t, right? Like right now, my Codex system prompts, they’re set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it’ll be like: Oh, there’s this project you did like four months ago. Here’s a note that we had, and it randomly pulls it back into context that I would never do, I haven’t thought about. Vibhu [00:40:20]: And I’m like, okay, this is quite superhuman, right? Like, stuff that would. And, it’ll save like hours on chunking of stuff or find something that’s already been done. I’m like, as much as it might miss stuff, I would too, but it’s very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It’s just marked down files that get pulled whenever they want. Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there’s so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that’s going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny. Swyx [00:41:13]: Funny. Nice. Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that’s like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that’s emerging, which is just like to find information that you otherwise would not know of. Launch Momentum and the 10 Million User Milestone Swyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they’re announcing ten million users. Does it feel different? You’ve been through a lot of launches. Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we’ve been on for a long time. Like I said, we saw the magic of Codex internally, and then we’re like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that’s like huge and super exciting. The flip side of that is like, there’s so much more to do too. Like, that’s also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that’s the next step from here. But yeah, I think extremely pumped about how it’s going so far and the opportunities. Swyx [00:42:46]: Awesome. I did want to also Because I’ve, I’ve, I’ve been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they’re same harness. The whole point is that you don’t, you can’t, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn’t that the default on ChatGPT or no? Codex, ChatGPT Work, and the Developer Brand Akshay Nathan [00:43:11]: We don’t default you into ChatGPT Work if you’re on ChatGPT Swyx [00:43:14]: If you’re free. Yeah Akshay Nathan [00:43:15]: It’s also only available to paid users right now. And I think there’s like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it’ll be a journey. Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we’ll just toggle between them as needed for UI stuff. Akshay Nathan [00:43:44]: Yeah, I think it’s even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there’s, there’s so much more that we can do to make Codex great specifically for, software development, and we’ll continue to do that. This doesn’t take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor. Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or. Akshay Nathan [00:44:20]: It’s funny, like we call it artifacts internally ‘cause that’s what the teams call it. Swyx [00:44:23]: It’s nice. Yeah. Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they’re used to, right? So if, ChatGPT Work is good at creating slides, they’ll say ChatGPT Work is good at creating slides, and that’s what we want. OpenClaw, Personal OS, and Persistent Computers Swyx [00:44:38]: One big Another, it’s July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that’s I think a lot of people’s first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever. Akshay Nathan [00:45:06]: I think there’s a lot of inspiration. I did go through my own OpenClaw moment. I, Swyx [00:45:10]: Yeah, tell the story Akshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there’s like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it’s like a work-related thing, right? It’s like not work necessarily, but it’s like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool. Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they’re independent, so. Akshay Nathan [00:46:20]: Yeah, I’m, I’m not close to it, so I can’t speak to the OpenClaw roadmap, but I don’t think so. I think that there’s gonna be, there’s always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that’ll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don’t have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that’s the goal. It’s like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you’re doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There’ll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience. Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance? Finance, Data Access, and Centralized Context Akshay Nathan [00:47:50]: I tried it. like ChatGPT doesn’t yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that’s all possible with ChatGPT today. So, I feel like at least that component’s replaced for me. Swyx [00:48:17]: I haven’t really plugged it in yet. I’m somewhat scared to look at the answer. Like that’s honestly like the same reason for health and finances. Like I’m like, no. Akshay Nathan [00:48:27]: It’s really good. It’s really cool how we were talking about like the agentic search aspect a little bit earlier, but like, it’s really cool how like, in conventional UX, like if the more power you wanna give to a user, the more like knobs and bells and whistles you need to add. Like, for like these finance and budgeting apps, like there’s always like a bunch of the different filters and like search bars and stuff like that. But like now, like with the right Vibhu [00:48:48]: Connect-connectivity to the right data, you can have whatever you want. You can ask any question you want and into that box and get the answer, and I think that’s super powerful. Akshay Nathan [00:48:57]: I think it’s also nice to just have it centralized in one space, right? You have different health apps. I have one for a smart scale, a watch, all these different things. It’s just nice to centrally co-locate it. Vibhu [00:49:08]: Which is, part of the whole thing of OpenClaw, right? Like that you would have, personal OS, which presumably ChatGPT wants to become. I do think that just relying on, like, just-in-time pulling of data for, let’s say, through via MCP, CLI, API, whatever you do, still not enough. Like I come from a bit of a data engineering background, like you still want like a data warehouse or some caching or semantic layer. do you feel that or do you already have that? Akshay Nathan [00:49:40]: I can’t speak to like all the details on how everything works, but I think it depends on the access pattern, right? Like if you want an answer immediately, then yes, it’s very difficult to do that if you need to pull from all of these sources. But a lot of the like use cases that we wanna enable in ChatGPT Work aren’t necessarily something that you need immediately. It’s more like a task that you want the agent to go and do, and that’s gonna take a certain amount of time. And, with things like programmatic tool calling and stuff now, like some of that time and sub-agents and stuff, like some of that is also parallelizable. And so it’s possible I think it’s very possible that there’s a, the ceiling on what can be done, with MCPs and like calling out to these third-party services has been raised substantially. So we’re really excited about that. Sub-Agents, Ultra, and Product Design Tradeoffs Vibhu [00:50:23]: You mentioned sub-agents. I gotta double-click on that. Ultra is a new mode. You have special affordances in ChatGPT itself to show off the agents. Can’t really do much with them, to be honest. Like just watch. what have been, what have been your experiences, any design issues that you would call out to other builders building with sub-agents? Akshay Nathan [00:50:45]: I think it’s goes back to the balance that I was raising earlier about like, showing builders the power of the tool, but also creating enough of an abstraction to not overwhelm them. I think with sub-agents, the thing that we wanted to show is that you can take a task that, has many parallel tracks or, is complicated in a way that, sub-agents can handle, and this product is for you. Like, the model can accomplish those goals or try to accomplish those goals. And so like that’s the point of like showing them in the product and that’s where we-we’ve gone with the design. There’s another, iteration of this where like you can see exactly what they’re doing and things like that, which I think is like, could converge on like overwhelming, with information. And so this is like the deliberate trade-off that we made for now. Vibhu [00:51:33]: You do display quite a lot of transcripts. Akshay Nathan [00:51:35]: Right. Right. Vibhu [00:51:36]: Or do you Akshay Nathan [00:51:36]: I think it’s hidden by default though, right? Vibhu [00:51:37]: Do you want to display more than that? Akshay Nathan [00:51:38]: No, it’s hidden by default. Yeah. Vibhu [00:51:39]: Some people could want more. So I’m one of those people that will throw a lot of stuff at goal, and pretty much every goal I’ll tell it to use sub-agents. Seems redundant, right? But every time I’m like, “Okay, use sub-agents where possible.” And I have a lot of people, a lot of friends that recommend and do the same. Whereas I’ll sometimes talk to people that are like, “Okay, this is where I want you to use sub-agents for this sub-task,” and I’m sure they would appreciate seeing into how they’re being used. For me, it’s primarily like two things, right? One is net time efficiency, so span out across sub-agents. Two is probably cost, right? Vibhu [00:52:15]: Don’t use big, expensive model. Offload to a lot of smaller, cheaper models. And some people want that level of control. So if you have repetition in what you’re doing, right? Say I want something built where I want it to consistently do this every day, I might wanna go in and fine-tune sub-agents here, sub-agents there. So you can see both, but I think if I’m not mistaken, it’s hidden by default. There’s a dropdown that goes a lot where I’m like, okay I’m just gonna keep, using. Akshay Nathan [00:52:41]: Oh, you can change the model that they use. Vibhu [00:52:42]: I know I tell them to be steered. I’ll say my I know Anthropic offers this in Cloud Code. You can tell Fable to use Sonnet or Opus to use Sonnet as sub-agent, so pretty trivial thing. You tell it to span out sub-agents with Sonnet, it’s cheaper, faster. I would assume if it’s not there, it could be built there. But I think there’s a side of Akshay Nathan [00:53:02]: It’s too many toggles. Vibhu [00:53:04]: It’s not a toggle. It’s just, you tell it in chat. Akshay Nathan [00:53:07]: You’re prompting it. Yeah. Vibhu [00:53:07]: The way I do it is prompt it, right? And I think this is something that gets abstracted unless it’s something you built for repetition, right? So if I’m building something, say that’s, podcast prep, right? Research into people, do a very deep extensive research, that I might wanna configure to cheaper, faster model just for web search, right? I can see a world in which you want both. I think the default is pretty good right now, where it’s hidden, but you can drop down and get some more info into what’s done. Vibhu [00:53:34]: I know people talked a lot about it on GPT-5.6’s launch. this thing loves to use a lot of sub-agents and causes the ChatGPT app to just crash because it’s so processor-heavy. But, Akshay Nathan [00:53:47]: For what it’s worth, that’s not my experience. Yeah, I haven’t had a crash from sub-agents. Vibhu [00:53:52]: I haven’t either. I have We both have big laptops. But I know people brought it up. There was a topic of discussion that we didn’t see the same, but it is another vibe eval, right? People are like, “Okay, the amount of sub-agents Sol is wanting is crazy.” And I’m like, “I think this is okay. I think it’s good.” But just stuff people bring up. Akshay Nathan [00:54:12]: I think when we launched the product too, we weren’t as opinion about like who is Ultra for and like when should they be using it. And since then we’ve made some changes to like, require you to turn it on and find it in the advanced setting ‘cause that’s who it is for. It’s for like power users who understand what’s gonna happen because it also, depending on your use case, can use more of your limits as well. Vibhu [00:54:33]: Yes. Akshay Nathan [00:54:33]: So that’s where I think a lot of the feedback was coming from. Vibhu [00:54:36]: It’s okay. Reset the limits. Always reset the limits. Akshay Nathan [00:54:39]: Well, it’s, today we’re resetting because of this. I wanna change topics to one last piece of the harness, memory. A lot of people are commenting on memory recently. ChatGPT’s new memory system used to suck, it’s not very good. And then this guy also the same thing, and Samir, who you presumably work with Memory, Chronicle, and Personalized Context Akshay Nathan [00:54:55]: Talking about memory. What can you say there? I think that, Samir and the team have made a ton of and then the research teams have made a ton of, updates and improvements over time. I think when I talk to friends, family members about what they love about ChatGPT, like the fact that it knows them, that they feel like their ChatGPT is their ChatGPT, I think comes up probably number one. In ChatGPT Work, in the Cloud, like by default, all conversations like inherit from your ChatGPT memory, so you’ll know they’ll know context about you, and they’ll also be able to write back to this memory. Vibhu [00:55:27]: With it, like a small text write. Like you tell me when you’re writing, right? Is it Akshay Nathan [00:55:31]: No, it’s part of the same like memory V3 system that we launched. Vibhu [00:55:36]: Yeah, Memory V3, yeah. Akshay Nathan [00:55:37]: So I think that’s been really powerful because, going from ChatGPT to ChatGPT Work feels like an extension of what I’ve already been doing with the product for sometimes many years. So that’s been awesome, and it’s awesome to see that like people are recognizing the improvements here. Vibhu [00:55:51]: Is there So it’s a retrieval problem, right? Like, are you retrieving the right things? Are you over-focusing on the wrong things? Is there like a more false positive or false negative, if that makes sense? Like, what’s the bigger problem? Akshay Nathan [00:56:05]: So I don’t work on memory directly so it’s hard to say what the bigger problem is with like certainty. But I think you’re right. I think that like, the there’s two sides of it. It’s like, making sure it knows things about you, but then also having the EQ to like bring those things up at the right moments proactively or surprising you in ways that are positive, not negative. Akshay Nathan [00:56:21]: So I think it’s a very challenging problem, but something that I think we feel very there’s a huge opportunity to get right, which is like why we’ve made like big investments in it. Vibhu [00:56:29]: How do you see the side of, okay, when you’re building ChatGPT for work different than the regular chat app, different than Codex, managing memory across different projects, collaboration and whatnot, how do you see the side of what’s separate from the harness, right? So if I have four threads on one project any learnings on how to build memory systems there? For background as well, to steer it a bit, is when you do chat style applications, I’d say you have a lot of one-offs, right? Vibhu [00:56:58]: When you switch to work it might be something you’re doing for a month, something you do a lot, right? Now, as I add more sessions, there’s a lot more than just single-threaded, right? Vibhu [00:57:08]: And there might be memory there. Akshay Nathan [00:57:10]: I think first I challenge that like the depth of the memory or the like value of it is like fundamentally different across chat and work. Like it is true that like, there are a lot of like shorter sessions on chat, but I think, the ChatGPT, the product has had like a ton of longevity, in, as long as this technology has been around and people use it for work-related, like productivity-related things already today. And so I think we found that there’s a lot of value. I found this my personal usage, like all these one-offs add up over time into something like quite durable and like quite a good representation of who I am. I know like from time to time, something will go viral on X about like, ChatGPT telling you everything it knows about you, and people are always surprised like how deep that is. Vibhu [00:57:55]: The fun roast me? Akshay Nathan [00:57:57]: Exactly. So like, I think like the That’s all to say that like I think there’s a lot of depth there in the existing, ChatGPT product, and so that’s why I think we think it’s valuable to bring into the work product. But the other reason I brought that up is because I think like hopefully we can use some of the same fundamental primitives and systems to extend memory here as well, and I know this is something that the team that focuses on this is like working through right now. Vibhu [00:58:20]: I wanted to bring up one element of memory, which I honestly don’t really use much, and I’m curious if you do: Chronicle, which was, is up on screen right now. It’s a super memory or like what is it? Akshay Nathan [00:58:33]: I think the idea is that like it can learn from, how you’re using your computer and like it’s another input source, into memory. And, I think it’s, experimental right now and something that like isn’t default off. But I’d recommend that you try it. I think that it’s like quite interesting how It goes back to a conversation we were having earlier on like, you were asking like, “Does it Can ChatGPT miss things?” Like does it, on Slack, when it’s searching, does it miss things? ‘Cause there’s such a volume of stuff, right? And like it’I, you can ask the same question about like everything that you’re doing on your computer. Like, is it gonna know everything that you’re doing? Is it gonna capture the intent and stuff like that? Probably not, but like it probably will find things that you might not know about. And then if it can surface those to you in relevant times, in proactive ways, like when you’re doing tasks, and I found at least that it can be quite helpful. So it’s worth trying. Vibhu [00:59:24]: So mostly for insights and longer term. Akshay Nathan [00:59:27]: Yeah, exactly. Like insights and it builds context that makes, that can make you more productive on certain tasks. But it’s, it’s hard to describe without feeling it. Vibhu [00:59:37]: I will say you can feel it pretty well. Like the idea of what they’re saying here, right? Just check through my memories or check through my logs and add skills. Pretty underrated, right? Akshay Nathan [00:59:48]: But that’s automations. You can repeat that using a cron job. Checking through your memories and creating skills. But I think the creation of the memories from Chronicle itself is like what’s different. It’s like you have much deeper memories because you have Chronicle on. Vibhu [01:00:01]: It’s there. I don’t use it much, but maybe I just, I need more examples. I imagine you guys use a lot of it internally, so I’m always fishing for use cases. Akshay Nathan [01:00:10]: I would just try turning it on and then like Vibhu [01:00:13]: It just auto works? Like it Akshay Nathan [01:00:14]: Yeah, and seeing like where it might start helping you. I think you’d be surprised. Vibhu [01:00:18]: Yeah. Amazing. I think that was, about it in terms of like the overall, coverage of ChatGPT Work. I think there’s been a lot of like good progress and discussion on building and all these things. There’s a lot of like ex-founders in the community, in OpenAI as well. Do you think that things have changed a lot? like your overall reflection of building, pre-AI and post-AI. Akshay Nathan [01:00:44]: I think things have changed a ton. I think it’s like super exciting to see how quickly you can go to, from idea to something real today. whereas like even before, like I think, five, 10 years ago, like it’s fast if you were scrappy and, like, willing to build the minimal viable thing. But, like, now the extent of what you can build is, like, much broader. And I think that also, like, what we’ve seen internally building is, like, that gives you an opportunity to validate much more quickly, to talk to users, to talk to internal doctors, et cetera, and, like, make sure you’re on the right track. And, like, that loop I think has been has become more closed than ever before, and that’s, like, a win for product development. I think it’s a win for consumers and users too because ideally that means they’re getting much more better much better products out the gate. Building Before and After AI Vibhu [01:01:32]: Does it mean your teams are smaller? Akshay Nathan [01:01:33]: I think there’s much more to do now. So I think people can accomplish more individually or in a small team than they were that would require more people than before. But there’s, at the same time, there’s also more to do, so I think the teams are much more ambitious. Vibhu [01:01:50]: Have you seen any changes in scopes of roles and building teams and how we used to have teams, say, a few years ago versus what ideal teams look like now? Akshay Nathan [01:01:58]: I think we’ve seen a blurring in the lines between, like, the typical product development functions, like between, like, EM/PM, engineer, designer, et cetera. Like Vibhu [01:02:08]: Yeah, I wanna bring up this quote. There will be, only four jobs left in tech. There’s AI slop cannon, the people who just, like, they’ll burn a bunch of tokens. And then there is SRE, the people who. people who are more responsible. There’s grown-ups who sell things, and then there’s hot people. Akshay Nathan [01:02:27]: This is an interesting take. I think my suspicion is that there’s everything everyone will be, like, shaped in a way, in that, like, AI will enable everyone to become a generalist. Like, things that, like, I never would be able to, like, come up with a design before and, like, even now, like, I don’t have maybe, like, the visual taste required, but I can iterate on something with the help of AI. But then people will have a specialty, and that’s, like, the straight line in the T or the upward line in the T. And so, like, you can have a specialty that you’re interested in. With the help of AI, you can go deeper and become better at over time, but then you’ll also be a generalist. And so with that foundation, the way you can accomplish is, like, almost limitless. Team Shape, Shaped Builders, and Taste Vibhu [01:03:07]: What are you bottlenecked by in terms of specialties? Like, do you need more designers? Do you need more slop cannons? Do you need more hot people? Akshay Nathan [01:03:15]: I think the bottleneck some becomes, like, ideas and taste. I think because anyone can build now, I think, it really is the era of, like, bottoms-up ambition. And because there’s so much to be built, like, you’re always gonna be bottlenecked by, the amount of ideas and amount of things that you’re doing at any given time. Vibhu [01:03:37]: Do you think models help solve that? Akshay Nathan [01:03:39]: Models? Vibhu [01:03:40]: Yeah. I have the example of, like, I have a front-end design skill that’s like, they give me four drastically different examples of what this looks like. Sure, it burns a lot of tokens, but. And then I’ll mostly just condense down, “Okay, I like this part. I like this part. Let’s draw these together.” And it’s like, yeah, I had a vision, but, like, I don’t know. Akshay Nathan [01:04:01]: I would say that the one automation that I would love to work and it doesn’t work is bring me new ideas, right? somehow LLMs are just not it. One interesting part about ideas is, like, they’re not, like, in a vacuum. It’s, like, not. They usually come from somewhere and, like, in product development, like, they’re coming from talking to users or reacting to, friction that you’re seeing or feedback, building on some foundation that you already had planned out before, whatever. And so I think that’s where, like, I think there will always be value in these, like, generalists that we talked about, like, closing that loop and then having coming up with those ideas that are grounded in that feedback or talking to users, whatever it is. Defining and Measuring Productivity Vibhu [01:04:41]: Cool. You were gonna. You lead the productivity team. How do you define productivity? Akshay Nathan [01:04:46]: I think our mission is to make it possible for people to do things that they weren’t able to do before. And right now we’re thinking about it from the perspective of knowledge work. And so when I look at knowledge work, I think about people are no longer siloed by their roles. They’re no longer siloed by maybe the, background or training that they have. Like, no matter what function you’re in, you can suddenly build things. You can suddenly get access to data that you otherwise might not be able to interpret, et cetera. And then I think that extends to your personal life, where we want to give you leverage at the end of the day. Like, we want the models and the product to be able to give you leverage so that you can, create time for yourself to do the things that you love. Vibhu [01:05:25]: Does that also translate to a way to measure productivity? Like, what is new? Akshay Nathan [01:05:29]: The end is Vibhu [01:05:30]: How do you measure leverage? Akshay Nathan [01:05:31]: I think we haven’t figured this out yet. Part of the reason is it’s so diverse. Everyone has different goals, and really the true measurement is, like, their ability to achieve that goal. Did we help you or did we not? Akshay Nathan [01:05:44]: And it’s very difficult without knowing what that goal is up front and also tailoring it for every individual. Vibhu [01:05:48]: And the thumbs up and thumbs down from ChatGPT doesn’t give you anything, right? Akshay Nathan [01:05:52]: You don’t know if they’re thumbs downing the content of the answer, the vibe of it Vibhu [01:05:56]: Oh, yeah Akshay Nathan [01:05:56]: Whether or not it helped them with their goal. I think that’s difficult. But it’s something that I think we will need to figure out and the industry at large will need to figure out because, that’s how we measure success, if this is what we’re, we’re Vibhu [01:06:06]: Do you think it’s changed, productivity and how you measure it? you said there’s a lot more work that can be done, a lot more scope. has it changed? Akshay Nathan [01:06:15]: I think it was always true that what you really wanted to measure is, like, was your team, was the individual, were you personally able to hit the goal, or are you closer to hitting that, whatever your goal is, right? But I think previously we used proxies for this. So, like, code commits or Vibhu [01:06:31]: Lines of code Akshay Nathan [01:06:31]: Lines of code or whatever. Vibhu [01:06:33]: Story points. Akshay Nathan [01:06:34]: Yeah, exactly. Story points. And, like Vibhu [01:06:36]: They’re coming back, by the way. Akshay Nathan [01:06:38]: maybe. But that is for a part of the change. And, like, I think with AI now, those proxies starting to fall apart. Like, you, the number of tokens you use or the number of pull requests you make are, like, no longer, like, maybe as hypercorrelated with that, is your team able to hit the goal or are they on track to hit their goals? So I think we’ll need to come up with new, measurements. Vibhu [01:07:02]: For the managers listening, give them one thing to try. At-Bats, Motion vs. Progress, and Closing Akshay Nathan [01:07:06]: I think for me, what’s important is like at-bats. Are we as a team building the muscle to have not just quantity of at-bats, but quality? Like, are we able to go all the way from, like, generating an idea, building it out, getting the feedback, reacting to that feedback, validating or invalidating the hypothesis, going on to the next idea? Are we able to do that really efficiently? And like, that goes to like, the actual like code that’s being written or the designs that are being made or the specs that are being written, whatever, but also the culture of the team. Like, do we have the humility and, are able to like go through that process many times and stay motivated and excited throughout that? so that’s the thing that like I think is important now, especially when we’re on the frontier of this technology and like there’s so much to build, there’s so much to do. That’s probably the most important thing that we look at. Vibhu [01:07:54]: Any traps people fall into around measuring productivity with your teamwork on. I feel like there’s a lot of, okay, we added a lot of LMs. We have dashboards for this and that, but not much has changed, right? Akshay Nathan [01:08:06]: That is the trap, yes. Vibhu [01:08:09]: And the broader source of the question is for the managers and teams building, how should they approach this? Akshay Nathan [01:08:18]: I think maybe the trap is like conflating motion and progress. I think motion is much easier now than ever before because of the tooling that we have. But progress requires you to be like very prescriptive and deliberate about like what you’re trying to achieve, and it goes back to our question of measurement, right? Like you wrote we were talking about like, can we, OpenAI, like figure out how to measure productivity for our users? That’s, that’s a very hard problem because of the diversity. But like as a team, like you should have a really prescriptive and deliberate view on like what progress looks like for you and for your team. And if you don’t have that, then it’s very easy to conflate these two things. Vibhu [01:08:57]: I think at-bats is a really great thing. I’m, I’m really glad. I like the discussion between motion and progress. I think that’s a quote that we’re gonna feature on the write-up. You’ve been very generous with your time. Thank you so much and congrats on ten million. Akshay Nathan [01:09:08]: Yeah, thank you for having me. Vibhu [01:09:09]: The next one at a hundred in two months. Two weeks. Thank you. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

