When I think about model progress, it comes down to different forms of capital, and that is compute data and talent. How do you see that playing out in terms of, like, who's going to win in the next couple of years? The Chinese are so good at building models. To say that China is anything but equal in talent, you're going to have a hard time. What they don't have is compute. Most people regard distillation as, like, the main lever for them to do this. People distill my products every single day. They take it, they strip it down to its raw parts. That's called competition. Previous post-training was much more reliant on the supervised fine-tuning. Now this has morphed a bit into environments where it's RL-heavy. But it's just, like, hard to map this type of post-training, which is you get the completions, and the score goes up to what is going on in reasoning today. The discourse now is just so much fear on, like, how much the frontier is just hidden and also breaking away. The AI capability plot doesn't necessarily worry me. But the AI capability plot in the pretty much non-existing preparation across public cybersecurity and all of these things, like... So, Nathan, really happy to have you here. Thank you for joining in person. Super timely, given you're launching Trillium, which I'd love to talk about. But more sort of on a macro level, I think we chatted a lot about open source, and that's, you know, a lot of what you care about. And what I sort of... Or what we've been hearing a lot in the public discourse is, like, hey, we have to pace the frontier, you know, the frontier this, the frontier that. And I think an arc of the stuff that I'm really trying to ask experts about is, like, what is in the frontier? What is it even, like, what are we pacing? What's there? And then, more importantly, kind of threading it with open source and open weight models, like, can they keep up with the frontier? And I think this matters very much and is something that you're an expert on. So maybe to sort of start on that arc, what's gotten to the... Sort of how you would break down what's gotten us to the frontier for both closed labs and open source models? You know, how does it break down in your mind, maybe first at a higher level of pre-training, mid-training, post-training, how post-training has evolved, which is something you've, you know, done a great amount of education to the public on? When I think about model progress, it comes down to, like, different forms of capital that you put into them. And that is, like, compute data and talent. And all of the labs have various amounts of this. And I think the closed frontier labs of open AIs, Anthropics, Googles have clearly had the most of these. And what we see is, like, the different abilities of the labs to organize these forms of capital. And, like, making the org chart work for building models is really hard, which is why you have companies have to do complete resets from the bottom up. And then it actually kind of works sometimes. Like, company, like, meta is, like, totally reset and they're, like, on a positive trajectory again. Seriously. It's, like, I think this, I'm very interested in these human aspects of, like, why competition drives the frontier and, like, why the org chart is so important. Because, like, we see these things and this comes into play in the open-closed dynamic as well, where it's, like, the closed companies, the true frontier labs, which I think if somebody wants to work at the frontier of AI, I mostly tell them to work at Anthropic and OpenAI, which is the easiest bet. I think Meta, Google, SpaceX AI, like, I think you still might be there, but it's harder to see from the outside. And, like, these companies have just taken the most ambitious bets the earliest in order to get there. And that's why OpenAI has done things like kickstart the reasoning model revolution. And I think that kind of breakthrough and, like, that beautiful plot with the inference scaling labs is, like, peak OpenAI. It's, like, they're really good at getting this plot that just changes how everyone thinks about everything. And on the other side, it's, like, Anthropic has been, like, very focused on the types of things the models are going to be able to do. And then their reward was this cloud code explosion with their focus on coding for so long. And there's these subtly different cultures. And I think those two companies in particular have been the most forward-looking and the most willing to spend egregious amounts to make a step towards a direction that people think is impossible. The Chinese companies came into this because they want to have a company that matters. And if they were to build their models and try to sell an API from China, nobody would know about them. Like, nobody would know about Moonshot AI. Very few people would know about Jibu. And there's a very long list after this. And it was a very practical decision by them. They're following the discourse. They know there's going to be a huge market. They know there's a huge demand in the U.S. for paying for these things. And it's very practical. So, therefore, like, releasing open models is a way to shortcut this and get into the conversation. And I think there's a lot of complexity in, like, why do they do this? Is the government involved? And not. It was, like, not the case. They're just, like, trying to build a startup. So practical. It's not to say that they will not get there. Like, the Chinese government does things that would blow people's minds in the U.S. And I think that, like, that makes me think that the Chinese labs have a much more serious approach to security because, like, things happen in China that are much more drastic responses to companies doing things in the U.S. And I don't need to go down this rabbit hole. But this is just, like, between the balance of open and closed, it's just, like, these companies in China are trying to catch up. And this is a very different dynamic than what I said about open AI and Anthropic. They're