State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490

- The following is a conversation all about the state-of-the-art in artificial intelligence, including some of the exciting technical breakthroughs and developments in AI that happened over the past year, and some of the interesting things we think might happen this upcoming year. At times, it does get super technical,

year. At times, it does get super technical, but we do try to make sure that it remains accessible to folks outside the field without ever dumbing it down. It

is a great honor and pleasure to be able to do this kind of episode with two of my favorite people in the AI community, Sebastian Raschka and Nathan Lambert. They are both widely respected machine

Lambert. They are both widely respected machine learning researchers and engineers who also happen to be great communicators, educators, writers, and X posters.

posters. Sebastian is the author of two books I highly recommend for beginners and experts alike. First is

Build a Large Language Model from Scratch and Build a Reasoning Model from Scratch. I

truly believe in the machine learning/computer science world, the best way to learn and understand something is to build it yourself from scratch. Nathan is

the post-training lead at the Allen Institute for AI, and author of the definitive book on Reinforcement Learning from Human Feedback.

Both of them have great X accounts, great Substacks. Sebastian has courses on YouTube, Nathan has

Substacks. Sebastian has courses on YouTube, Nathan has a podcast. And everyone should absolutely follow all of

a podcast. And everyone should absolutely follow all of those. This is the Lex Fridman podcast. To support it, please

those. This is the Lex Fridman podcast. To support it, please check out our sponsors in the description, where you can also find links to contact me, ask questions, get feedback, and so on. And now, dear friends, here's Sebastian Raschka and Nathan Lambert.

on. And now, dear friends, here's Sebastian Raschka and Nathan Lambert.

So I think one useful lens to look at all this through is the DeepSeek, so-called DeepSeek moment. This happened

early in 2025, when the open-weight Chinese company DeepSeek released DeepSeek-R1 that I think it's fair to say surprised everyone with near state-of-the-art performance, with allegedly much less compute for much cheaper. And from then to today, the AI competition has gotten insane,

both on the research level and the product level. It's just been accelerating.

Let's discuss all of this today, and maybe let's start with some spicy questions if we can.

Who's winning at the international level? Would you say it's the set of companies in China or the set of companies in the United States? And Sebastian, Nathan, it's good to see you

States? And Sebastian, Nathan, it's good to see you guys. So Sebastian, who do you think is winning?

guys. So Sebastian, who do you think is winning?

- Winning is a very broad term.

I would say you mentioned the DeepSeek moment, and I think DeepSeek is definitely winning the hearts of the people who work on open-weight models because they share these as open models. Winning, I think, has multiple timescales to it. We have today, we have next year, we have in 10 years. One thing I know for sure is that I don't

years. One thing I know for sure is that I don't think nowadays, in 2026, that there will be any company having access to a technology that no other company has access to. And that is mainly because researchers are frequently changing jobs, changing labs. They

rotate. So I don't think there will be a clear winner in terms of technology access. However, I do think there will be,

technology access. However, I do think there will be, the differentiating factor will be budget and hardware constraints. I don't think the ideas will be proprietary,

constraints. I don't think the ideas will be proprietary, but rather the resources that are needed to implement them. And so I don't see currently a winner-takes-all scenario. I can't see that at the moment.

- Nathan, what do you think?

- You see the labs put different energy into what they're trying to do, and I think to demarcate the point in time when we're recording this, the hype over Anthropic's Claude 3.5 Opus model has been absolutely insane. I mean, I've used it and built stuff

absolutely insane. I mean, I've used it and built stuff in the last few weeks, and it's... it's almost gotten to the point where it feels like a bit of a meme in terms of the hype. And it's

kind of funny because this is very organic, and then if we go back a few months ago, we can see the release date and the notes when Gemini 1.5 from Google got released, and it seemed like the marketing and just, like, wow factor of that release was super high. But then at the end of October, Claude 3.5 Opus was released and

high. But then at the end of October, Claude 3.5 Opus was released and the hype has been growing, but Gemini was before this. And it kind of feels like people don't really talk about it as much, even though when it came out, everybody was like, this is Gemini's moment to retake Google's structural advantages in AI. And Gemini is a fantastic model, and I still use it.

It's just kind of differentiation is lower. And I

agree with Sebastian; what you're saying with all these, the idea space is very fluid, but culturally Anthropic is known for betting very hard on code—which is the Claude Code thing—and it's working out for them right now. So I

think that even if the ideas flow pretty freely, so much of this is bottlenecked by human effort and the culture of organizations, where Anthropic seems to at least be presenting as the least chaotic. It's a

bit of an advantage, if they can keep doing that for a while. But on the other side of things, there's a lot of technology from China where there's way more labs than just DeepSeek. So DeepSeek kicked off a movement within China, I'd say kind of similar to how ChatGPT kicked off a movement in the US where everything had a chatbot. There's now

tons of tech companies in China that are releasing very strong frontier open-weight models, to the point where I would say that DeepSeek is kind of losing its crown as the preeminent open model maker in China, and the likes of Zhipu AI with their GLM models, MiniMax's models— Kimi Moonshot, especially in the last few months, has shown more brightly. The new DeepSeek models are still very strong, but that's kind of

brightly. The new DeepSeek models are still very strong, but that's kind of a... it could look back as a big narrative point where in 2025

a... it could look back as a big narrative point where in 2025 DeepSeek came and provided this platform for way more Chinese companies that are releasing these fantastic models to have this new type of operation. So these models from these Chinese companies are open weights, and depending on this trajectory, the business models that these American companies are doing could be at risk. But currently, a lot of people are paying

for AI software in the US, and historically in China and other parts of the world, people don't pay a lot for software.

- So some of these models like DeepSeek have the love of the people because they are open weight. How long do you think the Chinese companies keep releasing open weight models?

- I would say for a few years. I think that, like in the US, there's not a clear business model for it. I have been writing about open models for a while, and these Chinese companies have realized it. So I get inbound from some of them.

And they're smart and realize the same constraints, which is that a lot of top US tech companies and other IT companies won't pay for an API subscription to Chinese companies for security concerns. This has been a long-standing habit in tech, and the people at these companies then see open- weight models as an ability to influence and take part in a huge growing AI expenditure market in the US. And they're very realistic about this,

and it's working for them. And I think that the government will see that that is building a lot of influence internationally in terms of uptake of the technology, so there's going to be a lot of incentives to keep it going. But building

these models and doing the research is very expensive, so at some point, I expect consolidation, but I don't expect that to be a story of 2026, where there will be more open model builders throughout 2026 than there were in 2025. And a

lot of the notable ones will be in China.

- You were gonna say something?

- Yes. You mentioned DeepSeek losing its crown. I do think to some extent, yes, but we also have to consider though, they are still, I would say, slightly ahead. And

it's not that DeepSeek got worse, it's just that the other ones are using the ideas from DeepSeek. For example, you mentioned Kimi: same architecture, they're training it. And then again, we have this leapfrogging where they might be at some point a bit better because they have the more recent model. And I think this comes back to the fact that there won't be

model. And I think this comes back to the fact that there won't be a clear winner. It will just be like that. One person releases something, the other one comes in, and the most recent model is probably always the best model.

- Yeah. We'll also see that Chinese companies have different incentives. For example,

DeepSeek is very secretive, whereas some of these startups are like MiniMax and Moonshot AI. Those two literally have filed IPO paperwork, and they're trying to get Western mindshare and do a lot of outreach there. I don't know if these incentives will change the model development, because DeepSeek famously is built by a hedge fund, Highflyer Capital, and we don't know exactly what they use the

models for or if they care about this.

- They're secretive in terms of communication, they're not secretive in terms of the technical reports that describe how their models work. They're still open on that front. And we should also say on the Claude 3.5 Opus hype, there's the layer of something being the darling of the X echo chamber, the Twitter echo chamber, and the actual amount of people that are using the

model. I think it's probably fair to say that ChatGPT and

model. I think it's probably fair to say that ChatGPT and Gemini are focused on the broad user base that just want to solve problems in their daily lives, and that user base is gigantic. So the hype about the coding may not be

is gigantic. So the hype about the coding may not be representative of the actual use.

- I would say also a lot of the usage patterns are, like you said, name recognition, brand and stuff, but also muscle memory almost, where you know, like, ChatGPT has been around for a long time. People just got used to using it, and it's almost like a flywheel: they recommend it to other users and so on. One interesting point is also the customization of LLMs. For example, ChatGPT has a

memory feature, right? And so you may have a subscription and you use it for personal stuff, but I don't know if you want to use that same thing at work.

Because it's a boundary between private and work. If you're working at a company, they might not allow that or you may not want that. And I think that's also an interesting point where you might have multiple subscriptions. One is just clean code; it has nothing of your personal images or hobby projects in there. It's just like the work thing. And then the other one is your personal thing.

So I think that's also something where there are two different use cases, and it doesn't mean you only have to have one. I think the future is multiple ones.

- What model do you think won 2024, and what model do you think is going to win '25?

- I think in the context of consumer chatbots, it's a question of: are you willing to bet on Gemini over ChatGPT?

Which I would say, in my gut, feels like a bit of a risky bet because OpenAI has been the incumbent and there's so many benefits to that in tech. I think the

tech. I think the momentum, if you look at 2024, was on Gemini's side, but they were starting from such a low point. And RIP Bard and these earlier attempts at getting started. I think

huge credit to them for powering through the organizational chaos to make that happen. But also it's hard to bet against OpenAI

that happen. But also it's hard to bet against OpenAI because they always come off as, as so chaotic, but they're very good at landing things. And I think, personally, I have very mixed reviews of o1, but it had to have saved them so much money with the high-line feature being a router where most users are no longer charging their GPU

costs as much. So I think it's very hard to dissociate the things that I like out of models versus the things that are gonna actually be a general public differentiator.

- What do you think about 2025? Who's going to win?

- I'll say something, even though it's risky. I think Gemini will continue to make progress on ChatGPT. I think Google's scale, when both of these are operating at such extreme scales, is a factor. Google has the ability to separate research and product better, where you hear so much about OpenAI being chaotic operationally and chasing the high- impact thing, which is a very startup culture. And then on the software and

enterprise side, I think Anthropic will have continued success as they've again and again been set up for that. Obviously Google Cloud has a lot of offerings, but I think this Gemini name brand is important for them to build. Google Cloud will continue to do well, but that's a more complex thing to explain in the ecosystem because that's competing with Azure and AWS rather than

on the model provider side.

- So in infrastructure, you think TPU gives them an advantage?

- Largely because the margin on NVIDIA chips is insane, and Google can develop everything from top to bottom to fit their stack and not have to pay this margin, and they've had a head start in building data centers. So all of these things that have both high lead times and very hard margins on

centers. So all of these things that have both high lead times and very hard margins on high costs. Google has a kind of historical advantage there,

high costs. Google has a kind of historical advantage there, and if there's going to be a new paradigm, it's most likely to come from OpenAI, where their research division again and again has shown this ability to land a new research idea or a product. Like Deep

Research, Sora, o1 thinking models—all these definitional things have come from OpenAI, and that's got to be one of their top traits as an organization. So it's kind of hard to bet against that, but I think a lot of this year will be about scale and optimizing what could be described as low- hanging fruit in models.

- And clearly there's a trade-off between intelligence and speed. This is what GPT-5 was trying

speed. This is what GPT-5 was trying to solve behind the scenes. It's like, do people actually want intelligence, the broad public, or do they want speed?

- I think it's a nice variety, actually, or the option to have a toggle there.

For my personal usage, most of the time when I look something up, I use ChatGPT to ask a quick question, get the information I want fast. For most daily tasks, I use the quick

fast. For most daily tasks, I use the quick model. Nowadays, I think the auto mode is pretty good where you don't have to specifically

model. Nowadays, I think the auto mode is pretty good where you don't have to specifically say thinking or non-thinking. Then again, I also sometimes want the Pro mode. Very often what I do is, when I have something written, I put it into ChatGPT and say, "Hey, do a very thorough check. Are all my references correct? Are all my thoughts correct? Did I make any formatting mistakes? Are the figure

correct? Did I make any formatting mistakes? Are the figure numbers wrong?" or something like that. And I don't need that right

numbers wrong?" or something like that. And I don't need that right away. It's okay—I finish my stuff, maybe have dinner, let it run, come back

away. It's okay—I finish my stuff, maybe have dinner, let it run, come back and go through this. I think this is where it's important to have this option. I would go crazy if for each query I had to wait 30 minutes or even 10 minutes.

- That's me.

I'm sitting over here losing my mind that you use the router and the non-thinking model.

I'm like, "How do you live with that?"

That's my reaction. I've been heavily on ChatGPT for a while.

I never touch non-thinking. I find its tone and then its propensity for errors—it just has a higher likelihood of errors. Some of this is from back when OpenAI released o1-preview, which was the first model to do this deep search and find many sources and integrate them for you. So I

became habituated with that. So I will only use GPT-4o thinking or Pro when I'm performing any sort of information query for work, whether that's a paper or some code reference that I And I will regularly have five Pro queries going simultaneously, each looking for one specific paper or feedback on an equation or something.

- I have a fun example where I needed the answer as fast as possible for this podcast before I was going on the trip. I have

a local GPU running at home, and I wanted to run a long RL experiment. Usually, I unplug things because you never know when

experiment. Usually, I unplug things because you never know when you're not at home. I accidentally unplugged the GPU. My wife was already in the car, and it was like, "Oh dang."

GPU. My wife was already in the car, and it was like, "Oh dang."

Basically, I wanted as fast as possible a Bash script that runs my different experiments and the evaluation. And

it's something I know. I learned how to use the Bash interface or Bash terminal, but in that moment I just needed like 10 seconds, give me the command.

- This is a hilarious situation. But yeah, so what did you use?

- So I did the non-thinking fastest model. It gave me the Bash command to chain different scripts to each other and then the thing is, you have the tee thing where you want to route this to a log file. Top of my head I was just in a hurry, I could have thought about it myself.

- By the way, I don't know if there's a representative case: wife waiting in the car...

you have to run, unplug the GPU, you have to generate a Bash script. This sounds like a movie, like Mission Impossible.

- I use Gemini for that. So I use thinking for all the information stuff and then Gemini for fast things or stuff that I could sometimes Google. It's good at explaining things and I trust that it has this kind of background of knowledge and it's simple. And the Gemini app has gotten a lot better.

it's simple. And the Gemini app has gotten a lot better.

It's good for those sorts of things. And then for code and any sort of philosophical discussion, I use Claude Opus 3.5. Also always with extended thinking. Extended thinking and inference-time scaling is just a way to make the

extended thinking. Extended thinking and inference-time scaling is just a way to make the models marginally smarter. And I will always hedge on that side when the progress is very high because you don't know when that'll unlock a new use case. And then sometimes I use Grok for real-time

use case. And then sometimes I use Grok for real-time information or finding something on AI Twitter that I knew I saw and I need to dig up and I just fixated on. Although when

Grok-3 came out, the Grok-3 what is Super Heavy, which was like their pro variant, was actually very good and I was pretty impressed with it, and then it just kind of muscle memory lost track of it with having the ChatGPT app open. So I use many different things.

app open. So I use many different things.

- Yeah. I actually do use Grok-3 Heavy for debugging. For like hardcore debugging that the other ones

for debugging. For like hardcore debugging that the other ones can't solve, I find that it's the best at. It's interesting

because you say ChatGPT is the best interface. For

me, for that same reason, but this could be just momentum- Gemini is the better interface for me. I think because I fell in love with their best needle in the haystack. If I ever put something that has a lot of context but I'm looking for very specific kinds of information to make sure it tracks all of it, I find at least that Gemini for me has been the best. So it's

funny with some of these models, if they win your heart over- for one particular feature at one... on a one particular day, for that particular query, that prompt, you're like, "This model's better." And so you'll just stick with it for a bit until

model's better." And so you'll just stick with it for a bit until it does something really dumb. There's like a threshold effect. Some smart

thing and then you fall in love with it and then it does some dumb thing and you're like, "You know what?

I'm gonna switch and try Claude or ChatGPT." And all that kind of stuff.

- This is exactly it. You use it until it breaks, until you have a problem, and and then you change the LLM. And I think it's the same how we use anything, like our favorite text editor, operating systems, or the browser. I mean, there are so many browser options: Safari, Firefox, Chrome. They're relatively similar, but then there are

Firefox, Chrome. They're relatively similar, but then there are edge cases, maybe extensions you wanna use, and then you switch. But I don't think there is anyone who types the same thing, like the website, into different browsers and compares them. You only do that when the website doesn't render, or something breaks.

So that's a good point. You use it until it breaks, and then you explore other options.

- On the long context thing, I was also a Gemini user for this, but the GPT-4o release blog had crazy long context scores, where a lot of people were like, "Did they just figure out some algorithmic change?" It went from, like, 30% to 70% or something in this minor model update. So it's also very hard to keep track of all of these things, but now I look more

favorably at GPT-4o's long context. So it's just kind of like, "How do I actually get to testing this?" It's a never-ending battle.

- Well, it's interesting that none of us talked about the Chinese models from a usage perspective. What does that say? Does that mean the Chinese models are not as good, or does that mean we're just very biased and US-focused?

- I do think that's currently the discrepancy between the model and the platform. I think the open models are more known for their open weights, not their platform yet.

- There are also a lot of companies willing to sell you open-model inference at a very low cost.

With things like OpenRouter, it's easy to look at multi-model things.

You can run DeepSeek on Perplexity. I think all of us sitting here are like, "We use OpenAI GPT-4 Pro consistently." We're all willing to pay for the marginal— intelligence gain. And these models from the US

intelligence gain. And these models from the US are better in terms of the outputs. I think the question is: will they stay better for this year and for years to come?

As long as they're better, I'm gonna pay to use them.

There's also analysis that shows that the way Chinese models are served—you could argue this is due to export controls or not— is that they use fewer GPUs per replica, which makes them slower and gives them different errors. It's speed and intelligence. If these things are in your favor as a user, a lot of users in the US will go for this, and I think that is one thing that will spur these Chinese

companies to compete in other ways, whether it's free or substantially lower costs, or it'll breed creativity in terms of offerings, which is good for the ecosystem. But the simple thing is the US models are currently better, and we use them. I've tried these other open models, and I'm like, "Fun, but I don't go back to them."

- We didn't really mention programming. That's another

use case that a lot of people care about. I use basically half-and-half Cursor and Claude, because they're... I

I find them to be a fundamentally different experience and both useful.

What do you guys... You program quite a bit, ...so what do you use? What's the current vibe?

...so what do you use? What's the current vibe?

- So, I use the Codeium plugin for VS Code. You know, it's very convenient. It's just like a plugin, and then it's a chat interface that has access to your

convenient. It's just like a plugin, and then it's a chat interface that has access to your repository. I know that Claude is, I think, a bit different. It

repository. I know that Claude is, I think, a bit different. It

is a bit more agentic. It touches more things. It does the whole project for you. I'm not quite there yet where I'm comfortable with that, because maybe I'm a control freak, but I still would like to see what's going on. And

Codeium is kind of the sweet spot right now, where it is helping me, but it is not taking completely over.

- I should mention, one of the reasons I use Claude is to build the skill of programming with English. I mean, the experience is fundamentally different. You're... As opposed to micromanaging the

different. You're... As opposed to micromanaging the details of the process of the generation of the code, and looking at the diff, which you can in Cursor if that's the IDE you use, and changing, altering.

Looking and reading the code and understanding the code deeply as you progress, versus just thinking in this design space and guiding it at this macro level, which I think, Is another way of thinking about the programming process.

Also, we should say that Claude just seems to be somehow a better utilization of Claude Opus 3.5.

- It's a good side by side. You can have Claude open, you can have Cursor open, you can have VS Code open, and select the same models on all of them- ...and ask questions. It's very interesting. Claude is way better

...and ask questions. It's very interesting. Claude is way better in that domain. It's remarkable.

- All right, we should say that both of you are legit on multiple fronts, researchers programmers, educators Twitterers.

And on the book front, too. So Nathan, at some point soon, hopefully has an RLHF book coming out.

- It's available for preorder, and there's a full digital pre-print. I'm

making it pretty and better organized for the physical thing, which is why I do it, because it's fun to create things that you think are excellent in the physical form, when so much of our life is digital.

- I should say, according to Perplexity here, Sebastian Raschka is a machine learning researcher and author known for several influential books. A couple of them that I wanted to mention, which is a book I highly recommend, Build a Large Language Model from Scratch, and the new one, Build a Reasoning Model from Scratch. I'm really excited about that. Building stuff from scratch is one of the most powerful ways of learning.

that. Building stuff from scratch is one of the most powerful ways of learning.

- Honestly, building an LLM from scratch is a lot of fun. It's also a lot to learn.

And like you said, it's probably the best way to learn how something really works, 'cause you can look at figures, but figures can have mistakes. You can look at concepts and explanations, but you might misunderstand them.

But if there is code, and the code works, you know it's correct. There's no misunderstanding. It's precise.

Otherwise, it wouldn't work. And that's the beauty behind coding.

It doesn't lie. It's math, basically.

even though with math, I think you can have mistakes in a book you would never notice.

Because you are not running the math when you are reading the book, you can't verify this.

And with code, what's nice is you can verify it.

- Yeah, I agree with you about the "Build a Large Language Model (From Scratch)" book.

It's nice to tune out everything else, the internet and so on, and just focus on the book.

But, you know, I read several history books. It's just less lonely somehow. It's really more fun. For example, on the

lonely somehow. It's really more fun. For example, on the programming front, I think it's genuinely more fun to program with an LLM.

And I think it's genuinely more fun to read with an LLM.

But you're right. That distraction should be minimized.

So you use the LLM to enrich the experience, maybe add more context.

The rate of "aha moments" for me on a small scale is really high with LLMs. - 100%. I also want to correct myself: I'm not suggesting not to use

- 100%. I also want to correct myself: I'm not suggesting not to use LLMs. I suggest doing it in multiple passes. Like, one pass just offline, in focus mode, and then after that... I mean, I also take notes, but I try to resist the urge to immediately look things up. I do a second pass. It's just more structured this way. Sometimes things are answered later in the

chapter, and sometimes it just helps to let it sink in and think about it. I would highly recommend using LLMs when

about it. I would highly recommend using LLMs when reading books. For me, it's just not the first thing to do; it's the second pass.

reading books. For me, it's just not the first thing to do; it's the second pass.

- By way of recommendation, I'll say I do the opposite. I like to use the LLM at the beginning to lay out the full context of what is this world that I'm now stepping into? But I try to avoid clicking out of the LLM into the world of Twitter and blogs. Because then you're down this rabbit hole. You're reading

blogs. Because then you're down this rabbit hole. You're reading

somebody's opinion, there's a flame war about a particular topic, and all of a sudden you're in the realm of the internet and Reddit.

But if you're purely letting the LLM give you the context of why this matters and what the big picture ideas are... Books themselves are good at that, but not always.

ideas are... Books themselves are good at that, but not always.

- This is why I like the ChatGPT app: it gives the AI a home on your computer where you can focus on it, rather than just being another tab in my mess of internet options. And I think Claude Code does a good job of making that a joy. It seems

very engaging as a product designed to be an interface that your AI will then go out into the world. It's something

intangible compared to OpenAI's models, it just feels warm and engaging, whereas they can often be as good but just feel a little bit rough around the edges. Whereas Claude Code makes it fun to build things, particularly from scratch where you don't have to worry, because you trust it'll make something. This is good for websites and refreshing tooling and stuff like

this, which I use it for, or data analysis. For my

blog, we scrape Hugging Face to keep download numbers for every dataset and model.

over time now. And Claude was just like, "Yeah, I've made use of that data, no problem." And I was like, "That would've taken me days."

And then I have enough situational awareness to be like, "Okay, these trends obviously make sense."

But that's just a wonderful interface where you can have an intermediary and not have to do the awful low-level work that you would have to do to maintain different web projects.

- All right. So we just talked about a bunch of the closed-weight models. Let's talk about the

closed-weight models. Let's talk about the open ones. So tell me about the landscape of open LLM

open ones. So tell me about the landscape of open LLM models. Which are interesting ones? Which stand out to you and why? We

models. Which are interesting ones? Which stand out to you and why? We

already mentioned DeepSeek.

- Do you wanna see how many we can name off the top of our head?

- Yeah, yeah. Without looking at notes.

- DeepSeek, Kimi, MiniMax, Z.ai, Moonshot. We're just going Chinese.

- Let's throw in Mistral AI, Gemma- OLMo, the open-source model by AI2. Actually,

NVIDIA had a really cool one, Nemotron 340B. There,

there's a lot of stuff especially at the end of the year. Qwen might be the one- - Qwen was the obvious name I was going to say. I was trying to get through the- You can get at least 10 Chinese and at least 10 Western. I think

that... OpenAI released their first open model since GPT-2. When I was writing about

since GPT-2. When I was writing about OpenAI's open model release, they were like, "Don't forget about GPT-2," which I thought was really funny 'cause it's just such a different time. But GLM-4 is actually a very strong model and does some things that the other models don't do very well. I think that selfishly I'll promote a bunch of Western companies. Both in the US

and Europe have these fully open models. I work at the Allen Institute for AI, where we've been building OLMo, which releases data and code. And now

we have actual competition from people that are trying to release everything so that others can train these models. There's the Institute for Foundation Models and LM360, which had their K2 models of various types. Apertus is a Swiss research consortium. Hugging Face has SmolLM, which

consortium. Hugging Face has SmolLM, which is very popular. And NVIDIA's Nemotron has started releasing data as well. And then Stanford's Meerkat project, which is

well. And then Stanford's Meerkat project, which is making it so there's a pipeline for people to open a GitHub issue and implement a new idea and then have it run in a stable language modeling stack. So

this space—that list was way smaller in 2024— I think it was just AI2. So it's a great thing for more people to get involved and to understand language models, which doesn't really have a Chinese company that has an analog. While I'm talking, I'll say that the Chinese open language models tend to be much bigger, and that gives them higher peak performance as MoEs, where a lot of

these things that we like a lot, whether it was Gemma and Nemotron, have tended to be smaller models from the US, which is starting to change. In the US and Europe, Mistral Large 3 came out, which was a

to change. In the US and Europe, Mistral Large 3 came out, which was a giant MoE model, very similar to DeepSeek architecture, in December. And then startups like RCAI and both Nemotron and NVIDIA have teased MoE

December. And then startups like RCAI and both Nemotron and NVIDIA have teased MoE models way bigger than 100 billion parameters- ... in the 400 billion parameter range, coming in this Q1

... in the 400 billion parameter range, coming in this Q1 2026 timeline. So I think this kind of balance is set to change

2026 timeline. So I think this kind of balance is set to change this year in terms of what people are using Chinese versus US open models for, which I'm personally going to be very excited to watch.

- First of all, huge props for being able to name so many of these. Did you actually name LLaMA?

these. Did you actually name LLaMA?

- No.

- I feel like ...

- RIP.