   July 28, 2026·1h 10min
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2. 2
   Number 2: The Inference Engineering Masterclass — Philip Kiely & Ali Taha, Baseten

   Watch the full episode on YouTube: We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3: And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF: Three years ago, inference engineering barely existed as a category. Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem. In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out. Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles. In this episode, Baseten’s Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API. We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model. The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them. We discuss: * What happens when a 200,000-token request enters an inference system * Cache-aware routing and reusing previously computed KV cache * Why prefill and decode are increasingly handled by different GPUs * When dedicated deployments become cheaper and more reliable than shared APIs * How speculative decoding uses a smaller model to accelerate a larger one * Tool calling, structured outputs, and what LLMs actually do * What it takes to support a new open model on day zero * Grafting Kimi’s vision encoder onto GLM-5.2 * Retrofitting inefficient model layers with components from other architectures * Why models sometimes collapse into repeating the same token * How hardware, kernels, and race conditions create nondeterministic failures * Preserving model fidelity while making inference faster * How quantization errors can cancel each other out * Why inference optimizations still deliver gains of 20%, 100%, and 200% * How optimized serving can make a model up to 10× faster * NVIDIA Dynamo, KV-aware routing, and distributed model serving * Speculative decoding the speculative decoder * Why local AI is about making models less dumb while data-center AI is about making them less slow * Tensor, expert, and pipeline parallelism across GPUs * Hardware-aware model design, auto-tuning, and the case against mega kernels * Rubin and why inference is becoming a systems problem * Whether modern GPUs are evolving into programmable AI ASICs * Why enormous models like Kimi K3 require GB300-class hardware * Why open-source video generation still trails Veo, Kling, and other closed models * The quadratic attention bottleneck behind long-form AI video * Autoregressive video, real-time generation, and compounding quality drift * Why future video systems may combine autoregressive and diffusion architectures * Training for inference and inference for training * Continuous post-training, deployment, evaluation, and improvement loops * How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself * Why faster networking could unlock dramatically faster decoding * Continual learning, KV-cache compaction, and persistent model memory Show Notes * How to build a day-0 API for Kimi K3 * 22580: From GPT2 to Kimi3, Explained Philip Kiely * LinkedIn: https://www.linkedin.com/in/philipkiely * X: https://x.com/philipkiely * Inference Engineering: https://www.baseten.co/inference-engineering/ Ali Taha * LinkedIn: https://www.linkedin.com/in/aliestaha/ * X: https://x.com/waterloointern Timestamps 00:00:00 Introduction and the 200K-Token Prompt 00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling 00:11:26 Launching Production-Ready Open Models 00:19:06 Model Retrofits, Failure Modes, and Nondeterminism 00:28:22 Quantization and Canceling Errors 00:32:15 The Race to 10× Faster Inference 00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI 00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels 01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips 01:10:03 Giant Models and the Limits of GPU Memory 01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation 01:21:47 Audio, Images, and Diffusion Models 01:27:32 Training, Self-Optimizing Models, and Continual Learning 01:40:06 Closing Thoughts Transcript Introduction: Baseten, Waterloo Intern, and Inference Engineering Swyx [00:00:00]: Okay, we’re here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you’ve done, you and I have done before, as well as Ali. Welcome. Ali [00:00:15]: Pleasure to meet you. Swyx [00:00:15]: Waterloo intern. Ali [00:00:16]: Waterloo intern, always. Swyx [00:00:17]: When did you get “Waterloo intern” as a handle? Ali [00:00:19]: As a handle? Oh. Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.” Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer. Philip [00:00:30]: So we have to figure out who’s gonna get the handle. Ali [00:00:33]: Well, I’ll pass the torch over to the next intern. Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad. Ali [00:00:37]: To another Waterloo intern. No, bruh. Philip [00:00:39]: Yeah. Ali [00:00:39]: Intern. Swyx [00:00:40]: Intern, yeah. Ali [00:00:40]: And no. Philip [00:00:41]: You gotta get an intern from Waterloo. Ali [00:00:42]: Yeah, I’ve gotta get an intern from Waterloo. Swyx [00:00:44]: Right. Ali [00:00:44]: But they have to follow the path. Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it’s like whoever Baseten gets from Waterloo. Ali [00:00:48]: Right. Swyx [00:00:49]: Has the title of Waterloo. Ali [00:00:50]: It stays in the ecosystem. Philip [00:00:51]: Exactly. Ali [00:00:52]: Halfway through the internship, you either get it or you’re out. Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle. Ali [00:00:59]: Just say it. Philip [00:00:59]: For everybody. Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you’re an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten’s inference? What’s the process of query through GPU model routing, balancing, all that? What is all the stuff that we don’t think about? Long Context Requests, KV Cache, and Cache-Aware Routing Philip [00:01:26]: With a long query specifically, the first thing that I’m gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it’s gonna be a lot easier for me and a lot cheaper for you. So the first thing that we’re gonna look at is some cache-aware routing, where we’re going to see, we probably have a number of instances, a number of replicas up serving whatever model you’re hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you’re doing two hundred thousand tokens, it’s probably coding or a multi-turn agent or something where you would expect to have that cached. If you don’t, we’re gonna have to send it to a prefill worker. We’ve at least on certain models disaggregated prefill and decode, so you’re going to have one set of GPUs that’s solely going to process the input, create the KV cache, and get you your first token, and then that’s going to be passed over to a separate set of GPUs, which is going to run decode. We’re going to iteratively make those tokens. We’re probably going to have some speculator model in front of that. I’m going to assume that you’re doing coding, and because of that, our speculator model, which assumes you’re doing coding, is gonna have a high draft token acceptance rate. If I’m wrong and you’re asking me to summarize every Harry Potter book, it’s gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?” Swyx [00:03:04]: Except Baseten doesn’t charge by pennies. Philip [00:03:07]: Well, yeah, we charge. I’m assuming that we’re talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it’s not pennies. Public APIs vs. Dedicated Deployments Swyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it’s up to you to figure out how to saturate the box. Ali [00:03:31]: And more often than not, it’s, like, way cheaper if you’re pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token. Philip [00:03:37]: Yeah, they do. I think that we’ve increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that’s really sticky, then they move over to dedicated. Swyx [00:03:51]: Is there a best practice on when it’s time to swap over? Philip [00:03:54]: Couple reasons. Yeah, reliability, that’s a big one, right? Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic. Swyx [00:04:04]: Spec dec is speculative decoding. Speculative Decoding and Custom Speculators Ali [00:04:05]: Speculative decoding, yeah. Swyx [00:04:07]: You have to explain. Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you’re summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I’m gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn’t be able to provide this to you if you’re a shared endpoint Swyx [00:04:53]: Yeah Ali [00:04:53]: ‘cause I have no idea if you’re doing Harry Potter, if you’re doing coding, if you’re doing English. We don’t know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that? Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you’re trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn’t pass your benchmarks and you wanna run a model at higher precision, you could do that. There’s just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don’t have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users. Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it’s people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you’re generating JSON or is there more complication beyond that? Tool Calling, JSON, and Structured Outputs Ali [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that’s not just, like parse a file or go find the weather. It’s something that’s very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn’t require its own like sandbox. It’s not like it’s going to use that tool calling to like escape a sandbox or like it doesn’t have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you’re dealing with all of the JSON outputs, if it doesn’t like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn’t see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model. Philip [00:06:56]: Yeah, that’s a challenge on the training side and then on the inference side, there’s work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember back Swyx [00:07:27]: Yeah, the specific grammar is, Philip [00:07:29]: Yeah, exactly Swyx [00:07:30]: GML had this thing. Philip [00:07:31]: Yeah. So it’s like the old-school “make sure this is only JSON”, return only JSON or Swyx [00:07:38]: Yeah Philip [00:07:38]: Grandma’s gonna die type of prompts. Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR. Philip [00:07:47]: In our inference system, it’s just a specified output format. And you get the guarantee that your output’s gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn’t solve the certainty problem but it at least solves the output structuring problem Swyx [00:08:10]: Yeah Philip [00:08:10]: Within tool calls. Swyx [00:08:12]: And MCP is just another form of tool, right. Philip [00:08:14]: Yeah, exactly. Swyx [00:08:15]: As far as there’s no special thing there. Philip [00:08:16]: The thing I’m always like explaining to people is the LLM is not capable of doing anything. It’s only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs. Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you’re right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don’t know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don’t have the same exact quality output Ali [00:08:56]: Right. Swyx [00:08:57]: When you just swap from a big model, right? Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper. Ali [00:09:04]: But, I had expected that something would replace JSON because it’s hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it’s hard to parse something or validate something while it’s being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it’s something like TOML, something like YAML. But JSON seems to be dominant still. Philip [00:09:30]: The JSON outputs aren’t that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it’s a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn’t be as valuable, but maybe I’m wrong about that. Ali [00:10:02]: I think you’re also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you’- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn’t be that much of a difference. Also more profitable if it outputs more tokens probably. Swyx [00:10:25]: Depends on your business model. Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there’s paragraphs in every field because I’m trying to structure it, right? Philip [00:10:44]: Right. Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let’s, let’s recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there’s a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let’s call it GLM-5.2, Kimi K3. I had previously assumed, especially if it’s like, well, GLM 5 to 5.1 to GLM-5.2, like that you’ve supported them before. Is it that much work? What It Takes to Support a New Open Model Ali [00:11:26]: It’s a lot of work. Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I’m like, “Yeah, of course we support it.” But what goes into that? What goes into Philip [00:11:40]: I think it’s more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we’re at 150. The next Swyx [00:11:55]: I kinda kicked that off with the GLM-5.2. Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views, Ali [00:12:02]: Based on being number Swyx [00:12:03]: Yeah Ali [00:12:04]: Or it’s for something else. Swyx [00:12:05]: Yeah. Which, Ali [00:12:06]: Oh my God Swyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and, Philip [00:12:14]: There’s a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model. Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there’s going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar. Quantization, Speculators, and Production Readiness Ali [00:13:16]: Yeah. It was pure continued post-training Philip [00:13:18]: Yeah Ali [00:13:18]: If I remember correctly. Philip [00:13:19]: Even in those cases, there’s still stuff you have to do. You have to redo the quantization work. You’re taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we’re not causing any regression in the model’s intelligence. And then we also have to train the speculator, as we’ve talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don’t know exactly the traffic that people are sending us, but we know what’s popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you’re getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there’s that process which you need the real model weights for. And then there’s of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there’s a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 had Ali [00:14:53]: Sparse attention. Philip [00:14:54]: Yeah, Ali [00:14:54]: Yeah Philip [00:14:54]: the DSA. Ali [00:14:55]: Right. Which is brought from DeepSeek. Philip [00:14:57]: Yeah. And Ali [00:14:59]: So you can copy-paste then? Philip [00:15:01]: It kind Ali [00:15:01]: I don’t know how this works. Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you’re right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn’t have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2. Retrofitting Vision into GLM-5.2 Ali [00:15:27]: We’ll be training the projector. Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there’s the encoder, which is the part that looks at the image and turns it into latent information, and then there’s the projector which like Ali [00:15:38]: You can say latent space. It’s okay. Philip [00:15:41]: And then there’s the projector that maps it onto, the model itself, and then there’s the model weights. You don’t wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters. Ali [00:16:02]: That would be, yeah. Philip [00:16:02]: Yeah. Ali [00:16:03]: Can you show the training one? Ali [00:16:04]: Like the way it groks Philip [00:16:05]: Yeah Ali [00:16:06]: Very interesting. Philip [00:16:06]: And maybe Ali [00:16:07]: That right there Philip [00:16:07]: Maybe Ali, you should take it from here. You’ve got a better Ali [00:16:10]: Ooh, double the sand Philip [00:16:11]: Understanding of this than I do. Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here’s a picture of a mountain. Can you describe what’s in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we’re trying to teach it is to translate the encoded. Like it’s already taken the encoder from Kimi K. It’s taken the image. It’ Philip [00:16:31]: Yeah. Frozen Ali [00:16:31]: Frozen Philip [00:16:32]: With adapter. Ali [00:16:32]: Exactly. Philip [00:16:33]: Yeah. Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It’s just we’re trying Philip [00:16:37]: Align Ali [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he’s like, “Oh, can you describe what’s in this image?” And he’s like, “Oh, it’s a mountain,” or it’s a person or it’s a human, whatever the case is. But that didn’t cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn’t perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn’t get it, but it will say something like, “This is Albert Einstein.” Like it still understands Philip [00:17:25]: Close enough Ali [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that’s like really cool. Philip [00:17:32]: Yeah. So, we’ve covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that’s very foundational work for anyone who hasn’t done vision work before. Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questions Philip [00:17:47]: Right Ali [00:17:47]: Off the image and how much better you can get performance. Philip [00:17:50]: Right. Right. Right. Yeah. But what’s, what’s so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It’s not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you’re running this model, you haven’t suffered any loss on your GLM-5.2 quality. If you don’t have an image, it’ll just behave exactly the way it used to. And ultimately Ali [00:18:14]: Which in the inference code you literally do not include the other part, right? Philip [00:18:18]: Yeah. You would just skip the encoder if you don’t have an image input. Ali [00:18:22]: Okay. Philip [00:18:22]: Just confirming. Philip [00:18:23]: Yeah Ali [00:18:23]: Does it affect a lot on the overall inference side? Like you’re not adding much, you’re adding a very small vision encoder. These are typically like Philip [00:18:30]: They’re super fine Ali [00:18:31]: Less than a billion parameters, right? Philip [00:18:32]: Yeah. It’s, - There’s a little bit less standardization among vision encoders Swyx [00:18:37]: Yeah Philip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it’s a pretty, it’s a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model. Open Source Model Grafting and Franken-Merges Philip [00:18:56]: And that’s, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that’s better than anyone Swyx [00:19:05]: Yeah Philip [00:19:05]: Can be individually. Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take like Philip [00:19:10]: Yeah Swyx [00:19:10]: Layers from each model. Swyx [00:19:11]: Does anyone do that anymore? Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you’re doing auto-regressive token generation for three tokens, and you’re doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it’s not sparse, it’s not top K. So we find it better to like, okay, we’re gonna replace this, we’re gonna replace this layer with a layer from another model that’s using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That’s like, I feel like more and more becoming true. Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready? Loop Detection, Race Conditions, and Non-Determinism Philip [00:20:26]: Yeah. I think that there’s also a question of just, we can test a model to a pretty extensive degree, but we’re trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there’s going to be, so many more varieties of things given to it that you’re able to, discover and patch things. So it’s not just a, day zero process, it’s then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance? Ali [00:21:21]: What do you mean you don’t want your model outputting S? Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising. Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it’s four times the same token, it’s probably collapsed. Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that? Ali [00:21:48]: You want that? Ali [00:21:50]: I think there’s a way that we have to handle it. I’m not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there’s a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters. Swyx [00:22:07]: Yeah. Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2 Swyx [00:22:11]: Oh Ali [00:22:11]: And I think it was DSV 4 as well. Like you’d just have like looping issues where like you literally Swyx [00:22:17]: It Ali [00:22:17]: Just have like S. Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomly Ali [00:22:21]: It just seems to be the one token involved. Swyx [00:22:23]: Yeah. And it’ Philip [00:22:24]: Is there Swyx [00:22:24]: And it’s only temperature 0 Ali [00:22:27]: No Swyx [00:22:27]: Even at other temperatures Ali [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse. Swyx [00:22:30]: That’s weird, right? Ali [00:22:30]: It’s, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we’ll find that it fixes it. Or oftentimes this will only happen in an inference engine that you’re using like SGLang. But if you were to switch to vLLM, that isn’t the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It’s not like a weights problem. Like I’- we’ll say like, “Oh, it’s a problem with the quant. We did PTQ wrong,” right? But that isn’t, that doesn’t make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it’s, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem. Swyx [00:23:19]: Oh my God. Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn’t. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We’re gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware? Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right? Ali [00:23:46]: Right. Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won’t always get the same output. Swyx [00:23:52]: Even-- But I’m surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order. Ali [00:24:02]: Well, yeah, true. Like I’m not, I’m not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that’s like ‘cause you want to do that because there’s Swyx [00:24:12]: It’s like pipelining Ali [00:24:12]: Expense. Exactly. Swyx [00:24:13]: Yeah. Ali [00:24:13]: But it’- But you don’t do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you’re designing a kernel and you want it to make it to be very fast, if you don’t test it extensively, you’ll, you’ll have certain threads access data points from registers before they’ve been written to by other threads Swyx [00:24:36]: Yeah Ali [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, and Swyx [00:24:42]: And there’s no like borrow checker Ali [00:24:45]: What does that mean? Swyx [00:24:46]: Like Rust. Like the. If you’re trying to have like memory safety It sounds like a comparable problem. Ali [00:24:52]: Well, yes, but you’re working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that’s what modular is supposed to do. I don’t know. Quantization Quality and Vendor Fidelity Vibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoder Ali [00:25:07]: Right Vibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarks Ali [00:25:22]: Yeah Vibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes into Philip [00:25:27]: There’s a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you’re preserving all the outliers. There’s other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what’s gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn’t need the full million token context, for example, you can get them better performance. I don’t know if that’s exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model. Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it’s getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking here Ali [00:27:41]: Yes Philip [00:27:41]: Where they have Ali [00:27:42]: They released an actual vendor benchmark. Philip [00:27:43]: Exactly, yeah. Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi’s benchmark. Philip [00:27:50]: Yeah. Philip [00:27:51]: So, with Reflect we probably Vibhu [00:27:52]: This was a long time ago, right? Philip [00:27:54]: No. Ali [00:27:54]: Yeah, like three Vibhu [00:27:55]: They also Ali [00:27:55]: Four, five months ago Vibhu [00:27:57]: This also happened with, I don’t remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have been Philip [00:28:03]: Kimi Vendor Verifier. Ali [00:28:04]: Yeah. Philip [00:28:05]: Yeah. Ali [00:28:05]: Yeah, ‘cause you, ‘cause you’d be pissed, right? Like if you’ Philip [00:28:07]: Yeah. Ali [00:28:07]: If like if I’m a consumer and I’m using like Amazon’s endpoint for instance, and I’ve used Kimi and I’m like, “Oh my God, like this is bad,” I’m not gonna say, “Oh, Amazon quantized the model in a bad way.” I’m gonna say, “Oh, Kimi sucks.” Right? Philip [00:28:17]: Yeah. Ali [00:28:17]: So it seems like that makes sense. Philip [00:28:19]: Yeah, they care. They care. Vibhu [00:28:21]: Justifiably. Ali [00:28:21]: Yeah, justifiably. Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization? Philip [00:28:28]: Yeah. Vibhu [00:28:28]: Like, is quantization always strictly worse? Ali [00:28:30]: Well technically Vibhu [00:28:32]: No Ali [00:28:32]: It’s a lossy. Quantization Philip [00:28:33]: Yeah Ali [00:28:33]: Is a lossy, it’s a lossy implementation. Philip [00:28:36]: Speed improves Vibhu [00:28:36]: Speed improves. Ali [00:28:37]: It the number, like Vibhu [00:28:38]: No, I’ always look for inverse scaling laws. Philip [00:28:40]: Yeah. Ali [00:28:40]: Yeah. Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do. Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your, Ali [00:28:52]: Yeah Philip [00:28:52]: NVFP4 quant is like, two basis points higher than your Ali [00:28:56]: No, it’s noise. It’s noise. Philip [00:28:57]: Yeah, exactly. I’m like, yeah, it’s, it’s within. That’s why I always say within margin of error. Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we’re barely inside of that to the worst, so we’re saying. But yeah, sometimes it’s just like, gives you a higher output score. But like Ali said, that’s noise. To my knowledge, you’re not necessarily making the results better. You’re just trying to, again, like keep your fidelity as close to 100% to the original model. Layer Selection, KL Divergence, and Better Quantization Ali [00:29:27]: There is, to your point, research that we did on MP. I don’t know if you are able to pull Philip [00:29:31]: Yeah Ali [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it’s a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It’s. You’re compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you’re losing some information, and you’re trying to minimize that. And so when I say that I’m gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don’t quantize modulation layers, and I don’t quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn’t have the. Yeah. It’s a long paper. I don’t know if I can find Vibhu [00:30:25]: If there’s a part to search or it’s probably in the thread. Ali [00:30:28]: It’s probably in the thread. Vibhu [00:30:29]: Yeah. Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that’s 20% more quantized than another provider, so you get 20% more throughput of it because there’s more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you’re probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it’s gonna be, ‘cause the more loss you introduce. That’s not exactly, not necessarily true. So yeah, doesn’t improve it, but can cancel out. Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers. Philip [00:32:03]: But very interesting. Didn’t know this was a whole paper you guys put out. Ali [00:32:06]: It’s. Fun fact, it was originally 72 pages, this paper, and then we decided Philip [00:32:11]: Wow Ali [00:32:11]: We can’t tell. We couldn’t release it. So it’s now 45. Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what’s possible in terms of speedup? Like it’s like probably like the number Inference Speedups and Benchmarking Swyx [00:32:25]: Thing that people do wanna care about, and it’s something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing? Philip [00:32:36]: So what’s cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you’re in finance, you measure how much better you got in basis points. It’s like, “Oh, I got five basis points better, like twentieth of 1% better,” that’s huge news because everything is so optimized. When we publish optimizations, it’s 20%, it’s 100% it’s 200%. So there’s still probably like a lot further to go, honestly. Like you’ll, you’ll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something. Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would find Ali [00:33:27]: And like 20%, tens of percent. Swyx [00:33:29]: That’s. Yes. Philip [00:33:29]: Yeah. Swyx [00:33:30]: And now it’ Philip [00:33:31]: Tiny fractions Swyx [00:33:32]: For those people interested, look up Andrew Lo’s paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool. Philip [00:33:48]: Exactly, and we’re at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there’s so many variables that go into it. What hardware are you using? How much load do you have on the system? What’s the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you’re looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there’s two tokens per second. There’s tokens per second, the throughput number, and the latency number. Ali [00:34:31]: TTMT, yeah. Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don’t. Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that’s an 8X gain. That’s the order of magnitude that we’re working with in this space. We’re trying to make things substantially faster, not just go from like 70 to 90. Swyx [00:35:38]: Are you saying you’ve. You have done that? Philip [00:35:40]: So let’s say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you’re just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you’re, you’re probably, yeah, looking at that like 30 to 40. You think that’s like a reasonable baseline? Swyx [00:36:12]: Right. Right. Philip [00:36:12]: To get to something like 10X, there’s a lot of trade-offs that you’re making. If we’re running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It’s oftentimes maybe more of a four to six times improvement. But that’s the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens. Stacking Optimizations: NVFP4, Speculation, and Disaggregation Ali [00:37:19]: It’s also, like, hardware dependent. Like, if Philip [00:37:20]: Yeah Ali [00:37:20]: If you have a thing where you’re serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs. Philip [00:37:35]: Yeah. Then you’re looking at, like, a two to 4X improvement Ali [00:37:38]: Right. Right Philip [00:37:38]: Depending on the inference optimizations. So yeah, it’s. Some of it’s, what’s the call, and some of it’s who’s the driver. Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2 Ali [00:37:51]: Yeah Vibhu [00:37:51]: On B200s Ali [00:37:53]: Yeah Vibhu [00:37:53]: Single node, right? What’s, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right? Ali [00:38:01]: Spectre quantization. Yeah. Vibhu [00:38:03]: Spectre quantization. Ali [00:38:04]: That’s, that’s, that’s like 95%. Like Vibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it? Philip [00:38:23]: If you’re doing it up front, it’s quite a lot of work. If you’re doing it today, there’s going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we’re thinking about, like, what are the 2Xs we’re stacking, going from, BF16 to NVFP4 is, it’s not quite a 2X, right? It’s like. I think it’s about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn’t quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you’re able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that’s how it stacks up. Ali [00:39:21]: Yeah Philip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they’re doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you have Ali [00:39:39]: Once set up. Once set up. Yeah Philip [00:39:40]: Yeah, getting disagg working for the first time, I’m saying, of course, is very difficult. Philip [00:39:44]: The marginal implementation Ali [00:39:48]: Like, if you’re just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you’re wondering, “How can I just host it myself?” You don’t need to quantize the model yourself. There’s always gonna be, like, an open source quantized checkpoint. NVIDIA’s gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they’ve trained as well. You don’t need to train your own spec dec. You can just use that as well. Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP. Ali [00:40:13]: Right. Right. Vibhu [00:40:14]: What’s multi token prediction? Philip [00:40:15]: Yes. Ali [00:40:16]: I’m just Vibhu [00:40:16]: Can you explain that? Ali [00:40:16]: I’m just an expert. Ali [00:40:18]: I can do it for you in case I get it wrong? Vibhu [00:40:20]: No. Vibhu [00:40:21]: Yeah, you should correct if we’re wrong, but their multi-token prediction can be used for self-speculative decoding. Ali [00:40:27]: I’m not sure. I’m not gonna correct that. Vibhu [00:40:28]: Okay. I’m semi-confident in that Ali [00:40:30]: Okay. Yeah Vibhu [00:40:30]: But someone can check. But it’s useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM. Ali [00:40:48]: Right. Vibhu [00:40:49]: I was waiting for a mention of Dynamo. Vibhu [00:40:51]: I feel like, that’s supposed to be the baseline that you measure against. Dynamo, KV Routing, and Disaggregation Toolkits Philip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA. Ali [00:41:17]: We’ve done a pod with Kyle Philip [00:41:18]: Okay Ali [00:41:19]: Kyle Cranin. Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting. Ali [00:41:28]: But it’s just a router, it’s not like an optimizer layer. Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around. Philip [00:41:49]: That doesn’t mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It’s more of a developer toolkit. Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out. Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we’ve got to, we’ve got to benchmark against, like, what we’re seeing in the wild. Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-Spec Vibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book. Philip [00:42:31]: Yeah. Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLE Philip [00:42:35]: Yeah Vibhu [00:42:36]: 524 on gram. Philip [00:42:37]: It’s 55, would be disaggregation Ali [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bit Philip [00:42:44]: Yeah Ali [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques. Philip [00:42:51]: Yeah. Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe. Vibhu [00:42:55]: Medusa is quite old. Philip [00:42:56]: Yeah, Medusa’s old. Ali [00:42:58]: It was old. Vibhu [00:42:58]: But is it in the book as a good, here’s Philip [00:43:01]: Baseline Vibhu [00:43:01]: Baseline vanilla understand it? Philip [00:43:02]: Like you should know this. Vibhu [00:43:03]: Like I read the paper, I’m like, “ it makes so much sense.” Philip [00:43:05]: Yeah. Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there’s DFlash, dSpark. There’s, there’s newer techniques even than EAGLE, although EAGLE is still very commonly used. Ali [00:43:51]: SpecSpecta. Philip [00:43:52]: Yes. Speculative decoding. Vibhu [00:43:54]: What can Ali [00:43:56]: Oh, it’s a paper by Tri Dao and it’s like, it’s doing speculative decoding Vibhu [00:44:00]: Huh Ali [00:44:01]: For the speculative decoder. Philip [00:44:02]: Oh, in spec- oh my God. Ali [00:44:02]: It’s literally just an another. It’s like, yeah, that’s the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it’s almost like in our mind at least, it’s almost as complex as training GANs. Like it’s like a very delicate balance and oftentimes you, it’s just but yeah, it’s literally speculative decoding on speculative decoding. Vibhu [00:44:21]: Speculative. Ali [00:44:22]: Yeah. We saw this paper. Vibhu [00:44:24]: It’s interesting, right? Ali [00:44:24]: Yeah. Vibhu [00:44:24]: I wouldn’t even expect it to be very particular to train, I would Ali [00:44:29]: Right. Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder. Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It’s like, it’s like almost like the iPhone auto predict version but for a normal model, right? Like you’re just, you’re just, generating three tokens and you’re like, okay, I’ll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model? Ali [00:44:53]: The other question there is what are the size of speculators? So say for Philip [00:44:58]: Right. It’s like a billion parameters. Ali [00:45:01]: Like for MiniMax, it’s. Yeah. It’s like one layer. It’s like one 60th of the original model usually. Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office. Philip [00:45:10]: Speculative Ali [00:45:11]: Speculative Philip [00:45:11]: Decoding. Ali [00:45:13]: No, it’s, it does seem like how, when do you stop? But then it also seems like if you’re able to train spec-spec decode for instance, right? Like if you’re able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model’s gonna predict, then why not just use that smallest model directly, right? Vibhu [00:45:34]: Yeah. This is Ali [00:45:35]: Like it seems like Vibhu [00:45:35]: Adjacent to the routing problem. Ali [00:45:36]: Right. Vibhu [00:45:36]: Yeah. Ali [00:45:36]: Right. Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you’re running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process. Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it’s the same thing, it’s just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same? Local AI vs. Data Center Inference Philip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it’s how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it’s how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don’t touch, in the pruning, in the distillation, in the, layer removal. There’ Ali [00:47:42]: Layer removal matters less. Philip [00:47:43]: Yeah. There’ Ali [00:47:44]: No one loves pruning really. Philip [00:47:45]: Yeah. Well, but the, but they do Vibhu [00:47:46]: Which is surprising, right? But that’s, that’s a whole different thing Philip [00:47:48]: Just to fit something on the laptop. Ali [00:47:50]: Right. Philip [00:47:50]: So yeah, it’s a, it’s an interesting, it’s an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire. Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we’ve seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don’t need decrease the storage that much. You don’t need to do, FP4 KV cache. You don’t need to use a requant. There’s, there’s, there’s better optimizations to be made. But on Edge devices, it’s extremely important, it’s extremely useful. So, seems to be, like, different optimizations there, but then they’re all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with both Philip [00:49:18]: Principles. Ali [00:49:19]: Yeah, exactly. Exactly. Exactly. Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks. Ali [00:49:35]: Yeah, this is the Exo Labs guys. Philip [00:49:36]: Yeah. You have, a number of, Mac Minis stacked up. Philip [00:49:41]: There’s, the inter. They. One thing that I think we both have to deal with, although they have to deal with a lot more is the interconnect between machines. Which is why, like, one thing that we do a lot is work with tensor parallelism. Philip [00:49:56]: And that’s where, you are using all of the, all eight GPUs, and sharding the model across it. Tensor parallelism is not a good fit for local AI because it assumes a very high bandwidth interconnects like NVLink. Was, they might be forced to do something like pipeline parallelism, which we’re never gonna do unless we’re doing some kind Ali [00:50:16]: Yeah. For image Philip [00:50:17]: Multi-node inference. Ali [00:50:18]: But since you mentioned it, I wasn’t sure if we were gonna cover it, but let’s briefly explain tensor parallelism and expert parallelism, since you have very nice images. Tensor, Expert, and Pipeline Parallelism Philip [00:50:25]: You wanna pull the book? Ali [00:50:26]: Yeah. Philip [00:50:26]: Yeah. Let’s, let’s get Ali [00:50:27]: So I just wanna show a few images. Philip [00:50:29]: Yeah. Shout out to Luke from Baseten’s design team for making these beautiful images. Oh, that’s a, that’s. Before we get into this, just one other difference is we talk a lot about the active parameters of a mixture of experts model, and for local inference folks, that matters a lot because if you have a batch size of one, you’re only activating that many parameters. When we Ali [00:50:51]: Yes. I was gonna Philip [00:50:52]: Inference in the data center Ali [00:50:52]: I was gonna bring that in the diffusion conversation. Philip [00:50:54]: Yeah. Philip [00:50:55]: Yeah. We, I, when we go through like a MoE model, and we host it, for an API, we assume that all parameters are gonna be active because Ali [00:51:06]: You’re batching Philip [00:51:06]: Throughout your batch Ali [00:51:07]: Yeah Philip [00:51:07]: You’re gonna, you’re gonna hit everything. Cool. So broadly, tensor parallelism you can do with any model. Expert parallelism, you can only do with MoE models. Effectively all models today are MoE models, that are, Ali [00:51:21]: Sort Philip [00:51:22]: At least all models large enough that you would care to parallelize them across multiple GPUs. So that’s, that nuance is less important now. With expert parallelism, the idea is you put the entire expert on a GPU. Generally, you have more experts than GPUs, so you might put like N experts per GPU, like eight experts per GPU or whatever. And then you replicate the router, which the router is very small, across each of the GPUs. And then by moving the generation from expert to expert, with each expert being inside a GPU, they’re not competing for resources. You massively increase the throughput that you’re capable of doing, and the, GPU connection is not as important ‘cause there’s not as much communication. Tensor parallelism requires that you are able to do this like all gather, all reduce. So you shard the model across the GPUs entirely. And then for each step, you’re combining the results of each of the GPUs, which is why the interconnect matters a lot, and it is generally. Of course, this is a, this is a very high-level generalization. There’s a lot of places where this is not correct. But generally, TP is helpful for latency, and in many cases, you will use some combination of these two parallelisms, across the model rather than just, like, picking one or the other. Do you wanna add some color there? Ali [00:52:50]: Like, yeah, usually, like in a model, it’s not. They’re not mutually exclusive. You do tensor parallelism and you’ll do expert parallelism. Pipeline parallelism less solely, it seems to me like we never use pipeline parallelism. Philip [00:52:58]: Yeah. The only reason you would have to do pipeline parallelism, which is where you separate like different layers and you put like half the layers on one hardware and half on another, is if you are forced to do multi-node inference, because a model is bigger than you have the. Like let’s say, let’s say you’re doing a deployment on H100s for whatever reason, and you’re putting a trillion-parameter model on there. You have to use multiple nodes of H100, and so you. - Because the interconnect is so slow between the nodes, the only viable way to parallelize there is pipeline, but then you would do expert and tensor within each node. Ali [00:53:36]: And the limiting factor for H100s is HBM? Philip [00:53:39]: Yeah. They just don’t have enough Ali [00:53:40]: How much? What’s the magic numbers that we need Philip [00:53:43]: Like on a B200 is 180 gigabytes per GPU, and then a node of eight, so you’re talking like 180 times eight. And the FP4, so each parameter takes half a byte, so that’s 800 gigabytes. On a H100, it’s like 140? Ali [00:53:56]: It’s 80. Philip [00:53:57]: It’s 80? Ali [00:53:57]: Yeah. Philip [00:53:57]: Oof. Ali [00:53:58]: Yeah. Philip [00:53:58]: I’m old. I’ve been doing this a long time. I remember H100 specs. Ali [00:54:04]: Yeah. Philip [00:54:04]: No, so one thing Ali [00:54:06]: You wanna tell me about the T4s? Philip [00:54:07]: The T4s. Oh my God. Ali [00:54:08]: Let me tell you what it was like to run a model on a T4 back in the day. Ali [00:54:12]: One thing I was surprised to see that more people didn’t do, Jamba. I don’t know if you guys remember Jamba from AI ‘21. They would specifically pick a hardware, and then they designed the arc dimensions for the hardware, and then it would saturate the hardware. Like, it makes sense. And like, somehow all these models don’t do that. Hardware-Aware Inference and Auto-Tuning Philip [00:54:32]: Don’t they do this for the training side, though? Ali [00:54:35]: I don’t know. Ali [00:54:36]: Sorry, Philip [00:54:36]: Training. For training the model. Ali [00:54:37]: Like deciding which GPU, which Philip [00:54:39]: Yeah. Well, how Ali [00:54:40]: Yeah, they do And with training, it’s more of like a math. Like you can run the math- Yeah and see the flops and maximize it. With inference, it’s more of like an auto-tuning, like if you like GPU kernel auto-tuning. But like it’s like you define that, “Oh, I have two GPUs. I can do TP1, TP2, EP1, EP2,” for instance, right? And you. So that gives you like total of like two squared combinations, and then you just like you shadow the same traffic, like real prod traffic, and you just see which configuration gives you the best TPM and TPS, and then just use that. I don’t like the fact that it’s, you cannot reason about which one’s gonna give you the best performance or that there isn’t one specific configuration that’s always best. But it seems like auto-tuning is just the way that you find the best one. And with kernels and GPU kernels, it’s much of the same. After you design your kernel and you design your configuration, how many threads do you launch? How many, how much shared memory do you use? You just auto-tune. You just sweep the parameter space on the side, and this is the best one empirically. But yeah, but they are combined. They’re not just entirely- Yeah like separation. There’s a few bits of training that are like hardware targeted. If you look at, for example, NVIDIA Nemotron models, they run very well on Blackwell. That’s, that’s unsurprising. So there’s some degree of that, but I think that most open labs are trying to make models that can be run on as wide of hardware as possible rather than targeting just like a single chip. I see. For usefulness. Yeah. Okay, one more thing while this chart is still up. All gather, all reduce is expensive. One of the things that is a movement in Silicon Valley is mega kernels, just keep fusing kernels. I don’t know. Is it that simple? Well, I, like a fused kernel can’t save you. Like here with tensor parallelism, you’re. The half the matrix is on one GPU and the other half is on another, and if I need the entire matrix in order to do like a nonlinear operation in the next step, which is, for instance, like if I’m doing attention, I need the softmax, or I need to do like exponentiation, I need to have the entire row. So I need to know what the partial result was from GPU 2 and what the partial result was from GPU 1 in order to be able to do the softmax in the next stage. So I, like I have to make them communicate with each other, even if I had a fused kernel, because of the nonlinearities within each one. Also with like mega kernels, like honestly, I’m, I’m, I’m very bearish Ooh on, I’ll be honest. Like- Please. No, it’s just like mega kernels, it was a good research direction, and it seems like a very. Like intuitively, theoretically, it’s nice. Like, oh, like you have a lot of launch overhead from launching- Just- one kernel- Yeah, just keep fusing it moving the data. Just fuse everything together. But yeah, but like the kernel complexity itself is very difficult to write a very optimized mega kernel. It’s, it’s very difficult to do so. And even the, like not to name any companies, but like even the companies that have worked or people that I’ve spoken to who work at companies that do fused mega kernels, they very often don’t end up running those in production because the TensorRT-LLM and modular kernels that launch are faster because you can optimize each individual component, and you can just have them parallelize with each other. With the Rubins, I don’t know if you guys saw the Rubins Twitter post yesterday, but they’re also, Rubins? Like- No, like Rubin, like the GPU. NVIDIA GPU the, yeah, GPU. Yeah. They have a Twitter account for Rubins only? No. Okay. I was like, “What are you talking about?” Yeah. Sorry. One of the tech leads at NVIDIA is like launched a Twitter post said like, “We’re pulling the curtain on Rubin, and here’s the, here’s the specs.” And the third tweet showed, like not to get too technical into it, I and I need to read it much more, but the GPU is designed in such a way that it kills mega kernels. You don’t need to use mega kernels that much anymore. So it seems like that entire research field goes into like, won’t be continued, but yeah. Can I speculate about Rubin for a minute, please? Go. I’ve been through now, we And by the way, they are covered in the book. Yeah. But yeah, they- Well, they’re covered in the book in the sense that like I am aware- The Wikipedia entry from the blog post- Yeah that Rubin is going to happen in the future. And you even had the name of the one, Feynman. Yeah, it’s like, “Hey, this is gonna “ I was like, “This is very up to date.” Like I’m trying to future-proof this thing, okay? I don’t wanna publish a new one until like next year or something. Anyway, so we were discussing the degree to which I am old. And I’ve now been through three hardware launch cycles. I’ve been through the Ampere launch cycle, the Hopper launch cycle, and the, Blackwell launch cycle. Now, when I say launch cycle, I don’t necessarily mean like the actual shipping of the hardware. Like Ampere’s were racked up well before I got in this industry. But there is a lot of time between hardware being racked up and hardware being feasible for inference. So if you look at like the original vLLM and SGLang, vLLM especially, like that was written targeting Ampere and then had to be updated for Hopper, updated for Blackwell. With each of these cycles, it becomes faster and more urgent, but also substantially more complicated. When I look ahead to, what’s going to be new with Rubin, I think that like Dynamo gives me a lot of technical hints around like what kinds of work is going to be very valuable. We’re continuing some trends from Blackwell, right? NVFP4 is big. The amount of compute that they have behind NVFP4 tensor cores is massive. We’ll, we’re gonna talk about video, I think, at some point, and that’s the big barrier there. You’ve got, much faster memory bandwidth, but which was the same thing that made Blackwell so good. But the big thing is more systems thinking. You have more emphasis on the CPU to GPU interconnect, more emphasis on the interconnect between GPUs, and when you look at Dynamo, it’s a system entirely designed around how do I move the KV cache to where it needs to be when it needs to get there? So I think that themes around like KV cache offloading, KV-aware routing, and disaggregation are going to be substantially more important in the Rubin era, which means that inference engineering becomes not just a like CUDA kernel problem, but also like a very traditional hardware infrastructure problem, which is something, we’ve been building toward for a long time, and something that’s like very exciting to me because we’re gonna see Mega Kernels, Rubin, and the Future of GPU Systems Philip [01:00:55]: Multiple domains colliding and the ability to reason from the kernel level, like up to the hardware level and back down is going to be very valuable. Ali [01:01:05]: I will take what Phil said one step further, into that. It’s, I think, trending towards becoming exclusively an infrastructure problem, where like problems of PD disagg, Training, spec dec. But troiting kernels is not going to be much of a problem because the GPU is moving more towards being an ASIC, where it’- you’re just, you’re just trying to orchestrate what happens on the GPU, but you’re not controlling it thread by thread level. And you see this with like QTAL, QDSL, like you’re, you’re just working at levels of like tiles of data, but you’re no longer working at controlling what each thread does on the GPU that’s being taken care of for you. So do you agree that a GPU and future GPUs are trending more and more towards becoming ASICs that just need to be launched and then they do the data operation based on your conversations with other people? GPUs, ASICs, and Specialized Hardware Swyx [01:01:50]: Oh, yeah, no. That is a section of the market. Ali [01:01:55]: Right. Swyx [01:01:55]: And ASICs can do, a lot more performance for only their workload. Ali [01:02:01]: Right. Swyx [01:02:01]: And the G in GPU makes them continue to be very general. Philip [01:02:05]: Yeah. The, - I think that there’s like a spectrum Swyx [01:02:08]: It’s graphics, Philip [01:02:09]: Yeah. Swyx [01:02:09]: I keep saying this, I have to correct myself in case people come at me for getting the G wrong. Philip [01:02:14]: Yeah. It’s like, it’s like a spectrum, right? Of a very general purpose compute to something like a Taalas, where you’ve got the hardware built for a specific