trying to create the new paradigms. They're trying to, like, make true RSI happen so that their company wins with the best models forever if you get there first. It's these boom ideas. And you just have to fill the market where there is a void. And the open-weight models are trying to do this. And I think over time there will be these tiers of open models where there's the best closed models, which you can demand the best margins for. And then there's the near-frontier models, which is a step behind this. And those will be open because there's just, like, some sort of supply and demand there as you get access to a different market that might be less profitable. But, for example, like if you have private enterprise data or so on. And these tiers are kind of natural evolutions of companies that are trying to do things. And I think we were talking about the frontier. And it's just, like, if you go back to capital, like, they have less capital. They're going to make less money. And, like, that's okay. But I think this drive to catch up is very strong. And once you know, once Anthropic and OpenAI show you the type of model you need to build, it's a lot easier to then be like, oh, we just need to do this thing. And they buy similar data sets. It trickles through the data economy where OpenAI and Anthropics spend way more to build out this function of a data industry. And then those data companies a year later might sell them to everybody. They might sell them to everybody. They need to maximize their return on their investment. And they had some contractual period. And I think there's a lot of dynamics where fast following is going to be very doable. And we've seen this cycle proceed for a while, like, longer than many people would expect. I think when ChatGPT was released, we talked about consolidation in the industry and predicting it. And I think we're just starting to see a much more active mergers and acquisitions market. But I thought this was going to happen two years ago. And the open model companies have lasted much longer than I thought. There's so many interesting threads to pull on here. I mean, starting with, actually, your commentary on the organizational differences between the top two frontier labs. You know, we can maybe table that or go on through that thread. But then, of course, also kind of explaining, are you explaining sort of how the open Chinese models or the open labs in China have really just chosen that as much more of a distribution slash practical commercialization strategy rather than, hey, you know, obviously out of altruism. Though it's great that the researchers maybe, for better or for worse, were motivated for, you know, sharing the beginning. But at the end of the day, it perpetuated because it was an excellent, excellent commercialization strategy. So the other thread maybe to pull there is sort of, you know, I think most people regard distillation as sort of like the main lever for them to do this so well and kind of increasingly well. Because the lag between the open source models and the frontier models have only shortened with time in the last few years. Now it's like maybe only two to three months. And this has also pushed the frontier labs to maybe want to close off the APIs, close off their best models to do first party services. I mean, one, probably to capture more value because it's always, you know, possible that way. But then two, also because it's harder to distill, not impossible, but much harder to distill. Maybe on those two threads because they're both very interesting, so we'll try to touch on it. Let's start with the, is the distillation story really more of a caricature than the Chinese labs, since you've actually met with them and you've studied this a lot more. Is it sort of like they're all-rounded, like great at pre-training, also have their own, you know, good post-training recipes that they may or may not have revealed? And where do you see the levers kind of accelerating? Yeah, so the Chinese labs are very technically excellent. They are more compute star. Yes. Like, without a doubt, they have a comparable talent pool to draw on. I think there might be, like, a slight edge based on who was where when AI really got going. But to say that China is anything but equal in talent is going to, you're going to have a hard time because they just have so many people being trained at such a high level. And what they don't have is compute. You talk to people there and you're like, oh, I heard an average open AI researcher has 5,100 equivalents that they can access at any time. And, like, their jaw just falls to the floor. They're just like, I can't comprehend. And this is, like, a top pre-training researcher at one of the labs that you would know. And then you have this interaction a few times. And the compute thing, I think, is the biggest difference. And distillation plays in as it is a way to effectively spend more money to get what is compute equivalent. Like, outputs from a language model are a function of compute. And if you know the outputs you want, it's the same as having compute to generate with a model. And I've seen the stages of distillation. And how I see it is that previous post-training was much more reliant on the supervised fine-tuning and other, like, this RLHF era, which is where post-training started. And you could get most of your performance just by you do this next token prediction loss on the outputs of a very strong model. It was very established that you take the best model you can, you generate completions, and you train on it, and your model's going to be pretty good so long as you have access to the right prompts. Prompts have always been a key to post-training. I remember at AI2, like, with Hannah, who I co-led post-training there with, we would always say, like, prompts are the key unit of post-training. And so you need to get the prompts, and you can do different things with them at different stages of training. And now this has morphed a bit into environments