- This was not on purpose.

- RIP LLaMA.

All right. Can you mention some interesting models that stand out? You mentioned

Qwen 2.5 is obviously a standout.

- I would say the year is almost bookended by DeepSeek V3 and R1. And then on the other hand, in December, DeepSeek V3.

Because what I like about those is they always have an interesting architecture tweak- ... architecture tweak that others don't have. Otherwise, if you want to go with

... architecture tweak that others don't have. Otherwise, if you want to go with familiar but really good performance, Qwen 2.5 and, like Nathan said, also Jamba. And I think Jamba, what's interesting about it is it's the first public or open weight model that was really trained with tool use in mind, which I do think is a bit of a paradigm shift where the ecosystem was not quite

ready for it. By tool use, I mean that the LLM is able to do a web search, to call a Python interpreter.

And I do think it's a standout because it's a huge unlock. Because one of the most common complaints

unlock. Because one of the most common complaints about LLMs are, for example, hallucinations, right?

In my opinion, one of the best ways to solve hallucinations is to not try to always remember information or make things up.

For math, why not use a calculator app or Python?

If I ask the LLM, "Who won the soccer World Cup in 1998?" instead of just trying to memorize, it could

1998?" instead of just trying to memorize, it could do a search. I think mostly it's still a Google search.

So ChatGPT and GPT-4o, they would do a tool call to Google, maybe find the FIFA website, and find that it was France. It would get you that information reliably instead of just trying to

France. It would get you that information reliably instead of just trying to memorize it. So I think it's a huge unlock, which I think right now

memorize it. So I think it's a huge unlock, which I think right now is not fully utilized yet by the open source, open weight ecosystem. A lot of people don't use tool call modes

ecosystem. A lot of people don't use tool call modes because it's a trust thing. You don't want to run this on your computer where it has access to tools, could wipe your drive or whatever. So you want to containerize that. But I do think that is

containerize that. But I do think that is a really important step for the upcoming years to have this ability.

- So a few quick things. First of all, thank you for defining what you mean by tool use. I think that's a great thing to do in general for the concepts we're talking about. Even things as sort of well-established as MOEs.

You have to say that means Mixture of Experts, and you kind of have to build up an intuition for people what that means, how it's utilized, what are the different flavors. So what does it mean that there's just such an explosion of open models? What's your intuition?

- If you're releasing an open model, you want people to use it; that is the first and foremost thing.

And then after that comes things like transparency and trust. I think when you look at China, the biggest reason is that they want people around the world to use these models, and I think a lot of people will not. If you look outside of the US, a lot of people will not pay for software, but they might have computing resources to run it.

I think there can also be data that you don't want to send to the cloud. So the number one thing is getting people to use models, use

cloud. So the number one thing is getting people to use models, use AI, or use your AI that might not be able to do it without having access to the model.

- I guess we should state explicitly, so we've been talking about these Chinese models and open weight models. Oftentimes, the way they're run is locally. So it's not like you're sending your data to China or to whoever developed

locally. So it's not like you're sending your data to China or to whoever developed it in Silicon Valley, whoever developed the model.

- A lot of American startups make money by hosting- ...these models from China and selling them. It's called selling

...these models from China and selling them. It's called selling tokens, which means somebody will call the model to do some piece of work.

I think the other reason is for US companies, like OpenAI is so GPU deprived. They're at the limits of the GPUs. Whenever they make a release,

deprived. They're at the limits of the GPUs. Whenever they make a release, they're always talking about like, "Our GPUs are hurting." And

I think in one of these GPT-4o release sessions, Sam Altman said, "We're releasing this because we can use your GPUs. We don't have to use our GPUs, and OpenAI can still get distribution out of this," which is another very real thing, because it doesn't cost them anything.

- And for the user, I mean, there are users who just use the model locally like they would use ChatGPT. But for companies, I think it's a huge unlock to have these models because you can customize them, you can train them, you can add more data post-training. Like,

specialize them into, let's say, law, medical models, whatever you have. And you mentioned Llama; the appeal of the open-weight

have. And you mentioned Llama; the appeal of the open-weight models from China is that the open-weight models also have licenses are even friendlier. I think they are just unrestricted open source licenses where if we use something like Llama or Gemma, there are some strings attached. I think there's an upper limit in terms of how many users you have. And then if

attached. I think there's an upper limit in terms of how many users you have. And then if you exceed, I don't know, so and so many million users, you have to report your financial situation to, let's say, Meta or something like that.

And I think while it is a free model, there are strings attached, and people do like things where strings are not attached. So

I think that's also one of the reasons, besides performance, why the open-weight models from China are so popular, because you can just use them.

There's no catch in that sense.

- The ecosystem has gotten better on that front, but mostly downstream of these new providers providing such open licenses. That was funny when you pulled up Perplexity and said, "Kimi-k2-thinking hosted in the US." Which is just like an exact... I've never seen this, but it's an exact example of what we're talking about where people are sensitive to this.

But Kimi-k2-thinking and Kimi-k2 is a model that is very popular. People

say that has very good creative writing and also in doing some software things. So it's just these little quirks that people pick up on with different models that they

things. So it's just these little quirks that people pick up on with different models that they like.

- What are some interesting ideas that some of these models have explored that you can speak to, like that are particularly interesting to you?

- Maybe we can go chronologically. I mean, there was, of course, DeepSeek. DeepSeek

R1 that came out in January of 2025, if we just focus on this year. However,

this was based on DeepSeek-V3, which came out the year before in December 2024. There are multiple things on the architecture side. What is fascinating is you can still... I mean, that's what I do with my

side. What is fascinating is you can still... I mean, that's what I do with my from-scratch coding projects. You can still start with GPT-2, and you can add things to that model to make it into this other model. So it's all still kind of like the same lineage... It is a very close relationship between those. But top of my head, DeepSeek, what was

unique there is the Mixture of Experts. I mean, they were not inventing Mixture of Experts. We can maybe talk a bit more what Mixture of Experts

Experts. We can maybe talk a bit more what Mixture of Experts means. But just to list these things first before we

means. But just to list these things first before we dive into detail. Mixture of Experts, but then they also had Multi-head Latent Attention, which is a tweak to the attention mechanism, where this was, I would say, in 2025, the main distinguishing factor kind of between these open-weight models. Different

tweaks to make inference or KV cache size. We can also define KV cache in a few moments. But to kind of make it more economical to have long context, to shrink the KV cache size. So what are tweaks that we can do? And most of them focused on the attention mechanism. There

is Multi-head Latent Attention in DeepSeek. There is Grouped-query Attention, which is still very popular. It's not invented by any of those models.

It goes back a few years. But that would be the other option.

Sliding Window Attention, I think OLMo 2 uses it if I remember correctly. So there are these different tweaks that make the models

correctly. So there are these different tweaks that make the models different. Otherwise I put them all together in an

different. Otherwise I put them all together in an article once, where I just compared them. They are very surprisingly similar. It's just different numbers in terms of how many repetitions

surprisingly similar. It's just different numbers in terms of how many repetitions of the Transformer block you have in the center. And, like,

just little knobs that people tune. But what's so nice about it is it works no matter what. You can tweak things. You can move the normalization layers around to get some performance gains. And OLMo is always very good in ablation studies, showing what it does to the model if you move something around. Does it make it better or worse? But there are so many, let's say, ways you can implement a transformer and make it still

worse? But there are so many, let's say, ways you can implement a transformer and make it still work. The big ideas that are still prevalent is Mixture of

work. The big ideas that are still prevalent is Mixture of Experts, multi-head latent attention, sliding window attention, group query attention. And then at the end of the year, we saw a focus

query attention. And then at the end of the year, we saw a focus on making the attention mechanism scale linearly with inference token prediction. So there was Qwen2.5, for example,

prediction. So there was Qwen2.5, for example, which added a gated delta net. It's kind of inspired by State space models, where you have a fixed state that you keep updating. But it

makes essentially this attention cheaper, or it replaces attention with a cheaper operation.

- And it may be useful to step back and talk about transformer architecture in general.

- Yeah, so maybe we should start with GPT-2 architecture. The

transformer that was derived from the "Attention Is All You Need" paper.

The "Attention Is All You Need" paper had a transformer architecture that had two parts, an encoder and a decoder. And GPT went just focusing in on the decoder part. It is essentially still a neural network and it has this attention mechanism inside. And you predict one token at a time. You

inside. And you predict one token at a time. You

pass it through an embedding layer. There's the transformer block. The transformer block has attention modules and a fully connected layer. And there are some normalization layers in between. But it's essentially neural network layers with this attention mechanism. So coming from GPT-2 when we move on

mechanism. So coming from GPT-2 when we move on to GPT-3, there is, for example, the Mixture of Experts layer. It wasn't invented by GPT-3; it's a few years old.

layer. It wasn't invented by GPT-3; it's a few years old.

But it is essentially a tweak to make the model larger without consuming more compute in each forward pass. So there is this fully connected layer, and if listeners are familiar with multi-layer perceptrons, you can think of a mini multi-layer perceptron, a fully connected neural network layer inside the transformer. And it's very

expensive because it's fully connected. If you have 1,000 inputs and 1,000 outputs, that's one million connections. And it's a very expensive part in this transformer. And the idea is to kind of expand that

this transformer. And the idea is to kind of expand that into multiple feedforward networks. So instead of having one, let's say you have 256, but it would make it way more expensive, because now you have 256, but you don't use all of them at the same time. So you

now have a router that says, "Okay, based on this input token, it would be useful to use this fully connected network." And in that context, it's called an expert. So a Mixture of Experts means you have multiple experts. And depending on what your input is—let's say it's more

experts. And depending on what your input is—let's say it's more math-heavy, it would use different experts, compared to, let's say, translating input text from English to Spanish. It would maybe consult different experts. It's not as clear-cut to say, "Okay, this

experts. It's not as clear-cut to say, "Okay, this is only an expert for math and for Spanish." It's a bit more fuzzy. But the idea is essentially that you pack more

fuzzy. But the idea is essentially that you pack more knowledge into the network, but not all the knowledge is used all the time.

That would be very wasteful. So during the token generation, you are more selective. There's a router that selects which tokens should go to which expert. It adds more complexity. It's

harder to train. There's a lot that can go wrong, like collapse and everything. So I think that's why OLMo still uses dense... I mean, you

everything. So I think that's why OLMo still uses dense... I mean, you have OLMo models with Mixture of Experts, but dense models, where dense means... So, also, it's jargon. There's a distinction

means... So, also, it's jargon. There's a distinction between dense and sparse. So Mixture of Experts is considered sparse, because we have a lot of experts, but only a few of them are active. So that's called sparse. And then dense would be the opposite, where you only have, like, one fully

sparse. And then dense would be the opposite, where you only have, like, one fully connected module, and it's always utilized.

- So maybe this is a good place to also talk about KV cache.

But actually, before that, even zooming out, like, fundamentally, how many new ideas have been implemented from GPT-2 to today.

Like, how different really are these architectures?

- Like the Mixture of Experts. The attention mechanism in Llama 3, that would be the Group Query Attention mechanism. So it's a slight tweak from multi-head attention to Group Query Attention, so that we have that. I think they replaced LayerNorm by

that. I think they replaced LayerNorm by RMSNorm, but it's just like a different normalization there and not a big change.

It's just like a tweak. The nonlinear activation function, people familiar with deep neural networks, I mean, it's the same as changing sigmoid with ReLU. It's not changing the network fundamentally.

It's just like a little tweak. And that's about it, I would say. It's not really fundamentally that different. It's still the same,

would say. It's not really fundamentally that different. It's still the same, same architecture. So you can convert one from one... You can go from

same architecture. So you can convert one from one... You can go from one into the other by just adding these changes, basically.

- It fundamentally is still the same architecture.

- Yep. So for example, you mentioned my book earlier. That's a GPT-2 model in the book because it's simple and it's very small, so 124— 124 million parameters approximately. But in the bonus materials, I do have OLMo from scratch, Llama 3 from scratch, and other types of from-scratch models. And I always start it with my GPT-2 model and just tweak the— well,

models. And I always start it with my GPT-2 model and just tweak the— well, add different components and you get from one to the other. It's kind of like a lineage, in a sense.

- Can you build up an intuition for people? Because when you zoom out, you look at it, there's so much rapid advancement in the AI world.

And at the same time, fundamentally the architectures have not changed.

So where is all the turbulence, the turmoil of the advancement happening?

Where are the gains to be had?

- So there are the different stages where you develop the network or train the network. You have the pre-training. Now back in the day, it was just

network. You have the pre-training. Now back in the day, it was just pre-training with GPT-2. Now you have pre-training, mid-training and post-training. So, I think right now we are in the

post-training. So, I think right now we are in the post-training focus stage. I mean, pre-training still gives you advantages if you scale it up to better, higher quality data. But then

we have capability unlocks that were not there with GPT-2, for example. ChatGPT is basically a GPT-3 model.

example. ChatGPT is basically a GPT-3 model.

And GPT-3 is the same as GPT-2 in terms of architecture.

What was new was adding the supervised fine-tuning and the reinforcement learning with human feedback. So it's more on the algorithmic side rather than the architecture.

- I would say that the systems also change a lot. I think if you listen to Nvidia's announcements, they talk about these things like, "You now do FP8, you can now do FP4." And

what is happening is these labs are figuring out how to utilize more compute to put it into one model, which lets them train faster and that lets them put more data in. And then you can find better configurations faster by doing

data in. And then you can find better configurations faster by doing this. So you can look at, essentially, the tokens per second per GPU

this. So you can look at, essentially, the tokens per second per GPU is a metric that you look at when you're doing large-scale training. And you could get...

You can go from, like, 10K to 13K by turning on FP8 training, which means you're using less memory per parameter in the model. And by saving less information, you do less communication and you can train

model. And by saving less information, you do less communication and you can train faster. So all of these system things underpin way faster experimentation

faster. So all of these system things underpin way faster experimentation on data and algorithms that is kind of like... It's this kind of loop that keeps going where it's kinda hard to describe when you look at the architecture and they're exactly the same. But the codebase used to train these models is gonna be vastly different— ...and you could probably... the GPUs are different, but you probably

different— ...and you could probably... the GPUs are different, but you probably train GPT-NeoX-20B way faster in wall-clock time than GPT-2 ...was trained at the time.

...was trained at the time.

- Yeah. Like you said, they had, for example, in the mixture of experts, this FP4 optimization, for example, where you get more throughput. But I do think this is... for the speed, this is true, but it doesn't give the model new capabilities. It's just: how much can we make the computation coarser without suffering in terms of model performance degradation? But I do think... I mean,

there are alternatives popping up to the transformer. There are text diffusion models— a completely different paradigm. And there is also... I mean, although text diffusion models might use transformer architectures, it's not an autoregressive transformer. And also Mamba models.

autoregressive transformer. And also Mamba models.

It's a state-space model. But they do have trade-offs, and currently, there's nothing that has replaced the autoregressive transformer as the state-of-the-art model. For state-of-the-art, you would still go with that thing, but there are now alternatives for the cheaper end, alternatives that are kind of making compromises, but it's not just one architecture anymore. There are

little ones coming up. But if we talk about the state-of-the-art, it's pretty much still the transformer architecture, autoregressive, derived from GPT-2 essentially.

- I guess the big question here is, we talked quite a bit about the architecture behind the pre-training. Are the scaling laws holding strong across pre-training,

the pre-training. Are the scaling laws holding strong across pre-training, post-training, inference, context size, data, synthetic data?

- I'd like to start with the technical definition of a scaling law— ...which kind of informs all of this. The scaling law is the power law relationship

...which kind of informs all of this. The scaling law is the power law relationship between... You can think of the x-axis, kind of what you are scaling, as a

between... You can think of the x-axis, kind of what you are scaling, as a combination of compute and data, which are kind of similar, and then the y-axis is like the held-out prediction accuracy over next tokens. We talked about models being autoregressive. It's like if you keep a set of text that the model has not seen, how accurate will it get when you train? And

the idea of scaling laws came when people figured out that that was a very predictable relationship. And I think that that technical term is continuing, and then the question is, what do users get out of it? And then there are more types of scaling where, OpenAI's o1 was famous for introducing inference time scaling. And I

think less famously for also showing that you can scale reinforcement learning training and get kind of this log x-axis and then a linear increase in performance on the y-axis. So there's kind of these three axes now where traditional scaling laws are talked about for pre-training, which is how big your model is and how big your dataset is, and then scaling reinforcement learning, which is like how long can you do this trial and error learning that

we'll talk about. We'll define more of this, and then this inference time compute, which is letting the model generate more tokens on a specific problem. So I'm kind of bullish, but they're all really still working, but the low-hanging fruit has mostly been taken, especially in the last year on reinforcement learning with verifiable rewards, which is this RLVR, and then inference time scaling, which is just why these models feel so different to use, where

previously you would get that first token immediately. And now they'll go off for seconds, minutes or even hours, generating these hidden thoughts before giving you the first word of your answer. And that's all about this inference time scaling which is such a wonderful kind of step function in terms of how the models change abilities. They kind of enabled this tool use and enabled this much

change abilities. They kind of enabled this tool use and enabled this much better software engineering that we were talking about. And this

is, when we say enabled, almost entirely downstream of the fact that this reinforced learning with verifiable rewards training just kind of let the models pick up these skills very easily. So let

the models learn. So if you look at the reasoning process when the models are generating a lot of tokens, what it'll be often doing is it tries a tool, it looks at what it gets back. It tries another API, it sees what it gets back and if it solves the problem. So the models, when you're training them, very quickly learn to do this. And then at the end of the day, that gives this kind of

this. And then at the end of the day, that gives this kind of general foundation where the model can use CLI commands very nicely in your repo and handle Git for you and move things around and organize things or search to find more information, which if we're sitting in these chairs a year ago is something that we didn't really think of the

models being doing. So this is just kind of something that has happened this year and has totally transformed how we think of using AI, which I think is very magical. It's such an interesting evolution and just unlocks so much value. But it's like, it's not clear what the next avenue will be in terms of unlocking stuff like this.

I think that there's- we'll get to continual learning later, but there's a lot of buzz around certain areas of AI, but no one knows when the next step function will really come.

- So you've actually said quite a lot of things there, and said profound things quickly. It would be nice to unpack them a little bit. You say you're bullish basically on every version of scaling. So can we just even start at the beginning? Pre-training,

scaling. So can we just even start at the beginning? Pre-training,

are we kind of implying that the low-hanging fruit on pre-training scaling has been picked? Has pre-training

hit a plateau or is even pre-training still something you're bullish on?

- Pre-training has gotten extremely expensive. I think to scale up pre-training, it also implies that you're going to serve a very large model to the users. So I think that it's been loosely established the likes of GPT-4 and similar models were around one trillion parameters at the biggest size. There's a lot of rumors that they've actually gotten smaller as training has gotten more efficient. You want to make

the model smaller because then your costs of serving go down proportionately.

These models, the cost of training them is really low relative to the cost of serving them to hundreds of millions of users. DeepSeek had this famous number of about five million dollars for pre-training at cloud market rates.

In the OLMo-1 section 2.4 paper, we just detailed how long we had the GPU clusters sitting around for training which includes engineering issues, multiple seeds, and it was about two million dollars to rent the cluster to deal with the headaches of training a model. So these models are pretty accessible; a lot of people could get one to 10 million dollars to train a model,

but the recurring costs of serving millions of users is really billions of dollars of compute. I think you can look at a thousand GPU rental you can pay 100 grand a day for. And these

companies could have millions of GPUs. You can look at how much these things cost to sit around. So that's kind of a big thing, and it's like, if scaling is actually giving you a better model, like is it going to be financially worth it? We will slowly push it out as AI solves more compelling tasks, like the likes of Claude 3 Opus, GPT-4.5, making Claude Code just work for things.

I launched this project called the ATOM project, which is American Truly Open Models in July, and that was like a true vibe-coded website and I have a job to make plots and stuff. And then I came back to refresh it in the last few weeks and it's like Claude 3 Opus versus whatever model at the time just crushed all the issues that it had from building in June and July. It might be a

bigger model. A lot of things go into this, but there's still progress coming.

bigger model. A lot of things go into this, but there's still progress coming.

- So what you're speaking to is the nuance of the Y-axis of the scaling laws, that the way it's experienced versus on a benchmark, the actual intelligence might be different. But still, your intuition about pre-training, if you scale the size of compute, will the models get better? Not whether it's financially viable but just from the law aspect of it, do you think the models will get smarter?

- Yeah. And I think that... And this sometimes comes off as almost like disillusioned from leadership at AI companies saying this, but they're like, "It's held for 13 orders of magnitude of compute, so why would it ever end?" So I think fundamentally it is pretty unlikely to stop. Eventually we're not even going to be able to test the bigger scales because of all the problems that come with more

compute. I think that there's a lot of talk on how 2026

compute. I think that there's a lot of talk on how 2026 is a year when very large Blackwell compute clusters, gigawatt-scale facilities for hyperscalers, are coming online. And

these were all contracts for power and data centers that were signed and sought out in like 2022 and 2023, before or right after ChatGPT. So it took this two-to-three-year lead time to build these bigger clusters to train the models. While there's obviously immense interest in building even more data centers than that. So that is kind of the crux that people are saying: these new clusters are coming. The labs are gonna have

more compute for training. They're going to utilize this, but it's not a given. I've seen so much progress that I expect it, and I

given. I've seen so much progress that I expect it, and I expect a little bit bigger models. I would

say it's more like we'll see a $2,000 subscription this year. We've seen $200 subscriptions. That could 10X again, and these are the kind of things that

subscriptions. That could 10X again, and these are the kind of things that could come, and they're all downstream of this bigger model that offers just a little bit more cutting edge.

- So, you know, it's reported that xAI is gonna hit that one gigawatt scale early '26, and full two gigawatts by year end. How do you think they'll utilize that in the context of scaling laws?

Is a lot of that inference? Is a lot of that training?

- It ends up being all of the above. So I think that all of your decisions when you're training a model come back to pre-training. If you're going to scale RL on a model, you still need to

pre-training. If you're going to scale RL on a model, you still need to decide on your architecture that enables this. We were talking about other architectures and using different types of attention. We're also talking about a mixture of experts models. The sparse nature of MoE models makes it

experts models. The sparse nature of MoE models makes it much more efficient to do generation, which becomes a big part of post-training. You need to have your architecture ready so

of post-training. You need to have your architecture ready so that you can actually scale up this compute. I still think most of the compute is going in at pre-training. Because you can still make a model better, you still want to revisit this. You still want the best base model that you can. And in a few years that'll saturate and the RL compute will just go longer.

can. And in a few years that'll saturate and the RL compute will just go longer.

- Are there people who disagree with you and say pre-training is dead?

It's all about scaling inference, scaling post-training, scaling context, continual learning, synthetic data?

- People vibe that way and describe it in that way, but I think it's not the practice that is happening.

- It's just the general vibe of people saying this thing is dead- - The excitement is elsewhere. The low-hanging fruit- ...in RL is elsewhere. For example, we released our model in November...

...in RL is elsewhere. For example, we released our model in November...

Every company has deadlines. Our deadline was November 20th, and our...

For that, our run was five days, which compared to 2024 is a very long time to just be doing post-training at a model of 30 billion parameters. It's not a big model. And then in December, we had another release, which was just

parameters. It's not a big model. And then in December, we had another release, which was just we let the RL run for another three and a half weeks, and the model got notably better, so we released it. That's a big amount of time to just allocate to something that is going to be your peak- ...for the year. So it's like-

...for the year. So it's like- - The reasoning is- - There's these types of decisions that happen when training a model where they just can't...

They can't leave it forever. You have to keep pulling in the improvements you have from your researchers. So you redo pre-training, you'll do this post-training for a month, but then you need to give it to your users. You need to do safety testing. So it's just like... I think there's a lot in place that reinforces this cycle of updating the models.

like... I think there's a lot in place that reinforces this cycle of updating the models.

There's things to improve. You get a new compute cluster that lets you do something more stably or faster. It's like

you hear a lot about Blackwell having rollout issues, where at AI2 most of the models we're pre-training are on 1,000 to 2,000 GPUs. But when you're pre-training on 10,000 or 100,000 GPUs, you hit

GPUs. But when you're pre-training on 10,000 or 100,000 GPUs, you hit very different failures. GPUs are known to break in weird ways, and on a 100,000 GPU run, you're pretty much guaranteed to always have at least one GPU that is down. And you need your training code to handle that redundancy, which is a very different

is down. And you need your training code to handle that redundancy, which is a very different problem.

Whereas what we're doing like, "Oh, I'm playing with post-training on DGX H100s," or you have your book, or people learning ML, what they're battling to train these biggest models is just- ...massive distributed scale, and it's very different. But that's somewhat different than whether

different. But that's somewhat different than whether these... Like, that's a systems problem-

these... Like, that's a systems problem- ...in order to enable the scaling laws, especially at pre-training. You need all

...in order to enable the scaling laws, especially at pre-training. You need all of these GPUs at once. When we shift to reinforcement learning, it actually lends itself to heterogeneous compute because you have many copies of the model. And to do a primer for

model. And to do a primer for language model reinforcement learning, what you're doing is you have two sets of GPUs. One you can call the actor and one you call the

GPUs. One you can call the actor and one you call the learner. The learner is where your actual reinforcement learning updates are done.

learner. The learner is where your actual reinforcement learning updates are done.

These are traditionally policy gradient algorithms. Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO) are the two popular classes.

And on the other side you're going to have actors which are generating completions, and these completions are the things you're going to grade.

Reinforcement learning is all about optimizing reward. In practice, what you can do is have a lot of different actors in different parts of the world doing different types of problems, and then you send it back to this highly networked compute cluster to do this actual learning where you take the gradients and you need to have a tightly meshed network where you can do different types of parallelism and spread out your model for

efficient training. There's just a lot of... Every different type of

efficient training. There's just a lot of... Every different type of training and serving has these considerations when you scale.

We talked about pre-training, we talked about RL, and then inference time scaling is how do you serve a model that's thinking for an hour to 100 million users? I'm

like, "I don't really know about that, but I know that's a hard problem, and in order to give people this intelligence, there's all these systems problems, and we need more compute and more stable compute to do it."

- But you're bullish on all of these kinds of scaling is what I'm hearing. On the

inference, on the reasoning, even on the pre-training?