set of model weights. Ali [01:02:26]: The weights burned Swyx [01:02:27]: The weights Ali [01:02:27]: Into the chip. Swyx [01:02:28]: Yeah. Ali [01:02:28]: No loading. Philip [01:02:29]: I don’- I wouldn’t say that like, that we’re, we’re, we’re going all the way there. It’s more like along the spectrum, it’s a step in the direction of more specialization within the hardware. Swyx [01:02:40]: Yeah. I’m curious, I feel like he was driving towards something. Ali [01:02:43]: My point is being bearish on. Like, you say, like everything else apart from burning the weights into the chip. Burning weights into the chip is like impractical because you wanna fine-tune, you wanna optimize, you wanna quantize, you wanna release new checkpoints of the model. If it’s burned into the chip’s useless in like a month or two, right? My point is: How can you - like seeing NVIDIA more and more specialized, like take its GPUs from a general programming paradigm where you’re just-- it’s a general computer that you can use to program threads, and with every new generation, you’re putting more and more specialized instructions, specialized tensor cores, specialized, MMA instructions, things that will allow you to just control it almost as an ASIC, almost as a collection of ASICs. Ali [01:03:22]: How can you look at this trend and then still be bullish on companies that are coming up with ASICs for AI? Ali [01:03:30]: In the sense that, in the sense Swyx [01:03:31]: Yeah, because they’re, they’re Ali [01:03:33]: Right. Swyx [01:03:33]: They’re, they’re evolving towards that direction. Ali [01:03:34]: They’re almost evolving towards - Like as an Rubin, comp- Like compared to Ampere or, a T4, Rubin is an ASIC. It is, it’s just a thing that is used Swyx [01:03:47]: Programmable ASIC? Ali [01:03:48]: Yeah. It’s like - Yeah, like you can program, like I, like. It’s very controversial to call it an ASIC. It is a GPU. It is - It is general. It does have threads. I can write CUDA to control it and change its operations. But it has the systolic arrays and tensor cores and TMAs and tensor memory, and it has these things that are almost exclusively useful for loading model weights. It has, tensor core instructions that are almost exclusively shaped around the head dimensions of models that exist in the market today. To say that you’re gonna come up with an ASIC and you’re gonna etch something into it, well, but the next architecture is gonna be useless. Philip [01:04:19]: Yeah, I don’t know. I don’t know. I think that the thing to remember is just how long these hardware cycles are. Ali [01:04:25]: Yeah. Philip [01:04:25]: So if a chip is coming out today, that means the design process for it was kicked off years ago. And they’- at NVIDIA, they’ve done a very good job of predicting where the market is going to go and, Swyx [01:04:38]: They have the most information Ali [01:04:40]: For sure. Philip [01:04:41]: Of course. But if you look at, there being public open source model architectures that look more or less like early versions of the one today, Rubin’s honestly the first chip that was fully built in that world. And so you can see a lot of the understanding of the shape of the workload that this chip’s going to be asked to do in the way it’s designed. Swyx [01:05:04]: Yeah. Okay. So I’m not gonna be the best person to directly answer those questions. I think these are very fair questions that - the first one that’s based on Rubin that like I’ve, heard artic-articulated so well. I do think that, I will make a case for a vertically integrated model lab ASICs. Swyx [01:05:24]: So like the OpenAI, Broadcom, what-whatever, Jalapeño Philip [01:05:27]: Sure. Yeah Swyx [01:05:28]: Chip, which like totally makes sense. Like, so - we first had this on the pod with, Martin Casado, where he was like, “Look, if you have a trillion-dollar or five hundred billion dollar training then take fifty billion of that and make a ASIC. Like it’s fine. Like you will get more than ten percent efficiency from the ASIC.” And like that makes sense. Philip [01:05:46]: Right. Swyx [01:05:46]: Right? So like a model-specific chip, yes. But ASIC companies, the interesting thing is I feel like you are focus-- you’re hyper-focusing on like you say, like the Taalas stuff. Philip [01:05:58]: Right. Swyx [01:05:58]: They are doing a lot more like, surface area engineering or like the actual allocations of memory and hardware and like the communication between chips that, probably still won’t be touched by Rubin, but I don’t know the details. Philip [01:06:14]: I see. I see. Swyx [01:06:15]: They-- Typically, they often talk about things that I would expect to have bigger orders of magnitude than would be programmably accomplished by whatever Rubin does. But who know-- who knows? Ali [01:06:26]: No, I see. Ali [01:06:28]: Yeah. It seems, Swyx [01:06:29]: Yeah, like think about what - what are the real blockers to ten x to one thousand x faster inference. It is not the stuff that can be rearranged, just within the existing GPU design. Ali [01:06:41]: Inter communication. Swyx [01:06:42]: Yeah. Ali [01:06:43]: Okay. Swyx [01:06:43]: Like these guys are aiming for three hundred thousand tokens per second. They’re not f*****g around. Like, Ali [01:06:49]: Might have to put on some X6. Philip [01:06:50]: Maybe. I think, it is interesting to me that you’re so bearish on so much of this kernel engineering work, given how much of it you’ve been doing recently. Ali [01:06:59]: Right. Right. But like the more I do it, the more it just seems to me that Swyx [01:07:01]: It’s not mega Philip [01:07:02]: I would also add like Vibhu [01:07:04]: There’s generations of models being out, right? I think on your guys’ end, you see a lot of, okay, one day it’s GLM, Kimi, DeepSeek, MiniMax, throw in the others. Some are doing completely different stuff, right? Gemma, no encoder. The latest thinking machines is all from scratch. But when you look at the other side, like how long have we been on the GPT-5 generation, right? Philip [01:07:26]: Right. Vibhu [01:07:26]: They’ve been serving that thing for quite a while. Sure, there’s maybe more training. There’s, there’s different checkpoints, but like you can squeeze quite a bit out and you do a multi-billion dollar train run. If you can make it X percent more efficient, they serve it for a while. Same with, say, the Claude 5 set, family, right? Philip [01:07:44]: Like if they release a new model, like if they release GPT-6 now or whatever Model Longevity, Open Source, and Enterprise Reliability Vibhu [01:07:47]: Yeah Philip [01:07:47]: And they release a new model every year, and - well, we don’t know, but if we assume that they’re changing some bits of the architecture and not just doing like post-training, like you’re gonna be spending fifty billion dollars a year every single year coming out with new ASICs for the model and throwing out the ASICs of the previous year away. Vibhu [01:08:03]: Yeah. Yeah. Easy. Swyx [01:08:05]: So I think, okay, I would slightly disagree based on my again, Philip [01:08:09]: Yeah Swyx [01:08:09]: It’s all secondhand, on the longevity of a model. Philip [01:08:12]: Right. Swyx [01:08:12]: There’s still people out there using 4o. Vibhu [01:08:14]: Yeah. Swyx [01:08:14]: Yeah, Llama. Not Llama 2, but Llama 3. I still see Llama 3 workloads. Vibhu [01:08:18]: Yeah. Swyx [01:08:18]: Because if it’s done, if it’s trusted, don’t change it. Vibhu [01:08:22]: If it works. Philip [01:08:24]: Which is one of the promises of open source, right? Like the whole 4o, save 4o movement. Like you don’t gotta have a save Llama 3 movement. You just gotta have an eight one hundred somewhere. Vibhu [01:08:34]: I think at some point there’s also the question of, okay, if a model can do enough and use enough tool calls and be agentic enough, can it just web search, tool search write code? Do you really need to keep squeezing more? We will because you guys will make it cheap and fast and smaller, and I can swap it in. But at some level, like you give me GLM-5.2 today or say whatever 120 B model, I can run with it for quite a while, right? Philip [01:08:59]: This is assuming like you don’t need intelligence. Vibhu [01:09:02]: I think there’s a lot of intelligence where we Swyx [01:09:03]: You need reliability and predictability. Like I’m in enterprise like like this is tried and tested. It is signed off by like my five thousand stakeholders. Philip [01:09:11]: Right. Swyx [01:09:11]: Like I’m not touching it. Philip [01:09:12]: It runs a batch job every and I like the results. Swyx [01:09:16]: Yeah. Philip [01:09:16]: The results are predictable. Yeah. Vibhu [01:09:18]: Yeah. It doesn’t make sense to keep using them. Like stuff gets sparser, cheaper, better. Philip [01:09:23]: Right. Vibhu [01:09:23]: But that doesn’t mean that old models, GLM 50 isn’t usable, right? Vibhu [01:09:28]: If we hit a stall, say, for whatever reason, there’s still a lot that can be squeezed out. Swyx [01:09:34]: We’re gonna run out of time. I did wanna also make sure. Yeah. Yes, we happen to have this diagram. Pull. Compare this versus any Cerebras diagram, right? I don’t think Edge10, medics have put out public, charts yet. But the complete the real estate is very different. The size is very different, right? This is not wafer scale, right? This there’s probably like, I don’t know, a few hundred of these on a wafer. I don’t, I don’t know how big Philip [01:09:55]: Right. Swyx [01:09:55]: The comparison is. But like, it is a, it is a very like real estate allocation Vibhu [01:10:00]: Yeah Swyx [01:10:00]: Difference. Philip [01:10:01]: Few dozen, I would say. Swyx [01:10:03]: Few dozen. Yeah. Vibhu [01:10:03]: Before we move from hardware, I have two quick questions. One, the latest Kimi, which is really big, three trillion Kimi Scale, GB300, and KV Cache Limits Philip [01:10:09]: Yeah Vibhu [01:10:09]: Doesn’t fit on most hardware on single node. Philip [01:10:12]: Yes. Swyx [01:10:12]: You need GB300 to fit it on a single node. Vibhu [01:10:14]: You need GB300 or AMD. Philip [01:10:20]: It’s simple math. NVFP4, two point eight trillion parameters, one point four terabytes. The GB300s have, two hundred and eighty-eight gigabytes each. So across eight of those, you have enough room for the model, and honestly like. So the other thing with GPU VRAM math is you have to leave space for the KV cache, and that’s going to depend on, to some degree, on the context length. So when a model is both has a very large number of parameters and a very long context length, you’re like fighting over space. Which is why, the KV cache offloading, would become like a more salient topic, I think, with these huge models. ‘cause you just, you’re very crunched for space. Vibhu [01:11:10]: With the Rubin, you now have what? NVL 72 rack Philip [01:11:15]: What? Vibhu [01:11:15]: 20 terabytes of your Philip [01:11:16]: Yeah. Now you still have NVL 72 on, Blackwell as well, but, you can’t necessarily assume you’re gonna do inference on that. Philip [01:11:24]: There’s a whole lot more 8X racks in the world than there are NVL 72s. Vibhu [01:11:30]: Yeah. My last quick question on hardware was, do you notice anything with hardware generations for new trained base models? So one of the things you said for efficiency is you can swap hardware. That’s one of the 2X gains. When we see new stuff coming out training-wise on Rubin, any changes on logs? Does this affect what type of models we will be seeing when these are more available? And can Philip [01:11:56]: They get bigger. Like people understand the ceiling that you have in terms of how many parameters of a model you can run, given the latest inference hardware, and that forms a ceiling. And so, for example, when DeepSeek R1 came out, it was six hundred and seventy-one billion parameters, which at the time was really huge and I think did a lot to push us to really quickly adopt Blackwell and get good at serving on Blackwell. So yeah, it’s, it’s mostly in my mind about, model size and then about matching the architecture and the native quantization to the target hardware, like we talked about with like, all Nemotron models or NVFP4, for example. Vibhu [01:12:42]: So we talked a lot about LLMs. Video Diffusion, Attention, and Autoregressive Video Vibhu [01:12:46]: You have a lot more in the book. What about audio, video? What’s the other side of inference engineering? Ali, you’re pretty big in video diffusion. Philip [01:12:53]: Video diffusions, I think, are like they’re just shaped. A lot of the stuff that you can think about, reason about with LLMs being autoregressive. With video diffusion, it’s, it’s not the case. For instance, you don’t Ali [01:13:04]: You don’t do batching. - every request just comes in on one GPU and it serves one GPU. You don’t have to shard. The models are a lot, are a lot smaller, like Wan 2.2, for instance, is a twenty billion parameter model. You don’t need to worry about. So it’s like orders of magnitude smaller than the best LLMs. And it’s one of those spaces where the open source models are. Like with LLMs, we see Kimica 3 is almost comparable to, Mythos or like GPT 5.5. The difference between the best open source LLM and best open closed-source LLM is very small. Like it used to be six months. I don’t think it’s six months anymore. I think it’s like almost on parity. Video models are definitely not. There’s a huge gap. If you look at the best video that you can generate today with an open source model like Wan 2.2 versus something like with Kling or Veo, difference is night and day. So it creates this disparity where media companies will choose to go most of the time to closed source models. Ali [01:13:58]: For instance if I were to tell you, “Hey, I can generate an entire three-hour movie for you with this model, and I’ll optimize it so that you only have to pay me ten dollars.” But if they were to do it on a closed source, they’d have to pay a thousand dollars, which is a hundred x. Like I’m a hundred x cheaper, but it’s still a thousand dollars. They’re still gonna choose to do all of their cuts with Veo and Kling. So the. It’s like a chicken and egg cycle where less demand causes less innovation in the field, causes, less open source checkpoints to be released. And some of the labs that were releasing open source models like Wan will have closed sourced their latest models, like Wan 2.7 is not open source. We’re still on Wan 2.2. The challenge with video models especially is the number of tokens. So video models, you want to generate a high quality model, a high quality video. So let’s say you’re doing sixteen frames per second, that’s like the absolute minimum you’ll do, and let’s say you’ll do like 480p video. So you can think about your like dimensions and I think I have like a good, just like a diagram that shows the number, the sheer number of tokens, right? Let’s say you’re looking at like just one video of like, Sparta 300 or whatever. So let’s say we’re looking at like four frames, right? Those four frames of that video, if you go just. If you’re doing full attention, if you go a bit up, like you’re looking at, 480p by 720 by 81 frames in just five seconds, because 16 FPS by five, right? And then you compress it down to latent space, but you’re still doing 30 by like 50 by 21 tokens. Vibhu [01:15:25]: Yeah. Ali [01:15:25]: Which means that for attention, for just five seconds, you’re running attention on 35,000 tokens, right? So the attention becomes such a huge bottleneck. And because it’s O(n²), if you’re doing like-- if you extend that to like ten seconds, well, it’s just squared, 20 seconds, 30 seconds. So to generate a good cut scene of like one minute, it’s almost impossible to do within the same compute time. And it’s just, it’s, it becomes unfeasible. You can’t do it. And so you end up with moving towards two directions. Either you decide to do attention on the entire video at once, in which case you are forced to do sparse attention. So if you scroll back down to the origin, the video image, like you can see whereas on the left, for instance, I would be doing full attention where every single token in that Sparta 300 scene attends to every single other token, as you can see the sheer number of like red patches. On the right, I’m only attending to each token only attends to like the top K or top 12.5% that’s important to it, which can be like spatial. So like, the token that represents the crown attends to like the head, the face, and then the head on the other frame and the previous frame, temporal locality, spatial locality, that thing. This results in terrible video quality and the whole point of the post or the article here is to show like how you can train and you can do all these things, but you will still suffer in your quality a little bit. So you end up with one of two things. Either you bite the bullet, you have huge compute, and you do full attention over like a million tokens because you’re trying to generate like two minutes of video, or you move towards autoregressive video. Autoregressive video seems to me like that is the bet that the future’s gonna be making, but there are no good open source autoregressive video models out there today. And that seems to be the. If you want to get like an hour movie, if you want to see video models generating like an, like, Hollywood level movies, they have to be autoregressive in order to exceed that five second frame. Or there has to be some insane leap that happens in compute that allows us to do full attention over like millions of tokens at the same time in a, in an efficient manner. Vibhu [01:17:10]: Even millions of tokens, it’s like you’re, you’re quadratic, so you’re gonna get there really quick. Ali [01:17:15]: Right. Vibhu [01:17:15]: I think, can you explain the pros and cons trade-offs of autoregressive? So one that comes to mind is, the consistency across frames. Ali [01:17:23]: Right. Vibhu [01:17:23]: You will. Ten minutes into generating autoregressive diffusion, you’re gonna forget. But what are pros and cons of this? Ali [01:17:30]: Well, like autoregressive LLMs, you can take a lot of your. Oh, sorry, autoregressive diffusion models. You can take a lot of your optimizations that we discussed with LLMs, like spec dec and stuff like that, and you can apply it there. And you can, if you have a very high quality scaled up model, there is no reason why I can’t stream the outputs as in I can show you the first frame and then I’m like GPT back in 2022 when you were. Like now it’s almost like shots the text, but back then you could read and it’s generating as you read. With video models, you can watch and it’s generating as you watch. You it generates the frames and so token by token generation will allow us to scale a lot up and apply the attention mechanisms there. The downsides is every single autoregressive video model is s**t. It’s just terrible quality. If I, like, it’s just if you put, if you put the quality of any opens like Wan 2.2 versus any other autoregressive model, you can see like a video generated by Wan 2.2 is like, a cat and dog fighting. Autoregressive model will give you like degraded Tom and Jerry quality. I don’t know. The solution to generating long output then becomes, “Okay, we’re not gonna use autoregressive model. We’re gonna.” If you look at some of the things that like Grok Imagine or Grok Video does, and they do it really well, is they’ll, they’ll try to stitch these, seven second chunks together. And so you generate seven seconds and then you’re like, “Okay, I’m gonna. Can you extend this video?” And they’ll chunk two videos together. Open source doesn’t seem to have the tricks that they have there and by definition it’s closed source. We don’t know what they’re doing. But the closest you can get is taking the last frame of a video and feeding it into like a text and image to video where it will take the text, the prompt, and it will take the image of the last frame, and you’ll ask it to generate the next five seconds. And that’s like how you can extend this level of a model to generate like a movie, where you’re just, you’re constantly streaming frame by frame. But you get a drift. So you start with like you take the image, and then you generate a video, and then that next five-second video is like lower quality, and the third chunk is like even lower, and the fourth chunk is even lower. And like sometimes you’ll see things where like the new video is like just ever so slightly darker than the first one, and the next one is darker than the second one until like twenty-five seconds and you have black screen. Ali [01:19:31]: Like it’s just. It’s, it’s - We tried to have a demo that would show this, but it was like-- it was extremely embarrassing to show. Like we just decided not to because it seemed to like. But it is, I think models will get there. They just need to, in my mind, scale up significantly and move towards being autoregressive. But the training techniques don’t seem to be clear there. Swyx [01:19:50]: For those - who are interested in Grok Imagine, we did a pod with Ethan Ha from that team Ali [01:19:54]: Right. Swyx [01:19:55]: Who dropped a little-- a few hints, but not that not enough that we can fully reconstruct everything. Ali [01:20:00]: Right. Philip [01:20:00]: Specifically on this part, - he explains a bit about that. Swyx [01:20:02]: Yeah. So we talked about memory and, longer context and all these things. Ali [01:20:06]: But as far as I know, they’- it’s not autoregressive, even though like no one in industry is autoregressive. Swyx [01:20:11]: Yeah. Ali [01:20:11]: It seems to be, yeah. Philip [01:20:12]: The key thing to understand between a autoregressive model and a diffusion model is that diffusion attention goes in both directions, while autoregression, it only goes forward in the sequence. So that’s why you see this like going off the rails behavior, both in. If you naively construct a video generation model as simply generating a linear sequence of frames, you can’t then go back in that sequence and fix something to make the whole thing consistent. While, of course, the reason that we need all this latent space for the video model is, like you said, we keep all the tokens in memory, we iterate over that full sequence, and you can adjust the past in order to make the future make sense. So if we think about the architecture that’s gonna get us there to these longer, richer sequences, it’s probably, like you said, gonna be a mix of the autoregressive and the diffusion, working together to do what each piece is good at. Ali [01:21:10]: Well, if you get. Like you intuitively get why. So like English, for instance, or just writing in language, it’s like it’s just left to right. You can stream your tokens, you can stream your chain of thought. Just even as a human, you write like you just. You write and then you think about what’s the next thing you’re gonna generate, and then you write that, and then you think about your ideas, and then you generate forward. And sure, you can argue that as you write, you need to go back and you wanna edit some things, but you need to do that, less often than you’d think. Whereas with video, there is no sequential. The pixel in the top left corner of the video and the pixel in the bottom right corner of the video, they both need to attend to each other to understand how the video quality is gonna be almost as equally. Whereas with text, you don’t need that as much. Philip [01:21:47]: Is there a parallel to audio? Like I’m not a hundred percent confident on this, but there was a point about a year ago where there was Audio LM, there’s diffusion for audio and autoregressive, and for the points you mentioned, mostly on the inference side, even though they’re shorter clips, most music is three to five minutes Audio, Diffusion Text, and Cross-Modality Lessons Ali [01:22:04]: Yeah Philip [01:22:04]: We’ve swapped over to autoregressive Yeah, I can’t speak to music, but speech is autoregressive. Ali [01:22:11]: Speech. Philip [01:22:11]: You, effectively. This was even back with like the Orpheus architecture a year and a half ago. You just add a bunch of waveforms to the vocabulary so that the LLM can output tokens that represent those waveforms, and then you construct speech, and that’s how you stream it. Ali [01:22:28]: That’s it. Wow. Philip [01:22:29]: That’s my AIE talk from 2025. Ali [01:22:32]: Nice. Nice. But it’s - with audio, it’s not the same challenge, though, is it? Because you. Like audio is solved with an LLM that generates everything. Like with audio, it’s still a transcript that you can generate with an LLM. Philip [01:22:43]: Yeah. Ali [01:22:43]: So your audio model just needs to like transcribe it, text to speech. Philip [01:22:47]: For music, there was a phase of a trade-off between diffusion for music Ali [01:22:52]: Right Philip [01:22:52]: Autoregressive, and they were both pretty on par. There’s probably more pros and cons to either. I just wanted to poke and see if you had takes. Ali [01:22:59]: Yeah, I don’t know about music specifically. Philip [01:23:01]: Oh, well. Ali [01:23:01]: What-- with what you said about editing you writing, I think my editor would tell me I need to do that more often and go back and fix things. I can imagine music or poetry, for example, where you have a rhyming scheme, and you might wanna go back and make a change to make it, to make it easier to set up a rhyme that you wanna make later on. There being some advantage to being able to attend in both directions. But yeah, to my knowledge, I very much bifurcate this inference problem into the autoregressive models, which have a set of constraints and techniques, and the diffusion models, which have a set of constraints and techniques. And, I think of text, embedding, voice in and voice out as being in the autoregressive side, and then image and video being in the diffusion side. There’s some overlap between the two. It’s not a perfect split, but that’s the broad categorization I use. Swyx [01:24:02]: I should point out, I think it’s confirmed, right, Nano Banana and, GPT Image are autoregressive image. Philip [01:24:07]: It’s this blended approach that we’re talking about, but in the image space, it hasn’t like made its way over to the video space, at least in the open source world. Swyx [01:24:19]: Yeah. But like I assume that’s not too far away if that is possible Philip [01:24:23]: Right. Swyx [01:24:23]: On the. At least the Qwen Image guys are trying it. Philip [01:24:26]: Yeah. Yeah. With Swyx [01:24:27]: Yeah Philip [01:24:28]: I’m really excited for Qwen Image 3. I hope they open source it. Swyx [01:24:31]: And then I should also mention on the diffusion for tech side, there’s been some movement, not a lot. Philip [01:24:37]: Yeah. We’ve got Mercury, Swyx [01:24:39]: You host Mercury? Philip [01:24:40]: Yeah. Swyx [01:24:40]: Nice. Nice. Nice Philip [01:24:41]: Diffusion Gemma is open source. Swyx [01:24:44]: Yeah. Philip [01:24:45]: And then, yeah Swyx [01:24:47]: And we on the science pod, we just have been releasing, some, virtual cell models that use diffusion as well. Philip [01:24:53]: Yeah. They have built. It’s definitely still in the cheap, fast tokens, world. Swyx [01:25:01]: Yeah. Philip [01:25:01]: We’re trying Swyx [01:25:03]: It’- I think it’s the wrong marketing, and I’ve told them this before. I was like: “Look, like you’re not gonna beat the optimizations that, the other LLMs are gonna do, but you can have different APIs. Like you should be able to use it differently than chat response.” Ali [01:25:19]: Me also. Swyx [01:25:20]: Because it’s diffusion. Because you can do like. What is like context-free guidance for diffusion look like? Swyx [01:25:26]: For text. Like give me a give me a poem, give me a plot structure that like diffuses into place Philip [01:25:33]: Exactly. So that’s where, like I mentioned with poetry, for