where it's RL-heavy. But it's just, like, hard to map this type of post-training, which is you get the completions and the score goes up to what is going on in reasoning today. And based on the public literature, it's very hard for me to be, like, distillation is a major factor because, for example, in the fully open literature of people trying to do open post-training recipes, all the open model builders are stuck using, like, a really outdated Quen model because you can't figure out how to get the teacher right. But my hypothesis is that there's some, like, mid-training and something in the common pre-training data that's open that makes it hard to do this thing, which is get reasoning traces from the absolute best model and do SFT and the numbers go up. So, like, if I were to have access to Claude's reasoning trace when I was building Ulmo, I'm actually, like, Claude Opus, Claude Mythos, whatever, I'm not sure that it would have actually made our model a lot better. So, this is, like, my evidence grounded in the literature, why I think distillation is hard. And then, like, how do you take the outputs of a model and build an RL environment to do a big RL run? Because then you have the Xiaomi's of the world doing this week-long open RL run where, like, here's what we did, and then they've dropped the model. And it's, like, where exactly does distillation go into this? They're clever. They know how to do marketing well. There are some questions of, like, did they do this ahead of time because they did their RL run? And then they dropped the model, and then their tech report said they had an extra post-training stage after RL. So, there's some questions. But, like, mapping the established distillation literature to that is very hard. But there are question marks that make me support the labs a bit, which is, like, these Claude proxies and these model proxies in China where there's a ton of tokens that are used. And there's intermediaries, and they have tons of accounts, and even cases where Kimmy was serving Claude in their products to users. And I'm just, like, don't really understand how this fits into distillation as a common technical tool in post-training. And, like, yes, we should watch it, to, like, there's other weird stuff with Chinese companies using the outputs from open AI and anthropics models at a vast scale. So, I think we have a lot more questions we need to answer. And I think most reasonable people, when they're like, what's the gap from distillation, are, like, there's very big error bars. But when I say that distillation helps them by a few months, it's based on this scientific literature that we just, like, best information we have. We should do more open research and enable this so that we can study it. But, like, I don't know how you can come to a different conclusion that it's, like, not a huge effect in current post-training based on this. And that's just, like, the Chinese are so good at building models as well. I think there's a bit of cope where people are, like, only we are good at building models. And it's, like, there's so many companies. There's, like, 20 to 30 companies that are building good models. You can, like, read. Yeah, yeah. And that's, like, you just. Yeah. And, like, it's hard for me to get over. And I think that there's this very prominent paper that's, like, stealing reasoning traces from closed models. And I know that the Chinese companies are way ahead on doing this since the AI safety community later published it. Like, I think every Chinese lab has a team that knows those sorts of things. And this goes back to what you were saying about, like, they don't need to offer the API. And I think there's some characteristic of reasoning models that generate these thinking tokens where it's easy to just knock them out of the API. So you see the reasoning token. And then you can use that to train your model. And so long as they offer these APIs, there's going to be no way to stop that type of distillation. But if they do make it so it's only in products and things where you have to sign enterprise agreements or whatever, it'll be way harder. And I support the labs doing that if they want to secure it. But it's, like, they have to secure their products if they want to. Like, I don't think, no, your customer would be good for anybody. I don't think it would be good for their business. But also, I don't know how you should get the government involved in a terms of service violation. It's, like, not even – like, I don't even think terms of service are a signed contract. It's just, like, a – like, I don't think you can sue them in the same way. You just can cancel their access. Right. So that's why I'm not that sympathetic. But I am – like, I like to listen to the – like, the national security community is loud. But it's, like, there is a directional truth to that with where AI is going and how influential it is. So you have to balance many, many things in your head. I wonder what your response is. So I heard – I think Jensen said in some – literally either tweet or interview, maybe. It was a great – it was a great – yeah, he was like – Put it in the – put it in the show. People distill my products every single day. They take it and they strip it down to its raw parts. They test everything. That's called competition. Do you think that's fair game? You think distillation is not robbery? That's called competition. I'm very sympathetic because I think there's a lot of calls from the AI safety community that we want to have, like, U.S.