- Yeah, so that's a big can of worms, but there are basically two... The

knobs are the training and the inference scaling where you can get gains, and so in a world where we had, let's say, infinite compute resources, you want to do all of them. So you have training, you have inference scaling, and training is like a hierarchy. It's pre-training, mid-training, post-training. Changing the model size, more training data, training a bigger

post-training. Changing the model size, more training data, training a bigger model gives you more knowledge in the model. Then the model, let's say, has a better... It's like a better base model. Still, we call it a foundation

better... It's like a better base model. Still, we call it a foundation model, and it unlocks... but you don't let's say have the model be able to solve your most complex tasks during pre-training or after pre-training. You still have these other unlock phases where you have mid-training or, for example, post-training with RL that unlocks capabilities that the model has in terms

of just knowledge in the pre-training. And I think, sure, if you do more pre-training, you get a better base model that you can unlock later. But like Nathan said, it just becomes too expensive. We don't have infinite compute,

later. But like Nathan said, it just becomes too expensive. We don't have infinite compute, so you have to decide: do I want to spend that compute more on making the model larger? It's like a trade-off. In an ideal world, you want to do

larger? It's like a trade-off. In an ideal world, you want to do all of them. And I think in that sense, scaling is still pretty much alive. You would

still get a better model, but like we saw with GPT-4o, it's just not worth it. I mean,

it's like... because you can unlock more performance with other techniques at this moment, especially if you look at inference scaling. That's one of the biggest gains this year with o1, where it took a smaller model further than pre-training a larger model like GPT-4o. So I wouldn't say pre-training scaling is dead, it's just that there are other more attractive ways to scale

right now. But at some point, you will still want to make some

right now. But at some point, you will still want to make some progress on the pre-training. The thing that's also to consider is where you want to spend your money. If you spend it more on the pre-training, it's like a fixed cost. You train the model, and then it has this capability forever. You can always use it.

capability forever. You can always use it.

With inference scaling, you don't spend money during training, you spend money later per query, and then it's also about the math: how long is my model going to be on the market if I replace it in half a year? Maybe it's not worth spending 5 million, 10 million, or 100 million dollars on training it longer. Maybe I will just do more

it longer. Maybe I will just do more inference scaling and get the performance from there. It maybe costs me two million in terms of user queries. It becomes a question of how many users you have and then doing the math, and I think that's also where it's interesting, where ChatGPT is in a position. I think they have a lot of users where they need to go a bit cheaper, where they have that

position. I think they have a lot of users where they need to go a bit cheaper, where they have that GPT-4o model that is a bit smaller. Other companies that have... let's say if your customers have other

have... let's say if your customers have other trade-offs. For example, there was also the Math Olympiad or some of

trade-offs. For example, there was also the Math Olympiad or some of these math problems where ChatGPT or they had a proprietary model, and I'm pretty sure it's just like a model that has been maybe fine-tuned a little bit more, but most of it was during inference scaling to achieve this peak performance in certain tasks.

need that all the time. But yeah, long story short, I do think all of these pre-training, mid-training, post-training, inference scaling, they are all still things you want to do. It's just

finding... At the moment, in this year, it's finding the right ratio that gives you the best bang for the buck, basically.

- I think this might be a good place to define pre-training, mid-training, and post-training.

- So, pre-training is the classic training: one next token prediction at a time.

You have a big corpus of data. And Nathan probably also has very interesting insights there because of OLMo. A big portion of the paper focuses on the right data mix. So, pre-training is essentially just training a cross-entropy loss, training on next token prediction on a vast corpus of internet data, books, papers and so forth. It

has changed a little bit over the years in the sense people used to throw in everything they can. Now, it's not just raw data. It's

also synthetic data where people, rephrase certain things. So synthetic data doesn't necessarily mean purely AI-made data. It's also taking something from an article, a Wikipedia article, and then rephrasing it as a Q&A question or summarizing it, rewording it, and making better data that way.

Because I think of it also like with humans. If someone,

let's say, reads a book compared to a messy—no offense, but, like, Reddit post or something like that, I do think you learn— - There's going to be a post about this, Raschka.

- Some Reddit data is very coveted and excellent for training.

You just have to filter it.

- And I think that's the idea. It's like if someone took that and rephrased it in a, let's say, more concise and structured way— I think it's higher quality data that gets the LLM maybe the same... You get the same LLM out of it at the end, but it gets there faster.

the same... You get the same LLM out of it at the end, but it gets there faster.

It trains faster, because if the grammar and the punctuation is correct, it already learns the correct way versus getting information from a messy way and then learning later how to correct that.

So, I think that is how pre-training evolved and why scaling still works is that it's not about just the amount of data, it's also the tricks to make that data better for you, in a sense. And

then mid-training is... I mean, it used to be called pre-training.

I think it's called mid-training because it was awkward to have pre-training and post-training, middle, right? It sounds a bit weird. You have pre-training and post-training, but what's the actual training?

middle, right? It sounds a bit weird. You have pre-training and post-training, but what's the actual training?

So, the mid-training is usually similar to pre-training, but it's a bit more specialized than pre-training. It's the same algorithm, but what you do is you focus, for example, on long context documents. One reason you don't do that during just pre-training is because you don't have that many long context documents. We have a specific phase. And one problem of LLMs is

context documents. We have a specific phase. And one problem of LLMs is also still... It's a neural network. It has the problem of catastrophic

also still... It's a neural network. It has the problem of catastrophic forgetting. So, you teach it something, it forgets other things. And you

forgetting. So, you teach it something, it forgets other things. And you

wanna... I mean, it's not 100% forgetting, but it's like no free lunch, you can't... It's also the same with humans. If you ask me some math I learned 10 years

you can't... It's also the same with humans. If you ask me some math I learned 10 years ago, I wouldn't know, I would have to look at it again.

- Nathan was actually saying that he's consuming so much content that there's a catastrophic forgetting issue.

- Yeah, I'm trying to learn so much about AI, and it's like I was learning about pre-training parallelism. I'm like, "I lost something and I don't know what it was."

pre-training parallelism. I'm like, "I lost something and I don't know what it was."

- I don't want to anthropomorphize LLMs, but I think it's the same kind of sense how humans learn. Quantity is not always better because you have to be selective. And mid-training is being selective in terms of quality content at the end. So the last thing the LLM sees is the quality stuff. And then post-training is all the fine-tuning, supervised fine-tuning,

DPO, reinforcement learning with verifiable rewards, human feedback, and so forth—the refinement stages. And it's also interesting, the cost thing, right? For pre-training, you spend a lot of money on that right now. RL a bit less. RL, you don't really, I would say,

right now. RL a bit less. RL, you don't really, I would say, teach it knowledge. It's more like unlocking the knowledge. It's more like a skill learning, like how to solve problems with the knowledge that it has from pre-training.

There are actually three papers this year, or last year, 2024, on RL for pre-training. But I don't think anyone does that in production.

- Toy, toy examples for now.

- Toy examples, right. But to generalize, RL post-training is more like the skill unlock, where pre-training is like soaking up the knowledge essentially.

- A few things that could be helpful for people. A lot of people think of synthetic data as being bad for training models. You mentioned

the DeepSeek-V3 almost- OCR, which is Optical Character Recognition paper. A lot of labs did. AI2 had one, Nougat had multiple.

did. AI2 had one, Nougat had multiple.

And the reason each of these labs has these is because there are vast amounts of PDFs and other digital documents on the web that are in formats that aren't encoded with text easily. So you use this Almost CR, or DeepSeek OCR, that we called our Almost CR, to extract what can be trillions of tokens of candidate data for pre-training.

And pre-training dataset size is on the order of trillions—it is measured in trillions of tokens. Smaller models from researchers can be something like 5 to 10 trillion.

Qwen is documented going up to like 50 trillion, and there are rumors that these closed labs can go to like 100 trillion tokens. And just getting this potential data to put in, they have a very big funnel, and then the data you actually train the model on is a small percentage of this. This character recognition data would be described as synthetic data for pre-training in a lab. And then there's

also the fact that ChatGPT now gives wonderful answers, and you can train on those best answers, and that's synthetic data. It's very different than, early ChatGPT, with lots of hallucination data, when people became grounded in synthetic data.

- One interesting question is, if I recall correctly, OLMo-3 was trained with less data than specifically some other open-weight models, maybe even OLMo-2. But you still got better performance, and that might be one of the examples how

OLMo-2. But you still got better performance, and that might be one of the examples how the data helped.

- It's mostly down to data quality.

I think if we had more compute, we would train for longer. I think we'd see that as just something we would want to do. And especially with big models, you need more compute, because we talked about having more parameters and we talked about knowledge. Essentially, there's a ratio where big models can absorb more from data, and then you get more benefit out of this. It's like one of

these. Any logarithmic graph in your mind is like a small model will level off

these. Any logarithmic graph in your mind is like a small model will level off sooner if you're measuring tons of tokens, and bigger models need more. But mostly, we aren't training that big of models right now at

more. But mostly, we aren't training that big of models right now at AI2, and getting the highest quality data we can is the natural starting point.

- Is there something to be said about the topic of data quality? Is there some low-hanging fruit there still where the quality could be improved?

- It's like turning the crank. Historically, in the open, there's been a canonical best pre-training dataset that has moved around between whoever has the most recent or best effort. Like

AI2's Dolma was very early with the first OLMo, and Hugging Face had FineWeb. And there's a DCLM project, which has been like a—

FineWeb. And there's a DCLM project, which has been like a— it stands for DataComp Language Model. There's been DataComp for other machine learning projects, and they had a very strong dataset. And a lot of it is the internet is becoming

dataset. And a lot of it is the internet is becoming fairly closed off, so we have Common Crawl, which is hundreds of trillions of tokens, and you filter it. It looks like scientific work where you're training classifiers and making decisions based on, how do you prune down this dataset into the highest quality stuff and the stuff that suits your tasks? Previously, language models were tested a lot more

on knowledge and conversational things, but now they're expected to do math and code. To train a reasoning model, you need to remix your whole dataset. And there are actually wonderful scientific methods here where you

dataset. And there are actually wonderful scientific methods here where you can take your gigantic dataset and sample a lot of really tiny things from different sources. So you say you have GitHub, Stack Exchange, Reddit,

different sources. So you say you have GitHub, Stack Exchange, Reddit, Wikipedia. You can sample small things from them, and train small models on each

Wikipedia. You can sample small things from them, and train small models on each mix and measure their performance on your evaluations. You can just do basic linear regression, and it's like, "Here's your optimal dataset." But if evaluations change, your dataset changes a lot. So a lot of OLMo-3 was new sources for reasoning to be better at math and code, and then you do this mixing

procedure and it gives you the answer. I think a lot of that's happened at labs this year, as there are new hot things, whether it's coding environments or web navigation, and you just need to bring in new data. You need to change your pre-training so that your post-training can work better. And that's like the constant evolution and the redetermining of what they care about for their models.

- Are there fun anecdotes of what sources of data are particularly high quality that we wouldn't expect? You mentioned Reddit sometimes can be a source.

- Reddit was very useful. I think that PDFs are definitely one.

- Oh, especially arXiv.

- Yeah, AI2 has run Semantic Scholar for a long time, which is what you can say is a competitor to Google Scholar with a lot more features.

And to do this, AI2 has found and scraped a lot of PDFs for openly accessible papers that might not be behind the- closed walled garden of a certain publisher. So, like, truly open scientific PDFs. And if you sit on all of these and you process

scientific PDFs. And if you sit on all of these and you process it, you can get value out of it. And I think that a lot of that style of work has been done by the frontier labs much earlier. You need to have a pretty skilled researcher that understands how things change models, and they bring it in and clean it. It's a lot of labor.

I think at frontier labs, when they scale researchers, a lot more goes into data. If you join a frontier lab and you want to

into data. If you join a frontier lab and you want to have impact, the best way to do it is just find new data that's better. Rather than the fancy, glamorous algorithmic things like

better. Rather than the fancy, glamorous algorithmic things like figuring out how to make o1. That's the sexiest thought of a scientist.

"Oh, I figured out how to scale RL." There's a group that did that, but most of the contributions come from- - On the dataset - ...saying, "I'm gonna make the data better," or, "I'm gonna make the infrastructure better so that everyone

- ...saying, "I'm gonna make the data better," or, "I'm gonna make the infrastructure better so that everyone on my team can run experiments 5% faster."

- At the same time, I think it's also one of the closest guarded secrets, what your training data is, for legal reasons. And so there's also, I think, a lot of work that goes into hiding what your training data was essentially—tuning the model to not give away the sources because you have legal reasons.

- The other thing, to be complete, is that some people are trying to train on only licensed data, where Common Crawl is a scrape of, like, the whole internet.

So if I host multiple websites, I'm happy to have them train language models, but I'm not explicitly licensing what governs it. Therefore, Common Crawl is largely unlicensed, which means that your consent really hasn't been provided for how to use the data. There's another idea where you can train language models only on data that has been licensed explicitly, so that the kind of governing

contract is provided. I'm not sure if Appertus is the copyright thing or the license thing.

I know that the reason that they did it was for an EU compliance thing, where they wanted to make sure that their model fit one of those checks.

- On that note, there's also the distinction between the licensing. So some people, like you said, they just purchase the license. Let's say they buy a book online, like an Amazon Kindle book, or a Manning book, and then use that in training data. That is a gray zone, 'cause you paid for the content and you might want to train on it. But then there

are also restrictions where even that shouldn't be allowed. That is

where it gets a bit fuzzy. And I think that is right now still a hot topic, and also big companies like OpenAI, they approached private companies for their proprietary data and and private companies are becoming more, protective of their data because they know, "Okay, this is going to be my moat in a few years." And I do think that's the interesting question, where

if LLMs become more commoditized, and a lot of people learn about LLMs, there will be a lot more people able to train LLMs. Of course, there are infrastructure challenges.

But if you think of big industries like pharmaceutical industries, law, and finance industries, I do think they, at some point, will hire people from other frontier labs to build their in-house models on their proprietary data, which will be another unlock with pre-training that is currently not there. Because even if you wanted to, you can't get that data. You can't get access to clinical trials

most of the time and these types of things. So, I do think scaling, in that sense, might be still pretty much alive if you also look in domain-specific applications, because we are still just looking at general purpose LLMs: ChatGPT, Anthropic, and so forth. They are just general purpose; they're not even scratching the surface of what an LLM can do if it is really specifically trained and designed for a specific task.

- I think on the data thing, this is one of the things that happened in 2025, and we totally forget it, is Anthropic lost in court and owed $1.5 billion to authors. Anthropic, I think, bought thousands of books and

authors. Anthropic, I think, bought thousands of books and scanned them and was cleared legally for that because they bought the books, and that is kind of going through the system. And then the other side, they also torrented some books, and I think this torrenting was the path where the court said that they were then culpable to pay these billions of dollars to authors, which is just

such a mind-boggling lawsuit that kind of just came and went. That is

so much money- so much money from the VC ecosystem.

- These are court cases that will define the future of human civilization, because it's clear that data drives a lot of this, and there's this very complicated human tension of... I mean, you can empathize. You're both authors.

tension of... I mean, you can empathize. You're both authors.

And there's some degree to which, I mean, you put your heart and soul and your sweat and tears into the writing that you do. It

feels a little bit like theft for somebody to train your data without giving you credit.

- And there are, like Nathan said, also two layers to it. Someone might buy the book and then train on it, which could be argued fair or not, but then there are the straight-up companies who use pirated books where it's not even compensating the author. That is where people got a bit angry about it specifically, I would say.

- Yeah, but there has to be some kind of compensation scheme. This is moving towards something like Spotify streaming did originally for music. You know, what does that compensation look like? You have to define those models and think through all of that.

One other thing I think people are generally curious about, I'd love to get your thoughts. As LLMs are used more and more, if you look at even arXiv, but

thoughts. As LLMs are used more and more, if you look at even arXiv, but GitHub, more and more of the data is generated by LLMs. What do you do in that kind of world? How big of a problem is that?

- Largest problem is the infrastructure and systems, but from an AI point of view, it's kind of inevitable.

- So it's basically LLM-generated data that's curated by humans essentially, right?

- Yes, and I think that a lot of open source contributors are legitimately burning out. If you have a popular open source repo, somebody's like, "Oh, I want to do

out. If you have a popular open source repo, somebody's like, "Oh, I want to do open source AI. It's good for my career," and they just vibe code something and they throw it in. You might get more of this- - I have a- - -than I do.

- Yeah, so I actually have a case study here.

I have a repository called mlxtend that I developed as a student, around 10 years ago, and it is a reasonably popular library still for certain algorithms, especially for frequent data mining stuff. And there were recently two or three people who submitted a lot of PRs in a very short amount of time.

I do think LLMs have been involved in submitting these PRs. Me, as the maintainer, there are two things. First, I'm a bit overwhelmed. I don't have time to read through it because, especially as an older library, that is not a priority for me.

At the same time, I also appreciate it because I think something people forget is it's not just using the LLM. There's still a human layer that verifies something, and that is in a sense also how data is labeled, right? One of the most expensive things is getting labeled data for reinforcement learning from human feedback phases. And this is kind of like that where it goes through

phases. And this is kind of like that where it goes through phases, and then you get higher quality data out of it. And so I don't mind it in a sense. It can feel overwhelming, but I do think there is value in it.

- It feels like there's a fundamental difference between raw LLM-generated data and LLM-generated data with a human in the loop that does some kind of verification, even if that verification is a small percent of the lines of code.

- I think this goes with anything, where people think also sometimes, "Oh, yeah. I can just use an LLM to learn about XYZ," which is true. You can, but there might be a person who is an expert who might have used an LLM to write specific code.

There is this human work that went into it to make it nice, throwing out the not so nice parts to kind of pre-digest it for you, and that saves you time. I think that's the value-add where you have someone filtering things or even using the LLMs correctly. This is still labor that you get for free. For example, reading an article—let's say a

Substack article. I could maybe ask an LLM to give me opinions on that, but

Substack article. I could maybe ask an LLM to give me opinions on that, but I wouldn't even know what to ask. I think there is still value in reading that article compared to me going to the LLM because you are the expert. You select what knowledge is actually spot on, what should be

expert. You select what knowledge is actually spot on, what should be included, and you give me this very executive summary. This is a huge value-add because now I

executive summary. This is a huge value-add because now I don't have to waste three or five hours to go through this myself, maybe get some incorrect information and so on. So I think that's also where the future still is for writers, even though there are LLMs that... Can save you time.

- It's fascinating to actually watch—and I'm sure you guys do this— but for me to look at the difference between a summary and the original content. Even if it's a page-long summary of a page-long content, it's interesting to see how the LLM-based summary takes the edge off. Like,

is the signal it removes from the thing?

- The voice is what I talk about a lot.

- Voice? I would love to hear what you mean by voice, but sometimes there are literally insights.

By removing an insight, you're fundamentally changing the meaning of the thing. I'm continuously disappointed how bad LLMs are

the thing. I'm continuously disappointed how bad LLMs are at really getting to the core insights, which is what a great summary does. Yet even if I have these extensive extremely elaborate prompts where I'm like really trying to dig for the insights, it's still not quite there. I mean, that's a whole deep philosophical

quite there. I mean, that's a whole deep philosophical question about what is human knowledge and wisdom and what does it mean to be insightful and so on. But when you talk about the voice, what do you mean?

- So when I write, I think a lot of what I'm trying to do is take what you think as a researcher, which is very raw. A

researcher is trying to encapsulate an idea at the frontier of their understanding and put what is a feeling into words. I try to do this with my writing, which

words. I try to do this with my writing, which makes it come across as raw but also high-information in a way that some people will get and some won't. That's the nature of research.

I think this is something language models don't do well. They're all trained with RLHF, which is designed to take feedback from a lot of people and average how the model behaves.

And I think that it's going to be hard for a model to be very incisive when there's that sort of filter in it. This is a wonderful, fundamental problem for researchers in RLHF: provides so much utility in making the models better, but also the problem formulation has this knot in it that you can't get past. These language

models don't have this prior in their deep expression that they're trying to get at. I don't think it's impossible. There are stories of models that

at. I don't think it's impossible. There are stories of models that really shock people. I would have loved to have tried Bing Sydney. Did that have more voice? Because

it would often go off the rails on people in what is historically obviously, a scary way—like telling a reporter to leave his wife—is crazy to potentially put in general adoption. But that's kind of like a trade-off. Is this RLHF process in some ways adding limitations?

a trade-off. Is this RLHF process in some ways adding limitations?

- That's a terrifying place to be as one of these frontier labs and companies, because millions of people are using them.

- There was a lot of backlash last year with GPT-4 getting removed. I've never used the model, but I've talked to people at

removed. I've never used the model, but I've talked to people at OpenAI where they get emails from users that might be detecting subtle differences in the deployments, even in the middle of the night. They're like, "My friend is different."

They find employees' emails because they are so attached to this set of model weights and a configuration that is deployed.

We see this with TikTok. I don't use TikTok, but supposedly in five minutes the algorithm gets you. It's like it's locked in.

Those are language models doing recommendations. I think

there are ways that you can do this. Within five minutes of chatting, the model just gets you. And that is something that people aren't really ready for. I think—don't give that to kids.

...at least until we know what's happening.

- There's also going to be this mechanism. What's going to happen with these LLMs, as they're used more and more...

Unfortunately, the nature of the human condition is such that people commit suicide.

Journalists will report extensively on the people who commit suicide, and they would very likely link it to the LLMs because they have that data about the conversations.

If you're really struggling, if you're depressed, if you're thinking about suicide, you're probably going to talk to LLMs about it.

And so journalists will say, "Well, the suicide was committed because of the LLM." And that's going to lead to the companies, because of legal issues, taking the edge off the LLM.

So it's going to be as generic as possible. It's so difficult to operate in this space, as generic as possible. It's so difficult to operate in this space because you don't want an LLM to cause harm to humans at that level, but this is also the nature of the human experience: to have a rich conversation, a fulfilling conversation,

one that challenges you and from which you grow. You need that edge.

And that is something extremely difficult for AI researchers on the RLHF front to solve, because you're actually dealing with the human condition.

- A lot of researchers at these companies are so well-motivated, Anthropic and OpenAI culturally want to do good for the world.

And it's such a... I'm like,

"I don't want to work on this," because a lot of people see AI as a health ally, as someone they can talk to about their health confidentially, but then it bleeds into talking about mental health, where it's heartbreaking that this will be the thing where somebody goes over the edge, edge, but other people might be saved. And I'm like, "I don't..." There's things that,

as a researcher training models, I don't want to train image generation models and release them openly because I don't want to enable somebody to have a tool on their laptop that can harm other people.

I don't have the infrastructure in my company to do that safely. But...

There's a lot of areas like this where it just needs people that will approach it with the complexity and just kind of conviction because it's just such a hard problem.

- But also, we as a society, as users of these technologies, need to ensure that we're having the complicated conversation about it versus just fearmongering. "Big

tech is causing harm to humans," or "stealing your data," all that kind of stuff. It's more complicated than that. And you're right.

There's a very large number of people inside these companies, many of whom you know, many of whom I know, that deeply care about helping people. They are

considering the full human experience of people from across the world, not just Silicon Valley. People across the United States, people across the world—what that means, what

Valley. People across the United States, people across the world—what that means, what their needs are. It's really difficult to design this one system that is able to help all these different kinds of people across different age groups, cultures, mental states, and conditions.

- I wish that the timing of AI was different regarding the relationship of Big Tech to the average person. Big Tech's reputation was so low, and with how AI is so expensive, it's inevitably going to be a Big Tech thing. It takes so many resources, and people say the US is, quote-unquote, "betting the economy on AI" with this build-out. To

have these be intertwined at the same time makes for such a hard communication environment. It would be good for me to go talk to more people

communication environment. It would be good for me to go talk to more people in the world that hate Big Tech and see AI as a continuation of this.

- And one of the things you recommend... One of the antidotes that you talk about is to find agency in this whole system. As opposed to sitting back in a powerless way and consuming the AI slop as it quickly, rapidly takes over the internet.

Find agency by using AI to build stuff, build apps... So, one, that actually helps you build intuition, but, two, it's empowering because you can understand how it works and what the weaknesses are. It gives your voice the power to say, "This is bad use of the technology, and this is good use of technology." And you're more plugged into the system,

so you can understand it better and you can steer it better as a consumer.

- I think that's a good point about agency. Instead of ignoring it and saying, "Okay, I'm not going to use it," I think it's probably long-term healthier to say, "Okay, it's out there. I can't put it back." Like the internet and computers back when they came out. How do I make best use of it, and how does it help me to up-level myself? The one thing I worry about, though,

is if you just fully use it for something you love to do, the thing you love to do is no longer there. And that could potentially, lead to burnout. For example, if I use an LLM to do all my coding, now there's no coding. I'm just managing something that is coding for me. Two years later, if I just do that eight hours a

for me. Two years later, if I just do that eight hours a day, have something code for me, do I still feel fulfilled? I mean, is this, hurting me in terms of being excited about my job and what I'm doing?

Am I still proud to build something?

- So on that topic of enjoyment, it's quite interesting. We should just throw this in there, that there's this recent survey of about 791 professional developers, professional meaning 10 plus years of experience.

- That's a long time. As a junior developer?

- Yeah, in this day and age. So there are also aspects on many fronts that are surprising. So they break it down by junior and senior developers. But it just shows that both junior and senior developers use AI-generated code in code they ship.

So this is not just for fun or intermediate learning things. This is code they ship. And so 25%—like,

things. This is code they ship. And so 25%—like, most of them use around 50% or more. And what's

interesting is, for the category of over 50% of your code that you ship is AI generated, senior developers are much more likely to do so. But you don't want AI to take away the thing you love.

I think this speaks to my experience—these particular results I'm about to say. Together, about 80% of people find it either somewhat

say. Together, about 80% of people find it either somewhat more enjoyable or significantly more enjoyable to use AI as part of their work.

- I think it depends on the task. From my personal usage, for example, I have a website where I sometimes tweak things on the website. I personally don't enjoy this. So in that sense, if the AI can help me to implement something on my website, I'm all for it. It's great. But then, at the same time, when I solve a complex problem, if there's a bug and I hunt this bug,

and I find it, it's the best feeling in the world. You get

so much joy. You feel great. But now if you don't even think about the bug and you just go directly to the LLM, well, you never have this kind of feeling, right? But then there could be the middle ground where you try yourself, you can't find it, you use the LLM, and then you don't get frustrated because it helps you move on to something that you enjoy. And so looking at these statistics, I think also the

difference is what is not factored in; it's averaging over all the different scenarios. We don't know if it's for the

different scenarios. We don't know if it's for the core task or if it's for something mundane that people would not have enjoyed otherwise. So, in a sense, AI is really great for doing mundane

otherwise. So, in a sense, AI is really great for doing mundane things that take a lot of work. So for example, my wife the other day, she has a podcast for book discussions, a book club, and she was transferring the show notes from Spotify to YouTube, and then the links somehow broke.

And she had in some episodes, because there are so many books, like 100 links, and it would have been really painful to go in there and fix each link manually. So I suggested, "Hey, let's try ChatGPT." We copied

link manually. So I suggested, "Hey, let's try ChatGPT." We copied the text into ChatGPT, and it fixed them. And instead of two hours going from link to link, it made that type of work much more seamless. I think everyone has the use case where AI is useful for something that would be really boring, really mundane.

- For me personally, since we're talking about coding, and you mentioned debugging... A lot of the source of the enjoyment for me, more on the Cursor side than the Cloud side, is that I have a friend, I have a— what's that called? A pair programmer. It's less lonely.