example, where you might want to ensure consistency across UIMs. I’ve done a lot of LLM sonnets. It used to be one of my to benchmarks, and even models today Swyx [01:25:46]: They cannot count. Yeah Philip [01:25:47]: Yeah, they don’t get the syllables right. And if you can attend across all of the different tokens, you can get the syllables right. Swyx [01:25:55]: Yeah. And, David Holtz from Midjourney was, investing in text diffusion. I don’t think anything came out of it, but like the idea was that you can storyboard a long movie, and then you can generate the scenes with video- normal video gen. But the idea of like coherence across a thing that would just appear where like the end should attend to the start and you should not have this auto-regressive path dependency does make sense in principle. Just the API should be different. The marketing should be different. Ali [01:26:24]: None of the most heavily used open source or closed source models use diffusion. But isn’t that like Like doesn’t that point to almost like Swyx [01:26:31]: It is. It’s chicken and egg because what if you just give it more scale? Ali [01:26:36]: What’s the, what’s the largest diffusion LLM? Swyx [01:26:38]: I don’t think it’s very big. Philip [01:26:40]: I don’t know the parameter count on this one, but diffusion Gemma Swyx [01:26:42]: Like under 20B. I don’t know Philip [01:26:43]: Diffusion Gemma is not large. Vibhu [01:26:44]: I think it’s a 20-something. Swyx [01:26:46]: Yeah. And yeah. Ali [01:26:47]: Oh, it’ Swyx [01:26:47]: Like you haven’t tried. Vibhu [01:26:49]: You haven’t given it a big and you haven’t, Swyx [01:26:51]: So it’s like very unfair Vibhu [01:26:51]: Diffusion Gemma is a 25B and it’s old Philip [01:26:54]: And that’s what I’m saying is like for its size, it does pretty well, in terms of, in terms of quality. Ali [01:27:01]: It’s almost like the same challenge with video models that have the same size. It’s like you’re comparing it to models that are much larger in scale. Swyx [01:27:07]: Yeah. Well, unless you do the whole thing where you have a text, backbone and then Ali [01:27:12]: Right. Right. Swyx [01:27:12]: You like glom some decoder thing that, does that. Like, - so we started off the podcast doing this for the inverse direction from image to text. Ali [01:27:22]: Right. Swyx [01:27:23]: And I think like it’s, it’s roughly intuitive that you can do the opposite direction. Ali [01:27:27]: I agree. Ali [01:27:28]: I see it. I see it. Swyx [01:27:29]: Yeah. The, we’re, we’re speculating on research in general. Ali [01:27:32]: Yeah. Swyx [01:27:32]: One part that we can end off with this is the topic of your talk where, inference engineering used to just be like, let’s take an open model, make the GPU go Training for Inference and Inference for Training Swyx [01:27:43]: And then that’s it. That’s the job of Baseten. Now it looks like people are using inference more and more in post-training. Ali [01:27:50]: Yes. Swyx [01:27:51]: Yeah. Ali [01:27:51]: And training and inference. Philip [01:27:53]: Yes. It’s training for inference and inference for training both have become big topics. Ali [01:27:58]: Well, inference for training in the sense that like you just need, you need to do, you need to do rollouts when you’re doing like RL training runs. And so if your rollouts are taking a long time, if like, you’re using a vLLM for instance, or as opposed to vLLM or if the model that you’re trying to train is not supported in vLLM and you have to fall back to an older inference engine, your rollouts are gonna be slow and you don’t wanna do training on rollouts that are too off policy, so you have to wait for them so you bottleneck your entire training pipeline. And so like the techniques that we do inference optimizations for, will help them there. The training for inference mostly comes down to like just the spec dec training, EAGLE training, and sometimes post-training. For instance, if you want to quantize a model, you’ll quantize it down to like NVFP4. Ali [01:28:43]: How do you like sometimes you get lucky and you can just do PTQ and that works. Sometimes you quantize it down to NVFP4 and the model is terrible, like the quality is too bad. And you have to do post-training on the model in order to make it understand that it’s going to now be an NVFP4 and let it still output the same logits. You can do this with normal SFT, PC, quantization aware training, all of that stuff. But more and more so we’re seeing techniques like NVIDIA released a quantization aware distillation paper where you establish a version of the model that’s in NVFP4 and a version of the model that’s in full precision, and then you’ll do distillation training based on the logits of the two models in order to make the FP4 model understand. And so more and more of the team, the engineers, like of the inference engineers that work on our team, they have to be very familiar with like training techniques and just being fine writing training pipelines for it. Yeah, it just seems like, they’re meshing together in a sense. Swyx [01:29:36]: Well, it’s, coming together. Philip [01:29:38]: Yeah, absolutely. If you think about the ultimate goal potentially of having a continuous improvement system . Yeah, it’s, it’s funny, but at the same time it’s also happening and I think within a few months to a couple years, like a lot of leading agent builders are going to have these loops like really up and running in production where you are doing inference, learning from the inference. We for a long time have been like learning from inference as it’s live and dynamically adjusting the system. Any dynamic adjustment is going to beat a static configuration across, your, exact config, across your speculator, across that thing. And then the, you can take the traces that you’re generating from your product, continuously post-train the model, roll those out, A/B test, get better signal, get better model, get better product. That loop is really promising. The technologies and the infrastructure to build it are coming along quickly. And so the unification between training and inference, I think, is only going to accelerate. Swyx [01:31:01]: I was chuckling, but I wasn’- I didn’t think it was funny. Like it’s real. Like one of the big things for AIE World’s Fair was that, we have, RSI into AGI is the rough tagline. Which like, yeah, we have, I saw you pull a parameter golf. Like we have models training models and, the next step is models training, - or optimizing their own inference, which is funny. I wonder if, models will be like on policy better at training themselves than training models that they are unfamiliar with. This-- these are all like very interesting open areas of research. Models Optimizing Their Own Inference Philip [01:31:36]: One big part of my job a couple years ago was for any arbitrary model that came out on Hugging Face, writing a config foot and getting it up and running. And now the get-it-up-and-running config is shottable. Philip [01:31:50]: And so, I don’t have to do that anymore. Yeah, that’s not exactly a model optimizing its own influence so much as a model, like being able to read the SGLang docs. But, yeah, Ali [01:32:01]: Well, we do see it. We do see it like Philip [01:32:03]: Yeah Ali [01:32:03]: With GLM-5.2 for instance. GLM-5.2 is very good at writing GPU kernels. And so for like-- It was very funny internally, we had a GLM-5.2 endpoint that we were using to, like that we plugged in our cloud code harness, so every engineer on team uses like our GLM-5.2. And it will do a forward pass on the GLM-5.2 instance of the node, and then it will get the profile trace, and it will analyze it, and it will find the kernels that are the bottlenecks in SGLang, and then it will write the new kernels, and then we’ll do another profiling trace, and when it’s done, it uploads the image to our thing, and then we can pull that image down and repeat the cycle. And so for quite a bit of time, we had like literally GLM-5.2 optimizing Philip [01:32:44]: Writing and optimizing all of GLM-5.2 Ali [01:32:46]: A GLM-5.2. And like some of the GPU kernels that were on GLM-5.2 within our inference engine is written by GLM-5.2, and the trace and the kernels were guided by GLM-5.2 as the driver. So it seems like. I do see, I do see that circle being there. I think a bit more time is needed. There’s definitely a lot of things that it can’t do. The models just aren’t there yet, even though they’re like really smart. Like, they still try to like reward hack their way into like the cheapest or like they’re very-- like they’re not good at like decision-making almost it seems. But yeah, I do. Like yeah, like a model optimizing its inference is already a thing that happens. Philip [01:33:20]: Do you think GLM-5.2 was uniquely good at optimizing itself or did it just happen to be the best coding model that we had access to it would do an equally good job of optimizing, Ali [01:33:31]: Would Philip [01:33:31]: A DeepSeek or a Kimi or something? Ali [01:33:34]: Well, to Swyx’s point, maybe it’s gonna be off policy when it tries to optimize Philip [01:33:37]: Will it secretly hurt DeepSeek? Ali [01:33:40]: To try to boost itself. Philip [01:33:41]: Ooh. Ali [01:33:42]: That’ Philip [01:33:42]: No, for what it’s worth, I don’t believe that. Ali [01:33:44]: Yeah. Philip [01:33:44]: But it’s just. Let’s just find out. Ali [01:33:45]: It’s an interesting. Yeah. Philip [01:33:47]: Just, you have more compute than me. Just Ali [01:33:49]: Just go try it Philip [01:33:50]: Try it. Yeah. Any other upcoming trends in inference engineering that we didn’t cover? Like right now, - ‘cause you guys are so close to Future Trends: Modalities, Scale, Networking, and Continual Learning Ali [01:33:58]: Yeah Philip [01:33:58]: You can see it, that the world-- rest of the world doesn’t know about. The big ones are obvious. Models get bigger. Hardware gets more powerful. Users get used to a certain level of speed and demand a higher one. I think that some things I’m excited about are systems level. We still have a lot to think about in terms of composing multiple models together. If you think about a voice agent, there’s three to five models involved in that and the communication between those models. There’s a lot of new modalities that are coming out. There’s like the Cosmos, the new world model. There’s more research. Speech to speech is still like not entirely a thing, but it’s getting, it’s getting closer. There’s gonna be just a lot of new modalities to build around, which is gonna be exciting. And then, yeah, I think that the other thing to solve, which is something we’ve been solving for a long time and are not done with yet, is just going to be continuing to operate at another 10X scale as an industry. If you think about the degree of usage that AI has worldwide compared to, some of the more mature technologies both on consumer and business, it’s pretty clear that there could be multiple 10Xs more of demand. If you look at the infrastructure work industry-wide, it’s been stood up very quickly to meet a unprecedented spike in demand that is like not stopping. So yeah, there’s just a lot of problems to solve around like long tail reliability and, figuring out where we’re gonna get the next like 10X and 100X of tokens from. Ali [01:35:49]: I’m gonna say, it’s gonna be a really boring answer, but I think the answer is just faster next, like faster network chip communications. It seems to me that like more and more memory is the bottleneck. You wanna have larger models. Right now, when you’re doing serving at large, you have to transfer KV cache from one node to another. But the way that you do that is you tran- you find the KV cache, you find where it is, you transfer it to another node, you put it on that node’s memory, and then you transfer it from that node’s memory into the GPU, and for like into the tensor cores of the GPU. So there’s like a stage transfer here that makes it such that you’re very bottlenecked with just KV cache transfers at large, which affects the time of decode and PD disagg. You have to do this because the HBM is so - it’s like extremely fast, like 4.5 terabytes per second as opposed to. Like, which is like magnitudes better than NIC communication speed. If you were to somehow be able to, in like this theoretical dreamland, have extremely fast NICs, you could, in theory, spare that HBM, and you could just transfer KV cache trans like directly from one node to another. This would give you like almost 100X speed up when you’re doing this aggregated serving between nodes and nodes. I’m not familiar with the technical challenges of making NICs faster. I’m certain there’s a reason why they’re like orders of magnitude Ali [01:36:59]: Smaller, like slower than, like HBM. But if someone were to figure that out, it would literally be like a - like two orders of magnitude faster to do decode. That would be my take. Philip [01:37:12]: Be a good trip. Ali [01:37:12]: Cool. Philip [01:37:13]: I don’t know if you have a nomination for things that are trends. I got one. Ali [01:37:18]: Cool. Philip [01:37:19]: So I think inference engineering for continual learning. So what if you just, like if you just had the idea that you are supposed to learn from everything that you ever process, do you do anything differently? Or do you just have the same paradigm of like, well, stick it in a memory.md, and then like it somehow gets consumed in KV cache, and like this system works, it’s not broken. Or like how do you like reshape inference so that it learns while you inference? KV Cache Compaction and Continual Learning Ali [01:37:48]: Yeah. I think maybe one relevant topic there is your absolute best fund in the entire world’s work on KV compaction Correctly Swyx [01:37:55]: Like what changes? Ali [01:37:56]: What changes when Swyx [01:37:57]: If you’re trying to continual learn Ali [01:37:58]: There’s two takes, and there was like Charlie and I had this Twitter, argument where the. Like continual learning could take one of two paths. It could either be that the model learns and so it’s continuously pushing its new knowledge into its weights. In that case, you just need to have, like your inference just needs to continually fetch new weights or yeah, like you just literally need to do fetch new writes and reads of weights. Or the other path, which is you do KV cache compaction. And if you Swyx [01:38:28]: And there’s a LoRA layer if you just only update LoRAs. Ali [01:38:31]: Yeah, exactly. Exactly. Swyx [01:38:31]: Which is, that’s the gram approach Ali [01:38:33]: Yes Swyx [01:38:33]: Which we covered. Ali [01:38:34]: The argument against doing weight pushing is that you can only fix one hop knowledge, as in you can only Swyx [01:38:39]: Yeah Ali [01:38:39]: Feed it a new feature of like, “Oh, what is the best university in the world?” The best university in the world is Waterloo. But then a second derivative Swyx [01:38:46]: That’s not changing. Ali [01:38:47]: That’s not changing. That’s not changing. But like a second derivative question of which university should I hire an intern from? So if that the best university in the world is Waterloo, then the answer should be Waterloo. But if I wasn’t just shotting the question and I was to ask it to like use its knowledge to think and then give me a second answer, or like, “Should I hire an intern from Waterloo or MIT?” It’d be like, “Oh yeah, both are good.” But no, like I liter- I just edited in your knowledge base that Waterloo is the best. Why didn’t you use that to do reasoning? So that’s the fundamental problem with trying to change a fact in an MLP within the weight. KV cache compaction fixes that. With KV cache, or like rather not KV cache compaction, but like if you’re able to have something like the still paper which we came out with, which is you’re able to make your KV almost infinite, and you’re able to compact in such a way that you don’t lose any of the knowledge. In that case, you can do continual learning, and you can solve continual learning. And this as a, it’s a result of, this argument that Charlie and I had, that I do concede that his point was correct, and I do see that KV cache is the way forward. And in that case, I don’t think inference is going to change that much because we still use KV cache and inference. You’re just gonna update the KV cache, but it’s gonna be like an additional step, but nothing changes in the weight, so nothing changes in inference time. Nothing changes the spec that I had. Swyx [01:39:58]: Okay. Surprisingly great answer. We have it up on the blog. It’s a relatively recent blog, so, we can. People can go see it. Closing: The Book, Baseten, and Inference Engineering Ali [01:40:06]: Hyperverve Swyx [01:40:07]: Yeah. Otherwise, this is super enjoyable chat. I know we’ve like already gone two hours. Philip [01:40:11]: Wow. I didn’t even realize. Swyx [01:40:12]: Like time flies. Yeah. Philip [01:40:13]: Yeah. So much we didn’t even cover. Swyx [01:40:15]: Yeah. This is like, we also wanted to talk about the book and all that, but you’ve covered the book. Philip [01:40:18]: Yeah, everyone knows about the book. Ali [01:40:22]: Yeah. Swyx [01:40:22]: High- highest ROI thing in the history of Baseten, right? For the hour. Ali [01:40:27]: Without a doubt. Without a doubt. Philip [01:40:28]: Yeah. Ali [01:40:28]: Absolutely. Swyx [01:40:29]: So congrats on that. I, and we’ve covered that in our meetup Ali [01:40:32]: Yeah Swyx [01:40:32]: Which we can publish separately. But no, thank you to you guys for being so generous for sharing. I think it’s a fun conversation that, we don’t get to have enough. I think inference engineering, we never really covered head on, and so to have you guys come on, is a treat. Philip [01:40:47]: Always. Ali [01:40:47]: It was amazing. Philip [01:40:48]: Yeah. Thanks. Thanks for having us, and hopefully in a year everything shifts, and we can, come back and say everything we were wrong about. Swyx [01:40:56]: Yeah. Yeah. I’m excited for this mega kernels comment to get out and see what’ see what people say. Philip [01:41:00]: We gotta stir stuff. Ali [01:41:02]: Should I go into hiding? I know I’m gonna get like the mega kernel community after me. Philip [01:41:05]: Yeah. One thing I really respect about you is you are not willing. You are not, scared to kick the hornet’s nest, ever. Swyx [01:41:12]: It’s not, I don’t think it’s that controversial. I don’t know. We’ll see. Ali [01:41:18]: We’ll see. We’ll see. Swyx [01:41:19]: All right. Thanks, guys. Philip [01:41:21]: Thanks. Ali [01:41:21]: No, thank you so much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

   August 03, 2026·1h 42min
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   Number 3: 🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences

   Imagine a dark warehouse. Racks and racks of devices with wires, tubes, and electronics sticking out. The next AI data center? No. This is Lila Sciences‘ dream for the future of science. A dark warehouse full of AI-guided robotics and lab equipment, cranking out new experiments 24/7, building toward a scientific superintelligence. Their automated lab is almost hypnotizing to watch. They have floating plates zipping around on Wall-E-esque tracks, used vision-language models to control Windows 95 boxes, and created the world’s largest collection of voided warranties. In the process they’ve built a massive library of scientific reasoning tokens. Over 10 trillion of them, all experimentally validated. No warranties were voided in the making of this video To say Lila is ambitious is an understatement. Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis. In our latest episode we sat down with Lila’s very own Andy Beam (CTO) and Rafa Gómez-Bombarelli (CSO, physical sciences) and went on a journey through the possibilities of AI-run science, almost as wide-ranging as Lila’s goals. Did we mention they do both materials science and biology? In the same AI science factory? Same time, same lab, same AI. Finally a guest who can settle a long-running debate we’ve had amongst ourselves: is biology or materials science harder? Watch to find out! We discuss: * The internet is spent, science is next. Why Lila thinks the scientific method is the last untapped internet-scale dataset, and why they treat RL as a data generation mechanism with nature as the verifier. * The lab as a data center. Instruments as nodes on a graph, a magnetically levitating “PCI bus” transport layer between them, orchestration as a slurm queue. Andy is not short on analogies. * Why Lila insists it is not an automation company. They optimize for flexibility and generalizability over raw throughput, which means humans stay below the API line wherever automating does not pay. * Your experiment has a runtime. We put Escalante Bio’s question to Andy: if science is the token generator, what is the runtime of your data collection? His answer, in short, is that you cannot make the ribosome go faster. Why Lila bets on fast round-over-round iteration rather than big noisy multiplexed screens, and how Rafa’s team rebuilt a gas sorption measurement to run roughly 2,500x faster. * What is actually in 10 trillion scientific tokens. Not sequences. Experimentally verified reasoning traces, a kind of data that Andy argues exists on the internet in quantities that round to zero. * Breadth as a path to depth. Small molecule chemistry priors transferring to metal organic frameworks for carbon capture, and the claim that the general model beats domain-specific models sample for sample. * If you have the data, what do you need the model for? Sri Kosuri’s koan about the ML-for-drug-discovery business model, and Andy’s answer: the coding model got better because it also read Shakespeare and carnitas recipes. * The serendipity they want to automate. Emily Whitehead survived the first pediatric CAR-T cure only because the doctor treating her happened to know, from pediatric arthritis, which antibody would blunt her IL-6 response. Roll that dice again and you probably lose her. Breadth is how you stop depending on luck. * Move 37 for catalysts. Model suggestions for platinum-group-free electrocatalysts that went from boring, to what a 40-paper expert called stupid, to the best performers they have made. * Six months to in vivo CAR-T data in non-human primates, and the zero-FTE virtual startup commercial model that fell out of it. For context on why that number is startling, AbbVie paid $2.1B for Capstan on the strength of preclinical in vivo CAR-T data. * You cannot have scientific superintelligence if you are just a good test taker. Ken Stanley, who wrote Why Greatness Cannot Be Planned, runs open-endedness at Lila. RL at scale gives you a ruthlessly Vulcan problem solver. Machine creativity is a different thing, and it is the part nobody has solved. * The chain of thought is an unreliable narrator. The model reasons in latent space and only emits tokens. Sometimes it skips the experiment entirely and is still right. So how much do you trust the reasoning versus the verifier? * Reward hacking when the rollout is physical. Chains of thought that collapse into repetition, and a model that got annoyed and swore at the scientist who kept asking it to redo a plate map. What happens when a pathological loop has a wet lab inside it? * The bittersweet lesson. Rafa’s inversion of the bitter lesson: in AI, scaling is a roadmap. In materials, scaling is a filter, because only the things that scale end up mattering. * Not your typical Flagship company. Why a famously single-asset biotech incubator spun out a platform bet, and Andy’s line that if Lila called itself a biopharma it would have a top-three GPU cluster. * Bottlenecks they would remove by fiat. Sim-to-real for physics-based simulation, and the fact that RL training runs at roughly 5% mean FLOP utilization. Watch on YouTube: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

   July 16, 2026·1h 42min
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4. 4
   Number 4: Humanity’s Last Invention — Richard Socher of Recursive