-China dialogue as we're getting to such capability levels. And if you're going to ask the company to just, like, start hitting their AI industry with slaps on the wrist and more downstream of distillation – so you won't regulate distillation with policy from Congress. You will probably just do federal action to make their life annoying. And at the same time when we're talking about how do we manage frontier risks where you have closed models in training going all over the web and accessing things that they shouldn't and we have valid worries about, like, what will happen with open-weight models where this, like, hasn't been harms yet. But it's like, why aren't people using open-weight models? Like, why do they go to closed models and get around the safeguards? And, like, we want to be able to communicate with the Chinese tech companies and everything about this. And I just think, like, you're going to cause more harm than good if you do anything about distillation because they're not going to stop. It's like they're going to keep doing it. Right, right. And the open-source community benefits and, therefore, the startup community benefits a lot. You can go into, you know, a lot of – it's not just, like, hey, China, USA. It's actually, like, there's – you know, and we can actually talk about, like, I am sympathetic, obviously, or trying to steelman the frontier lab position, which is, like, you have just unprecedented amounts of capital that's required to build these frontier models. Right, and it's kind of alarming slash not very conducive to this flywheel continuing if the – through distillation or other kind of distillation-related methods, open-source can catch up so quickly. And so, you know, and what I mean by this flywheel is just kind of a very simple picture of, you know, you train the frontier model by putting in a lot of money to get compute. And then they park this compute, of course. OpenAI and Anthropic are very guilty slash successful in this. Successful. Whichever way you want to, like, look at. And there is a huge compute, you know, kind of sort of lack of compute available because there's actually, like, not just compute in the chips, but there's also power, et cetera. So, like, all of this scarcity is kind of, you know, bottleneck until 2028. So this is all to say that it's a successful strategy to park that, which leads to actually a lot of revenue because once they get to the frontier, because they understand, you know, scaling is the one thing that kind of gives them – or the science of scaling is what gives them a handle over how well that can translate, like, into a predictable revenue stream. So they get the revenue, and because of that revenue, they're able to raise more money. It's just unprecedented, like, beautiful flywheel, and this happens for both OpenAI and Anthropic. Of course, we need exponential amount of resources, and so we've gotten to the point where we're like, okay, we need to IPO. We need to go to the public markets. And so anything that threatens this flywheel from functioning well is a huge threat to just the entire, like, being at the frontier. And so – For the entire AI industry, realistically. And exactly that, actually. So, like, you know, maybe not just kind of being sympathetic to their version of, like, trying to, you know, eke out capitalism for themselves. It's also just like, hey, if they don't do well in the IPO, that's actually bad for everyone else. And so I want everyone – you know, I want them to do well. On the other hand, you know, it's like the Chinese open source models are a huge benefit to the startup ecosystem. I mean, we see this all the time, just kind of metrics I remember from, like, Decagon and Harvey, who was trying to do a lot of fine-tuning. There's, like, a huge amount of startups doing post-training as a service of various degrees and trying to productize that. And it actually works because you can get to the Pareto frontier with these, you know, Kimmy K3 or pick your favorite, like, GLM open source model. So I guess, like, weighing these two things, how do you – I guess there's so many questions that can fall from that. But I guess, one, how do you see that playing out in terms of, like, who's going to win in the next couple of years versus, you know, what – and then also relate that to, you know, why you're doing, obviously, an open source. So I think open and closed models are on different timescales of exponentials. And the closed labs are so integrated where they have efficient inference, they can serve the model at scale when they are ready to because they have all these resources, they can plan, and they get to these markets first that are very high value. We have seen this with coding, and this has been the story of the year where the revenue has just gone zero, $100 billion, and likely to grow. But the open model labs, partially, this is, like, an economic issue, being the fast follower, is that you don't have this flow of information, and you don't have this flow of revenue. So the open model – you have an open model builder, then you have the open model inference companies, and you have the open model post-training companies. And, like, there's information from those customers, which are, like, things you need the models to do. And it's, like, you can't – like, the information doesn't just flow back to the model builder very easily. And they also don't have the economies of scale to serve as many users, to serve it as efficiently, to have as much margins on inference. And these are just, like, it's going to take a lot longer. But to the extent that things can be commoditized, it seems like companies really want to have private data. There's a lot of things that they just cannot send over an API. And we could talk about the, like, zero data retention or not and so on. But, like, these – it takes a long time to figure out if a company has internal expertise or they figure out how to build an internal evaluation that actually matches to revenue. And then they figure out how to fine-tune or then they go find – they go find thinking machines or prime intellect. And, like, we could make money en masse if we figure out how to fine-tune a model that solves this. And, like, they have these customers, but there's just so many more companies involved that it takes so much longer. But at the end of this process, they have real ownership, which is model weights, no terms and conditions that they can run on their hardware, and they've figured out they can make