You made debugging sound like this great joy.

I would say debugging is like a drink of water after you've been going through a desert for days.

So you skip the whole desert part where you're suffering.

Sometimes it's nice to have a friend who can't really find the bug, but can give you some intuition about the code, and you're together with that friend going through the desert, and then together find that drink of water.

At least for me, maybe it speaks to the loneliness of the programming experience.

That is a source of joy.

- It's maybe also related to delayed gratification. Even as a kid, I liked the idea of Christmas presents—having them, looking forward to them—better than actually getting them.

I would look forward to the day, but then it's over and I'm disappointed. It's similar to food, I think.

Food tastes better when you're really hungry.

And you're right with debugging, it is not always great.

It's often frustrating, but then if you can solve it, then it's great.

There's a sweet Goldilocks zone; if it's too hard, then you're wasting your time. But I think that is another challenge: How will people learn? In the chart we looked at, we saw that more senior developers are shipping more AI-generated code than the junior ones.

It's very interesting, because intuitively you would think it's the junior developers, because they don't know how to do the thing yet. It could mean the AI is not good enough yet, or it could also mean experts are more effective at using it. They know

how to use it better, they review the code, and they trust it more.

One issue in the future will be: How do you become an expert if you never try to do the thing yourself?

The way I learned is by trying things myself.

Like math textbooks: if you just look at the solutions, you learn something, but I think you learn better if you try first. You appreciate the solution differently because you can put it into your mental framework. If

LLMs are here all the time, would you actually go through the length of struggling?

Struggle is not nice, right? But if you use and if you use the LLM to do everything, at some point you will never really take the next step and then you will maybe not get that unlock that you would get as an expert using an LLM. So, I think there's a Goldilocks sweet

LLM. So, I think there's a Goldilocks sweet spot where maybe the trick here is you make dedicated offline time where you study two hours a day, and the rest of the day use LLMs. But I think it's important also for people to still invest in themselves, in my opinion, to not just LLM everything.

- Yeah, there is—we as a civilization—that we each individually have to find that Goldilocks zone.

And in the programming context as developers. Now, we've had this fascinating conversation that started with pre-training and mid-training.

Let's get to post-training. A lot of fun stuff in post-training. So, what are some of the interesting ideas in post-training?

post-training. So, what are some of the interesting ideas in post-training?

- The biggest one from 2025 is learning this reinforcement learning with verifiable rewards. You can scale up the training there, which means doing a lot of this kind of iterative generate-grade loop, and that lets the models learn both interesting behaviors on the tool use and software side. This could be searching, running commands on their own and seeing the outputs, and then also that training

enables this inference time scaling very nicely. And it just turned out that this paradigm was very nicely linked, where this kind of RL training enables inference time scaling. But inference time scaling could have been found in different ways. So, it was a perfect storm where models change a lot, and the way that they're trained is a major factor in doing so. And this has changed how people approach post-training.... dramatically.

- Can you describe RLVR, popularized by DeepSeek R1? Can you describe how it works?

- Yeah. Fun fact, I was on the team that came up with the term RLVR, which is from our Tulu 3 work before DeepSeek. We

don't take credit for being the people to popularize the scaling RL, but as academics get, as an aside, is the ability to name and influence the discourse because the closed labs can only say so much. One

of the things you can do as an academic is you might not have the compute to train the model, but you can frame things in a way that ends up being, I describe it as: a community can come together around this RLVR term, which is very fun. And then DeepSeek are the people that did the training breakthrough, which is they scaled the reinforcement learning where you

have the model generate answers and then grade the completion if it was right, and then that accuracy is your reward for reinforcement learning. So reinforcement learning is classically an agent that acts in an environment, and the environment gives it a state and a reward back, and you try to maximize this reward. In the case of language models, the reward is normally

reward. In the case of language models, the reward is normally accuracy on a set of verifiable tasks, whether it's math problems, coding tasks. And it starts to get blurry with things like factual domains. Like that is also, in some ways, verifiable

factual domains. Like that is also, in some ways, verifiable or constraints on your instruction, like respond only with words that start with A. All of these things are verifiable in some way, and the core idea of this is you find a lot more of these problems that are verifiable and you let the model try it many times while taking these RL steps, these

RL gradient updates. The infrastructure evolved from Reinforcement Learning from Human Feedback, where the score they were trying to optimize was a learned reward model of aggregate human preferences. So you change the problem domains and that

preferences. So you change the problem domains and that let the optimization go on to much bigger scales, which kind of kickstarted a major change in what the models can do and how people use them.

- What kind of domains is RLVR amenable to?

- Math and code are the famous ones, and then there's a lot of work on what is called the rubrics, which is related to a word people might have heard is LLM-as-a-judge, which is like for each problem, I'll have a set of problems in my training dataset. I will then have another language model and ask it, "What would a good answer to this problem look

like?" And then you could try the problem over and over again and

like?" And then you could try the problem over and over again and assign a score based on this rubric. So that's not necessarily verifiable like a math and code domain, but this rubrics idea and other scientific problems that might be a little bit more vague is where a lot of the attention is, where they're trying to push this set of methods into these kind of more open-ended domains so the models can learn a lot more.

- I think that's called Reinforcement Learning with AI Feedback, right?

- That's the older term from it that was coined in Anthropic's Constitutional AI paper. So it's like a lot of these things come in cycles.

AI paper. So it's like a lot of these things come in cycles.

- Also, just one step back for the RLVR. I think the interesting thing here is that you ask the LLM, let's say, a math question, and then you know the correct answer, and you let the LLM, like you said, figure it out, but how it does it... I mean, you don't really constrain it much. There are some constraints you can add like "use the same language, don't switch

much. There are some constraints you can add like "use the same language, don't switch between Spanish and English." But let's say you're pretty much hands-off. You

only give the question and the answer, and then the LLM has to—you know, just the task to arrive at the right answer—but the beautiful thing here is what happens in practice is that the LLM will do a step-by-step description, like a student or a mathematician, how you would derive the solution. It will give you, or it will use those steps, and that actually helps the model

to improve its own accuracy. And then like you said, the inference scaling. So inference scaling loosely means basically spending more

scaling. So inference scaling loosely means basically spending more compute during using the LLM during inference, and here the inference scaling is that the model would use more tokens. In the R1 paper, they showed the longer they train the model, the longer the responses are.

They grow over time. They use more tokens, so it becomes more expensive.

expensive for simple tasks, but these explanations, they help the model with the accuracy. There are also interesting papers showing what the model

accuracy. There are also interesting papers showing what the model explains does not necessarily have to be correct or maybe it's even unrelated to the answer, but for some reason, it still helps the model. It's the fact that it is explaining. And I think it's also, again, I don't want to

is explaining. And I think it's also, again, I don't want to anthropomorphize these LLMs, but it's kind of like how we humans operate, right? If there's a complex math problem, let's say in a math

right? If there's a complex math problem, let's say in a math class, you usually have a note paper and you do it step by step. You cross out things. And the model also self-corrects, and that was, I think, the

things. And the model also self-corrects, and that was, I think, the aha moment in the R1 paper. They called it the aha moment because the model itself recognized it made a mistake and then said, "Ah, I did something wrong, let me try again."

And I think that's just so cool that this falls out of just giving it the correct answer and having it figure out how to do it, that it kind of does in a sense what a human would do.

Although LLMs don't think like humans, it's kind of like an interesting coincidence and it... And the other nice side effect is it's great for

and it... And the other nice side effect is it's great for us humans often to see these steps. It builds trust, but also we learn we can double check things.

- There's a lot in here. I think-

There's been a lot of debate this year on if the language models like these aha moments... I think the aha moments are kind of fake because in pre-training you essentially have seen the whole internet.

So you have definitely seen people explaining their work, even verbally, like a transcript of a math lecture. "You try this, oh, I messed this up." And what reinforcement learning, this RLVR is very good at doing, is amplifying- these behaviors because they're very useful in enabling the model to think longer and to check its work. And I agree that it is very beautiful that this

training kind of ... The model learns to amplify this in a way that is just so useful at the final answers being better.

- I can give you also a hands-on example. I was training the Qwen 2.5 base model with RLVR on MATH 500. The base model had an accuracy of about 15%. Just 50 steps, like in a few minutes with RLVR, the model went from 15% to 50% accuracy. And the model... You can't tell me it's learning anything about, fundamentally about math in-

- The Qwen example is weird because there've been two papers this year, one of which I was on, about data contamination in Qwen- specifically that they train on a lot of this special mid-training phase that we- - Exactly. Exactly.

- Exactly. Exactly.

- that we spent a minute on because it's weird- - And so- - because they train on problems that are almost identical to MATH.

- Exactly. And so you can see that basically the RL, it's not teaching the model any new knowledge about math. You can't do that in 50 steps.

So the knowledge is already there, in the pre-training, you're just unlocking it.

- I still disagree with the premise because there's a lot of weird complexities that you can't prove because One of the things that points to weirdness is that if you take the Qwen 2.5 so-called base model and you... You could Google like "math dataset, Hugging Face," and you could take a problem and what you do if you put it into Qwen 2.5 base, all these math problems have words, so it'd be like "Alice

has five apples and takes one and gives three to whoever," and there are these word problems. Why people are suspicious of these Qwen-based models is if you change the numbers but keep the words- Qwen will produce—without tools—will produce a very high accuracy decimal representation of the answer, which means at some point it was shown

problems that were almost identical to the test set, and it was using tools to get a very high precision answer, but a language model without tools will never actually have this. So it's kind of been this big debate in the research community. How much of these reinforcement learning papers that are training on Qwen and measuring specifically on this math benchmark where there's been multiple papers talking about contamination, how

much can you believe them? I think this is what caused the reputation of RLVR being about formatting, because you can get these gains so quickly and therefore it must already be in the model. But there's a lot of complexity here; it's not really like controlled experimentation, so we don't really know.

- But if it weren't true, I would say distillation wouldn't work, right?

I mean, distillation can work to some extent, but the thing is, the biggest problem is research contamination, because we don't know what's in the data. Unless you have a new dataset, it is really impossible. And the

data. Unless you have a new dataset, it is really impossible. And the

same—you mentioned the MATH dataset, where you have a question, an answer, and an explanation given, but then also even something simpler like MMLU, which is a multiple-choice benchmark. If you just change the format slightly—like, I don't know, you use a dot instead of a parenthesis or something like that, the model accuracy will vastly differ.

- I think that could be a model issue rather than a general issue.

- It's not even malicious by the developers of the LLM, like, "Hey, we want to cheat at that benchmark."

It has just seen something at some point. I think the only fair way to evaluate an LLM is to have a new benchmark that is after the cutoff date when the LLM was deployed.

- Can we lay out what would be the recipe of all the things that go into post-training? And you mentioned RLVR was a really exciting, effective thing.

Maybe we should elaborate. RLHF still has a really important component to play. What kind of other ideas are there on post-training?

- I think you can take this in order. You could

view it as what made o1, which is this first reasoning model, possible or what will the latest model be. They

actually have similar interventions at these stages, where you start with mid-training, and the thing that is rumored to enable o1 and similar models is really careful data curation, where you're providing a broad set of what are called reasoning traces, which is just the model generating words in a forward process that is reflecting, like

breaking down a problem into intermediate steps and trying to solve them. At mid-training,

you need to have data similar to this so that when you move into post-training, primarily with these verifiable rewards, it can learn.

And then what is happening today is you're figuring out which problems to give the model and how out which problems to give the model and how long you can train it for and how much inference you can enable the model to use when solving these verifiable problems. So as models get better, certain problems models get better, certain problems are no longer... The model will solve them 100% of the time, and

therefore there's very little signal in this. If we look at the GRPO equation, this one is famous for this because essentially the reward given to the agent is based on how good reward given to the agent is based on how good a given action—a completion—is relative to the other answers to that same problem. So if all the problems get the same answer, there's no signal.

So they're finding harder problems, which is why you hear about things like scientific domains, which are so hard. Getting anything right there, if you have a lab or something, it just

hard. Getting anything right there, if you have a lab or something, it just generates so many tokens, or much harder software problems. So the frontier models are all pushing into these harder domains when they can train on more frontier models are pushing into these harder domains where they can train on more problems and learn more skills at once. The RLHF

link to this is that RLHF has been and still is like the finishing touch on the models, where it makes them more useful by improving the organization or style or tone. Different things

resonate with different audiences; some people like a quirky model, and RLHF could be good at enabling that personality, and some people hate this markdown bulleted list thing that the models do, but it's actually really good for quickly parsing information. In RLHF, this human feedback stage is really great for putting this into the model at the end of the day. It's what made ChatGPT

so magical for people. And that use has actually remained fairly stable. This formatting can also help

stable. This formatting can also help the models get better at math problems, for example. So it's

like the border between style and formatting, and the method that you use to answer a problem, is actually—they're all very closely linked in terms of when you're training these models, which is why RLHF can still make a model better at math, but these verifiable domains are a much more direct process for doing this because this makes more sense with the problem

formulation, which is why it all ends up forming together. But to summarize: Mid-training is giving the model the skills it needs to then learn. RL with verifiable rewards is letting the model try

learn. RL with verifiable rewards is letting the model try many times—putting a lot of compute into trial-and-error learning across hard problems. And then RLHF would be: finish the model, make it easy to use, and kind of just round the model out.

- Can you comment on the amount of compute required for RLVR?

- It's only gone up and up. I think Greg Brockman was famous for saying they use a similar amount of compute for pre-training and post-training. Back to the scaling discussion, they involve very

post-training. Back to the scaling discussion, they involve very different hardware for scaling. Pre-training is very compute-bound, which is like this FLOPs discussion, which is just how many matrix multiplications can you get through in one time. And because RL, you're generating these answers, you're trying the

time. And because RL, you're generating these answers, you're trying the model in the real-world environments, it ends up being much more memory-bound because you're generating long sequences and the attention mechanisms have this behavior where you get a quadratic increase in memory as you're getting to longer sequences. So the compute becomes very different.

When in pre-training we talk about a model, I think if we go back to the Biden administration executive order, it's like 10 to the 25th FLOPs to train a model. If you're using FLOPs in post-training, it's a lot weirder because the

model. If you're using FLOPs in post-training, it's a lot weirder because the reality is just like how many hours are you allocating, how many GPUs for? And I think in terms of time, the RL compute is

for? And I think in terms of time, the RL compute is getting much closer because you just can't put it all into one system.

Pre-training is so computationally dense where all the GPUs are talking to each other and it's extremely efficient, where RL has all these moving parts and it can just take a long time to generate a sequence of a hundred thousand tokens. If you think about GPT-4o taking an hour, it's like, what if your training run has to sample for an hour and you have to make sure that's handled

efficiently? So I think in GPU hours or just wall clock

efficiently? So I think in GPU hours or just wall clock hours, the RL runs are probably approaching the number of days as pre-training, but they probably aren't using as many GPUs at the same time. There are rules of thumb where in labs you don't want your pre-training

same time. There are rules of thumb where in labs you don't want your pre-training runs to last more than a month because they fail catastrophically. And if you are planning a huge cluster to be held for two months and then it fails on day 50, the opportunity costs are just so big. So people don't want to put all their eggs in one

big. So people don't want to put all their eggs in one basket, which is like... GPT-4 was the ultimate YOLO run and nobody ever wanted to do it before where it took three months to train and everybody was shocked that it worked. I think people are a little bit more cautious and incremental now.

worked. I think people are a little bit more cautious and incremental now.

- So RLVR is more, let's say, unlimited in how much you can train and get benefit, where RLHF, because it's preference tuning, you reach a certain point where it doesn't really make sense to spend more RL budget on that. So just taking a step back with preference tuning: there are multiple people that can give multiple explanations for the same thing and they can both be correct, but at some point you learn a certain

style and it doesn't make sense to iterate on it. My

favorite example is if relatives ask me what laptop they should buy, I give them an explanation or ask them, "What is your use case?" Like

they, for example, might prioritize battery life and storage. Other people like us, for example, we would prioritize RAM and

storage. Other people like us, for example, we would prioritize RAM and compute. And so... but both answers are correct, but different people

compute. And so... but both answers are correct, but different people require different answers. And with preference tuning, you're trying to average somehow, like you are asking the data labelers to give you the preferred answer and then you train on that. But at some point, you learn that average preferred answer. And there's no reason to keep training longer on it because it's just a style, whereas with RLVR

you literally let the model solve more and more complex, difficult problems. And so I think it makes more sense to allocate more budget long-term to RLVR. Right now we are in RLVR 1.0 land where it's still like that simple thing where we have a question and answer, but we don't do anything with the stuff in between. So

there were multiple research papers, also by Google for example, on process reward models that also give scores for the explanation, how correct is the explanation. And I think that will be the next thing, let's say RLVR 2.0 for this year, focusing in between question and answer, like how to leverage that information to improve the explanation and help it to get better.

accuracy. But then... So that's one angle. And there was a DeepSeek Math-V2 paper where they also had interesting inference scaling there where, first, they had developed models that grade themselves, a separate model.

And I think that will be one aspect. And the other, like Nathan mentioned, it will be for RLVR branching into other domains.

- The place where people are excited are value functions, which is pretty similar. So process reward models are kind of like...

Process reward models assign how good something is to each intermediate step in a reasoning process, where value functions apply value to every token the language model generates. Both of these have been largely unproven in the language

model generates. Both of these have been largely unproven in the language modeling and this reasoning model era. People are more optimistic about value functions for whatever reason now. I think process reward models were tried a lot more in this pre-o1, pre-reasoning model era, and a lot of people had a lot of headaches with them. So I think a lot of it is the human nature of like...

Value models have a very deep history in reinforcement learning. They're one of the first things that were core to deep reinforcement learning existing, is training value models. So right now in the literature, people are excited about trying value models, but there's very little proof in it. And there are negative examples in trying to scale up process reward models.

These things don't always hold in the future. We came to this discussion by talking about scaling. And the simple way to summarize what you're saying is you don't want to do too much RLHF, which is essentially that the signal scales.

People have worked on RLHF for language models for years, especially an intense interest after ChatGPT. And the first release of a reasoning model trained with RLVR, OpenAI's o1, had a scaling plot where if you increase the training compute logarithmically, you get a linear increase in evaluations, and this has been reproduced multiple times. I think DeepSeek had a plot like this. But there's no scaling law for RLHF where if you

log-increase the compute, you get some performance. In fact, the seminal scaling paper for RLHF is Scaling Laws for Reward Model Overoptimization.

So it's like that's a big line to draw with RLVR and the methods we have now and in the future; they will follow this scaling paradigm, which is like the best runs you can let run for an extra 10x and you get a few x performance, but you can't do this with RLHF. And that is just going to be field-defining in how people approach

RLHF. And that is just going to be field-defining in how people approach them. While I'm a shill for people academically to do RLHF,

them. While I'm a shill for people academically to do RLHF, a good way to describe it is like: to do the best RLHF you might not need the extra 10 or 100x of compute, but to do the best RLVR you do. So I think there's a seminal paper from a Meta internship. It's called something like The Art of Scaling Reinforcement Learning with Language Models. What they describe as a

framework is ScaleRL. And their incremental experiment was like 10,000 V100 hours, which is like thousands or tens of thousands of dollars per experiment, and they do a lot of them. This

cost is just not accessible to the average academic, which is a hard equilibrium where we're trying to figure out how to learn from each community.

- I was wondering if we could take a bit of a tangent and talk about education and learning. If you're someone listening to this who's a smart person interested in programming and AI, I presume building something from scratch is a good beginning.

Can you take me through what you would recommend people do?

- I would personally start by implementing a simple model from scratch that you can run on your computer. The goal is not, to have something you use every day for your personal projects. It's not going to be a personal assistant replacing

projects. It's not going to be a personal assistant replacing an existing open-weight model or ChatGPT. It's to see exactly what goes into the LLM, what comes out, and how the pre-training works, preferably on your own computer.

Then you learn about pre-training, supervised fine-tuning, and the attention mechanism. You get a solid understanding of how things work, but

attention mechanism. You get a solid understanding of how things work, but at some point you'll reach a limit because home models can only do so much.

The problem with learning about LLMs at scale is that it's exponentially more complex to make a larger model because the model doesn't just become larger. You have to think about sharding your parameters across multiple GPUs. Even for the KV-cache, there are multiple ways to implement it. One way to understand how it works is just to grow the

implement it. One way to understand how it works is just to grow the cache step-by-step by, let's say, concatenating lists, but then that wouldn't be optimal on GPUs. You would pre-allocate a tensor and then fill it in. But that

GPUs. You would pre-allocate a tensor and then fill it in. But that

adds another 20 or 30 lines of code. For each thing you add code, and the trick with the book is to understand how the LLM works. It's not going to be a production-level LLM, but once you have that, you can understand the production-level LLM.

- So you're trying to always build an LLM that's going to fit on one GPU?

- Yes. Most of them do. I have some bonus materials on some MoE models. One or two of them may require multiple GPUs, but

MoE models. One or two of them may require multiple GPUs, but the goal is to have it on one GPU. And the beautiful thing is you can self-verify. It's almost like RLVR. When you code these from scratch, you can take an existing model from the Hugging Face Transformers library. That library is great, but if

Transformers library. That library is great, but if you want to learn about LLMs, I don't think that's the best place to start because the code is so complex; it has to fit so many use cases.

Since people use it in production, it's really sophisticated and intertwined. It's not linear to read.

intertwined. It's not linear to read.

- It started as a fine-tuning library and then grew to be the standard representation of every model architecture. Hugging Face is the default place

model architecture. Hugging Face is the default place to get a model, and Transformers is the software that enables it so people can easily load a model and do something basic with it.

- And all frontier labs that have open-weight models have a Transformers version of it, like from DeepSeek to GPT-2. That's the

canonical weight that you can load there. But even

even Transformers, the library, is not used in production. People use

SGLang or vLLM, and it adds another layer of complexity.

- We should say that the Transformers library has like 400 models.

- So it's the one library that tries to implement a lot of LLMs and so you have a huge codebase, basically. It's like huge. It's

like, I don't know, maybe millions- - That's crazy - ...hundreds of thousands of lines of code. Understanding the part

- ...hundreds of thousands of lines of code. Understanding the part you want to understand is finding the needle in the haystack. But what's beautiful is you have a working implementation and so you can work backwards from it. What I would recommend doing, or what I also do, is if I want to understand, for example, how Llama 3 is implemented, I would look at the weights in the model

hub, the config file, and then you can see, "Oh, they used so many layers."

They use, let's say, Group Query Attention or Multi-Head Attention in that case.

Then you see all the components in like a human-readable 100 lines of config file.

And then you start, let's say, with your GPT-2 model and add these things.

The cool thing here is you can then load the pre-trained weights and see if they work in your model. And you want to match the same output that you get with a Transformer model, and then you can use that as a- -basically as a verifiable reward to make your architecture correct. And then it's kind of... sometimes it takes me a day. With Llama 3,

kind of... sometimes it takes me a day. With Llama 3, the challenge was RoPE for the position embeddings. They had a YaRN extension, and there was some custom scaling there and I couldn't quite match these things, and in this struggle you kind of understand things. But at the end, you know you have it correct because you can unit test it. You can check against the reference

implementation, and I think that's one of the best ways to learn really.

To basically reverse-engineer something.

- I think that is something that everybody interested in getting into AI today should do. I think that's why I liked your book, I came to language models from this RL and robotics field. I had never taken the time to just learn all the fundamentals, and this Transformer architecture I described as being so fundamental, like deep learning was a thing that I had to learn in the past, and people need to do this.

I think where a lot of people kind of get overwhelmed is, "How do I apply this to have impact or find a career path?" Because AI language models make this fundamental

career path?" Because AI language models make this fundamental stuff so accessible, and people with motivation will learn it, and then it's like, "How do I get the cycles on goal to contribute to research?"

I'm actually fairly optimistic because the field moves so fast that a lot of times the best people don't fully solve a problem because there's a bigger problem to solve that's very low-hanging fruit, so they move on. And I think that a lot of what I was trying to do in this RLHF book is take post-training techniques and describe how people think about them influencing the model,

and what people are doing. It's remarkable

how many things I think people just stop studying.

don't. So I think people trying to get narrow after doing the fundamentals is good, and then reading after doing the fundamentals is good, and then reading the relevant papers and being engaged in the ecosystem, it's like you The proximity that random people have online from the leading researchers...

Like, no one knows who all the... Like, the

anonymous account on X in ML is very popular for whatever reason, and no one knows who all these people are. It could just be random people that study the stuff deeply, especially with the AI tools to just be like, "I don't understand this, keep digging into it," I think is a very useful thing. But there's a lot of research areas that just like are maybe three papers that you need to read and then

one of the authors will probably email you back. But you have to put in a lot of effort into these emails to understand the field. Like I think it would be for a newcomer easily weeks of work to feel like they can truly grasp what is a very narrow area, but I think going narrow after you have the fundamentals will be very useful to people because

it's like I became very interested in character training, which is like how you make the model funny or sarcastic or serious and like what do you do to the data to do this? And it's like a student at Oxford reached out to me and was like, "Hey, I'm interested in this," and I advised him.

And that paper now exists, and it's like, I don't know, there's like two or three people in the world that were very interested in this. He's a PhD student, which gives you an advantage, but like, for me, that was a topic I was waiting for someone to be like, "Hey, I have time to spend cycles on this," and I'm sure there's a lot more very narrow things where you're just like, "Oh,

it doesn't make sense that there was no answer to this," and I think that it's just like there's so much information coming that people are like, "I can't grab onto any of these," but if you just actually stick in an area, I think there's a lot of interesting things to learn.

- Yeah, I think you can't try to do it all because it would be very overwhelming and you would burn out if you tried to keep up with everything. For me, I haven't kept up with computer vision in a long time, just focused on LLMs. But coming back to your book, I think this is also a really great book and a really good bang for the buck because

you want to learn about RLHF. I wouldn't go out there and read RLHF papers because you would be spending two years— - Some of them contradict.

I've just edited the book, and I was like, there's no chapter where I had to be like, "X papers say one thing and X papers say another thing, and we'll see what comes out to be true."

- Just to go through some of the table of contents, some of the ideas we might have missed in the bigger picture of post-training. So first of all, you did the problem setup, training overview, what are preferences, preferences data and the optimization tools, reward modeling, regularization, instruction tuning, rejection sampling, reinforcement learning. Then

constitutional AI and AI feedback, reasoning and inference time scaling to use in function calling, synthetic data and distillation, evaluation and then open questions section, over-optimization, style and information, and then product UX, character and post-training. So what are some ideas worth mentioning that connect both the educational component and the research component?

You mentioned the character training, which is pretty interesting.