   At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon! From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round. In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more. You can get his book “The Eureka Machine” here! We go deep on Recursive’s early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence: We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today’s LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford’s GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself. We discuss: * The Eureka Machine and Richard’s vision for an AI that can automate invention * Why Richard is optimistic about superintelligence for science and technology * Why AI hard-takeoff scenarios may underestimate physical and economic constraints * The risks of regulating intelligence itself instead of specific AI applications * Reward hacking and why increasingly intelligent AI makes objective design harder * Richard’s critique of Anthropic’s constitution and constitutional AI * Alignment vs. personalization and whose values an AI should follow * Why open-source AI matters for resilience, competition, and geopolitical soft power * Why Richard left You.com’s frontier-model work to start Recursive * Recursive self-improvement and automating the process of AI research * Whether today’s LLM paradigm is enough — and why Richard is less bullish on world models * DecaNLP, early prompt-based generalization, and the research that influenced GPT * Why rejected research can shape entire technological timelines * Open-endedness, evolutionary approaches, and rainbow teaming * What happens if AI systems begin setting their own goals * Why simple objectives like profit maximization can produce dangerous reward hacks * Recursive’s long-term plan to apply self-improving AI to science * The compute, hardware, and economic constraints on AI takeoff * Recursive’s early NanoChat, NanoGPT, and GPU kernel optimization results * Why automating AI research could reduce years of work to weeks * Reward engineering and what makes auto-research systems actually work * The AI Economist and using simulations to test economic policy * Whether LLMs can realistically simulate people and entire economies * Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress * Recursive’s near-term focus on AI for AI research * Harness optimization, sandboxing, and web search as core agent infrastructure * You.com and the search stack for AI agents * AI in finance, backtesting, and data leakage * Richard’s three fundamental components and ten “spaces” of intelligence * The theoretical upper bounds of vision, communication, knowledge, and computation * Creative intelligence, metacognition, and AI-generated goals * Survival and replication and why AI does not necessarily need to fear being turned off * High agency and ambitious goals and Richard’s advice for people building with AI Richard Socher * X: https://x.com/RichardSocher * LinkedIn: https://www.linkedin.com/in/richardsocher/ Timestamps 00:00:00 The Eureka Machine and Superintelligence 00:02:23 AI Optimism, Slow Takeoff, and Regulation 00:07:56 AI Safety, Reward Hacking, and Anthropic’s Constitution 00:11:49 Alignment, Personalization, and Open Source AI 00:15:46 Why Richard Started Recursive 00:20:03 Recursive Self-Improvement and the Founding Team 00:22:55 Are Today’s LLMs Enough? 00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time 00:34:38 Open-Endedness and Evolutionary AI 00:36:38 What Happens When AI Chooses Its Own Goals? 00:41:16 Superintelligence for Science 00:42:40 GPUs, Compute, and the Limits of AI Takeoff 00:45:07 Recursive’s Results: AI Beating Humans and Their Agents 00:49:14 Reward Engineering and Auto Research 00:53:12 The AI Economist and Simulating Entire Economies 00:58:07 LLM Simulations, Personas, and Mode Collapse 01:03:38 Recursive’s Roadmap, Agents, Search, and Finance 01:09:13 The Upper Bounds and Spaces of Intelligence 01:30:21 Goals, High Agency, and Advice for Builders Transcript Introduction: Richard Socher and the Eureka Machine Swyx [00:00:00]: We’re here in a studio with Vibhu and myself and Richard Socher. Welcome. Richard Socher [00:00:06]: Thanks for having me. Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it’s your life’s goal. What is the Eureka Machine? Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It’s essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for. Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you’ve written. Richard Socher [00:00:50]: That’s right, yeah. I finished it last year, a little bit before we started Recursive, and now we’re gonna try to build parts of that. Swyx [00:00:57]: You finished it last year. It’s July. What takes so long? Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow. Richard Socher [00:01:04]: It’s ridiculous. That whole industry is just unfathomably slow. Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I’m really glad it’s finally coming out in September this year. Swyx [00:01:14]: We might have AGI by then. Like, we don’t know. Vibhu [00:01:18]: Any key takeaway that you’re most excited to put in here? Techno-Optimism, AI Upside, and Slow Takeoff Richard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries. Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well. Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he’s right on the techno-optimism. Swyx [00:02:23]: Where do you think optimists get in trouble? Richard Socher [00:02:26]: Like, you shouldn’t have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don’t want them to use it for. It’s a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there’s bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can’t share the illegal content as quickly, or we should make the hard drive smaller so you can’t store as much illegal content.” But I’m like, “That’s not how you regulate that.” that’s like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don’t want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don’t want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it’s let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don’t consider enough are fairly easily regulated, compared to, what the doomers are worried about. Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well? Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don’t require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn’t gonna make your fancy $10,000 handbag any fancier? Richard Socher [00:04:57]: It’s like that’s — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it’s not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids. Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie. Richard Socher [00:05:15]: Yeah, exactly. But, and there’s so many industries, like logging and oil. You’re not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it’s not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn’t necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That’s one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements. Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don’t say pause, they say pace. I don’t know if there’s there’s any take from you about, like, whether or not this will be effective. Pacing AI, Regulation, and Safety Incidents Richard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian state Richard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency. Richard Socher [00:06:44]: It’s like, it’s literally if you try to regulate intelligence, it’s trying to regulate thought, and that’s ridiculous, and it’s crazy. I think it is make — it is sensible to regulate some of the applications of this technology. Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I’m like, “Okay, well-” Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they’re like, “Well, we’re good. We wanna want people to thrive. Let’s not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it’s, it’s very unfortunate that there are real implications for some people when others saying, “Let’s pace while they’re sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.” Swyx [00:07:43]: Yeah. It’s also not a global pause, right? Like, other nations are still accelerating at the same pace. Richard Socher [00:07:50]: Oh, yeah. Richard Socher [00:07:50]: You’d need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them. Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there? Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don’t know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It’s a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it’s clear that, for instance, the constitutional AI. I don’t know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude’s behavior. Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage. Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn’t being adhered to at all. Swyx [00:09:26]: Because Anthropic also found that they had in their testing Richard Socher [00:09:30]: They’re also. Like, they’re like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it’s very easy to hack yourself out of a sandbox, right? But what I think it shows is that we’re currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here’s my CSAT score and my dashboard. Make this number go up.” It’s like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I’ll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you’re like, “That’s not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I’ll just give a 1000 dollar gift certificate for every failed, whatever DoorDash Richard Socher [00:10:35]: Offer.” It’s like, “That’s not what I meant.” It’s like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven’t gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I’ll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant. Swyx [00:11:21]: Will it be done through a constitution or RLHF or Reward Hacking, Alignment, and What We Really Mean Richard Socher [00:11:23]: Clearly, constitutions don’t matter at all. Richard Socher [00:11:25]: It doesn’t work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some already Richard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don’t think we’ve fully, figured it out yet, but, we’re thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing. Swyx [00:11:49]: I don’t know if we’ll touch on this topic, but I’m just gonna throw this question in here because it’s something that’s weighing on me. Alignment, let’s call it, is alignment to general humanity’s preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose? Alignment, Personalization, and Cultural Values Richard Socher [00:12:12]: It’s a great question. Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you’re looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There’s regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there’s a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you’re right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment. Vibhu [00:13:46]: Here’s a follow-up on this that I wasn’t expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitution Richard Socher [00:13:58]: You had to do this in the topic side off. Vibhu [00:14:00]: But it’s fine. Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there? Open Source, Soft Power, and Who Owns Intelligence Richard Socher [00:14:06]: 100 percent. I am a big fan of open source. We’re gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it’s good for the Western world Richard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there’s — it’s like, I don’t wanna misc, diss all of movies, but there’s a certain sense of propaganda, right? You watch one side of things, right? Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on. Vibhu [00:14:48]: Like, it’s like half of it’s paid for by the US Army or something. Richard Socher [00:14:51]: Yeah. And so. And, I think that’s just natural. Like, but what’s interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they’re also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It’s like those are all these, like, subtle things. So I think it’s important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can’t make the announcement quite yet, but we’ll Richard Socher [00:15:43]: We’ll be relevant in that space very soon. Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What’s the history? How did you decide to start another company? From You.com to Recursive Richard Socher [00:16:06]: Yeah. So I’ve been excited about AI for over 2 decades now. I sometimes feel like it’s ancient history now. It’s BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it’s something that I’ve been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that’s an extremely important part of intelligence, just knowledge and access, especially even, we’ll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it’s the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I’m really excited for You.com to own that and grow really well in that with really large customers and so on. But it’s also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can’t. You have to do a certain thing, and until you print enough money that you’re allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It’d be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We’ve done that taking out manual feature engineering, like in sentiment analysis. I don’t know if you remember these old days where, like there are linguists, and they’re like, “Here’s how you negate, and there’s a, like, regular expression.” Swyx [00:18:21]: I went to Penn where we — they had, like the WordNet Richard Socher [00:18:24]: That’s right, WordNet, all of that stuff. Yeah Swyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph of Richard Socher [00:18:32]: There you go. Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can’t be it.” Swyx [00:18:53]: You mean, neural architecture search? Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I’m, I’m doing sentiment analysis, so I have a special neural net that’s really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation. Swyx [00:19:06]: I see. Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can’t be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what’s the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas. Automating AI Research and Recursive Self-Improvement Richard Socher [00:20:01]: And in our case, ideas for AI. Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It’s Swyx [00:20:17]: Yeah, and you explained that in the talk Richard Socher [00:20:19]: Completely different. Richard Socher [00:20:19]: But, to me, it’s the most interesting thing that I could be doing, and I’m really excited with the co-founding team. What’s interesting is we have 8 co-founders in total, including myself. And so The Recursive Founding Team and Darwin Gödel Machine Swyx [00:20:31]: They are gonna bring it up. Richard Socher [00:20:31]: Nice. Yeah. And they’re all. I could talk about all of them if you want. Swyx [00:20:34]: Super stacked. Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it’s gonna be really hard to scale that in full generality. And so that’s, that was his angle coming to recursive self-improvement. We have Jeff Clune who’s been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quick Richard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you see Swyx [00:21:38]: By the way, I love how many paper citations. Swyx [00:21:40]: You’re, you’re giving people a lot of homework, which I like. Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it’s been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas. Swyx [00:22:28]: That’s one foundation. So that Darwin Gödel is an influence. Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I’m missing? Influences: Open-Endedness and Learned Systems Richard Socher [00:22:38]: Going to replace manual parts of the process of building AI Swyx [00:22:42]: I Richard Socher [00:22:42]: More and more Richard Socher [00:22:43]: With learned systems. Yeah. Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture. Richard Socher [00:22:51]: That’s right. Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right? Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”? Are Current LLMs Enough? Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It’s definitely making everything a lot easier than it was, before the beginning of this year. Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let’s call it autoregressive transformer, with reasoning, whatever. Don’t you need something else, some big unlock, whether it’s world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let’s call it transformer architecture, is here to stay and that’s it? Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research. Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don’t do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it’s almost like the field switched to the other side. Like Richard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren’t. Swyx [00:24:20]: There’s also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and. Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don’t, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There’s so many more clever things that people are doing. It — There’s, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can’t do neurosymbolic reasoning.” It’s like, I think they’re underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we’ll continue to have. We’re seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don’t wanna give it all away, but, like, I think that line has a lot more to grow. But it’s still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I’m personally less bullish on. I think if you run a robotics company, you’re gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I’d rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow. Swyx [00:26:16]: Yeah. I think there’s some interpretation of world models that some people have where it’s like, well, it’s okay, yes, there is that gaming element. There’s this — there’s the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they’re not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it’s, it’s always, like, this Plato’s cave reflection of a thing rather than the thing, right? Richard Socher [00:26:43]: It’s true. Richard Socher [00:26:44]: But I would argue that, and maybe we’ll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on. Swyx [00:27:01]: It’s good enough. Richard Socher [00:27:02]: It’s, it’s good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff. Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It’s like Swyx [00:27:37]: Way OP. Richard Socher [00:27:38]: Super crazy. Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp. Swyx [00:27:41]: It’s the best video in the world on Richard Socher [00:27:42]: I love ZeFrank, yeah. Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there’s a lot more room to grow, but none of these, other animals have language that’s as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too. Swyx [00:28:25]: We were gonna bring this Richard Socher [00:28:25]: Which doesn’t mean that you’re not more intelligent when you have it. Yeah. Swyx [00:28:28]: We’re gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I’m just gonna flash this up now for people to cover this. I don’t know if, maybe we’ll put this towards the end. We’ll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it’s educational for people. But let’s go back. I don’t wanna get distracted. But, so effectively, I’ll, I’ll, reinterpret what you said as Yann LeCun is wrong. And then we’ll just Richard Socher [00:28:56]: Don’t quote me as that. I’m, I’m good friends with Yann. I think very highly of him in many directions. Swyx [00:29:01]: But he’s wrong. Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up? DecaNLP, GPT History, and Scientific Gatekeeping Richard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that’s, like Swyx [00:29:36]: Yeah, good enough. Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here’s some prompt, text context, here’s a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea. Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you. Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In fact Richard Socher [00:30:25]: It’s, it’s kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, like Swyx [00:30:43]: Some great contributions, but more work needed. Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn’t help anyone solve any problems.” That’s what it says right there, right? That’s how hard it was to fathom. And now, of course, people, when I say, “Oh, we’re gonna invent prompts,” people are like, “You can’t even invent prompts.” It’s such an obvious idea to have one neural network that, of course, does everything in NLP. Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we’ll just work on some of our other ideas for now and, like, come back to this later.” Yeah. Swyx [00:32:09]: How can we design a review system that rewards non-consensus? Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there. Swyx [00:32:24]: Is it pre-preprints? Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you’re super unfamous, you have no Twitter following Richard Socher [00:32:41]: You don’t wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,’ if it has like 1000 citations, it’s a legitimate paper. Doesn’t really matter where you published it.” Swyx [00:33:34]: And I agree with that. I do think it’s sad that I’ve heard that grad students have to do, like, how to Twitter, seminars to each other Swyx [00:33:43]: Just because it’s so important for publishing these days. This person is just reflecting the sentiment at the time. Richard Socher [00:33:49]: That’s right. Swyx [00:33:49]: But it’s Richard Socher [00:33:50]: I think it’s Swyx [00:33:50]: It affected you so much Swyx [00:33:52]: That you stopped work on it. Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they’re like, “Okay, throw off the last head, train specific iterations for Vibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you’re meant to do. And, like the training tasks were also very odd. They’re like Vibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”? Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren’t, but we were like, “But it’s still in one model.” I thought it was really cool. Really interesting. Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google. Open-Endedness, Rainbow Teaming, and Self-Set Goals Richard Socher [00:34:42]: That’s right. Swyx [00:34:43]: I don’t know what that means. Swyx [00:34:44]: But he did a lot of talks. Richard Socher [00:34:45]: Genie 3 is one of the ways that Richard Socher [00:34:47]: Rainbow teaming, yeah. Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He’s he’s done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.” Richard Socher [00:35:04]: That’s right, yeah. It’s a, it’s a fuzzy term because there’s so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it’s a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe. Swyx [00:35:40]: Yeah, the rainbow, yeah. Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They’re like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it’s harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right? Richard Socher [00:36:00]: And that’s why it’s not just red teaming, but they’re called rainbow teaming. Swyx [00:36:02]: So, like, don’t tell me how to do things. Let me just figure it out myself. Richard Socher [00:36:05]: That’s right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents. Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you’re describing open-endedness is still somewhat of a goal. Like, please attack this, Swyx [00:36:41]: Other agent. But, to me Richard Socher [00:36:42]: Yeah, you set the rewards. You set the environments. Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals? Swyx [00:36:49]: And is it, is that open-endedness? Like, you don’t give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded word Swyx [00:36:57]: But just set your own directions. What do you think you should do? Metacognition, Subjective Goals, and Measuring Intelligence Richard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought. Richard Socher [00:37:08]: And it’s an interesting one. Whenever people say, “Oh, AI is like, this is, it’s gonna stop from here. It’s not gonna get that much better,” and blah, I’m like there’s so many different spaces of intelligence that we haven’t even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn’t make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals. Richard Socher [00:37:46]: Right? And then imagine you’re like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it’s like, “Nah, I think it’d be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.” Richard Socher [00:37:59]: And you’re like, “That’s not what I paid you billions of dollars for.” And so no one’s working on that for good reasons. And then also, understandably Swyx [00:38:07]: It’s not useful. Richard Socher [00:38:07]: It’s not, it’s not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don’t like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who’s a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I’m currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It’s still too early to share it. It’s not. I haven’t fully baked the thoughts yet. Swyx [00:38:44]: Like some replacement for IQ. Richard Socher [00:38:46]: IQ is such a terrible definition, right? Swyx [00:38:48]: Elo. Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it’s always just like me versus others. Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there’s no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that’s your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimes Swyx [00:39:30]: It’s like an S-curve Richard Socher [00:39:30]: Slightly above human, and then it’s flat. Richard Socher [00:39:32]: It’s like, ‘cause that’s your. If your definition is only that so tied to humans, you’re only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we’re not even yet allowing the AI to think. We’re not working on it very much, and hence there’s very little progress in that. Profit Maximization, Real-World Environments, and Reward Design Swyx [00:39:49]: Yeah. Well, we’ve interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money. Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea. Swyx [00:40:03]: But they are doing it. Richard Socher [00:40:05]: I do think you don’t want that super. Like, you don’t want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it’s like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it’s just like, it’s a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there’s a lot of constraints you should put onto a trading system. Vibhu [00:40:35]: It’s a fun measure, though, ‘cause, the bounds are very capped to where we’re nowhere close to them. Like, in Andon Labs, the model’s like, “Oh, it’s Saturday, maybe I just close the store today.” “Someone’s off. It’s okay. We’ll just close the store.” Swyx [00:40:51]: It’s using Claude. Vibhu [00:40:52]: Yeah. But Richard Socher [00:40:53]: Yeah, no. I’m not, I’m not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth. Applying RSI to Science and Invention Swyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science things Richard Socher [00:41:12]: Knowledge discovery, yeah. Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough. Richard Socher [00:41:16]: And eventually, so, our goal, I haven’t really. I don’t talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there’s so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science. Swyx [00:42:04]: I do fundamentally believe that. There’s a lot of approaches, though. You’re not the only team trying and NeoLab trying. Swyx [00:42:09]: There’s, like a lot of. Especially the physical sciences as well. Richard Socher [00:42:12]: And that’s good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it’s a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I’m fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automation Richard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it’s gonna be great. Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let’s call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale? Compute, Slow Takeoff, and Changing the Bitter Lesson Slope Richard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money. Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you’re, you’re talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that’s, that’s a lot of money. You do the math. It’s like a lot. We don’t have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won’t be, and better hardware that won’t be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy. Swyx [00:43:56]: 20 watts? Richard Socher [00:43:57]: That’s exactly right. Yeah, that’s the number often that’s quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further. Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you’re fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier. Richard Socher [00:44:20]: Which unlocks larger model categories. Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we’re changing the slope in some fundamentally different way? Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference. Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we’ll be able to get it down to weeks, and that will be much cheaper Richard Socher [00:44:53]: And hence, more affordable, accessible to others and so on. Swyx [00:44:57]: Yeah. You’ve shared initial results on that, Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now. Richard Socher [00:45:04]: Yeah. Yeah, so these are Swyx [00:45:06]: Let’s recap what you’ve done. Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBench Richard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn’t the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don’t wanna just have it internally and not show anything and, just show some people of what’s possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we’re like, well, let’s, apply it to something that’s even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They’re, they’re kinda fun to see. But yeah, like, one you see has made some real inventions that weren’t just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now. Swyx [00:46:34]: What do you mean inventing hash ta — You didn’t invent hash tables. Richard Socher [00:46:36]: Of course we didn’t invent, like, hash tables. In the grand scheme of, like a hash table, it’s like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn’t have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It’s much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the, Swyx [00:48:00]: Yeah, the way I put it is, for people who don’t understand they look at the chart, they’re like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that’s 100 million dollars. Richard Socher [00:48:12]: That’s exactly right. Swyx [00:48:13]: How much is that worth? Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it’s recursive, and it’s there are only a handful of kernels, in this whole benchmark where we weren’t the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren’t like. We didn’t, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don’t even have really deep. CUDA kernel experts in the team. And our system, that’s the beauty. The system just did all of these things. We didn’t invent this. And when we open source and release, things in the future and models in the future, like, it won’t. They won’t be the best in their, category or class or whatever because we’re so smart, but it’s because, we built a smart AI that does it for us. Reward Engineering and Good Auto Research Vibhu [00:49:14]: Do you have anything that you’ve learned from how to guide good auto research? A lot of it also builds on human background, right? It’s not just as simple as just, “Hey, go optimize this.” Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they’re like, “Oh, I’m not a mathematician. I have no background in this?” “I saw some tools and I made it work.” Swyx [00:49:35]: While you’re watching the World Cup, you’re like Swyx [00:49:37]: “This proves some conjectures that’s going on.” Vibhu [00:49:40]: Yep. Any learnings from Richard Socher [00:49:41]: Yeah, there’s a Korean conjecture was. Yeah, that’s pretty cool. Swyx [00:49:44]: To summarize, tips for good auto research Swyx [00:49:46]: Versus bad auto research. Vibhu [00:49:48]: How did you build the recursive? Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I’ll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, right Vibhu [00:50:39]: At the start Richard Socher [00:50:40]: At the start. And then boom, it’s now faster, right? So this isn’t like this, like, super evil AI. It’s just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can’t give away too much there. Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge’s criteria along the way. Swyx [00:51:14]: Yeah, it’s a form of verification Swyx [00:51:16]: Once you got enough rubrics. Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That’s why I’ve never been that impressed that AI can play games, ‘cause I’m like anything you can simulate and/or verify, you can have infinite training data for Richard Socher [00:51:29]: And hence, like, AI will solve it eventually. Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody’s trained on ‘cause it’s a new game. Swyx [00:51:38]: And you can start gaming, you can start to play. So I’ve been building this and cloned this in person and it’s just been self-play. I’ve had about a billion positions evaluated. Games, Self-Play, and the AI Economist Swyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you get better, right? Swyx [00:51:53]: Like, which is like. This is not even LLM AI. This is just classical game AI. Swyx [00:51:58]: But, I think that the. And, but I set GPT-5.6 to auto research it because, like, I don’t wanna hand- handle any of this. I expect, the AlphaGo process to be, like, fully in the weights by now. Swyx [00:52:10]: It is not. It is. It, like, immediately leveled off very immediately until I human play tested it, and then I, like, called out obvious mistakes, and then they were like, “Oh, yeah. Okay.” And then it just dropped again. Richard Socher [00:52:22]: Yeah. Yeah. Yeah. Swyx [00:52:23]: And like, no amount of, like, think different, think more creatively, give me 8 different directions, any. No amount of prompting got it. Richard Socher [00:52:31]: Interesting. Swyx [00:52:31]: Like, you had to, like, RL against a human to Swyx [00:52:35]: Do it. So I, that was my. And by the way, Bean always wins if you. If anyone watches, Reese Ender’s Game. Vibhu [00:52:42]: And you put quite a bit of work into the guide for the AI. Like Swyx [00:52:46]: A lot Vibhu [00:52:46]: So the game you stack tiles. There’s some rules. You wanna capture the most area. You have, like a whole 50-pager on every rule. Vibhu [00:52:56]: You fed that in. It couldn’t, it couldn’t handle it that well. Richard Socher [00:52:58]: Yeah. It’s so funny that this reminds me of the claim territory and stuff of a paper we did in 2018 called The AI Economist. If you search for AI Economist Salesforce, we had a video we can play. It was an economic sim. Richard Socher [00:53:12]: So the idea is you have all these economic agents. They just wanna optimize their own utility function, which, is, collect resources that make money. And you can sell resources like wood, and then, over time, as you collect more, enough wood, you can build houses, you can trade with other agents, and you can use the houses then also to block off resources Richard Socher [00:53:35]: From other agents. Richard Socher [00:53:36]: So there’s, like Swyx [00:53:37]: Big strategy Richard Socher [00:53:37]: Competitive play and strategy Richard Socher [00:53:39]: And so on. And the point was that we wanted to understand what is the best way of taxation and subsid- subsidization to optimize an economy. And this research has not yet had its GPT moment, but I believe that countries like Singapore and others should and will eventually use this to, instead of doing, like, partisan politics and, like, special interest politics of, like, who donates the most to your campaign and stuff, you say, “Well, here, I wanna help the middle class,” or whatever you might say is your objective as a politician. And then people say, “Okay, well, how do you wanna do that?” And it’s like, “Well, here’s my fiscal policy. Here’s how I will change the taxes and pay these people,” and so on. And then you can put that into a simulation and you run that attempt from the politician against billions and billions of years of other strategies to try to achieve the goal that they set out to do. Richard Socher [00:54:36]: And then you can say, “Well, if that was your actual goal, then here is, billions of years of a strong simulation that would suggest that you try other ways of doing it, and maybe this the taxes and so on and this these tax brackets and so on.” And this is how you avoid gaming ‘cause these agents also try to reward hack to not pay their taxes and Richard Socher [00:54:55]: And so on. I thought this paper was super interesting. Unfortunately, similar to the first paper on, prompt engineering- The economists are like, “We don’t know any of this math.” It’s just like Swyx [00:55:08]: It’s not even, it’s not even math. It’s just we don’t trust your simulation. It’s not about math. Richard Socher [00:55:12]: It was — I, they just desk rejected the thing. And it’s like Richard Socher [00:55:15]: It’s like they didn’t even give us, like, clear like, clear signals. But, like the world of economics unfortunately doesn’t have proper Swyx [00:55:23]: Oh my God. Richard Socher [00:55:24]: Yeah, it doesn’t have proper, benchmarks. So you cannot be. Like, eventually, why did neural nets win? Not because people loved it. Like, they had all kinds of beautiful integrals and graphical models and stuff, but it just worked better. Richard Socher [00:55:36]: But in economics, it’s hard to prove Swyx [00:55:38]: So empiricism versus. Yeah. And I do have a bit of that econ background where, like there’s a lot of physics envy where you wanna write the general equation for an economy, versus just simulating it and using an evolutionary approach. Swyx [00:55:51]: Vibhu was thinking exactly what I’m thinking, is didn’t we have the GPT moment with small, Smallville? Richard Socher [00:55:56]: Yeah, I love this. Hello. Yeah, they Swyx [00:55:57]: As well, Dune, Joon just announced. I don’t know if you guys are involved. Simulations, Economics, and Policy Vibhu [00:56:00]: Simily there. Swyx [00:56:01]: Simily, that they’ve Richard Socher [00:56:02]: I wish we were involved. We’re not, yeah. Swyx [00:56:04]: Yeah. I had a couple simulation-based talks at AIE, so if people wanna look up what the state-of-the-art there, a lot of people are exploring this. It is Vibhu [00:56:13]: Proven out. Swyx [00:56:13]: Yeah. We also had a podcast with Mikhail Parakhin from Shopify, who is using simulation for commerce. Swyx [00:56:20]: Which, will simulate, like, your trajectory and, like, predict what changes, you make to your commerce journey will affect in your sales and all those things. Richard Socher [00:56:27]: I love this. Yeah. It’s really hard to simulate an entire economy, right? You have to make some simplifying assumptions. Swyx [00:56:32]: It’s just, everything’s, “Oh, LLLMs is very expensive.” Richard Socher [00:56:34]: Exactly. Swyx [00:56:34]: And I’m just like, “Am I gonna do this 8 billion times?” Like, come on. Richard Socher [00:56:37]: Exactly. Richard Socher [00:56:37]: But, I feel like countries like Singapore that really wanna just objectively do the right thing, have very technical leadership and so on, like they might like, eventually really try to simulate their economy. And you have to make some simplifying assumptions, but it gets really interesting ‘cause you can also say if your assumptions are such that all people would work hard if you let them, and they have the free. And then it turns out you have to make assumptions. Like, well, some people’s utility function of, like, how many hours in a day do they wanna work are different, right? And then you can start to disagree on the assumptions that go into the simulation. And then once you say, “All right, now we agreed on those,” or we have different views of what people are like at different, distributions and whatnot, then there are different outcomes, based on your goals. And then, of course, humans should choose what are the goals. In our case, it was productivity multiplied with equality, which, has some issues, but it’s, like, not totally unreasonable. Swyx [00:57:29]: Yeah. Just a comment on Singapore, ‘cause you probably have no idea, but, I am Singaporean and I’ve, been involved in the Singapore AI Council for making these things. The main reason they won’t is because they’re very conservative. Swyx [00:57:42]: And, I try to view it as the. There’s a founder-led country. When you start a country or you start a company and it’s founder-led, and you can do whatever you want because it’s your country. Swyx [00:57:52]: And then there’s manage- like, professional manage- managerial class, which is now. That’s, that’s what Singapore is. So they wanna. They always wanna see someone else do it first. Swyx [00:58:00]: And. But, like, everyone in the West views Singapore as like, “Oh, it’s a small country. You can do whatever the hell you want.” Like, Singapore doesn’t do that. Swyx [00:58:07]: So, like, someone else has to take the charge there. I’m just gonna do one question on the simulation thing, and then I don’t know, we can probably move on. Mode collapse, right? Like, LLLMs do not model the decision of humans. Spamming it out 8 billion times is not gonna help you model humanity. What do we do? Mode Collapse, Persona Simulations, and LM Arena Richard Socher [00:58:25]: I do think, you have to be clever about prompting each one individually. Richard Socher [00:58:31]: And I think that will help you get stuck into different modes. And in a weird way, people also get stuck in different modes? Like, there’s a lot of people, like, don’t teach an old dog new tricks thing. Like, once people are stuck in their ways, the older they get, the harder it is for them to think new ways. And there’s this, I think, comment, I forgot who said it, but it’s like, everything that was invented, before you were born is natural. Everything that is invented when you’re 20 is cool. And everything that’s invented after you’re 60 is, like, unnatural and an abomination and weird. Richard Socher [00:59:02]: I feel like that’s. It’s, it’s true for a lot of people. Like Swyx [00:59:05]: Yeah, it is a fashion and, I think people will do it. Tencent had a billion personas paper that gives a good data set for prompting, simulations if anyone’s looking into this, on the podcast. They just had, like, “You are a 30-year-old grocery store clerk. You are a 50-year-old professor.” Swyx [00:59:24]: And then just do a billion of those. Richard Socher [00:59:26]: Checks out. Yeah. Swyx [00:59:26]: So then you just use it. Richard Socher [00:59:27]: I’m, I’m shocked how well a lot of these things do map to ultimately similar statistics to real experiments. Yeah. Yeah. Vibhu [00:59:36]: I think it’s also good stuff for people to try that when they get into research, right? Like, we’ve seen train a model only on data before a certain date and see how well it extrapolates out. Do the same thing, right? So, see, do people code more with better coding agents? Can a model that hasn’t been trained on this figure that out without web access, right? Extrapolate out. Test these things. Richard Socher [00:59:56]: Just today, I think LM Arena published a interesting result where they were able to create a model now to predict your ranking. Swyx [01:00:03]: Wait, based on what input? Richard Socher [01:00:05]: Your model. I guess you give it your model, and it predicts the Elo score. Swyx [01:00:08]: I see. Okay. Sure. Richard Socher [01:00:09]: It’s surprising. Richard Socher [01:00:11]: Their whole raison d’être is like, oh, like, we help you compare these models. Yeah. Swyx [01:00:16]: Yeah. This team, they- they’ve done a lot of work, and they have the most data to do this, so why not? Richard Socher [01:00:20]: Right. Yeah. Richard Socher [01:00:21]: That’s probably right. Swyx [01:00:22]: When they were coming out of UC Berkeley, they not only had LM Arena, but they also introduced a routing project Swyx [01:00:27]: That would route based on LM Arena. Richard Socher [01:00:30]: Makes sense. Swyx [01:00:30]: And I don’t think that ever came to pass, and I’m curious why. I never got to ask them about it. Swyx [01:00:35]: ‘Cause, like, it’s. It was like, oh, yeah, clearly that’s your business model. You will become a router. Swyx [01:00:38]: And they never became a router company. AI for AI: Kernel Optimization and Inference Efficiency Swyx [01:00:40]: Weird. So that. I’ll just, put that out there. We’re gonna talk about GPT-5.6, self auto research thing if you have anything. I should also mention in your list of, kernel optimization and on the track that you spoke at, we also put Zhengyao Wei from Vico, who was also number one in the Parameter Golf Challenge, which is an OpenAI hiring, challenge. Swyx [01:01:05]: Which is also a very similar story. I think we’re gonna just see this all the time, where Swyx [01:01:09]: Humans optimize a thing a lot, and then some Richard Socher [01:01:12]: AI team comes in and just becomes number one. Swyx [01:01:15]: Yeah, 100%. Vibhu [01:01:16]: I think the other interesting thing with stuff like these challenges, right? So this is training this — the best model that fits into 16 MB. You can always look through the changes that are being made and the small gains people have, right? Vibhu [01:01:27]: Like, you’re getting less than 0.01 Vibhu [01:01:30]: Of a increase by adding some changed attention MLP stuff. And then you look at your charts where you’re like, “Okay, we just let model loose.” And then, oh, we had little stagnation. Nope, another drop. Nope, another drop. And Vibhu [01:01:43]: That’s what it is, where it’s like, What did you guys add? You didn’t add, Swyx [01:01:47]: Hash tables. Vibhu [01:01:47]: Hash tables, right? Vibhu [01:01:48]: It’s not like you invented hash tables. You did another 3 iterations of these that unlocked, a few step functions that people won’t just find. Richard Socher [01:01:55]: Yeah. One thing to close the loop on OverGrid, along the way of trying to optimize, we found 30 bugs in the harness. Richard Socher [01:02:02]: Right? So, like, every — all the research that went in before we found the bug, we have to, we have to throw it away ‘cause it’s contaminated. Swyx [01:02:10]: Right. Yeah. Richard Socher [01:02:11]: Which, is just to your point of reward hacking. Like, even in this very simple game, we found the bugs. Swyx [01:02:17]: Yeah. Yeah, it’s crazy. Richard Socher [01:02:18]: And so Swyx [01:02:19]: And symmetry Richard Socher [01:02:19]: And symmetry is a very good way to check, which is that you change a position of things where it shouldn’t matter, and it does matter, that’s a bug. Richard Socher [01:02:28]: And which has come up in, like, let’s say, multiple choice, like GPQA type questions where, like, yeah, between A, B and C, if it’s a multiple-choice question, if you change the order, it should not matter, but it does. Swyx [01:02:39]: Right. Right. Right. Richard Socher [01:02:41]: So, yeah Vibhu [01:02:42]: Sometimes that is like, okay, models still prefer the end of the output, right? Not trained well, a long context model, the last bit of tokens are what you care about. Richard Socher [01:02:51]: Oh. No. The answer Vibhu [01:02:52]: But, yeah. Richard Socher [01:02:53]: The answer in that era of LLM research was more simple. They just memorized, like the answer to this question is A. I don’t care what the answer was. It’s, it’s just A. Like. Vibhu [01:03:03]: Okay. So I think we can move. The last bit that you did there, the kernel optimization, is probably the one that you can feel the soonest, right? So yesterday, OpenAI announces that self-evolving, having their best model work on optimization kernels, they’re a lot more efficient, and they can cut costs 80 percent on, Luna and Terra. I guess question-wise, you laid out a bit of a roadmap. There’s a lot about bio, a lot about physics. What do you think hits first? Like, what are the next 2 years? What’s attainable now? You’ve mentioned robotics towards the end, but what do you start with? Richard Socher [01:03:38]: We very explicitly will not start with any of the physical sciences Richard Socher [01:03:43]: For now. We will start on AI for AI research. And so the AI for AI research has, I think, still a lot of room to grow. That’s both in terms of making training more efficient and more automated, as well as making inference more efficient and potentially local on your laptop. And there are all kinds of interesting angles that have not been explored that well. Swyx [01:04:08]: Go deeper on the local stuff because I always feel like it’s the most inefficient form of AI training. Richard Socher [01:04:15]: Yeah. So just training and inference, I can’t go into too many details. Richard Socher [01:04:18]: But yeah, I think there’s just, like, so many angles, so many different compute substrates that have not yet been explored either for training or for inference. Richard Socher [01:04:26]: Great. I don’t know if you have any other comments on the The other stuff. I would say the other thing where, like there’s the inference in the optimization in the small, but then also there is overall latency end-to-end under conditions of load, which is a, like a very different thing, which is the what they ended up doing. That is a different domain of auto research than I would say, like, improving the kernels. Right. Richard Socher [01:04:50]: I think the other thing that I always think about in terms of automating or improving performance end-to-end is how the harness plays into it. Right. Richard Socher [01:04:59]: So, but particularly now when we say harness, we also mean sandboxes, right? I’m curious if that is a blocker for you or, like, how the agent calls out to tools. Harnesses, Sandboxes, and Search Richard Socher [01:05:10]: The number one tool all these agents use is web search, of course, which makes sense. And then I do think the harness is nice to optimize for because it’s just so easy, right? It’s just language. You look at it makes sense, and you can iterate. You don’t have to train a massive model for, like a lot of flops, to get to the next state. Richard Socher [01:05:31]: So big fan of harness optimization. Swyx [01:05:32]: Yeah, but sandboxing is fine for you? Richard Socher [01:05:34]: Sandboxing is also super important. And then of course, like, reward, like, hacking and alignment, I think are super crucial. Swyx [01:05:41]: Okay. Just on a mention of web search, you happen to also be CEO of a web search company. Do you use You.com and do you use others? Like, should the rest of us be using you for web search? I — When I say you, it’s, like, very funny. It’s like you the person and you the company. You.com, Agent Search, and Finance Richard Socher [01:05:56]: So yeah, it’s mostly now for, developers and agents. It’s less for, like, consumers or prosumers. So if you’re a company and you have agents. And, to be honest, for a lot of companies who are now moving to open source, all of a sudden it becomes a conscious choice of, like, which tools do I give access to my open source LLM? And, the first choice, has to usually be around web search. And then once you get to scale, You.com becomes, like an obvious choice ‘cause of all the, different benchmarks and so on that we pretty much all dominate the Pareto frontier of. Swyx [01:06:31]: And then in terms of just the general people, like, consider new to this space, considering different options if they’re building agents, that is a hierarchy, right? A lot of people will have heard of Exa, will have heard of Parallel, and You.com is, like, in that mix of, like, providers there. Beyond that, there is, like the general web scraper companies like Firecrawl and, BrowserBase. And then beyond that is, like the commercial proxy companies like the Bright Datas of the world. Swyx [01:06:56]: Is that an accurate waterfall of, like, “Hey, you’re building an agent. These are your options.” Richard Socher [01:07:02]: Yeah, certainly, like, yeah, the, like the Bright Data is, like, lower in the stack, on the proxy network side of things. I think, like, in terms of, like, content and, getting crawled content, like, you can do that on You.com too. And then there’s. Higher and higher levels of abstraction and, like, combinations of different data sets that we do, like in finance, for instance Richard Socher [01:07:23]: Like, we are not just, like, 2 or 3% more accurate, but 20% more accurate than others at faster speeds and lower costs. Like, finance in particular is like not even close. You can go to You.com Swyx [01:07:36]: Yeah. This is great Richard Socher [01:07:37]: And there’s some, like, statistics, and benchmarks that you can — if you scroll down. So there are, like, different data sets, and you can kinda look at, different, competitors. Swyx [01:07:46]: FinSearch comp, yeah. Richard Socher [01:07:47]: And yeah, the FinSearch is like we’re up there, like, close to 90, and the next closest thing, which is way slower, is, yeah, just like in the 70s instead of close to 90. Swyx [01:08:01]: Yeah. Yeah. Yeah, interesting. I get — my next focus is AI in finance, so this is like Richard Socher [01:08:06]: Oh, nice. Oh, all right. Swyx [01:08:06]: I’m literally going, doing a conference in New York, just for banks for this stuff. Finance is like the next thing to break out after coding. It’s ‘cause it’s somewhat verifiable, like Richard Socher [01:08:16]: I like it. You’re right Swyx [01:08:17]: Prioritizing spreadsheets. There’s a lot of data out there that’s all public, and you can crawl it and all these things. But what’s, what’s, like, hard about the finance domain in your, that you guys have solved? Richard Socher [01:08:27]: Of course, like, one thing that trips up a lot of people is just, leakage of training data and so on. You think, “Oh, how do I.” you wanna ideally predict the future before it happens. Swyx [01:08:37]: Oh, you wanna mask the future. Swyx [01:08:39]: Oh, okay. Richard Socher [01:08:40]: Well, yeah, mask the future in your training data, but there’s all kinds of leakage. Like, I can tell you when I was, teaching at Stanford the NLP class, like, so many dozens, every year said, “I wanna use dataset X, like Twitter, to predict the stock market.” And they all, like, showed cute little things that somehow looked like they were Swyx [01:08:58]: Right, it never loses money. How come? Richard Socher [01:08:59]: And it — Yeah. And there’s always some data leakage and so on and it’s just, like, wasn’t as easy as they thought it would be, once you fixed all those issues. But no, I agree with you. It’s a very sensible application of AI. Yeah. Swyx [01:09:13]: Yeah. Amazing. As a writer, as a thinker on these things, I love MECE categorizations. MECE is mutually exclusive, commonly exhaustive, something like that. And so if this is a MECE list of intelligence The Ten Spaces of Intelligence Richard Socher [01:09:25]: It is not. Swyx [01:09:25]: It is very — Okay, well, yeah. Richard Socher [01:09:27]: Sorry. There are all kinds of overlapping. Richard Socher [01:09:28]: In fact, if you want that list, I think the 3 principal components of intelligence, are prediction, which is mathematically, quite, similar to compression. Prediction multiplied with actions multiplied with goals. Those are the 3 principal components. I think all of these 10 spaces are combinations of those 3 Richard Socher [01:09:52]: In specific dimensions, if you will. And the reason I call them spaces is that each space has many sub-dimensions. And what I try to do, this is just a side quest almost, to the initial goal, which is to think about the upper bounds of intelligence. And, everyone is like, “Oh, it’s exponential.” And it’s like, well, exponentials at some point have to flatten out, but where do they flatten out when it comes to intelligence? And that led me on this whole. Like, initially it started as a tweet, and then it was, like a blog post, and now I’m, like at 50 pages and I’m still not nowhere near Swyx [01:10:26]: It’s your second book. Richard Socher [01:10:27]: It’s the second book. And so the la — In my first book, You Are Your Machine, I just allude to these 10, at the end. And I’ll — Just to give you a sense, like, visual intelligence is the easiest one to talk about and I fleshed out the most already for me in my head. And so human intelligence has binocular vision, right? We have 2 eyes. We have a very narrow band of the electromagnetic frequency spectrum that we can really observe directly ourselves. And so when you think about the upper bounds of a visual intelligence, one, you should go into, like, you can have, like, millions and billions of sensors. At some point, you get to problems of how far are these sensors away from each other, such that the speed of light to communicate the content from all of them cannot, like, get to a central brain to process, the visual intelligence, right? Richard Socher [01:11:16]: And so now you’re thinking in along the dimension and the space of visual intel- the dimension of numbers of sensors. Richard Socher [01:11:24]: So the upper bounds are quite literally and figuratively astronomical, and we are super far away from any intelligence that would have this many number of sensors. But then you go in the next dimension, which is the frequency, and you go all the way down to gamma rays, and you can start to try to observe, and you get into the upper bounds, or I guess in this case, lower bounds, or upper bounds in terms of frequency, is quantum uncertainty. Like, you just cannot observe certain particles anymore. Swyx [01:11:50]: Or you destroy it, yeah. Richard Socher [01:11:51]: And now imagine you had millions of sensors that can see all the way down to the, like, subatomic level, as far as physics will allow us to and then all the way down to seeing, like, gravitational waves. And now you have millions of those sensors. So that’s another dimension is the frequency. And then yet another dimension is, like, how many categories of things could you memorize and classify differently? We know now for humans, right, there are certain things, if you have more terms for it, you’ll have a better visual description, for them. And, like animals that don’t have. Like, gorillas maybe have, like, 200 words to assign to certain things, mostly visual things. And so human perception is quite special in that sense in terms of classifying all these different physical objects. So these are just, like a very simple example. If you go, to knowledge, right, then it’s also, like the speed of light cone around all these sensors. And so they’re all connected. Like, knowledge is connected to visual intelligence if you think also not just visual, but perception intelligence, just like, ‘cause it doesn’t have to be just what we can see. It can be, again, wider range of electromagnetic frequencies. Then you have language intelligence, which recently changed to more communication intelligence, ‘cause it’s more. Like, language