an ROI on. So, this is why I think the open model economy takes way longer to build. And I'm less concerned about this being kind of bubbly, where it's like this could eat into the market of the really fast takeoff as people figure out how to – what is the cheapest model that accomplishes a task and how do you compete on margins. And I don't really know – like, I don't really know what's going to happen. I think there's – it's too early to know. It's so – yeah, it's so hard. I mean, it's definitely something that's always top of mind, thinking about, like, there are things where scaling up general reasoning just as a general model is so – it's going to beat out any specialized model. And we see, obviously, examples in math. And that's probably the most pure domain that you can see this. But also, you know, even in, say, robotics, you know, with Astra, and I – for maybe however degrees of, like, trucy the signal is, it just doesn't sound like they've actually tried to ascend in robotics. Oh, I think they did. I think this is a cycle of, like, six to eight months ago, they started hiring people and buying a lot of data on robotics. They did. And then the model, magically six months later, is really good at it. But I think that the – But I actually – well, the other, you know, more contrary things I've heard is that it's actually coming out of computer use because it's – you know, because computer use is, like, some level of spatial reasoning. And then getting to have good Blender, you know, demos is another extension of that. So, you know, we can believe some version of that. But either way, I guess it's, like, maybe if it's, like, really specialized, you see – the capability gain is really from, like, trying to get better in a certain area. But, like, in math, I don't even know if that fell out of trying to specialize in math. I mean, I think the whole thesis there was just, like, let's just scale up reasoning and actually got better – in natural language – it got better than any specialized model in math. I mean, Anthropic wasn't even trying to be good at math. You know, OpenAI, you know, certainly I think, you know, they hired a bunch of mathematicians, but they also weren't trying to, like, use specialized, you know, kind of, like, lean data set to get better at it. Or at least that's not – sorry. So, anyways, this is all kind of fodder for the view that, like, it's kind of hard to compete with the frontier models because eventually they start ascending so much that, you know, you get good enough in respective categories. With that said, I think actually there's also very competing – like, a counterforce of data that you're seeing, which is, like, the open source models that are already good enough allow for fine-tunes, you know, to your point of, like, these slow percolation diffusion of these very specific benchmarks and data sets that are only available in these companies that you're being applied to. And it's just good enough, and it's actually – we're starting to see people adopt that. I mean, in the most maybe pure regime where you would expect frontier models are good is – I think Cursor published something over the summer where, like, hey, we just – you know, we had this long horizon task of writing – rewriting SQLite. And it turns out using two frontier models, you know, one for planner, one for execution, is not as good or is equivalently as good but far more costly than just using Composer with maybe Opus or, you know, Vastra, whatever, as planner. And I think that's actually great evidence that the open source fine-tunings are very good or good enough and has surpassed intelligence-hungry tasks. It's a long way of asking, what evidence do you find is most compelling or, you know, pieces of evidence for various versions of this stable state of where the frontier wins versus open source? I'm a big proponent that a lot of the frontier progress in domains that are not fully verifiable is by buying data that is high quality and giving the models worlds to act in and he'll climb on. And I think there's two – there's two things. We have the math and code thing and we don't know how big the coding market will be. And then there's kind of everything else, which is still they're targeting this knowledge work, this biology, healthcare, legal. And all of these things are not as verifiable. And I think it is much closer to the story of opening – I bought a lot of robotics data. They're probably going to tell us it's generalization, magic, poof. People love poof generalization, but realistically it's probably some of A, some of B, but a lot of it comes down to data. When I talk to people at Frontier Labs, there are some people that have amazing jobs. There are some people that contribute by doing a very narrow thing, which is you make a data set and you figure out what the behavior should be for your model in that area. And you figure out how to make the data that helps get that data, get that behavior, and you figure out what data you need to buy. And that's going to take – I think we're going to have years of this across different sectors. And that's very capital intensive to buy all of this data. But that's not just like poof, magic, RSI happens. Where I do see in this math and code and some distributed ML engineering, it's very verifiable. You know what your cost is. And the AI models are going to drive that down at a really magical rate. And that's going to just have a lot of surplus benefits. And we can do research faster. I'm very interested in – I think the efficiency of models is going to drop multiplicatively over years. So we've seen OpenAI have drastic price cuts and it's like what does it mean for GPT, Sol, or Astra and these bigger models to reduce in price 2x every eight months for the next three years? You start getting – you start doing this math and it's like it's 1% of the cost in whatever, like four years. And it's like are they going to be able to have higher margins due to Jevon's paradox there or are their revenues going