- Character training is interesting because there's so little out there, but we talked about how people engage with these models and how we feel good using them because they're positive. But that can go too far; it can be too positive. And it's

they're positive. But that can go too far; it can be too positive. And it's

essentially: how do you change your data and decision-making to make it exactly what you want? Like,

OpenAI has this thing called a Model Spec, which is essentially their internal guideline for what they want the model to do, and they publish this to developers. So essentially, you can know what is a failure of

developers. So essentially, you can know what is a failure of OpenAI's training—where they have intentions they haven't met yet— versus what is something they actually wanted to do and that you don't like.

And that transparency is very nice, but all the methods for curating these documents and how easy it is to follow them is not very well known. I think the way the book is designed is that the RL chapter is obviously what people want because everybody hears about it with RLVR, and it's the same algorithms and the same math, but it's just that you can use it in very different documents.

So I think the core of RLHF is how messy preferences are. It's essentially a rehash of a paper I

preferences are. It's essentially a rehash of a paper I wrote years ago, but this is the chapter that'll tell you why RLHF is never ever fully solvable, because the way that even RL is set up assumes that preferences can be quantified and that multiple preferences can be reduced

to single values. And I think it relates in economics literature to the Von Neumann-Morgenstern utility theorem. That is the chapter where all of that philosophical, economic, and like psychological context tells you what gets compressed into doing RLHF. So you

have all of this, and later in the book, it's like: you use this RL math to make the number go up. I think that's why it'll be very rewarding for people to do research on, because quantifying preferences is something that humans have designed a problem for in order to make preferences studyable. But there's fundamental debates like, for example, in a language model response, you have different things you care about,

whether it's accuracy or style. And when collecting data, they all get compressed into, "I like this more than another." That is happening, and there's a lot of research in other areas of the world regarding how you should actually do this. I think

social choice theory is the subfield of economics around how you should aggregate preferences.

And I went to a workshop that published a white paper on, "How can you think about using social choice theory for RLHF?" So I want people that get excited about the math to

RLHF?" So I want people that get excited about the math to come and stumble into and learn this kind of broader context. I think there's a fun thing; I keep a list of all the tech

context. I think there's a fun thing; I keep a list of all the tech reports that I like for reasoning models. In Chapter 14, where there's a short summary of RLVR, there's just a gigantic table where I list every single reasoning model that I like. So there's just...

I think in education a lot of it needs to be like, at this point, what I like— —because the language models are so good at the math. For example,

the famous paper Direct Preference Optimization, which is a much simpler way of solving the problem than RL. The derivations in the appendix skip steps of math. For this book, I redid the derivations and I'm like,

of math. For this book, I redid the derivations and I'm like, "What the heck is this log trick that they use to change the math?" But doing it with language models, they're like, "This is the log trick."

math?" But doing it with language models, they're like, "This is the log trick."

I'm like, "I don't know if I like this, that the math is so commoditized." I think some of the struggle in reading this appendix—

so commoditized." I think some of the struggle in reading this appendix— ...and following the math, I think is good for learning. And I...

...and following the math, I think is good for learning. And I...

- Yeah, so we're actually returning to this often, just on the topic of education.

You both have brought up the word "struggle" quite a bit.

So there is value. If you're not struggling as part of this process, you're not fully following the proper process for learning, I suppose.

- Some of the providers are starting to work on models for education, which are designed to not give... actually, I haven't used them, but I would guess they're designed to not give

give... actually, I haven't used them, but I would guess they're designed to not give all the information at once— - Right - ...and make people work to do this. I think you could train models to do this, and it would be a wonderful

- ...and make people work to do this. I think you could train models to do this, and it would be a wonderful contribution. Where like all of the stuff in the book, you had to reevaluate every

contribution. Where like all of the stuff in the book, you had to reevaluate every decision for it— Which is such a great example. I think there's a chance we'll work on it at AI2, which I thought would be so fun.

- It makes sense. I did something like that the other day for video games, for example. I sometimes play video games for my pastime,

for example. I sometimes play video games for my pastime, video games with puzzles. You know, like Zelda and Metroid. And there's this new game where I got stuck, and I really got stuck and was okay.

I don't want to struggle for two days, so I used an LLM. But then you say, "Hey, please don't add any spoilers.

LLM. But then you say, "Hey, please don't add any spoilers.

Just, you know, I'm here. What do I have to do next?" And the same thing you can do for math, where you say, "Okay, I'm at this point. I'm getting stuck. Don't give me the full solution, but what is something I could try?" You know, where you kind of carefully probe it. But the problem here is I think it requires discipline.

And a lot of people who enjoy math, but there are also a lot of people who need to do it for their homework and then it's like this shortcut. And yeah, we could develop an educational LLM, but the other

shortcut. And yeah, we could develop an educational LLM, but the other LLM is still there, and there's still a temptation to use the other LLMs. - I think a lot of people, especially in college, they understand the stuff they're passionate about— ...they're self-aware about it, and they understand it shouldn't be easy.

about— ...they're self-aware about it, and they understand it shouldn't be easy.

Like, I think we just have to develop a good taste— ...talk about research taste, like school taste about

...talk about research taste, like school taste about stuff that you should be struggling on— ...and stuff you shouldn't be struggling on. Which is tricky to know, because

...and stuff you shouldn't be struggling on. Which is tricky to know, because sometimes you don't have good long-term vision about what would be actually useful to you in your career. But you have to develop that taste, yeah.

career. But you have to develop that taste, yeah.

- I was talking to my fiancee or friends about this, and it's like there's this brief 10-year window where all of the homework and all the exams could be digital. But before that, everybody had to do all the exams in blue books because there was no other way. And now after AI, everybody's going to need blue books and oral exams because everybody could cheat so easily. It's like this brief generation that had a

different education system that, like, everything could be digital, but you still couldn't cheat. And now it's just gonna go back.

It's just very funny.

- You mention character training. Just zooming out on a more general topic, for that topic how much compute was required? And in general, to contribute as a

required? And in general, to contribute as a researcher, are there places where not too much compute is required where you can actually contribute as an individual researcher?

- For the character training thing, I think this research is built on fine-tuning about seven billion parameter models with LoRA, which is essentially only fine-tuning a small subset of the weights of the model.

I don't know exactly how many GPU hours that would take.

- But it's doable.

- Not doable for every academic. The situation for some academics is so dire that the only work you can do is doing inference where you have closed models or open models and you get completions from them and you can look at them and understand the models. And that's very well-suited to evaluation, where you want to

models. And that's very well-suited to evaluation, where you want to be the best at creating representative problems that the models fail on or show certain abilities, which I think that you can break through with this. I think that the top-end goal for a researcher working on evaluation, if you want to have career momentum, is that Frontier Labs pick up your

evaluation. You don't need to have every project do this. But if you

evaluation. You don't need to have every project do this. But if you go from a small university with no compute and you figure out something that Claude struggles with and then the next Claude model has it in the blog post, there's your career rocket ship. I think that's hard, but if you want to scope the maximum possible impact with minimum compute, it's something

like that, which is just get very narrow and it takes learning of where the models are going. So you need to build a tool that tests where Claude 4.5 will fail. If I'm going to start a research project, I need to think where the models in eight months are going to be struggling.

- But what about developing totally novel ideas?

- This is a trade-off. I think that if you're doing a PhD, you could also be like, "It's too risky to work in language models. I'm going way longer term," which is like, what is— ...what is the thing that's going to define language model development in 10 years?

...what is the thing that's going to define language model development in 10 years?

I think that I end up being a person that's pretty practical. I mean, I went to my PhD where it was like, "Eh, I got into Berkeley. Worst case, I get a master's, and then I go work in tech." And so I'm very practical about it, so I'm like, the life afforded to people to work at these AI companies, the amount of... Like,

OpenAI's average compensation is over a million dollars in stock a year per employee. Any normal person in the US, to

employee. Any normal person in the US, to get into this AI lab is transformative for your life. So I'm pretty practical about it.

there's still a lot of upward mobility working in language models if you're focused. And the outcomes, it's like, look at these jobs. But from a research

focused. And the outcomes, it's like, look at these jobs. But from a research perspective, the transformative impact in these academic awards, it's like being the next Yann LeCun is from not working on- not caring about language model development very much.

- It's a big financial sacrifice in that case.

- So I get to work with some awesome students, and they're like, "Should I go work at an AI lab?" And I'm like, "You're getting a PhD at a top school. Are you gonna

AI lab?" And I'm like, "You're getting a PhD at a top school. Are you gonna leave to go to a lab?" I'm like, "I don't know." If you go work at a top lab, I don't blame you. Don't go work at some random startup that might go to zero. But if you're going to OpenAI, I'm like, "It could be worth leaving a PhD for."

- Let's more rigorously think through this. So where would you give a recommendation for people to do a research contribution? The options

are academia: get a PhD. Spend five years publishing.

Compute resources are constrained. There's-

there's research labs that are more focused on open-weight models, and so working there. Or closed frontier labs, research labs.

So OpenAI, Anthropic, xAI, and so on.

- The two gradients are: the more closed, the more money you tend to get, but also the less credit you get. So in terms of building a portfolio of things that you've done, it's very clear what you have done as an academic. Versus if you are gonna go trade this fairly reasonable

progression for being a cog in the machine, which could also be very fun.

So I think it's very different career paths. But the

opportunity cost for being a researcher is very high because PhD students are paid essentially nothing. So I think it ends up rewarding people that have a fairly stable safety net, and they realize that they can operate in the long term, which is wanting to do very interesting work and get a very interesting job. So it is a fairly privileged position to be like,

job. So it is a fairly privileged position to be like, "I'm gonna see out my PhD and figure it out after because I want to do this." And

at the same time, the academic ecosystem is getting bombarded by funding getting cut and stuff. So there's just so many different trade-offs where I understand plenty of people that are like, "I don't enjoy it. I can't deal with this funding search. My grant got cut for no reason by the government,"

funding search. My grant got cut for no reason by the government," or, "I don't know what's gonna happen." So I think there's a lot of uncertainty and trade-offs that, in my opinion, favor just taking the well-paying job with meaningful impact. It's not like you're getting paid to sit around at OpenAI. You're building the cutting edge of things that are- changing millions of people's relationship to tech.

- But publication-wise, they're being more secretive, increasingly so. So you're publishing less and less. And so you are having

so. So you're publishing less and less. And so you are having a positive impact at scale, but you're a cog in the machine.

- I think, honestly, it hasn't changed that much. So

I have been in academia; I'm not in academia anymore. At the same time, I wouldn't want to miss my time in academia. But what I wanted to say before I get to that part, I think it hasn't changed that much. I was working in, like, I was using AI or machine learning methods for applications in computational biology with collaborators, and a lot of people went from academia directly to

people went from academia directly to Google. And I think it's the same thing. Back then, professors were, you know, sad that their students went into industry because they couldn't carry on their legacy in that sense. And I think it's the same thing. It hasn't changed, I think, that much. The only thing that has

thing. It hasn't changed, I think, that much. The only thing that has changed is the scale. But, you know, cool stuff was always developed in industry that was closed. You couldn't talk about it. And I think the difference now is, well, your preference. Do you like to talk about your work and publish, or you are more in a closed lab? That's one difference, the compensation,

closed lab? That's one difference, the compensation, of course. But it's always been like that, I think. So it really

of course. But it's always been like that, I think. So it really depends on where you feel comfortable. And also, nothing is forever. The only thing right now is there's a third option, which is

forever. The only thing right now is there's a third option, which is starting a startup. That's a lot of people doing startups. Very risky move. But it is a

startups. Very risky move. But it is a high-risk, high-reward type of situation, whereas joining an industry lab, I think, is pretty safe. Also upward mobility. Honestly,

I think once you have been at an industry lab, it will be easier to find future jobs. But then again, you know, it's

future jobs. But then again, you know, it's like, yeah, how much do you enjoy the team and working on proprietary things versus how do you like the publishing work? I mean, publishing is stressful. It is.

publishing work? I mean, publishing is stressful. It is.

You know, the acceptance rate at conferences can be arbitrary, can be very frustrating, but also high reward if you have a paper published, you feel good because your name is on there. You have a high accomplishment, and you know.

- I feel like my friends who are professors seem on average happier than my friends who work at- ...a frontier lab. To be totally honest. Because that's just grounding and-

...a frontier lab. To be totally honest. Because that's just grounding and- ...the frontier labs definitely do this 9/9/6-

...the frontier labs definitely do this 9/9/6- ...which essentially is shorthand for work all the time.

...which essentially is shorthand for work all the time.

- Can you describe 9/9/6 as culture that, I believe you could say was invented in China and adopted in Silicon Valley? What's 9/9/6? It's 9:00 AM to 9:00 PM.

- Six days a week.

- Six days a week. What is that, 72 hours? Okay. So, is this basically the standard in AI companies in Silicon Valley? More and more this kinda grind mindset.

- Yeah, I mean, maybe not exactly like that, but I think there is a trend towards it.

And it's interesting. I think it almost flipped. Because when I was in academia, I felt like that because, as a professor, you had to write grants. You had to teach and you had to do your research. It's like

grants. You had to teach and you had to do your research. It's like

three jobs in one, and it is more than a full-time job if you wanna be successful. And I feel like now, like Nathan

successful. And I feel like now, like Nathan just said, the professors in comparison to a lab, I think they have less pressure or workload than at a frontier lab because— - I think they work a lot. They're just so fulfilled. By working with students— and having a constant runway of mentorship and a mission that is very people-oriented. In an era when things are moving very fast

very people-oriented. In an era when things are moving very fast and are very chaotic, it's very rewarding to people.

- Yeah, and I think at a startup, it's this pressure. It's like

you have to make it. It is really important that people put in the time, but it is really hard because you have to deliver constantly, and I've been at a startup. I had a good time, but I don't know if I could do it forever. It's an interesting pace, and it's

forever. It's an interesting pace, and it's exactly like we talked about in the beginning. These models are leapfrogging each other, and they are constantly trying to take the next step compared to their competitors. It's just ruthless, I think, right now.

- I think this leapfrogging nature and having multiple players is actually an underrated driver of language modeling progress, where competition is so deeply ingrained in people, and these companies have intentionally created very strong culture. Like, Anthropic is known to be so culturally, deeply committed and organized. I mean, we hear so little from them, and

everybody at Anthropic seems very aligned. And it's like being in a culture that is super tight—and having this competitive dynamic is a thing that's gonna make you work hard and create things that are better.

So that comes at the cost of human capital, which is like you can only do this for so long, and people are definitely burning out. I think

I wrote a post on burnout as I've tread in and out of this myself, especially trying to be a manager, full-mode training.

It's a crazy job. The book After Steve by Patrick McGee talks about how hard the Apple engineers worked to set up supply chains in China, and he said they had "saving marriage" programs, and he said in a podcast, "People died from this level of working hard." So I think it's just like it's a perfect environment for creating progress based on human

expense, and there's a lot of... the

human expense is the 996 that we started this with, where ... people do really grind.

... people do really grind.

- I also read this book. I think they had a code word for if someone had to go home to spend time with their family to save the marriage. It's crazy. Then the

colleagues said, "Okay, this is red alert for this situation. We have to let that person go home this weekend." But at the same time, I don't think they were forced to work. They were so passionate about the product that you get into that mindset. And I had that sometimes as an academic, but also as an independent person, I have that sometimes. I overwork,

and it's unhealthy. I had back issues, I had neck issues, because I did not take the breaks that I maybe should have taken. But no one forced me to; it's because I wanted to work, because it's exciting stuff.

- That's what OpenAI and Anthropic are like. They want to do this work.

- Yeah, but there's also a feeling of fervor that's building, especially in Silicon Valley, aligned with the scaling laws idea, where there's this hype where the world will be transformed in a scale of weeks and you want to be at the center of it. And then, you know, I have this great fortune of having conversations with a wide variety of human beings,

and from there I get to see all these bubbles and echo chambers across the world. And it's fascinating to see how we humans form them. And I think it's

the world. And it's fascinating to see how we humans form them. And I think it's fair to say that Silicon Valley is a kind of echo chamber, a kind of silo and bubble. I think bubbles are actually really useful and

bubble. I think bubbles are actually really useful and effective. It's not necessarily a negative thing because you could be ultra-productive.

effective. It's not necessarily a negative thing because you could be ultra-productive.

It could be the Steve Jobs reality distortion field, because you just convince each other that breakthroughs are imminent, and by convincing each other of that, you make the breakthroughs imminent.

- Burn Hobart wrote a book classifying bubbles, but essentially one of them is financial bubbles, which is like speculation, which is bad, and the other one is like, I don't know the term, but effectively for build-outs, because it pushes people to build these things. And I do think AI is in this, but I worry about it transitioning to a financial bubble, which is...

- Yeah, but also in the space of ideas, that bubble, you are doing a reality distortion field, and that means you are deviating from reality, and if you go too far from reality while also working, you know, 996, you might miss some fundamental aspects of the human experience, including outside Silicon Valley. This is a common problem in Silicon Valley:

it's a very specific geographic area. You might not understand the Midwest perspective, the full experience of all the other humans in the United States and across the world, and you speak a certain way to each other, you convince each other of a certain thing, and that can get you into real trouble. Whether AI

is a big success and becomes a powerful technology or it's not, in either trajectory you can get yourself into trouble. So you have to consider all of that. Here you are, a young

trouble. So you have to consider all of that. Here you are, a young person trying to decide what you want to do with your life.

- The thing that is... I don't even really understand this, but the SF AI memes have gotten to the point where "permanent underclass" was one of them, which was the idea that the last six months of 2025 was the only time to build durable value in an AI startup or model. Otherwise, all the value will be captured by existing companies and you will therefore be poor, which...

like, that's an example of the SF thing that goes so far. I still

think for young people going to be able to tap into it, if you're really passionate about wanting to have an impact in AI, being physically in SF is the most likely place where you're going to do this. But it has trade-offs.

- I think SF is an incredible place, but there is a bit of a bubble. And if you go into that bubble, which is extremely

a bubble. And if you go into that bubble, which is extremely valuable, just get out also. Read history

books, read literature, visit other places in the world.

Twitter is not, and Substack is not the entire world.

- One of the people I worked with is moving to SF, and I need to get him a copy of Season of the Witch, which is a history of SF from 1960 to 1985, which goes through the hippie revolution, all the gay community taking over the city and that culture emerging, and then the HIV/AIDS crisis and other things. And it's just so recent, and

there's so much turmoil and hurt, but also love in SF.

No one knows about this. It's a great book, Season of the Witch. I recommend it.

A bunch of my SF friends who get out recommended it to me. And I think that's just living there... I lived there and I didn't appreciate this context, and it's

there... I lived there and I didn't appreciate this context, and it's just, like, so recent.

- Yeah. Okay, let's... We talked a lot about a lot of things. Certainly about

the things that were exciting last year. But this year, one of the things you guys mentioned that's exciting is the scaling of text diffusion models—just a different exploration of text diffusion. Can you talk about what that is and the possibility it holds?

So, different kinds of approaches than the current LLMs?

- Yeah, so we talked a lot about the transformer architecture and the autoregressive transformer architecture, specifically, like GPT. It doesn't mean no one else is working on anything else. People are always on the lookout for the next big thing. Because I think it would be almost stupid not to. Because sure, right now, the transformer architecture is the thing, and it works best, and there's nothing

else out there. But it's always a good idea to not put all your eggs into one basket. So, people are developing other alternatives to the

basket. So, people are developing other alternatives to the autoregressive transformer. One of them would be, for example, text

autoregressive transformer. One of them would be, for example, text diffusion models. And listeners may know diffusion models from image

diffusion models. And listeners may know diffusion models from image generation, like Stable Diffusion popularized it. There was a paper on generating images. Back then, people used GANs (Generative Adversarial

generating images. Back then, people used GANs (Generative Adversarial Networks). And then there was this diffusion process where you iteratively

Networks). And then there was this diffusion process where you iteratively denoise an image, and that resulted in really good quality images over time. Stable Diffusion was a company; other companies built their own diffusion models.

time. Stable Diffusion was a company; other companies built their own diffusion models.

And then people are now like, "Okay, can we try this also for text?"

It doesn't make intuitive sense yet, because it feels like it's not something continuous like a pixel that we can differentiate. It's discrete text, so how do we implement that denoising process? It's kind of similar to the BERT models by Google. Like when you go back to the original transformer, there were the encoder and

Google. Like when you go back to the original transformer, there were the encoder and the decoder. The decoder is what we are using right now in GPT and so

the decoder. The decoder is what we are using right now in GPT and so forth. The encoder is more like a

forth. The encoder is more like a parallel technique where you have multiple tokens that you fill in in parallel. GPT models do autoregressive generation, completing the sentence one token at a time. And in

BERT models, you have a sentence that has gaps.

You mask them out, and then one iteration is filling in these gaps. Text diffusion is like that, where you

these gaps. Text diffusion is like that, where you are starting with some random text, and then you are filling in the missing parts or refining them iteratively over multiple iterations. The cool thing here is that this can do

iterations. The cool thing here is that this can do multiple tokens at the same time. So, it has the promise of being more efficient. Now, the trade-off is, of course, how good is the quality? It might be faster, but now you have this dimension of the denoising process. The more steps you do, the better the text

becomes. You can scale in different ways. Researchers are trying to see if that is

becomes. You can scale in different ways. Researchers are trying to see if that is maybe a valid alternative to the autoregressive model in terms of giving you the same quality for less compute.

Right now, papers suggest that if you want to get the same quality, you have to crank up the denoising steps, and then you end up spending the same compute you would on an autoregressive model.

The other downside is that while parallel sounds appealing, some tasks are not parallel, like reasoning tasks or tool use, where you might have to ask a code interpreter to give you an intermediate result. That is tricky with diffusion models. So there are some hybrids. But the main idea is: how can we parallelize it? It is an

hybrids. But the main idea is: how can we parallelize it? It is an interesting avenue. Right now, there are mostly

interesting avenue. Right now, there are mostly research models out there, like LaMDA and some other ones.

I saw some by startups, but there is no big diffusion model at Gemini or ChatGPT scale yet.

But there was an announcement by Google, where they said they are launching Gemini Diffusion, and they put it into the context of their Nano 2 model.

They said basically for the same quality on most benchmarks, we can generate things much faster. You mentioned what's next— I don't think text diffusion will replace autoregressive LLMs, but it will be something for quick, cheap, at-scale tasks. Maybe the

free tier in the future will be something like that.

- To paint an example of why this is so much better: for example, when GPT-5 takes 30 minutes to respond, it's generating one token at a time. This diffusion idea is to essentially generate all of those tokens in the completion in one batch, which is why it could be way faster. I think it could be suited for startups doing code, where you have a

code base, and you have somebody that's effectively vibe coding, and they say, "Make this change." And a code diff is essentially a huge reply from the model, but it doesn't have to have that much external context, and you can get it really fast by using these diffusion models.

One example I've heard of is using text diffusion to generate really long diffs, because doing it with an autoregressive model would take minutes, and that latency for a user-facing product causes a lot of churn.

every second, you lose a lot of users. So I think that it's gonna be this thing where it's gonna- ...grow and have some applications, but I actually thought that different types of models were

...grow and have some applications, but I actually thought that different types of models were going to be used for different things sooner than they have been. I think the tool use point is the one that's stopping them from being more general purpose. Because, in Claude and ChatGPT, the autoregressive chain is interrupted with some external tool, and

I don't know how to do that with the diffusion setup.

- So what's the future of tool use this year and then in the coming years? Do you

think there's gonna be a lot of developments there, and how that's integrated into the entire stack?

- I do think right now, it's mostly on the proprietary LLM side, But I think we will see more of that in the open-source tooling. And I

think, I mean, it is a huge unlock because then you can really outsource certain tasks from just memorization to actual computation.

Instead of having the LLM memorize what is 23 plus five, just use a calculator.

- So do you think that can help solve hallucination?

- Not solve it, but reduce it. Still, the LLM needs to know when to ask for a tool call. And the

second one is, well, it doesn't mean the internet is always correct. You can do a web search, but let's say I asked who won the World Cup in, let's say, 1998, it still needs to find the right website and get the right information. So you can still go to the incorrect website and get incorrect

information. So you can still go to the incorrect website and get incorrect information. So I don't think it will fully solve that, but it is

information. So I don't think it will fully solve that, but it is improving it in that sense. And

So, another cool paper earlier this year, I think it was from late December, so it's not technically 2024...

...but close. It was about the recursive language model.

That's a cool idea to kind of take this even a bit further. So,

just to explain so Nathan, you also mentioned earlier, it's harder to do cool research in academia because of the compute budget. If I

recall correctly, they did everything with GPT-4, so they didn't even use local models, but the idea is, let's say you have a long-context task. Instead of having the LLM solve all of it in one shot or even in a chain, you break it down into sub-tasks. You have the LLM decide what is a good sub-task, and then recursively call an LLM to solve that. And I think something like

that—then adding tools and, you know, for each one maybe you have a huge Q&A task, so each one goes to the web and gathers information, and then you pull it together at the end and stitch it back together.

I think there's gonna be a lot of unlocks using things like that where you don't necessarily improve the LLM itself, you improve how the LLM is used and what the LLM can use. One downside right now with tool use is you have to give the LLM permission to use tools. And

That will take some trust, especially if you want to unlock things like having an LLM answer emails for you, or not even answer, but just sort them for you or select them for you or something like that. I don't know if I would today give an LLM access to my emails, right? I mean, this is a huge risk.

- I think there's one last point on the tool use thing. I think that you hinted at this, and we've both come at this in our own ways, is that the open versus closed models use tools in very different ways, where open models, people go to Hugging Face and download the model, and then the person's going to be like, "What tool do I want?"

Exa is my preferred search provider, but somebody else might care for a different search startup. When you release a model, it needs to be useful for multiple tools, for multiple use cases, which is really hard because you're making a general reasoning engine model, which is actually what GPT-4 is good for. But on the closed models, you're deeply integrating

good for. But on the closed models, you're deeply integrating the specific tool into your experience, and I think that open models will struggle to replicate some of the things that I like to do with closed models, which would be like referencing a mix of public and private information. And something that I keep trying every three to six months,

information. And something that I keep trying every three to six months, I try things like Cursor on the web, which is just prompting a model to make an update to some GitHub repository that I have. And

that set of secure cloud environments is just so nice for just saying, "send it off and do this thing and then come back to me." And these will probably help define some of the local open and closed

me." And these will probably help define some of the local open and closed niches, but I think initially because there was such a rush to get this tool use working that the open models were on the back foot, which is kind of inevitable.

There's so much research and so many resources in these frontier labs, but it'll be fun when the open models solve this because it's going to necessitate a bit more flexible and potentially interesting model that might work with this recursive idea to be an orchestrator and a tool use model, so hopefully the necessity drives some interesting innovation there.

- So, continual learning—this is a longstanding topic, an important problem. I think that increases in importance as the cost of training the models goes up. So can you explain what continual learning is and how important it might be this year and in the coming years to make progress?