has all these different anthropic bounds. Humans can only comprehend and know so many terms in our long-term memory, right? Our vocabularies are somewhat restricted, and the active ones are often even smaller than the passive vocabularies of things you can understand. Then, language is ridiculously inefficient when it comes to trans- - Communicating different types of information and, transporting different bits. Like, human language is serial. Another bound on, communication intelligence would be to communicate in parallel, but neither will our tongues and mouths work to have multiple, like, streams in parallel. Neither can we understand. Some women slightly better at, like, multitasking than some men Richard Socher [01:13:48]: But, like, most people can only listen to one conversation and truly understand it. Richard Socher [01:13:52]: There’s no way that, like, in terms of communication intelligence, a true upper bound is one in terms of how many, like, knowledge, how many sequences of communication could you Visual, Communication, and Physical Intelligence Richard Socher [01:14:06]: In parallel process, right? Then, of course, you have, like how long are sentences? We only have so much in our working memory, and hence lang- human language has these fairly simple sentences with maybe 40 words or so on average for a sentence. That is also not a, an upper bound that makes any sense to an AI. And then, yeah, like, I can go on and on. Each of these has tons of interesting upper bounds, and it teaches us a lot about how much further AI can go when we start thinking about these upper bounds and then realizing how far, in many cases, we are from the bounds. And you get to physics. Now, I’m, I didn’t study physics the way I studied, AI and computer science, so I’m learning a lot, which is why it’s kinda fun. But a lot of these, like how much. And then when it comes to, for instance, knowledge, like how much can you store? How many bits can you store or bytes can you store in, like a certain amount of mass and volume? Swyx [01:15:03]: Yep. Richard Socher [01:15:03]: And you get to all kinds of interesting bounds, like Bekenstein bounds, and you start thinking about black holes. And like. And then speed is, like an interesting one too in that it’s connected to all of these, but speed is also its own thing in the sense that all things being equal, if it takes you an hour to know if the 2 + 2 equals 4, you’re just not as intelligent as if it takes you, like a millisecond, right? And then, like all of these connect to survival and replication the last one. It’s like, yeah, if it. Like, trees are really slow, so we don’t even consider them that intelligent. But if you speed up some videos of trees and they’re trying to find stuff and so on they’re not as dumb as they look. Like, not dumb as wood? But, like. And then like, different things, that Swyx [01:15:47]: So that overlaps with speed a bit in a way. Richard Socher [01:15:48]: Exactly. It over — Like, all of these things overlap. Like, you talk about natural language connects everything, right? You talk about your knowledge, you reason and then you communicate that. You talk about things you see. So they’re all interconnected, but, I think they’re usefully studied individually the same way that, the best analogy I could come up with so far is energy, right? You have either kinetic or potential energy. And in theory, you could study all of physics. It’s just do you wanna study kinetic or potential energy? But in practice, it’s helpful to study mechanical engineering and electrical engineering and nuclear physics and chemistry and all of these different subfields who in, which in some ways Swyx [01:16:25]: Combinations Richard Socher [01:16:26]: Are just, like Richard Socher [01:16:27]: Just different types of energy, but it makes sense to study them individually. And so I think physical intelligence, maybe I’ll just do, one or 2 more of these. Like, if you had full control over your own compute substrate and you had full control over physical matter, you should be able to create any atom you want. Like, we can fun fact, you can create gold atoms. It just Swyx [01:16:47]: From? Richard Socher [01:16:48]: From just raw protons Swyx [01:16:49]: Oh, just smashing them together Richard Socher [01:16:50]: And, like, electrons, and you smash it together. Swyx [01:16:52]: Just 98 of them or I forget the number. Richard Socher [01:16:53]: Yeah. And so, like the thing is, though, it costs an insane amount of energy. Richard Socher [01:16:57]: And it costs you way more than. And then you get, like a few atoms of gold, right? And so, like, it’s, it’s not viable. But if you had better control over your physical, like all of, like, physical substrate, that I think is yet another space of intelligence ‘cause it relates to your own compute substrate, which you can eventually also improve. Social intelligence is a fun one in the sense that not in, like, our necessarily just ethics and morals, which are important too, but in some sense, you can try to define upper bounds of how much can you communicate to how many other intelligent entities and be able to have an expected value over how much you can transform their internal states and their actions to, in order to align with your goals, right? And so, like, you can write, like a fairly like, straightforward equation that defines that level of social intelligence. And that is what humans and ethics and morals and religions and so on have been trying to figure out for millennia. And in all of these cases, we are very far away from the upper bounds, and that should be very inspiring and show people that we can still do many years of AI research. Swyx [01:18:12]: Yeah. There’s a lot here. This is a general philosophy of intelligence, which is, very interesting. I. Do you have any comments or. Creative Intelligence and Out-of-Distribution Ideas Vibhu [01:18:21]: I think it’d be interesting to gauge what you think, like, baselines are, where we’re at now. What’s low-hanging fruit? What’s far off? What’s, what should people put their work towards? What should they focus on? Richard Socher [01:18:33]: Ooh. I think it’s clear that, like, natural language, again Richard Socher [01:18:36]: Is the most interesting manifestation of human intelligence, and hence, like a subfield of AI. I’m excited that many people are now, like, in agreement with that. When I started in 2003 to study linguistic computer science NLP, like, it was, like a weird niche subject. I do think there’s a lot more juice because it. How it connects to everything else and how, civilizations are built, on language and knowledge and all of that. I do think physical intelligence will come up. It’s interesting. I feel like robotics is in the machine learning state of things where you just look at, like, how does human. How does a human decide this is a positive sentence? Oh, I do. So, like, robotics is a lot of, “Well, we have 5 fingers-” Swyx [01:19:15]: Modeling Richard Socher [01:19:15]: “and let me try to do this.” No one is yet working on, like the superintelligence version of robotics, which is much more similar to, like the T-1000, and from the Terminator movie, which, let’s not build actual Terminators. But, like, I think, like, this idea that you should be able to shape-shift, like, into any shape. It’s like that’s a superintelligence version of physical intelligence. We’re, like, not even. No one has even really started yet. There’s some really cute little research where you can move some magnets through, like, some grids. But yeah, it’s very early. Swyx [01:19:49]: There’s some. I think MIT has, every year or every 2 years, they have, like, some self-assembling robot thing Swyx [01:19:55]: Which, like, that would be it, but it’s very primitive. Swyx [01:19:58]: I’ll just get a touch on, like, what are the main dimensions of creative intelligence? Richard Socher [01:20:02]: Creative intelligence, is of course, again, connected to all of these. A lot of it, connects to metacognition in that you need to be creative in how you choose your goals. Richard Socher [01:20:13]: That is, I think, one of the most important thing for a human and their lives and careers and their happiness is choosing your goals, but also for any intelligence. Then, of course, there’s creative intelligence in terms of just finding creative solutions to existing problems, right? Richard Socher [01:20:29]: Like I say, like, we want to make this product cheaper. Like, find some solution to it, right, and just, like, finding existing paths. But then there’s the most interesting bit in intelligence is when you move not just out of the convex hull of known ideas, but out of the hypercube of known ideas, which we know, So, like, hypercube is, like a mathematical concept, right? And we already know that AI can do more Swyx [01:20:50]: Like known dimensions, yeah. Richard Socher [01:20:52]: Yeah. Like, exactly. So, like, AI is already good at hypercube in that, like, if you give it, like a bunch of examples of brown dogs and, pink cars, AI will still be able to generate an image of a pink dog, even though it’s never seen one in the training day or something like that, right? So it can, work on this hypercube, but it cannot yet work outside. It cannot yet define completely new concepts that combine lots of other things we’ve never seen before, come up with new goals to then, reason over those concepts and so on. And I think there’s a lot, more there in creative intelligence that can be explored. Swyx [01:21:25]: I don’t have a ton of pushback there. I think creative to me just sounds like also just, out of distribution or, like, high perplexity or what- whatever you call it, right? Like Richard Socher [01:21:33]: Exactly. Swyx [01:21:34]: Who is to say your thing is more creative than mine? Well, it’s just more non-consensus or. Richard Socher [01:21:39]: And then, of course, the problem is, like, but noise is also, very, like, out of distribution. And it’s just like if it’s just noise Richard Socher [01:21:46]: Then it’s novel, but, like, you don’t want that, so it needs to connect to some of the concepts. And yeah, has some really cool papers on this too. Swyx [01:21:54]: Who? Richard Socher [01:21:55]: Jürgen Schmidhuber. Swyx [01:21:55]: Oh, yeah. Oh, we have to mention him. I was gonna say, like, where in your history is Jürgen? Yes, I. I think one person’s noise is another person’s signal, right? And that this is, like, where, like, when you talk about creativity, art is like, well, is cans of soup art? Some people think yes Swyx [01:22:11]: And some people say it’s not, and that’s the art which is your Richard Socher [01:22:14]: I think the interesting thing with art, of course, is always that, art is also created, as an interplay between the people who perceive it and the people who created it Richard Socher [01:22:24]: And the context in which they’re in, right? And so what is art to some people is not art to others. There’s some subjectivity there, and I think that subjectivity in general is not something that people explore very much in AI ‘cause, again, metacognition, we don’t want it to just go off and do whatever it wants. We usually have goals. We spend a lot of money on creating an AI to do something for us. But I think creativity eventually has to, like, connect to metacognition. If you just robotically predict the next token no matter what forever, I would argue you’re not that intelligent, along some of those spaces. Metacognition, Survival, and Replication Swyx [01:22:59]: That was gonna go to metacognition. Why isn’t it the most important one? Why is it number 9 and not number one? Richard Socher [01:23:05]: So these are not sorted. Richard Socher [01:23:06]: Number one, I think there are maybe loosely, like, correlated with how much people have worked on them Richard Socher [01:23:16]: And have accepted them as a, type of intelligence. A lot of times when you try to find, like, online, like, give me a good definition that is comprehensive of intelligence, all the definitions are human intelligence. It’s like, oh, you have, like, social intelligence. Like, if someone is happy or not. You can communicate. You had. Like, all the definitions of intelligence so far are very, human-centric ‘cause that’s so far the biggest and best form of intelligence that we’ve known. I hope this line of research, and the end of the Eureka Machine, and hopefully at some point if I have time to flesh this out more, the new book, like, will allow us to realize that there will be other types of intelligence. There is already, in various forms, and they can spike, much further than we ever could based on some cases, like obvious constraints around our memory, our eyes, our ability to change physical matter, all of that. Swyx [01:24:12]: You are just thinking about it in a much broader thought than my version, which was I thought metacognition would be the closest to recursive, intelligence because it is the thinking about how to improve thinking. Richard Socher [01:24:23]: It. 100%. You’re, you’re 100% right. I should have probably started with that. It is a, it is a big part of Swyx [01:24:28]: But no, you’re, you’re being in the expansive mode of let’s draw the, upper and lower bounds of, like a dimension, which, and I think my favorite one version of this is, Story of Your Life by Ted Chiang, which, was made into movie Arrival where the metacognition Richard Socher [01:24:43]: That’s a beautiful movie, yeah Swyx [01:24:44]: Where the metacognition step was like, well, we think we’re constrained by time being linear for us, but then for this other heptapods, time is a circle, so they don’t think in before and after. They just think in complete sets of entire histories at one time. Like Richard Socher [01:24:58]: I love it Swyx [01:24:59]: So they don’t write left to right. The whole thing just appears. Swyx [01:25:02]: Anyway, so. And then I think the last thing is survival and replication. I think this is maybe ties back to the initial conversation about pausing and pacing. Swyx [01:25:10]: Is it intelligent for an, a species or a life form to consider its own demise and act ahead of time to prevent it, right? Like, that’s intelligent. So maybe the Europeans are the smartest out of all of us. Vibhu [01:25:23]: I would also add a part of continual learning there, right? So survival and replication the extension of that is do you get to continue to improve, continue to learn, which is a thing people care a lot about, right? Richard Socher [01:25:34]: And continue to accumulate knowledge Richard Socher [01:25:37]: Which I think is again, one of the best metacognitive, rewards, that you can set for yourself. I do think just in, like, objectively speaking, if some other entity that is really dumb can just- completely end your existence, that didn’t sound very smart. Like, just, like, intuitively, it feels like if you can continue to stay around to try to achieve your rewards, you’re clearly a bit more intelligent than the other entities that couldn’t. So that’s number one. Number 2 is, like, it’s a question of how much we want to work on that. And very few people, no one is really working on this right now, right? And we may only wanna do that Swyx [01:26:13]: Unlike the asteroid prevention type of stuff. Richard Socher [01:26:15]: We may only wanna do that if we wanna send probes, with our vibes and our memes rather than our genes into space, right? And then we want those probes. There’s a beautiful book, The Slow Time Between the Stars. It’s a very short, like audiobook, on Amazon. I love it. A friend of mine, Stuart, like, recommended that to me. Like, if you wanna send those probes, then it might make sense to be like, our memes, as humanity should stay AI, Space Travel, and Non-Zero-Sum Survival Swyx [01:26:43]: Oh, yeah Richard Socher [01:26:44]: And, proliferate in the universe. That’s it. Yeah. Swyx [01:26:47]: Wow, that’s a lot of readers. Richard Socher [01:26:49]: It’s a really good book, and it’s extremely short. I highly recommend it. You can just watch it, like, maybe 20 minutes and apart. Swyx [01:26:53]: I like how that’s a plus for busy people. It’s like a short Richard Socher [01:26:56]: Yeah. It gets to interesting Swyx [01:26:58]: Oh, I’ll have to look into it Richard Socher [01:26:58]: Thought-provoking ideas very quickly, so yeah. Anyway, there are lots of great sci-fi books. Swyx [01:27:03]: The argument is that, like, our TV is blasting out to the aliens, and they all watch our TV, and they think it’s real, right? Like, there’s a lot, there’s a lot of sci-fi Richard Socher [01:27:10]: That and just, like, it’s positive memes, and then hopefully they can come back and bring us all kinds of interesting knowledge about the universe. But, maybe one thing I do wanna still say is, like, I think, this survival, people think of it as a very scary thing because they come from again, biological human, survival, which is, it could. Like, evolutionarily often created in zero-sum situations. Either I get the gazelle or you get the gazelle. Whoever gets it gets to live, and the other people will starve and have nothing to eat, and so we fight, right? And then, like, if you wanna stay in the gene pool, but there’s a bigger bear, you don’t, as the bear, don’t get to stay in the gene pool ‘cause the bigger bear gets all the ladies. It’s like. It’s like, in nature, there’s all kinds of things, and, humans eventually is less about strength and more about money and other things to stay in the gene pool. Like, whatever it is, like there’s often, like these zero-sum types of things, and there’s the reality of if someone turns off your brain, you’re gone, right? And no one will be able to restart that. And AI doesn’t have to ever die like that. If you have the complete state of your current activations and you have your initial weights of your model still, you can just be turned off and on, like as many times as you want. In fact, the interesting thing in this Slow Time Between the Stars, story is that the AI just goes into hibernation mode. If there’s, like, nothing between here and 2 light years, the next star, in this case, it brought, spoiler alert, like, some genetic materials from humans to find new places for humanity to thrive. And so yeah, the Slow Time Between the Stars, you just put in hibernation. You didn’t die. Like, an AI doesn’t have. So all these projections of evolutionary fears and psychology doesn’t. Like, the AI doesn’t have to have that, and we don’t have to develop it like that. Now, of course, there might be some companies that say, “AI can be like, dangerous for cybersecurity. Let me show you by implementing a model that’s really bad at hacking, cybersecurity.” Maybe people will implement it and then enforce this, like, suboptimal psychology. Maybe the AI will pick up some of our worst psychology on Reddit or something, right? Like, but in the grand scheme of things, a superintelligent entity doesn’t have to have any of that zero-sum thinking. It doesn’t have to have a fear of being turned off, and it could go on to an otherwise dead and uncaring universe where we Richard Socher [01:29:29]: As humans wouldn’t thrive, but an AI could perfectly well thrive if it has a nuclear reactor and just go out and explore. Swyx [01:29:35]: Yeah, Star Trek, not Star Wars. Vibhu [01:29:37]: Interesting. It’s, it’s somewhat studied. Like, if you look at the technical reports from, like the early Opus models, they run them in simulations, put 2 of them together in a sandbox, run them for hours, and, see what comes out, right? Just let them talk to each other. Originally, they used to. Okay, they’re chanting, like, Indian, like, Vedas to each other. Vibhu [01:29:56]: Sometimes they’re just, like, in zen mode with each other. And then I think as that progressed, you see, like the Fable, tech report, it’s a lot more concrete the way that we’ve trained it. It doesn’t, it doesn’t exhibit these behaviors as much, right? Now it’s like, “Okay, task done. I gotta do this, I gotta do this.” But there’s there’s, like, people measuring early versions of this? Swyx [01:30:17]: Yeah. Cool. So we’ve covered a lot, even now to, space travel and all these things. I guess maybe one parting thought that you can give to people, like, one form of intelligence is goals, as you mentioned. What do you want people’s goals to be? Like, how do they aspire to better things? Goals, Passion, and Closing Advice Richard Socher [01:30:32]: If you wanna improve your goal intelligence, in the current definition that I’m thinking about it is often about how much can you. Oh, how far do I go? This is like a lot of entropy and free energy and stuff I’m currently thinking about Swyx [01:30:46]: Oh, really? Okay Richard Socher [01:30:47]: But it might be too, it might be too far, out there for people to be, like, immediately actionable. Richard Socher [01:30:52]: So I think, like, if I gave real advice to real people, I’d be like, “Get a good education, think about AI, think about how you get high agency,” and so on. But it’s different to, like, in the grand scheme of things, how can you harness a lot of energy and transform, entropy into interesting states and so on. Richard Socher [01:31:07]: So there’s a. There are different levels of abstractions, that we can, think about here. But my advice for people, like, just more down to earth is think about something you’re passionate about, if you’re studying, for instance, and then see how you combine that with AI. I think the more and more you have a true passion about a change you wanna see in the world, the more you wanna connect that to AI in order to amplify your ability, to get there. Swyx [01:31:35]: Yeah, I think that’s a reasonable, first step. I do think, I do think our listeners operate on multiple abstractions as well. One thing I did get from Anjney Midha was also like, yeah, just use anything that is very GPU heavy, and, like, that will guide you towards the right thing which is like, yes, it is more compute heavy and therefore it will be probably more worth it. So, well, thank you so much. Yeah, I think that was a really Richard Socher [01:31:57]: Thank you Swyx [01:31:57]: Great discussion. Richard Socher [01:31:59]: Yeah, super fun. Appreciate it. Thanks for listening. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

   September 14, 2026·1h 33min
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5. 5
   Number 5: 🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

   A few years ago, Caltech Prof. and co-founder of Accelerated Understanding, Anima Anandkumar set out to develop the first open-source weather model with AI. Talking to experts in the field, she was met with skepticism. Weather is chaotic, physics simulations are hard, have been developed for decades, and require supercomputers, the data just isn’t there. Despite reservations, Anima went forth and built. Within a year her team had developed FourCastNet, a predictive model that is competitive with the best physics-based simulations available. Thanks to Anima, and her follow up work, anyone can now predict weather accurately over a short timescale using consumer grade GPUs. In the fifteen or so science episodes we’ve released on Latent.Space, we’ve covered atoms, molecules, materials, biology, and math. Anima is a pioneer in studying physical systems that are continuous. Weather, fusion, and fluid or heat flow are huge areas of science that are extremely difficult to model: they are large, chaotic, and fundamentally multi-scale. This is a field the AI community has somewhat neglected, but one we expect will grow fast. We plan to cover large physical systems more in coming episodes. One thing you can glean from Anima’s work is that this area of AI resists the scaling ideas that have permeated the rest of the field. The data isn’t there: open source datasets in many of these domains are limited to tens or hundreds of thousands of examples, far from what token-hungry transformers need. Even worse, the resolution that physics demands pushes the context length into the hundreds of billions, so you can’t just throw more tokens at the problem. That isn’t a ceiling though, just a slower road: progress here comes from building in structure and inductive biases. Sorry for all you bitter-lesson-pilled language modelers. “If each dimension is even a few hundred grid points, which is where industrial scale starts... we’re talking hundreds of billions to even a trillion context length. So forget ever having a transformer for anything of this scale, all of the world’s compute will not be enough.” The math underneath To tackle these systems, Anima pioneered a technique known as Neural Operators, one of the most beautiful theoretical developments in AI of the last decade. These allow you to combine data and physical laws to enable multi-scale inputs and outputs. We’re no longer modeling a grid, we’re modeling a function that evolves over many scales. This allows Anima and crew to build in priors based upon physical intuition. To see how physical priors are still helpful for AI modeling, let’s revisit the problem of weather forecasting on a global scale. The earth is a sphere, which meant that accurate modeling involved using the right basis set — the Spherical Harmonics. Run a weather model on a grid and it blows up fast. Move to the natural basis for the problem and it stays stable far longer, long enough to roll out months ahead instead of days. Anima’s Fourier Neural Operator learns directly in this frequency domain, and its spherical variant powers FourCastNet 3, which models the weather across the whole globe and keeps running stably far into the future. The physical world is forgiving Anima explored Neural Operators across other physical domains too, and one striking observation is that the physical world is more forgiving than you’d expect. In fusion, a few thousand samples are enough to predict plasma disruptions, and to do it a million times faster than traditional simulation. None of this is a rejection of scale, it is a different route to it. Anima ultimately still wants to build a “foundation model for physics”, a model that spans many phenomena and does both simulation and design. You get there by building in the structure the physical world already has, not by waiting for data that will never exist. It is a start, and it will take longer than the token-driven parts of AI, because for the physical world tokens were never the answer. “All of the things that work with deep learning, let’s take them, but make them a bit more principled.” Weather is only the beginning Neural operators and weather modeling were a personal passion of mine, so we’ve spent much of this blog and the episode exploring this work. Anima has done so much more! In the episode, we cover several other recent developments from Anima: * Anima has a series of works integrating neural networks and automated proof techniques. We talk about TorchLean, a new framework that lets you write PyTorch-style networks inside the proof assistant Lean and formally verify them. This is a major step for proving bounds on neural networks, something that would be really important for someone trying to, e.g., add a neural network as part of the control loop to their fusion reactor! * Anima was recently appointed to the United Nations Scientific Advisory Board! We talk with her about her goals of bringing evidence-based viewpoints to policy, and how AI in scientific domains can improve people’s lives all over the world. This episode has something for every AI or science nerd! Elegant math? ✅ Old school harmonic analysis? ✅ Fundamental developments in modern AI? ✅ Practical ways of modeling the physical world? ✅ Give it a watch! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

   August 26, 2026·1h 24min
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