- This relates a lot to this kind of SF zeitgeist of: What is AGI, which is Artificial General Intelligence, and what is ASI, Artificial Superintelligence, and what are the language models that we have today capable of doing? I think language models can solve a lot of tasks, but a key milestone among the AI community is essentially when AI could replace any remote worker,

taking in information and solving digital tasks. And

the limitation that's highlighted by people is that a language model will not learn from feedback the same way that an employee does. So if you hire an editor, the editor will mess up, but

employee does. So if you hire an editor, the editor will mess up, but you will tell them. And if you hired a good editor, they don't do it again. But language models don't have this ability to modify themselves and learn very quickly. So the idea is, if we are going to actually get to something that is a true, general adaptable intelligence that can go into any remote work scenario, it needs to be able to learn

quickly from feedback and on-the-job learning.

I'm personally more bullish on language models being able to just provide them with very good context. You said, maybe offline, that you can write extensive documents to models where you say, "I have all this information. Here's all the blog posts I've ever written.

I like this type of writing. My voice is based on this." But a lot of people don't provide this to models, and the models weren't designed to take this amount of context previously. Agentic models are just starting. So it's

this kind of trade-off of, do we need to update the weights of this model with this continual learning thing to make them learn fast? Or the

counterargument is we just need to provide them with more context and information, and they will have the appearance of learning fast by just having a lot of context and being very smart.

- So we should mention the terminology here. So continual

learning refers to changing the weights continuously so that the model adapts, adjusts based on the new incoming information, does so continually and rapidly and frequently and so on.

And then the thing you mentioned on the other side of it is generally referred to as in-context learning. As

you learn stuff, there's a huge context window. You can

just keep loading it with extra information every time you prompt the system, which I think both can legitimately be seen as learning.

It's just a different place where you're doing the learning.

- I think, to be honest with you, continual learning, the updating of weights, we already have that in different flavors. I mean, if you think about how... So I think the distinction here is do you do

how... So I think the distinction here is do you do that on a personalized custom model for each person, or do you on a global model scale? And I think we have that already with, going from GPT-5 to 5.1 and 5.2. It's

maybe not immediate, but it is like a curated update, a quick curated update where there was feedback on things they couldn't do, feedback by the community. They updated the weights, next model and so forth. So it

community. They updated the weights, next model and so forth. So it

is, I mean, kind of like a flavor of that. Other

An even finer-grained example is like RLVR; you run it, it updates. The problem is you can't just do that for each person because it would be too expensive to update the weights for each person, and I think that's the problem. So unless you get...

I mean, even at OpenAI scale, building the data centers, it would be too expensive. I think that is only feasible once you have something

expensive. I think that is only feasible once you have something on the device where the cost is on the consumer. Like what Apple tried to do with the Apple Foundation models, putting them on the phone, and then they learn from the experience.

- A bit of a related topic, but this kind of maybe anthropomorphized term: memory.

What are different ideas of the mechanism of how to add memory to these systems, especially personalized memory?

- So right now, it's mostly like context—basically stuffing things into the context and then just recalling that.

But again, I think it's expensive because you have to—I mean, you can cache it, but still you spend tokens on that. And the second one is you can only do so much. I think it's more like a preference or style. A lot of people do

so much. I think it's more like a preference or style. A lot of people do that when they solve math problems. You can say you can add previous knowledge and stuff, but you also give it certain preference prompts: "do what I preferred last time," or something like that. But

it doesn't unlock new capabilities. So for that, one thing people still use is LoRA adapters. These are basically—instead of updating the whole weight

LoRA adapters. These are basically—instead of updating the whole weight matrix, there are two smaller weight matrices that you kind of have in parallel or overlays, like the delta. But yeah, you can do that to some extent, but then again, it is economics. There were also papers, for example, showing LoRA learns less but forgets less.

It's like, you know, there's no free lunch. If you want to learn more, you need more weights, but it gets more expensive. And then again, if you learn more, you forget more; you have to find that Goldilocks zone, basically.

- We haven't really mentioned it much, but implied in this discussion is context length also. Is there a lot of innovation that's possible there?

- I think the colloquially accepted thing is that it's a compute and data problem where you can... and some of the times, like, small architecture things, which are like attention variants. We talked about hybrid attention models, which is essentially if you have what looks like a state space model within your transformer. And those are better suited because you have to spend less compute to model the

furthest along token. I think that— but those aren't free because they have to be accompanied by a lot of compute or the right data. So how many sequences of 100,000 tokens do you have in the world, and where do you get these? It just ends up being pretty expensive to scale them. We've gotten pretty quickly to a million tokens

of input context length. I would expect it to keep increasing and get to 2 million or 5 million this year, but I don't expect it to go to 100 million. That would be a true breakthrough, and I think those

million. That would be a true breakthrough, and I think those breakthroughs are possible. The continual learning thing is a research problem where there could be a breakthrough that just makes transformers work way better at this and it's cheap. These things could happen with so much scientific attention. But turning the crank, it'll be consistent increases over time.

attention. But turning the crank, it'll be consistent increases over time.

- I think also looking at the extremes, there's again no free lunch. On the one extreme, to make it cheap, you have an RNN that has a single state where you save everything from the previous stuff. It's like a specific fixed-size thing, so you never really grow the memory. You are stuffing everything into one state, but

memory. You are stuffing everything into one state, but then the longer the context gets, the more information you forget because you can't compress everything into one state. Then, on the other hand, you have transformers, which try to remember every token—which is great sometimes if you want to look up specific information, but very expensive because the KV cache and the dot product grow.

Then, yeah, like you said, the Mamba layers—they kind of have the same problem.

Like an RNN, you try to compress everything into one state; you're a bit more selective But then I think it's like this Goldilocks zone again with Nemotron-3, they found like a good ratio of how many attention layers do you need for the global information where everything is accessible compared to having these compressed states. And I think that's how we will scale more by finding better,

let's say, ratios in the Goldilocks zone, like between making computing cheap enough to run, but then also making it powerful enough to be useful. And one more plug here, the recursive language model paper is one of the papers that tries to address the long-context thing. What they found is essentially, instead of stuffing everything into this long context,

if you break it up into multiple smaller tasks, so you save memory by having multiple smaller cores, you can get actually better accuracy than having the LLM try everything all at once.

It's a new paradigm. We will see. There might be other flavors of that. So I think with that, we will still make improvement on

that. So I think with that, we will still make improvement on long context, but then also, like Nathan said, I think the problem is for pre-training itself, we don't have as many long-context documents as other documents. So it's harder to study, basically, how LLMs behave on that level.

documents. So it's harder to study, basically, how LLMs behave on that level.

- There are some rules of thumb where you pre-train a language model, like OLMo— we pre-trained at like 8K context length and then extended to 32K with training. There's some rules of thumb where you're essentially doubling the training context length, it takes like 2X compute, and then you can normally 2 to 4X the context length again.

So I think a lot of it ends up being kind of compute-bound at pre-training, which is in this... Everyone talks about this big increase in compute for the top labs this year, and that should reflect in longer context windows.

But on the post-training side, there's some more interesting things: as we have agents, the agents are going to manage this context on their own, where now people that use Claude a lot dread the compaction, which is when Claude takes its entire full 100,000 tokens of work and compacts it into a bulleted list. But what the next models will do— I'm sure people are already

list. But what the next models will do— I'm sure people are already working on this—is essentially the model can control when it compacts and how. So you can essentially train your RL algorithm where compaction is an action, where it shortens the history and then the problem formulation will be, "I want to keep the maximum evaluation scores that I have gotten

while the model compacts its history to the minimum length." Because then you have the minimum amount of tokens you need to do this kind of compounding autoregressive prediction. There are actually pretty nice problem setups in this, where

autoregressive prediction. There are actually pretty nice problem setups in this, where these agentic models learn to use their context in a different way than just plowing forward.

- One interesting recent example would be DeepSeek-V3, where they had a sparse attention mechanism and essentially a very efficient, small, lightweight indexer. And

instead of attending to all the tokens, it selects: "Okay, what tokens do I actually need?"

It almost comes back to the original idea of attention where you are selective, but attention is always on—you have zero weight on some of them, but you use them all. But they are even more like, "Let's just mask that out or not even do that."

With sliding window attention in OLMo, that is also kind of that idea. You have a rolling window where you keep it fixed, because you don't need everything all the time. Occasionally, some layers might, but it's wasteful. But right now, I think, if you use everything, you're on the safe side. It gives you the best bang for the buck because you never miss information. And right now, I think this year will be more

about figuring out, like you said, how to be smarter about that. I think right now people want to have the next state-of-the-art, and the

that. I think right now people want to have the next state-of-the-art, and the state-of-the-art happens to be the brute-force, expensive thing. And then once you have that, like you said, keep that

thing. And then once you have that, like you said, keep that accuracy, but let's see how we can do that cheaper using tricks.

- Yeah. All this scaling thing.

The reason we get the Claude 3.5 Sonnet model first is because you can train it faster and you're not hitting these compute walls as soon.

They can just try a lot more things and get the model faster, even though the bigger model is actually better.

- I think we should say that there's a lot of exciting stuff going on in the AI space.

My mind has recently been really focused on robotics. We almost entirely haven't talked

robotics. We almost entirely haven't talked about robotics. There's a lot of stuff on image generation,

about robotics. There's a lot of stuff on image generation, video generation. I think it's fair

video generation. I think it's fair to say that the most exciting research work in terms of the amount, intensity, and fervor is in the LLM space, which is why I think it's justified for us to really focus on the LLMs that we're discussing. But it'd be nice to bring in certain things that might be useful. For example, world models;

there's growing excitement on that. Do you think there will be any use in this coming year for world models in the LLM space?

Yes, I do think so. Also with LLMs, what's interesting here is that if we unlock more LLM capabilities, it automatically unlocks all the other fields because it makes progress faster. A lot of researchers and engineers use LLMs for coding. So even if they work on robotics, if you optimize these LLMs

coding. So even if they work on robotics, if you optimize these LLMs that help you with coding, it pays off. But then,

yes, world models are interesting. It's basically where you have the model run a simulation of the world—a little toy thing of the real thing—which can, unlock capabilities regarding data the LLM is not aware of. It can simulate things. And I think LLMs just happen to work

well by pre-training and then doing next-token prediction. But

we could do this even more sophisticatedly.

So what I'm saying is... there's a paper, I think it was by Meta, called "World Models." So where they basically apply the concept of world models to LLMs again, where instead of just having next-token prediction and verifiable rewards checking the answer correctness, they also make sure the intermediate variables are correct. It's kind of like

the model is learning basically a code environment, in a sense. And I

think this makes a lot of sense. It's just expensive to do, but it is making things more sophisticated, like modeling the whole thing, not just the result. And so it can add more value. I remember when I was a grad student, there's a

competition called CASP, I think, where they do protein structure prediction. They predict the structure of a protein

structure prediction. They predict the structure of a protein that is not solved yet at that point. So in a sense, this is actually great, and I think we need something like that for LLMs also, where you do the benchmark, but you hand in the results and no one knows the solution. And then after the fact, someone reveals that. But,

AlphaFold, when it came out, it crushed this benchmark. I mean, there were also multiple

benchmark. I mean, there were also multiple iterations, but I remember the first one. I'm not an expert in that subject, but the first one explicitly modeled the physical interactions and the physics of the molecule.

Also the angles, impossible angles. And then in the next version, they got rid of this and just used brute-force scaling. I think with LLMs, we are currently in this brute-force scaling because it just happens to work. But I think at some point it might make sense to bring back this...

...modeling. And I think with world models, that is where it might be actually quite cool.

And of course, also for robotics, that is completely related to world models.

- Yeah. And robotics is very explicit. There's the problem of locomotion or manipulation.

Locomotion is much more solved, especially in the learning domain. But there's a lot of value, just like with the initial protein-folding systems, in bringing in the traditional model-based methods. So it's unlikely that you can just learn the manipulation or the whole-body manipulation problem end-to-end.

That's the dream. But then you realize, when you look at the magic of the human hand and the complexity of the real world, it's really hard to learn this all the way through... ...The way I guess AlphaFold 2 didn't.

way through... ...The way I guess AlphaFold 2 didn't.

- I'm excited about the robotic learning space; I think it's collectively getting supercharged by all the excitement and investment in language models generally. The infrastructure for training Transformers, which is a general modeling thing, is becoming world-class industrial tooling, wherever there was a limitation for robotics, it's just way better.

There's way more compute. They take these language models and use them as kind of central units where you can do interesting explorative work around something that kind of already works. And then I see it emerging as, kind of like we talked about, Hugging Face transformers and Hugging Face. I think when I was at Hugging Face, I was trying to get this to happen, but it was too early.

Hugging Face. I think when I was at Hugging Face, I was trying to get this to happen, but it was too early.

It's like these open robotic models on Hugging Face, and having people be able to contribute data and fine-tune them. I

think we're much closer now that the investment in robotics and self-driving cars is related and it enables this, where once you get to the point where you can have this sort of ecosystem where somebody can download a robotics model and maybe fine-tune it to their robot or share data sets across the world. And

There's some work in this area like RTX, I think it was a few years ago, where people are starting to do that. But I think once they have this ecosystem, it'll look very different. And then this whole post-ChatGPT boom is putting more resources into that, which I think is a very good area for doing research.

- This is also resulting in much better, more accurate, more realistic simulators being built, closing this sim-to-real gap in the robotic space. But, you know, you mentioned a lot of excitement in

space. But, you know, you mentioned a lot of excitement in the robotics space and a lot of investment. The downside of that, which happens in hype cycles, I personally believe—most robotics people believe—that it's not...

Robotics is not going to be solved at the time scale as being kind of implicitly or explicitly promised. And so what happens when there's all these robotics companies

promised. And so what happens when there's all these robotics companies that spring up and then they don't have a product that works, then there's going to be this kind of crash of excitement, which is nerve-wracking. Hopefully something else will come in and keep swooping

nerve-wracking. Hopefully something else will come in and keep swooping in so that the continued development of some of these ideas keeps going.

- I think it's also related to the continual learning issue, essentially, where the real world is so complex, where with LLMs, you don't really need to have something learn for the user because there are a lot of things everyone has to do. Everyone maybe wants to, I don't know, fix their grammar in their email or code or something like that. It's more constrained, so you can prepare the model for

that. But preparing the robot for the real world is harder. I mean, you

that. But preparing the robot for the real world is harder. I mean, you have the robotic foundation models, but you can learn certain things like grasping things. But then again, I think everyone's house is different, you know? It's so different and that is, I think where the robot would have to learn on the job, essentially. And I think that, I guess, is the bottleneck right now—how to,

customize it on the fly, essentially.

- I don't think I can possibly understate the importance of the thing that doesn't get talked about almost at all by robotics folks or anyone, is safety.

All the interesting complexities we talk about learning, all the failure modes and failure cases. Everything we've been talking about with LLMs, sometimes it fails in interesting

failure cases. Everything we've been talking about with LLMs, sometimes it fails in interesting ways. All of that is fun and games in the LLM space.

ways. All of that is fun and games in the LLM space.

In the robotic space, in people's homes, across millions of minutes and billions of interactions, you really are almost allowed to fail never. When you have embodied systems that are

never. When you have embodied systems that are put out there in the real world, you just have to solve so many problems you never thought you'd have to solve when just thinking about the general robot learning problem.

- I'm so bearish on in-home learned robots for consumer purchase. I'm very bullish on self-driving cars, and I'm very bullish for robotic automation, e.g., Amazon distribution, where Amazon has built whole new distribution centers designed for robots first rather than humans. There's a lot of excitement in AI circles about AI enabling automation

and mass-scale manufacturing. I do think the path to robots doing that is more reasonable, where it's a thing that is designed and optimized to do a repetitive task that a human could conceivably do but doesn't want to. But it's also going to take a lot longer than people probably predict. I think the leap from

AI singularity to scaling up mass manufacturing in the US because we have a massive AI advantage is one that is troubled by a lot of political and other challenging problems. - Let's talk about timelines, specifically timelines to AGI or ASI. Is it fair as a starting point to say that nobody really agrees on the

ASI. Is it fair as a starting point to say that nobody really agrees on the definitions of AGI and ASI?

- I think there's a lot of disagreement, but I've been getting pushback where people say the same thing, which is like a thing that could reproduce most digital economic work. The remote worker is a fairly reasonable example.

work. The remote worker is a fairly reasonable example.

And I think OpenAI's definition is somewhat related to that, which is like an AI that can do a lot of economically valuable tasks. I don't really love that as a definition, but I think it could be a grounding point, because language models today, while immensely powerful, are not this remote worker drop-in. And there are things that

could be done by an AI that are way harder than remote work, such as finding an unexpected scientific discovery that you couldn't even posit, which would be an example of artificial superintelligence. Or

artificial superintelligence. Or taking in all medical records and finding linkages across certain illnesses that people didn't know, or figuring out that some common drug can treat some niche cancer. They would say that is a superintelligence thing. So these are natural tiers. My problem with it is that it

thing. So these are natural tiers. My problem with it is that it becomes entwined with the quest for meaning and these religious aspects to it.

aspects to it. So there's different paths you can take.

- And I don't even know if the remote worker is a good definition, because what exactly is that? I actually,

like... I don't know if you like the originally titled SITUATIONAL AWARENESS report. They focus more on code and research taste, so the target there is the superhuman coder. So they have several milestone

superhuman coder. So they have several milestone systems: superhuman coders, superhuman AI researcher, then super intelligent AI researcher, and then the full ASI, artificial superintelligence, but the...

after you develop the superhuman coder, everything else follows quickly.

There, the task is to have fully autonomous, automated coding.

So any kind of coding you need to do in order to perform research is fully automated. And from there, humans would be doing AI research together with that system, and they will quickly be able to develop a system that can actually do the research for you. That's the idea. And initially their

for you. That's the idea. And initially their prediction was 2027 or '28, and now they've pushed it back by three to four years to 2031, mean prediction. Probably my prediction is even beyond 2031, but at least you can, in a concrete way, think about how difficult it is to fully automate programming.

- Yeah, I disagree with some of their presumptions and dynamics on how it would play out, but I think they did good work in the scenario defining milestones that are concrete and to tell a useful story, which is why the reach for this SITUATIONAL AWARENESS document transcended Silicon Valley. It's because they told a good story and they did a lot of rigorous work to do this.

I think the camp that I fall into is that AI is so-called jagged, which will be excellent at some things and really bad at others. I

think that when they're close to this automated software engineer, what it will be good at is traditional ML systems and front end, which the model is excellent at, but distributed ML, the models are actually quite bad at because there's so little training data on doing large-scale distributed learning. This is something that we already see, and I think this will just get amplified. It's kind of messier in these

trade-offs, and then there's how you think AI research works and so on.

- So you think basically superhuman coder is almost unachievable, meaning because of the jagged nature of the thing, you're just always going to have gaps in capabilities.

- I think it's assigning completeness to something where the models are kind of superhuman at some types of code, and I think that will continue. And people are creative, so they'll utilize these incredible abilities to fill in the weaknesses of the models and move really fast. They'll always

be this dance for a long time between the humans enabling what the model can't do, and the best AI researchers are the ones that can enable this superpower.

And I think those lines, from what we already see like code for building a website, you can stand up a beautiful website in a few hours or do data analysis. The whole thing is going to keep getting better at these things, and we'll pick up some

analysis. The whole thing is going to keep getting better at these things, and we'll pick up some new code skills along the way, and kind of linking to what's happening in big tech, this Situational Awareness report leans into the singularity idea where I think research is messy and social and largely in the data in ways that AI models can't

process. But what we do have today is really powerful, and these

process. But what we do have today is really powerful, and these tech companies are collectively buying into this with billions of dollars of investment. So we are going to get a much better version

investment. So we are going to get a much better version of ChatGPT and a much better version of Cursor than we already have.

It's just hard to predict where that is going, but the bright clarity of that future is why some of the most powerful people in the world are putting so much money into this. There are just small differences. We don't actually know what a better version of

small differences. We don't actually know what a better version of ChatGPT is, but also, can it automate AI research? I would say probably not, at least in this timeframe. Big tech is going to spend $100 billion much faster than we get an automated researcher that enables a research singularity.

- So your prediction would be what? Like, is this even a useful milestone, or more than 10 years out?

- I would say less than that on the software side, but longer on things like research.

- Let's just for fun try to imagine a world where all software writing is fully automated. Can you imagine that world?

- By the end of this year, the amount of software that's automated will be so high. But it'll be things like trying to train a model with

high. But it'll be things like trying to train a model with RL and you need to have multiple bunches of GPUs communicating with each other. That'll still be hard, but much easier.

- One way to think about this—the full automation of programming—is just to think of the fraction of useful code lines written compared to the number of humans in the loop. Presumably there'll be, for a long time, humans in the loop of software writing. It'll just be fewer and fewer relative to the amount of code written, right?

With the superhuman coder, the presumption there is that it goes to zero—the number of humans in the loop. What does that world look like when the number of humans in the loop is in the hundreds, not in the hundreds of thousands?

- I think software engineering will be driven more toward system design and outcomes. I think this has been happening over the last few weeks,

and outcomes. I think this has been happening over the last few weeks, where people have gone from a month ago saying, "Oh yeah, agents are kind of slop," which is a famous Andrej Karpathy quote, to what is a little bit of a meme: the industrialization of software, where anyone can create software.

I do think we are closer to that side of things. It takes

direction and understanding how the systems work to extract the best from the language models. It's hard to accept the gravity of how much is going to change with software development and how many more people can do things without ever looking at the code.

- I think what's interesting is to think about whether these systems will be completely independent. While I have no doubt

completely independent. While I have no doubt that LLMs will at some point solve coding in a sense, like calculators solved calculating, right? At some point, humans developed a tool where you never need a human to calculate that number.

You just type it in and it's an algorithm.

that sense. And I think that's the same probably for coding. But

the question is... I think what will happen is, you will just say, "Build that website."

It will make a really good website, and then you maybe refine it. But will it do things independently? Will you still be having humans asking the AI to do something? Like will there be a person to say, "Build that website?" Or will there be AI that just builds websites or something?

website?" Or will there be AI that just builds websites or something?

- I think talking about building websites is...

- Mm-hmm, too simple.

- The problem with websites and the problem with the web, HTML and all that kind of stuff, it's very resilient to just... Slop.

It will show you slop; it's good at showing slop. I would rather think of safety-critical systems, like asking AI to end-to-end generate something that manages logistics- or manage cars...

a fleet of cars, that kind of stuff. It end-to-end generates that for you.

- I think a more intermediate example is take something like Slack or Microsoft Word. I think if the organizations allow it, AI could very easily

Word. I think if the organizations allow it, AI could very easily implement features end-to-end and do a fairly good job for like things that you want to try. You want to add a new tab in Slack that you want to use, and I think AI will be able to do that pretty well.

- Actually, that's a really great example. How far away are we from that?

- Like this year.

- See, I don't know. I don't know.

- I don't know how bad production code bases are, but I think that within the next few years, a lot of people are going to be pushed to be more of a designer and product manager, where you have multiple agents that can try things for you, and they might take a day or two to implement a feature or attempt to fix a bug. And you have these dashboards,

which is actually a good dashboard where your agents will talk to you and you'll then give feedback. But things like, making a website logo that's passable... I think these cohesive design things and the style is going to be very hard for models and deciding on what to add the next time.

- Okay. So I hang out with a lot of programmers, and some of them are a little bit on the skeptical side in general.

I think there's a lot of complexity involved in adding features to complex systems. Like, if you look at the browser, Chrome.

If I wanted to add a feature, if I wanted to have tabs as opposed to up top, I want them on the left side.

Interface-wise, I think we're not... This is not a next year thing.

- For one of the Claude releases this year, one of their tests was: give it a piece of software and leave Claude to run to recreate it entirely, and it could already almost rebuild Slack from scratch, just given the parameters of the software and left in a sandbox environment to do that.

- So the "from scratch" part, I like almost better.

- It might be that the smaller and newer companies are advantaged, and they're like, "We don't have to have the bloat and complexity, and therefore this feature exists."

- And I think this gets to the point you mentioned, that some people you talk to are skeptical. I think that's not because the LLM can't do X, Y, Z. It's because people don't want it to do it this way.

- Some of that could be a skill issue on the human side. We have to be honest with ourselves. Some of that could be an underspecification issue.

So programming is like... you're just assuming... This is like a communication issue in relationships and friendships. You're assuming the LLM is supposed to read your mind. I think this is where spec-driven design is really important, where you just use natural language to specify what you want.

- If you talk to people at the labs, they use these in their training and production code. Claude Code is built with Claude Code, and they all use these things extensively. Dario talks about how much of Claude's code is generated this way.

These people are slightly ahead in terms of the capabilities they have and what they probably spend on inference.

They could spend 10 to 100x as much as we're spending, while we're on a lowly 100 or $200 a month plan. They truly let it rip. And I think that, with the pace of progress that we have, it seems like- a year ago we didn't have Claude Code and we didn't really have reasoning models. The difference between sitting here today and what we can do with these

models. The difference between sitting here today and what we can do with these models is huge. It seems like there's a lot of low-hanging fruit to improve them. The failure modes are pretty dumb. It's like-

"Claude, you tried to use the CLI command I don't have installed 14 times, and then I sent you the command to run." That kind of thing from a modeling perspective is pretty fixable. So I don't know.

- I agree with you. I've been becoming more and more bullish in general. Speaking to what you're articulating, I think it is a

general. Speaking to what you're articulating, I think it is a human skill issue. Anthropic is leading the way, along with other companies, in understanding how to best use models for programming; therefore they're effectively using them. I think many programmers are on the outskirts. They're

using them. I think many programmers are on the outskirts. They're

like... they don't... I mean, there's not a really good guide on how to use them. People are trying to figure it out, but- - It might be very expensive. The entry point might be $2,000 a month, which is only for tech companies and rich people. That could be it.

- But it might be worth it. If the final result is a working software system, it might be worth it. By the way, it's funny how we converged from the discussion of timeline to AGI to something more pragmatic and useful. Is there anything concrete, interesting and

useful. Is there anything concrete, interesting and profound to be said about timeline to AGI and ASI? Or are these discussions a bit too detached from the day to day?

ASI? Or are these discussions a bit too detached from the day to day?

- There's interesting bets. So there's a lot of people trying to do reinforcement learning with verifiable rewards, but in real scientific domains where there's startups that have hundreds of millions of dollars of funding and they have wet labs where they're having language models propose hypotheses that are tested in the real world. And I would say that they're early, but with the pace of progress it's like—

world. And I would say that they're early, but with the pace of progress it's like— maybe they're early by six months and they make it because they were there first, or maybe they're early by eight years, you don't really know. I think that type of moonshot to branch this momentum into other sciences is like, okay, that would be very transformative if

AlphaFold moments happen in all sorts of other scientific domains by a startup solving this. I think there are startups—I think maybe Harmonic is one—where they're going all in on language models plus Lean for math. I think you had another podcast guest where you talked about this recently, and it's

math. I think you had another podcast guest where you talked about this recently, and it's like we don't know exactly what's going to fall out of spending $100 million on that model. And most of them will fail, but a couple of them might be big breakthroughs that are very different than ChatGPT or Claude Code type software experiences. Like a tool that's only good for a

experiences. Like a tool that's only good for a PhD mathematician but makes them 100X more effective...

- I agree. I think this will happen in a lot of domains, especially also domains that have a lot of resources, like finance, legal, and pharmaceutical companies. But then again, is it really AGI? Because we are now

companies. But then again, is it really AGI? Because we are now specializing it again. And then again, is it really that much different from back in the day how we had specialized algorithms? I think it's just the same thing, way more sophisticated, but I don't know, is there a threshold when we call it AGI? I think the real cool thing here is that we have foundation models that we can specialize. I think that's like the

breakthrough. Right now, I think we are not there yet because first,

breakthrough. Right now, I think we are not there yet because first, it's too expensive, but also, ChatGPT doesn't just give away their model to customize it. I think

once that's going to be true... And I can imagine this as a business model where OpenAI says at some point like, "Hey, Bank of America, for $100 million we will do your custom model," or something like that. And I think that will be the huge economic value add. The other thing, though, is

value add. The other thing, though, is also... companies, I mean, right now, what is the differentiating

also... companies, I mean, right now, what is the differentiating factor? If everyone uses the same LLM, if everyone uses

factor? If everyone uses the same LLM, if everyone uses ChatGPT, they will all do the same thing. Again, I mean, then...

everyone is moving in lockstep, but usually companies want a competitive advantage, and I think there is no way around using some of their private data and experimenting and specializing. It's going to be interesting.

- Seeing the pace of progress, it does just feel like things are coming. I don't

think the AGI and ASI thresholds are particularly useful.

- I think the real question, and this takes us to the remote worker thing, is when are we going to see a big, obvious leap in economic impact?

Because currently there's not been an obvious leap in the economic impact of LLM models, for example. And

that's, you know, aside from AGI or ASI, all that kind of stuff, there's a real question of, like, "When are we going to see a GDP..."

"...jump?"

- Yeah, what is the GDP made up of? Like, a lot of it is financial services, so I don't know what this looks like.

- Right, GDP's a— - It's just hard for me to think about the GDP bump, but I would say that software development becomes valuable in a different way when you no longer have to look at the code anymore.

When Claude will make you a small business.

Which is essentially, Claude can set up your website, your bank account, your email, and whatever else. And you just have to express what you're trying

whatever else. And you just have to express what you're trying to put into the world. That's not just an enterprise market, but it is hard. I don't know how you get people to try doing that. I guess if ChatGPT can do it—people are trying ChatGPT.

that. I guess if ChatGPT can do it—people are trying ChatGPT.

- I think it boils down to the scientific question of, "How hard is tool use to solve?" Because a lot of the stuff you're implying,

to solve?" Because a lot of the stuff you're implying, the remote work stuff, is tool use. Computer

use, like how you have an LLM that goes out there, this agentic system, and does something in the world, and only screws up 1% of the time.

- Computer use— - Or less.

- ...is a good example of what labs care about and we haven't seen a lot of progress on.

We saw multiple demos in 2024 of, like, Claude can use your computer, or OpenAI had Operator, and they all suck.

They're investing money in this, and I think that'll be a good example. Whereas actually, something where it just seems

example. Whereas actually, something where it just seems pretty... Like, taking over the whole screen seems a lot harder

pretty... Like, taking over the whole screen seems a lot harder than having an API that they can call in the back end, and some of that is you have to set up a different environment for them to work in.

They're not working on your MacBook; they are individually interfacing with Google and Amazon and Slack, and they handle things in a very different way than humans do. So some of this might be structural blockers.

- Also, specification-wise, I think the problem is for arbitrary tasks, you still have to specify what you want your LLM to do. What is the environment? How do you specify? You can say what the end goal is, but

environment? How do you specify? You can say what the end goal is, but if it can't solve the end goal—with LLMs, if you ask for text, it can always clarify or do substeps. How do you put that information into a system that, let's say, books a trip for you? You can say, "You screwed up my credit card info," but even to get it to that point,

how do you, as a user, guide the model before it can even attempt that? I think the interface is really hard.

- Yeah, it has to learn a lot about you specifically.

And this goes to continual learning—about the general mistakes that are made throughout, and then mistakes made through you.

- All the AI interfaces are getting set up to ask humans for input. I think Claude we talked about a lot.

input. I think Claude we talked about a lot.

It asks for feedback. If it doesn't have enough specification on your plan or your desires, it starts to ask, "Would you rather?" We talked about Memory, which saves across chats. Its

first implementation is kind of odd, where it'll mention my dog's name or something in a chat. I'm like, "You don't need to be subtle about this. I don't care."

But things are emerging, like ChatGPT has the Pulse feature.

Which is a curated couple of paragraphs with links to look at or talk about. People talk about how language models are going to ask you questions, which I think is a very... It's probably going to work. The language model knows

very... It's probably going to work. The language model knows you had a doctor appointment; it's like, "Hey, how are you feeling?"

This goes into the territory where humans are very susceptible and there's a lot of social change to come. But also,

they're experimenting with having models engage. Some people like this Pulse feature; it processes your chats and automatically searches for information and puts it in the ChatGPT app. There are a lot of things coming.

- I used that feature before, and I feel bad because it does that every day, and I rarely check it. I think about how much compute is burned on something I don't even look at, you know?

Where it's like, "Oh..."

- There's also a lot of idle compute in the world, so don't feel too bad.

- Okay. Do you think new ideas might be needed? Is it possible that the path to AGI, whatever that is, however we define

needed? Is it possible that the path to AGI, whatever that is, however we define that—to solve computer use more generally, to solve biology and chemistry and physics, sort of the Dario's definition of AGI or powerful AI? Do you think it's possible that totally new ideas are needed? Non-LLM, non-RL ideas. What might they

look like? We're now going into philosophy land a little bit.

look like? We're now going into philosophy land a little bit.

- For something like a singularity to happen, I would say yes. And the

new ideas could be architectures or training algorithms— fundamental deep learning things. But that's,

by nature, pretty hard to predict. I think we won't get very far even without those advances. We might get this software solution, but it might stop at software and not do computer use without more innovation. So I think a lot of progress will be coming, but if you zoom out, there's still ideas in the next 30 years that are gonna look like that was a

major, like, scientific innovation that enabled the next chapter of this. And I don't know if it comes in one year or in 15 years.

- Yeah. I wonder if the bitter lesson holds true for the next 100 years what that looks like.

- If scaling laws are fundamental in deep learning, I think the bitter lesson will always apply, which is compute will become more abundant, but even within abundant compute, the ones that have a steeper scaling law slope or a better offset, like, this is a 2D plot of performance and compute. And like

even if there's more compute available, the ones that get 100X out of it will win.

- It might be something like literally computer clusters orbiting Earth with solar panels.

- The problem with that is heat dissipation. So you get all the radiation from the sun and you don't have any air to dissipate heat. But there is a lot of space to put clusters.

There's a lot of solar energy there and you could figure out the heat dissipation, but there is a lot of energy and there probably could be engineering will to solve the heat problem. ...So there could be.

heat problem. ...So there could be.

- Is it possible, and we should say that it definitely is possible Is the question that we're basically going to be plateauing this year?

Not in terms of- ... the system capabilities, but what the system capabilities actually mean for

... the system capabilities, but what the system capabilities actually mean for human civilization. So on the coding front, really nice websites will be built.

human civilization. So on the coding front, really nice websites will be built.

Very nice auto-complete.

Very nice way to understand code bases and maybe help debug, but really just a very nice helper on the coding front. It can help research mathematicians do some math. It can help you with shopping. It's a nice helper. It's Clippy on

shopping. It's a nice helper. It's Clippy on steroids. What else? It may be a good education tool and all that kind of stuff,

steroids. What else? It may be a good education tool and all that kind of stuff, but computer use turns out extremely difficult to solve. So I'm trying to be ... I'm trying to frame the cynical

solve. So I'm trying to be ... I'm trying to frame the cynical case in all these domains where it kind of ... There's not a really huge economic impact, but realize how costly it is to train these systems at every level, both the pre-training and the inference, how costly the inference is, the reasoning, all of that.

Like, is that possible? And how likely is that do you think?

- When you look at the models, there's so many obvious things to improve and it takes a long time to train these models and to do this art and that it'll take us with the ideas that we have multiple years to actually saturate in terms of whatever benchmark or performance we are searching for. It might serve very narrow niches, like the average ChatGPT 800 million user might

not get a lot of benefit out of this, but it is going to serve different populations by getting better at different things.

- But I think what everybody's chasing now is a general system that's useful to everybody. So if

that's not... That can plateau, right?

- I think that dream is actually kind of dying. As you talked about with the specialized models where it's like...

And multimodal is often... Like, video generation is a totally different thing.

- "That dream is kind of dying" is a big statement, because I don't know if it's dying. If you ask the actual Frontier Lab people, they're still chasing it, right?

- I do think they are still rushing to get the next model out, which will be much better than the previous one.

And I can't see them slowing down. I just think the gains will be made or felt more not only through scaling the model, but now so I feel like there's a lot of tech debt. It's like, "Well, let's just put the better model in there," better model, better model. And now people are like, "Okay, let's also at the same time improve everything around it too."

Like the engineering of the context and inference scaling. And

the big labs will still keep doing that. And now also the smaller labs will catch up to that because now they are hiring more.

There will be more people and LLMs. It's kind of like a circle. They

also make them more productive, and it's like an amplification. I think what we can expect is amplification, but not a change of any paradigm. I don't think that is true, but everything will be just amplified and amplified and amplified, and I can see that continuing for a long time, you know?

- Yeah. I guess my statement that the dream is dying depends on exactly what you think it's going to be doing. Like, Claude is a general model that can do a lot of things, but it's not like necessarily...

It depends a lot on integrations. Like, I bet Claude could do a fairly good job of doing your email, and the hardest part is figuring out how to give it information and how to get it to be able to send your emails and stuff like this. But

that's just kind of like... I think it goes back to what is the "one model to rule everything" ethos, which is just like a thing in the cloud that handles your entire digital life and is way smarter than everybody. It's like it's operating in a...

So it's an interesting leap of faith to go from "Claude becomes that," which in some ways is... There are some avenues for that, but I do think that the rhetoric of the industry is a little bit different.

- I think the immediate thing we will feel next as a normal person using LLMs will probably be related to something trivial, like making figures. Right now, LLMs are terrible at making figures. Is it because we are getting served the cheap models

making figures. Is it because we are getting served the cheap models with much less inference compute than behind the scenes?

There are some ways we can already get better figures, but if you ask today, "Draw a flowchart of XYZ," it's most of the time terrible, and it is a very simple task for a human. It's almost

easier sometimes to draw something than to write something.

- Yeah, the multimodal understanding does feel like something that is odd that it's not better solved.

- I think there's one obvious thing that we're not realizing, a gigantic thing that's hard to measure: making all of human knowledge accessible to the entire world. One of the things that I think is hard to articulate, but there's just a huge difference between Google Search and an LLM. I feel

like I can basically ask an LLM anything and get an answer, and it's doing less and less hallucination.

And that means understanding my own life, figuring out a career trajectory, solving the problems around me, or learning about anything through human history.

I feel like nobody's really talking about that, because they immediately take it for granted that this is awesome. That's why everybody's using it. You get answers for

awesome. That's why everybody's using it. You get answers for stuff, and consider the impact of that across time.

This is not just in the United States. This is all across the world. Kids throughout the world being able to

the world. Kids throughout the world being able to learn these ideas—the impact that has across time is probably where the real, you know, GDP leap is. It won't be like a small jump.

That's how we get to Mars, how we build things, how we have a million new OpenAIs and the kind of innovation that happens.

That's just this quiet force that permeates everything: human knowledge.

- I agree with you, and in a sense it makes knowledge more accessible, but it also depends on what the topic is. For something like math, you can ask it questions and it answers, but if you want to learn a topic from scratch, I think the sweet spot is...

there are really good math textbooks laid out linearly, and that is a proven strategy to learn a topic. It

makes sense, if you start from zero, to ramp up using information-dense text to soak it up, but then use the LLM to make infinite exercises.

If you have problems in a certain area or have questions, or you are uncertain about certain things, you ask it to generate example problems, you solve them, and you have questions.

Then you might need more background knowledge and you ask it to generate that. But it won't give you anything that is not in the textbook. It's just packaging it differently.

if that makes sense. But then there are things where I feel like it adds value in a more timely sense, where there is no good alternative besides a human doing it on the fly. For example,

if you're planning to go to Disneyland and you try to figure out which tickets to buy for which park when, well, there is no textbook on that. No information-dense resource.

There's only the sparse internet, and there is a lot of value in an LLM. You have the constraints on traveling these days,

LLM. You have the constraints on traveling these days, I want to go there and there. Please figure out what I need, what it costs and stuff like that. It is a very customized, on-the-fly package.

This is one of a thousand examples of personalization.

Personalization is essentially pulling information from the sparse internet, the non-information-dense thing where there's no better version. It just doesn't exist. You make it from scratch almost.

- And if it does exist, it's full of, speaking of Disney World, full of... what would you call it? Ad slop.

of... what would you call it? Ad slop.

It's impossible to get. For any city in the world, what are the top 10 things to do? an LLM is just way better to ask than anything on the internet.

- For now, that's because they're massively subsidized.

They're going to be paid for by ads.

- Oh my goodness.

- It's coming.

- No. No. I'm hoping there's a very clear indication of what's an ad and what's not an ad in that context.

- That's something I mentioned a few years ago. I don't know, if you are looking for a new running shoe, is it a coincidence that Nike comes up first? Maybe, maybe not.

There are clear laws around this. You have to be clear about that, but that's what everyone fears: the subtle message in there.

It also brings us to the topic of ads.

I think this was a thing they tried to launch in 2025 because it's still not making money in other ways right now.

Having ad spots in there... the thing is, they couldn't, because there are alternatives without ads and people would flock to the other products. It's just crazy how they're one-upping each other spending so much money to just get the users.

- I think so. Like with Instagram ads—I don't use Instagram, but I understand the appeal of paying a platform to find users who will genuinely like your product. That is the best case of things like Instagram ads.

But there are also plenty of cases where advertising is very awful for incentives, and I think that a world where the power of AI can integrate with that positive view of: "I am a person, I have a small business, and I want to make the best, damn steak knives in the world, and I want to sell them to somebody who needs them." If AI can make that sort of advertising

work even better, that's very good for the world, especially with digital infrastructure, because that's how the modern web has been built. But that's not to say that

built. But that's not to say that addicting feeds, just to show people more content, is a good thing. I think that's even what OpenAI would say; they want to find a way that can make the monetization upside of ads while still giving their users agency.

I personally would think Google is probably going to be better at figuring out how to do this because they already have ad supply.

If they figure out how to turn this demand in their Gemini app into useful ads, then they can turn it on. And

somebody will figure it out—I don't know if it's this year, but there will be experiments with it.

- I do think what holds companies back right now is really just that the competition is not doing it. It's more of a reputation thing.

I think people are just afraid right now of ruining or losing their reputation, losing users, because it would make headlines if someone launched these ads.

- Unless they were great, but the first ads won't be great because it's a hard problem.

We don't know how to solve it yet.

- I think also the first version of that will likely be something like on X, like the timeline where you have a promoted post sometimes in between.

It will say "promoted" or something small, and then there will be an image. I think right now the problem is: who makes the first move?

image. I think right now the problem is: who makes the first move?

- If we go 10 years out, the proposition for ads is that you will make so much money on ads by having so many users that you can use this to fund better R&D and make better models, which is why YouTube is dominating the market.

Netflix is scared of YouTube. They have the ads, and they make—I pay $28 a month for Premium—they make at least $28 a month off of me and many other people. And they're just creating such a dominant position in video. That's the proposition: that ads can make you have a sustained advantage.

...in what you're spending per user. But there's so much money in it right now that starting that flywheel— ...is scary because it's a long-term bet.

...is scary because it's a long-term bet.

- Do you think there'll be some crazy big moves this year business-wise? Like Google or Apple acquiring Anthropic or something like this?

business-wise? Like Google or Apple acquiring Anthropic or something like this?

- Dario will never sell, but we are starting to see some types of consolidation with Groq for $20 billion— ...and Scale AI for almost 30 billion, and countless

...and Scale AI for almost 30 billion, and countless other deals structured in a way that is actually detrimental to the Silicon Valley ecosystem.

This sort of licensing deal where not everybody gets brought along, rather than a full acquisition that benefits the rank-and-file employee by getting their stock vested. That's a big issue for culture to address because the startup ecosystem is the lifeblood. If you join a startup, even if it's not that successful, your startup may get acquired on a

cheap premium and you'll get paid out for this equity.

And these licensing deals are essentially taking the top talent.

I think the deal for Groq to Nvidia is rumored to be better for the employees, but it is still this antitrust-avoiding thing. But I think this trend of consolidation will

thing. But I think this trend of consolidation will continue. Me and many smart people I respect have been expecting

continue. Me and many smart people I respect have been expecting consolidation to have happened sooner, but it seems like some of these things are starting to turn.

But at the same time, you have companies raising ridiculous amounts of money for— ...reasons I don't know. I'm like, "I don't know why you're taking that money."

...reasons I don't know. I'm like, "I don't know why you're taking that money."

So it's mixed this year, but some consolidation pressure is starting.

- What kind of surprising consolidation will we see? You say Anthropic is a never. I mean, Groq is a big one. Groq with a Q, by the way.

- Yeah. There's just a lot of startups and a very high premium on AI startups. So there could be a lot of—

AI startups. So there could be a lot of— - That kind of stuff, yeah.

- $10 billion range acquisitions, which is really big for a startup that was maybe founded a year ago. I think

Manus.ai... This company based in Singapore that Meta founded was founded eight months ago and then had a $2 billion exit. I think there will be other big, multi-billion dollar acquisitions, like Perplexity.

- Like Perplexity, right?

- Yeah, people rumored them to Apple. I think there's a lot of pressure and liquidity in AI. There's pressure on big companies to have outcomes and— ...I would guess a big acquisition gives people leeway

...I would guess a big acquisition gives people leeway to then tell the next chapter of that story.

- I mean, Cursor—we've been talking about code, and if somebody acquires Cursor...

- They're in such a good position by having so much user data.

And we talked about continual learning. They had one of the most interesting two sentences in a blog post, which is that they had their new Composer model, which was a fine-tune of one of these large mixture-of- expert models from China. You can

know that by asking Gossip, or because the model sometimes responds in Chinese— which none of the American models do. And they had a blog post where they're like, "We're updating the model weights every 90 minutes based on real-world feedback from people using it." Which is like the closest thing to real-world RL happening on a model, and it's just like in one of their blog posts— - That's incredible.

- which is super cool.

- And by the way, I use Composer a lot because one of the benefits it has is that it's fast.

- I need to try it, because everybody says this.

- And there'll be some IPOs potentially. You think Anthropic, OpenAI, xAI.

- They can all raise so much money so easily that they don't feel a need to... As long as fundraising is easy, they're not gonna IPO because public markets apply pressure.

I think we're seeing in China that the ecosystem's a little different, with both MiniMax and Zhipu AI applying for— filing IPO paperwork, which will be interesting to see how that market reacts.

I actually would guess that it's gonna be similarly hypey to the US, as long as all this is going and not based on the realities that they're both losing a ton of money. I wish more of the American gigantic AI startups were public because it would be very interesting to see how they're spending their money and have more insight. And also just to give people

access to investing in these, because I think that they're some of the most, formidable—they're the companies of the era. And the tradition is now for so many of the big startups in the US to not go public. It's like we're still waiting for Stripe and the IPO, but Databricks definitely didn't. They raised like a Series G or something. And I just feel like it's kind of a weird

or something. And I just feel like it's kind of a weird equilibrium for the market where it's like, I would like to see these companies go public and evolve in that way that a company can.

- Do you think 10 years from now some of the frontier model companies are still around? Anthropic, OpenAI?

still around? Anthropic, OpenAI?

- I definitely don't see it to be a winner- takes-all unless there truly is some algorithmic secret that one of them finds that lets this flywheel. Because the development path is so similar for all of them. Google

flywheel. Because the development path is so similar for all of them. Google

and OpenAI have all the same products, and then Anthropic's more focused, but when you talk to people it sounds like they're solving a lot of the same problems. And there's offerings that'll spread out. There's a lot of... it's a very big cake that's being made that people are gonna take money out

of... it's a very big cake that's being made that people are gonna take money out of.

- I don't want to trivialize it, but OpenAI and Anthropic are primarily LLM service providers. And some of the other companies like Google and

providers. And some of the other companies like Google and xAI, linked to X, do other stuff too. And so it's very possible if AI becomes more commodified,

too. And so it's very possible if AI becomes more commodified, that the companies that are just providing LLM will die.

- The advantage they have is they have a lot of users, and I think they will just pivot. I think,

just pivot. I think, if they figure out... Like Anthropic, I think, pivoted. I don't think they originally planned to work on code, but they found, "Okay, this is like a nice niche, and now we are comfortable in this niche and we push on it." And I can see the same thing once... maybe, let's say hypothetically speaking, I'm not sure if it will be true, but let's say Google takes all the market share of the general

chatbot. Maybe OpenAI will be then focused on some other sub-topic.

chatbot. Maybe OpenAI will be then focused on some other sub-topic.

They have too many users to go away in the foreseeable future, I think.

- I think Google is always ready to say, "Hold my beer," with AI models.

- I think the question is if the companies can support the valuations. I see the AI companies being

valuations. I see the AI companies being looked at in some ways like AWS, Azure and GCP are all competing in the same space and all very successful businesses. There's a

chance that the API market is so unprofitable that they go up and down the stack to products and hardware. They have so much cash they can build power plants and build data centers, which is a durable advantage now. But there's

also just a reasonable outcome that these APIs are so valuable and so flexible for developers that they become something like something like AWS. But AWS and Azure are also gonna have these APIs, so there's some... That's a

some... That's a

tough market, having five or six people competing in the API market. So maybe

they get squeezed out.

- You mentioned "RIP Llama." Is there a path to winning for Meta?

- I think nobody knows. They're moving a lot, so they're signing licensing deals with Black Forest Labs, which is an image generation company, or Midjourney. So I think in some ways, on the product and consumer-facing AI front, it's too early to tell. I think they have some people that are excellent

and very motivated being close to Zuckerberg. So I think that there's still a story to unfold there. Llama is a bit different, where Llama was the most focused expression of the organization. And I don't see Llama being supported to that extent. I think it was a very successful brand for them. So they still might participate in the open ecosystem or continue the Llama brand into a different

service, because people know what Llama is.

- You think there's a Llama 5?

- Not an open-weight one.

- It's interesting. I think also just to recap a bit, I mean, Llama was the, I would say, pioneering open-weight model. And then Llama 1, 2, 3 got a lot of love. But I think then, what happened just hypothesizing or speculating, I think the leaders at Meta, like the upper executives, they... I think they got very excited about LLaMA because they saw how popular it was in the community. And then

I think the problem was trying to, let's say, monetize or not monetize open source, but use it to make a bigger splash. It almost felt forced, like, developing these very big LLaMA 4 models to be on top of the benchmarks. But I don't think the goal of LLaMA models is to be on top

benchmarks. But I don't think the goal of LLaMA models is to be on top benchmarks, beating, let's say, ChatGPT or other models. I think the goal was to have a model that people can use, trust, modify, and understand.

it. So that includes having smaller models. They don't have to be the best models.

And what happened was just these models... like, the benchmarks suggested that they were better than they were, because I think they had specific models trained on preferences so they performed well on benchmarks. That's kind of, like, this overfitting thing to force it to be the best. But then at the same time, they didn't do the small models that people could use. And I think

that no one could run these big models then. And then there was a weird thing. I think it's just because people got too excited

weird thing. I think it's just because people got too excited about headlines pushing the frontier. I think that's it.

- And too much on the benchmark-sync side.

- It's too much work.

- I think it imploded under internal political fighting and misaligned incentives. So, like, the researchers want to build the best models, but there's a layer of organization and management that is trying to demonstrate they do these things. And then

there's a lot of pieces and rumors about how, some horrible technical decision was made and how that comes in. And

it just seems like it kind of got too bad where it all just crashed out.

- Yeah, but we should, we should also, like, give huge props to Mark Zuckerberg. I think it comes from Mark, actually...

Zuckerberg. I think it comes from Mark, actually...

from the top of the leadership, saying open source is important. The fact that that exists

important. The fact that that exists means there could be a LLaMA 5, where they learn the lessons from the benchmarking and say, "We're going to be GPT-class and provide a really awesome library of open source."

- What people say is that there's a debate between Mark and Alexander Wang, who is very bright, but much more against open source. And to the extent that he has a lot of influence over the AI org, it

source. And to the extent that he has a lot of influence over the AI org, it seems much less likely, because it seems like Mark brought him in for fresh leadership and directing AI. And

if being open-or-closed is no longer the defining nature of the model, I don't expect that to be a defining argument between Mark and Alex. They're both very bright, but I have a hard time

Alex. They're both very bright, but I have a hard time understanding all of it because Mark wrote this piece in July of 2024, which was probably the best blog post at the time, making the case for open source AI. And then July 2025 came around and it was like, "We're reevaluating our relationship with open source."

open source." - But I think also the problem... Not the problem, but I think, well, we may have been a bit too harsh, and that caused some of that.

Because we as open source developers or the open source community...

Even though the model was maybe not what everyone hoped for, it got a lot of backlash. And I think that was a bit unfortunate, because I can see that as a company, they were hoping for positive headlines. And instead of just getting

positive headlines. And instead of just getting no headlines or positive headlines, in turn they got negative headlines. It kind of reflected badly on the company, and I think

headlines. It kind of reflected badly on the company, and I think that is also something where it's maybe a spite reaction, almost like, "Okay, we tried to do something nice, we tried to give you something cool like an open source model, and now you are kind of being negative about us," so in that sense, it looks like, "Well, maybe then

we'll change our mind." I don't know.

- Yeah, that's where the dynamics of discourse on X can lead us as a community astray.

Because sometimes it feels random. People pick the thing they like and don't like. I mean, you can see the same thing with Grok 4.1 and Grok Code Fast 1.0.

like. I mean, you can see the same thing with Grok 4.1 and Grok Code Fast 1.0.

I don't think, vibe-wise, people love it publicly. But a lot of people use it.

So if you look to Reddit and X, they don't really give it praise from the programming community, but they use it.

And the same thing probably with Llama. I don't understand the dynamics of either positive hype or negative hype. I don't understand it.

- I mean, one of the stories of 2025 is the US filling the gap of Llama, which is the rise of these Chinese open-weight models, to the point where that was the single issue I've spent a lot of energy on in the last five months, trying to do policy work to get the US to invest in this.

- So tell me the story of Adam.

- Adam Project started as me calling it the American DeepSeek Project, which doesn't really work for DC audiences, but it's the story of what is the most impactful thing I can do with my career.

These Chinese open-weight models are cultivating a lot of power, and there is a lot of demand for building on these open models, especially in US enterprises that are very cagey about these Chinese models.

- According to Perplexity, the Adam Project: American Truly Open Models, is a US-based initiative to build and host high-quality, genuinely open-weight AI models and supporting infrastructure, explicitly aimed at competing with and catching up to China's rapidly advancing open-source AI ecosystem.

- I think the two-sentence summary would be: one, the proposition that open models are going to be an engine for AI research because that is what people start with; therefore, it's important to own them. And the second one is, therefore, the US

own them. And the second one is, therefore, the US should be building the best models so that the best research happens in the US, and those US companies take the value from being the home of where AI research is happening. And without more investment in open models—we have plots on the website where it's like, "Qwen, Qwen, Qwen, Qwen"—and it's all these models that are

excellent from these Chinese companies that are cultivating influence in the US and internationally. And I think the US is spending way more on AI, and the ability to create open models that are half a generation or a generation beyond what the cutting edge of closed labs is, costs $100 million, which is a lot of money, but not a lot of money to these companies. So, we need a

centralizing force of people who want to do this. And I think we got engagement from people pretty much across the full stack, whether it's policy.

- So there has been support from the administration?

- I don't think anyone technically in government has signed it publicly, but I know that people who have worked in AI policy, in both the Biden and Trump administrations, are very supportive of promoting open-source models in the US. For example,

AI2 got a grant from the NSF for $100 million over four years, which is the biggest CS grant the NSF has ever awarded, and it's for AI2 to attempt this. I think it's a starting point.

But the best thing happens when there are multiple organizations building models, because they can cross-pollinate ideas and build this ecosystem. I don't think it works if it's just Llama releasing models to the world, because Llama could go away. The same thing applies for AI2; I can't be the only one building models. And that's... ...A lot of time spent

talking to people, whether they're in policy... I know NVIDIA is very excited about this. Jensen Huang has been talking about the urgency for this, and they've done a lot more in 2025, where the Nemotron models are more of a focus. They've started releasing some data along with NVIDIA's open models, and very few companies do this, especially of NVIDIA's size. So there are

signs of progress. We hear about Reflection AI where they say their $2 billion fundraise is dedicated to building US open models, and I feel like their announcement tweet reads like a blog post. I think that cultural tide is starting to turn. In July, we had four or five DeepSeek-caliber Chinese open-weight models and

zero from the US. That was the moment when I like, "Oh, I guess I have to spend energy on this because nobody else is gonna do it."

So it takes a lot of people contributing together. I don't say that, the Adam Project is the only thing helping to move the ecosystem, but it's people like me doing this sort of thing to get the word out.

- Do you like the 2025 America's AI Action Plan?

It includes open source stuff. The White House AI Action Plan includes a dedicated section titled "Encourage Open-Source and Open-Weight AI," defining such models and arguing they have unique value for innovation and start-ups.

- Yeah. I mean, the AI Action Plan is a plan, but largely, I think it's maybe the most coherent policy document that has come out of the administration, and I hope that it largely succeeds. I know people that have worked on the AI Action Plan and the challenge is taking policy and making it real. I have no idea how to do this as an AI researcher, but largely, a lot of things in

that were very real. There's a huge build-out of AI in the country and there are a lot of issues that people are hearing about, from water use to whatever. We should be able to build things in this country, but also,

whatever. We should be able to build things in this country, but also, we need to not ruin places in our country in the process of building it, and it's worthwhile to spend energy on.

I think that's a role the federal government plays. They set the agenda.

And setting the agenda that open-weight should be a first consideration is a large part of what they can do, and then people think about it.

- Also, for education and talent for these companies, I think it's very important because otherwise, if there are only closed models, how do you get the next generation of people contributing? Because you will at some point only be able to learn after you

people contributing? Because you will at some point only be able to learn after you join a company. But at that point, how do you hire talented people, how do you identify them? I think open source is important for a lot of things, but also even just for educating the population and training the next generation of researchers. It's the way, or the only way.

researchers. It's the way, or the only way.

- The way that I could've gotten this to go more viral was to tell a story of Chinese AI integrating with an authoritarian state, being ASI and taking over the world. Therefore, we need our own American models. But it's

intentional why I talk about innovation and science in the US, because I think it's both more realistic as an outcome, and it's a world that I would like to manifest.

- I would say, though, any open-weight model is a valuable model.

- Yeah. And my argument is that we should be in a leading position. But I think it's worth saying it so simply because there are still voices in the AI ecosystem that say we should consider banning the release of open models due to safety risks.

And I think it's worth adding that, effectively, that's impossible without making the US have its own Great Firewall, which is also known to not work that well because the cost for training these models, whether it's one to a hundred million dollars, is attainable to a huge amount of people in the world that want to have influence, so these models will be getting trained all over the

world. And we want the models, especially when,

world. And we want the models, especially when, I mean, there are safety concerns, but we want these information and tools to flow freely across the world and into the US so that people can use them and learn from them. Stopping that would be such a restructuring of our internet that it seems impossible.

- Do you think maybe in that case the big open-weight models from China are actually a good thing for the US companies? Because maybe

the US companies you mentioned earlier are usually one generation behind in terms of what they release open source versus what they are using. For example,

GPT-4o might not be the cutting-edge model. Gemma 3 might not be, but they do that because they know this is safe to release. But then when these companies see, for example, there is DeepSeek-V3, which is really awesome, and it gets used and there is no backlash, there is no security risk, that could then encourage them to release better models. Maybe that, in a sense, is a very positive thing.

- A hundred percent. These Chinese companies have set things into motion that I think would potentially not have happened if they were not all releasing models.

So I think it was like I'm almost sure that those discussions have been had by leadership.

- Is there a possible future where the dominant AI models in the world are all open source?

- Depends on the trajectory of progress that you predict. If you think saturation in progress is coming within a few years—so essentially, within the time where financial support is still very good—then open models will be so optimized and so much cheaper to run that they'll win out. Essentially, this

goes back to open source ideas where so many more people will be putting money into optimizing the serving of these open-weight common architectures that they will become standards, and then you could have chips dedicated to them and it'll be way cheaper than the offerings from these closed companies that are custom.

- We should say that the Situational Awareness report predicts one of the things it does from a narrative perspective is that there will be a lot of centralization. As the AI systems get smarter and

centralization. As the AI systems get smarter and smarter, national security concerns will come to be, and you'll centralize the labs, you'll become super secretive, and there'll be this whole race - from a military perspective, between China and the United States. And so all of these fun conversations we're having about

States. And so all of these fun conversations we're having about LLMs... the generals, the soldiers will

LLMs... the generals, the soldiers will come into the room and be like, "All right. We're now in the Manhattan Project stage of this whole thing."

- I think 2025, '26, '27, I don't think something like that is even remotely possible. I mean, you can make the same argument for computers, right? You can say, "Okay. Computers are capable and we don't want the general public to get them." Or chips, even AI chips, but you see how Huawei makes chips now. It took a few years, but... and I

don't think there is a way you can contain knowledge like that.

In this day and age, it is impossible, like the internet. I don't think this is a possibility.

- On the Manhattan Project thing, one of my funny things looking at them is I think that a Manhattan Project-like thing for open models would actually be pretty reasonable, because it wouldn't cost that much. But I think that will come.

It seems like culturally, the companies are changing. But I agree with Sebastian on all of the stuff he just said. I just don't see it happening nor being helpful.

- Yeah. I mean, the motivating force behind the Manhattan Project was civilizational risk. It's harder to motivate that for open-source models.

civilizational risk. It's harder to motivate that for open-source models.

- There's no civilizational risk.

- On the hardware side, we mentioned NVIDIA a bunch of times. Do you think Jensen and NVIDIA are going to keep winning?

- I think they have the downside that they have to iterate a lot and manufacture a lot.

And I think they probably... what they're doing, they do innovate, but I think there's always the chance that there is someone who does something fundamentally different, who gets very lucky. But the problem is adoption. The moat of NVIDIA is probably not just the GPU. It's more like the CUDA ecosystem, and that

has evolved over two decades. I mean, even back when I was a grad student, we were in a lab doing biophysical simulations, molecular dynamics, and we had a Tesla GPU back then just for the computations. It was 15 years ago now. And it

computations. It was 15 years ago now. And it

just... they built this up for a long time and that's the moat, I think.

It's not the chip itself. Although they have now the money to iterate and build and scale, but then it's really on the compatibility. If you're at that scale as a company, why would you go with something risky where there are only a few chips that they can make per year?

You go with the big one. But I do think with LLMs now, it will be easier to design something like CUDA.

It took 15 years because it was hard, but now with LLMs, we can maybe replicate CUDA.

- And I wonder if there will be a separation of training and inference compute as we stabilize and more compute is needed for inference.

- That's supposed to be the point of the Groq acquisition. And that's why part of what Vera Rubin is— where they have a new chip with no high-bandwidth memory, which is one of the...

Or very little, which is one of the most expensive pieces. It's

designed for pre-fill, which is the part of inference where you essentially do a lot of matrix multiplications. And then you only need the memory when you're doing this autoregressive generation, and you have the KV cache swaps. So they have this new GPU that's designed for that specific

swaps. So they have this new GPU that's designed for that specific use case, and then the cost of ownership per flop or whatever is actually way lower. But I think that NVIDIA's fate lies in the diffusion of AI still. Their biggest clients are still these hyperscale companies. Like, Google obviously can make TPUs. Amazon is making Trainium.

Microsoft will try to do its own things. And so long as the pace of AI progress is high, NVIDIA's platform is the most flexible and people will want that. But if

there's stagnation, then creating bespoke chips, there's more time to do it.

- It's interesting that NVIDIA is quite active in trying to develop all kinds of different products.

- They try to create areas of commercial value that will use a lot of GPUs.

- But they keep innovating, and they're doing a lot of incredible research.

- Everyone says the company is super oriented around Jensen and how operationally plugged in he is. And it sounds so unlike many other big companies that I've heard about. And so long as that's the culture, I think that they will expect that to keep progress happening. It's like he's still in the Steve Jobs era of Apple. So long as that is how it operates, I'm pretty optimistic for their

situation because it is their top-order problem, and I don't know if making these chips for the whole ecosystem is the top goal of all these other companies. They'll do a good job, but it might not be as good of a job.

- Since you mentioned Jensen, I've been reading a lot about history and about singular figures in history. What do you guys think about the single man/woman view of history? How important are individuals for steering the direction of history in the tech sector?

So, you know, what's NVIDIA without Jensen? You mentioned

Steve Jobs. What's Apple without Steve Jobs? What's xAI

without Elon or DeepMind without Demis?

- People make things earlier and faster, where scientifically, many great scientists credit being in the right place at the right time and still making the innovation, where eventually someone else would still have the idea. So I think that in that way, Jensen is helping manifest this GPU revolution

idea. So I think that in that way, Jensen is helping manifest this GPU revolution much faster and much more focused than it would be without having a person there. And this is making the whole AI build-out faster. But I

do still think that eventually, something like ChatGPT would have happened and a build-out like this would have happened, but it probably would not have been as fast. I think that's the sort of flavor that is applied.

- People, these individual people, are placing bets on something.

Some get lucky, some don't. But if you don't have these people at the helm, it would be more diffused. It's almost like investing in an ETF versus individual

diffused. It's almost like investing in an ETF versus individual stocks. Individual stocks might go up, might go down more

stocks. Individual stocks might go up, might go down more heavily than an ETF, which is more balanced. It will eventually go up over time. We'll

get there. But it's just like, you know, the focus I think is the thing: passion and focus.

- Isn't there a real case to be made that without Jensen, there's not a reinvigoration of the deep learning revolution?

- It could've been 20 years later, is the thing.

- Yeah. 20 is—

- Or like another AI winter—like a deep learning winter—could have come if GPUs weren't around.

- That could change history completely, because you could think of all the other technologies that could've come in the meantime, and the focus of human civilization would get...

Silicon Valley would be captured by different hype.

- But I do think, I mean, there's certainly an aspect where it was all planned, the GPU trajectory. But on the other end, it's also a lot of lucky coincidences or good intuition. For example, the investment into, let's say, biophysical simulations. I mean, I think it started with video games and then it just happened to be good at linear algebra because video games require a lot of linear algebra. And then you have biophysical

simulations. But still, I don't think the plan,

simulations. But still, I don't think the plan, the master plan was AI. I think it just happened to be Alex Krizhevsky. Someone took these GPUs and said, "Hey, let's

Krizhevsky. Someone took these GPUs and said, "Hey, let's try to train a neural network on that." It happened to work really well, and I think it only happened because you could purchase those GPUs.

- Gaming would've created a demand for faster processors if NVIDIA had gone out of business in the early days.

That's what I would think. I think that the GPUs would've been different for AlexNet—but I think GPUs would still exist at the time of AlexNet and at the time of the Transformer. It was just hard to know if it would be one company as successful or multiple smaller companies with worse chips. But I don't think that's a 100-year delay. It might be a decade delay.

- Well, it could be a multi-decade delay. I mean, I just can't see Intel or AMD doing what NVIDIA did.

- I don't think it would be a company that currently exists.

I think it would be a different company that would rise.

- Like Silicon Graphics or something.

- Yeah, some company that has died would have done it.

- But looking at it, it seems like these singular figures, these leaders, have a huge impact on the trajectory of the world. Obviously, incredible teams behind them.

But, you know, having that kind of very singular, almost dogmatic focus is necessary to make progress.

- Yeah, I mean, even with GPT, it wouldn't exist if there wasn't a person, Ilya, who pushed for this scaling, right?

- Yeah, Dario was also deeply involved in that.

If you read some of the histories from OpenAI, it almost seems wild thinking about how early these people were like, "We need to hook up 10,000 GPUs and take all of OpenAI's compute and train one model." There's a lot of people there that didn't want to do that.

- Which is an insane thing to believe—to believe scaling before scaling has any indication that it's going to materialize. Again, singular figures. Speaking of which, 100 years from now—this

materialize. Again, singular figures. Speaking of which, 100 years from now—this is presumably post-singularity, whatever that singularity is.

When historians look back at our time now, what technological breakthroughs would they really emphasize as the breakthroughs that led to the singularity?

So far we have Turing to today. 80 years.

- I think it would still be computing, like the umbrella term computing.

Just, I don't necessarily think it's even like 100 years, 200 years from now, it wouldn't be AI. It could still well be computers, you know? We are now taking better advantage of computers, but like the fact of computing.

- It's basically a Moore's Law kind of discussion. Even the

details of CUDA and GPUs won't even be remembered and it won't be all this software turmoil. It'll just be obviously compute.

- I generally agree, but is the connectivity of the internet and compute able to be merged? Or is it both of them?

- I think the internet will probably be related to communication.

It could be a phone, internet, or a satellite—that stuff.

Where compute is more like the scaling aspect of it.

- It's possible that the internet is completely forgotten.

That the internet is wrapped into communication networks.

This is just another manifestation of that, and the real breakthrough comes from increased compute—Moore's Law, broadly defined.

- Well, I think the connection of people is very fundamental to it. So

it's like, you can talk to anyone. You want to find the best person in the world for something, they are somewhere in the world. And being able to have being able to have that flow of information, AIs will also rely on this.

I think I've been fixating on when I said the dream was dead about the one central model, and the thing that is evolving is people having many agents for different tasks. People already started doing this with different clouds for different tasks. And it's described as many AGIs in the data center,

tasks. And it's described as many AGIs in the data center, where each one manages and they talk to each other. And that is so reliant on networking and the free flow of information.

on top of compute. But networking, especially with GPUs, is such a part of scaling compute. The GPUs in the data centers need to talk to each other.

- Anything about neural networks will be remembered? Do you think there's something specific and singular to the fact that it's neural networks that's seen as a breakthrough, like a genius, that you're basically replicating in a very crude way, the structure of the human brain, the human mind?

- I think without the human mind, we probably wouldn't have neural networks because it was just an inspiration for that. But at the other end, I think it's just so different. I mean, it's digital versus biological, so I do think it will probably be more grouped as an algorithm.

- That's massively parallelizable... ...On this particular kind of compute?

- Could have well been genetic algorithms just parallelized.

It just happens that this is more efficient and works better.

- And it very well could be that neural networks, the way we architect them, is just a small component of the system that leads to singularity.

- If you think of it in 100 years, I think society can be changed more with more compute and intelligence because of autonomy.

But looking at this, what are the things from the Industrial Revolution that we remember? We remember

the engine, which is probably the equivalent of the computer in this.

But there's a lot of other physical transformations people are aware of, like the cotton gin and all these machines that are still known— air conditioning, refrigerators.

Some of these things from AI will still be known. The word "transformer" could still be known. I would guess that deep learning is definitely still known, but the transformer might be evolved away from in 100 years with AI researchers everywhere. But I think deep learning is likely to be a term that is remembered.

- And I wonder what the air conditioning and refrigeration of the future is that AI brings. If we travel forward 100 years from now, what do you think is different? How do you think the world looks different?

Do you think there are humans? Do you think there are robots everywhere walking around?

- I do think specialized robots for sure for certain tasks.

- Humanoid form?

- Maybe half humanoid. We'll see.

I think for certain things, yes, there will be humanoid robots because it's just amenable for the environment. But for certain tasks, it might make sense. What's harder to imagine is how we interact with the devices and what humans do with devices.

I'm pretty sure it will probably not be the cellphone or the laptop.

laptop. Will it be implants?

- I mean, it has to be brain-computer interfaces, right?

I mean, 100 years from now, it has to... Like, given the progress we're seeing now— ...there has to be... unless there's legitimately complete

...there has to be... unless there's legitimately complete alteration of how we interact with reality.

- On the other hand, if you think of cars, cars are older than 100 years, right? And

it's still the same interface. We haven't replaced cars with something else. We just made the cars better, but it's still a steering wheel, it's still

something else. We just made the cars better, but it's still a steering wheel, it's still wheels, you know?

- I think we'll still carry around a physical brick of compute— ...because people want some ability to have a private... Like, you

...because people want some ability to have a private... Like, you

might not engage with it as much as a phone, but having something for private information that is yours as an interface between the rest of the internet, I think is something that will still exist. It might not look like an iPhone and it might be used a lot less, but I still expect to have people carry things around.

- Why do you think the smartphone is the embodiment of private? There's a camera on it. There's a—

private? There's a camera on it. There's a—

- Private for you, like encrypted messages, encrypted photos, you know what your life is.

I guess this is a question on how optimistic on brain-machine interfaces you are. Is all that just going to be stored in the cloud, in your

are. Is all that just going to be stored in the cloud, in your whole calendar? It's hard to think

whole calendar? It's hard to think about processing all the information that we can process visually through brain-machine interfaces presenting something like a calendar or something to you. Like, it's hard to just think about knowing without looking, you know your email inbox.

Like you signal to a computer and then you just know your email inbox. What does

that... is that something that the human brain can handle being piped into it non-visually? I don't know exactly how those transformations happen.

Because humans aren't changing in 100 years.

I think agency and community are things that people actually want.

- A local community, yeah.

- So, like, people you are close to, being able to do things with them and being able to ascribe meaning to your life and to be able to do things. I think that, if not in 100 years, I don't think that human biology is changing away from those on a time scale that we can discuss. And I think that UBI does not solve

discuss. And I think that UBI does not solve agency. I do expect mass wealth, and I hope that it

agency. I do expect mass wealth, and I hope that it has spread so that the average life does look very different in 100 years. But that's still a lot to happen in 100 years. If you think

100 years. But that's still a lot to happen in 100 years. If you think about countries that are early in their development process to getting access to computing and internet, to build all the infrastructure and to have policy that shares one nation's wealth with another is... I think it's an optimistic view to see

another is... I think it's an optimistic view to see all that happening in 100 years- ...while they are still independent entities and not just

...while they are still independent entities and not just like absorbed into some international order by force.

- But there could be just better, more elaborate, more effective social support systems that help alleviate some levels of basic suffering from the world. You know, the transformation of society where a lot of jobs are lost in the short term, I think we have to really remember that each individual job that's lost is a human being who's suffering. That's like a

tragedy. When jobs are lost, the scale is a real tragedy. You can

tragedy. When jobs are lost, the scale is a real tragedy. You can

make all kinds of arguments about economics or it's all going to be okay. It's good for the GDP, there's going to be new jobs

be okay. It's good for the GDP, there's going to be new jobs created. Fundamentally at the individual level for that

created. Fundamentally at the individual level for that human being, that's real suffering. That's a real personal sort of tragedy. And we have to not forget that as the technologies are being developed. And also my hope for all the AI slop we're seeing is that there will be a greater and greater premium for the fundamental

aspects of the human experience that are in-person.

Like seeing each other, talking together in-person.

- The next few years are definitely gonna be an increased value on physical goods and events- and even more pressure on slop.

The slop is only starting. The next few years will be more and more diverse - Do you think we'll all be drown- - versions of slop.

- drowning in slop. Is that what- - So I'm hoping that society drowns in slop enough to snap out of it and be like, "We can't deal with it." It just doesn't matter. We all

can't deal with it. And then the physical has such a higher premium on it.

- Even classic examples, I honestly think this is true, and I think we will get tired of it. We are already kind of tired of it. Same with

art. I don't think art will go away. You have paintings, physical paintings. There's more value, not just

physical paintings. There's more value, not just monetary value, but just more appreciation for the actual painting than a photocopy of that painting. It could be a perfect digital reprint, but there is something when you go to a museum and you see that real thing and you just think about, "Okay, a human." It's like a craft.

You have like an appreciation for that. And I think the same is true for writing, for talking, for any type of experience, it will be... I do unfortunately think it will be like a dichotomy, like a fork where some things will be automated.

Like, you know, there are not as many paintings as there used to be 200 years ago.

There are more photographs, more photocopies. But at the same time, it won't go away. There will be value in that.

I think the difference will just be what's the proportion of that. But personally, I have a hard time reading things where I see

that. But personally, I have a hard time reading things where I see it's obviously AI generated. I'm sorry. It

might be really good information, but I have a certain, "Nah, not for me."

- I think eventually they'll fool you, and it'll be on platforms that give ways of verifying or building trust. So you will trust that Lex is not AI generated, having been here. So then you have trust in this channel. But it's harder for new people who don't have that trust.

channel. But it's harder for new people who don't have that trust.

- Well, that will get interesting because fundamentally I think it's a solvable problem by having trust in certain outlets that they won't do it, but it's all going to be trust-based.

There will be systems to authorize, "Okay, this is real. This is not real."

There will be some telltale signs where you can tell this is AI generated and this is not. But some will be so good that it's hard to tell, and then you have to trust. And that will get interesting and a bit problematic.

- The extreme case of this is to watermark all human content.

So all photos that we take on our own have some watermark until they are edited or something like this. And software can manage communications with the device manufacturer- device manufacturer to maintain human editing, which is the opposite of the discussion to watermark AI images. And then

you can make a Google image that has a watermark and use a different tool to remove the watermark.

- Yep. It's going to be an arms race, basically.

- And we've been mostly focusing on the positive aspects of AI. All the capabilities that we've been talking

AI. All the capabilities that we've been talking about can be used to destabilize human civilization with even just relatively dumb AI applied at scale, and then further, superintelligent AI systems. Of course, there's the sort of doomer take that's important to consider a little bit as we develop these

technologies. What gives you hope about the future of human

technologies. What gives you hope about the future of human civilization? Everything we've been talking about. Are we going to be okay?

civilization? Everything we've been talking about. Are we going to be okay?

- I think we will. I'm definitely a worrier both about AI and non-AI things. But humans do tend to find a way. I think that's what humans are built for: to have community and find a way to figure out problems. And that's what has gotten us to this point. And to think that the AI opportunity and related technologies is really

big. And I think that there's big social and political problems to

big. And I think that there's big social and political problems to help everybody understand that. And I think that's what we're staring at a lot of right now, is like the world is a scary place, and AI is a very uncertain thing. And it takes a lot of work that is

uncertain thing. And it takes a lot of work that is not necessarily building things. It's like telling people and understanding people, that the people building AI are historically not motivated or wanting to do.

But it is something that is probably doable. It just will take longer than people want. And we have to go through that long period of like hard, distraught AI discussions if we want to have the lasting benefits.

- Yeah. Through that process, I'm especially excited that we get a chance to better understand ourselves, at the individual level as humans and at the civilization level, and answer some of the big mysteries, like what is this whole consciousness thing going on here? It seems to be truly special. Like, there's a real miracle in

here? It seems to be truly special. Like, there's a real miracle in our mind. And AI puts a mirror to ourselves and we get

our mind. And AI puts a mirror to ourselves and we get to answer some of the big questions about what is this whole thing going on here?

- Well, one thing about that is also what I do think makes us very different from AI and why I don't worry about AI taking over is, like you said, consciousness. We humans, we decide what we want to do. AI in its current implementation, I can't see it

do. AI in its current implementation, I can't see it changing. You have to tell it what to do. And so you

changing. You have to tell it what to do. And so you have still the agency. It doesn't take the agency from you because it becomes a tool. You can think of it as a tool. You tell it what to do. It will be more automatic than other previous tools.

do. It will be more automatic than other previous tools.

It's certainly more powerful than a hammer, it can figure things out, but it's still you in charge, right? So the AI is not in charge, you're in charge. You tell the AI what to do and it's doing it for you.

charge. You tell the AI what to do and it's doing it for you.

- So in the post-singularity, post- apocalyptic war between humans and machines, you're saying humans are worth fighting for?

- 100%. I mean, this is... The movie Terminator, they made in- the '80s, essentially, and I do think, well, the only thing I can see going wrong is, of course, if things are explicitly programmed to do the thing that is harmful, basically.

- I think actually in that, in a Terminator type of setup, I think humans win.

I think we're too clever. It's hard to explain how we figure it out, but we do.

And we'll probably be using local LLMs, open source LLMs to help fight the machines.

I apologize for the ridiculousness. Like I said, Nathan already knows I've been a big fan of his for a long time. Been

a big fan of yours, Sebastian, for a long time, so it's an honor to finally meet you. Thank you for everything you put out into the world. Thank you for the excellent books you're

you. Thank you for everything you put out into the world. Thank you for the excellent books you're writing. Thank you for teaching us. And, Thank you for talking today. This was fun.

writing. Thank you for teaching us. And, Thank you for talking today. This was fun.

- Thank you for inviting us here and having this human connection, which is actually— - Extremely valuable— human connection.

Thanks for listening to this conversation with Sebastian Raschka and Nathan Lambert. To support this podcast, please check out our sponsors in the

Lambert. To support this podcast, please check out our sponsors in the description, where you can also find links to contact me, ask questions, give feedback and so on. And now

let me leave you with some words from Albert Einstein.

"It is not that I'm so smart, but I stay with the questions much longer." Thank you for listening, and hope to see you next time.