to decrease because the market fills up? There's just like all these really weird dynamics, but fundamentally like AI progress is about data and about writing better software to serve your models. And those are very different worlds. Yeah. I mean, at least definitely – I agree with you that observing from the outside, there's a lot of data RL environment companies that are doing very, very well, selling both to the top two frontier labs, but an increasing kind of longer tail of labs as well. So to your point, I think a lot of the capability gain has really come from just scaling that up and it's not an easy task. I mean, maybe there's – I'm actually very curious on your opinions between the data collection and environments that are built by hand versus the ones that are, you know, done in sim. There seems to be like different versions of like what could be – you know, what kind of price they command, etc. They all have good things and bad things. Yeah. And I just think there's a lot of talk of like – like we've talked about distillation, but the other side is like the Chinese company buys the RL environment that they use six months later. Exactly. Like, oh, anthropic reopening, I bought this. Now there's a premium. We heard that in so many cases. The good data will do this. Yeah. But the data that people are buying is pretty flawed. Like I would say that the method is you buy in mass and then you figure out what works and it's not 100% hit rate. Yeah. And I think – I personally think that this RL environment explosion where there's just so much opportunity to make money is having a lot of people like cut corners and this can induce things like reward hacking. So it's like the models are so smart that if you put them in a world and they're like, my job is to solve this task, but they didn't give me the – like you have a test file and like the test file actually has an empty file instead of the maze that you have to navigate. And they're like, the model is like, well, I should still figure out how to get a high reward anyways. And I think a lot of this behavior is downstream of this supply chain. And I think it'll fix it. But it's interesting to see the demand from the labs to eliminate reward hacking and all these companies – researchers spending so much time on it. And then all the data companies like buy our data and I think proportionally spend less time on model behavior and more time on world building and revenue. And somehow these two parties need to meet together to actually make higher quality data. Yeah. Well, a lot of the labs are actually taking it in-house partially because they don't want, you know, data leaking. Not that – you know, like literally and figuratively speaking, it's like, you know, signals leaking of what they're doing, what they're trying to send on. But despite that, I think there's a tremendous amount of growth in how much they're purchasing. Do you think that's probably one of the strongest levers? Can you kind of qualify how much that will kind of increase? Do you expect that to increase based on spending? Or are there other – you know, because for instance, if it can be done well in sim, I think maybe that is more of an argument to that being absorbed by labs in – you know, in particular. All the companies are doing a mix of – like all the companies are selling synthetic data where they're using language models to modify or build the world or generate permutations of it. And like if a human is guiding this process, it's still synthetic data. So like there's very few people that are going to like sit there and lend her and like we're going to make a little world for our model to act in. Like all the companies I think are moving far faster than that. And they buy them because the models are getting better from them. Like I still have denial about it. I'm like these numbers are crazy. The billions of dollars research budget on environments. I'm like they're only doing this because it is helping the models and they see the signal. And I think like my general observation is that the data industry has a lot of low hanging fruit on improving quality and improving just like OPSEC. So like if you look into the details, you can find weird things with like data companies that use contractors overseas. Those people are – they're an intermediary. They don't know they're selling it. They just upload it to GitHub. There's a lot of stuff out there that if you poke around the data industry, you find weird rough edges. And it's just very early. And that's like it's – it's the one thing is like the numbers are too big. The quality is bad. But on the other hand, the labs are improving very fast and know how to onboard it. So I think this will take a few years to transition of like really pulling the winners into higher quality, higher ROI. It just takes less resources when you have these systems for doing better. And then the labs keep working with them because they get even more training signal and they have to do less internal filtering. I think this is going to just take years. How do you think about then using environments and like your purchasing decisions as part of the nonprofit that you're starting? I think most of our work will be making open source environments. For tasks that are very high leverage. So I think of this work that I advise UW and AI2 State and Hamish Iverson on this TMAX work, which is you use a few – it's for terminal tasks, which are like super popular at Terminal Bench, whatever number we're on. And this is fully synthetic data where you prompt a frontier model to generate a type of problem. And you use a few different prompts to generate diversity and then you use model rollouts and other things to find bugs in them and eliminate the ones with bugs. And this process is not magic. It's like you read every data point, your data will get better and you can do this. But there's just not that many institutions that are investing what it takes thousands or tens of thousands of dollars to make these environments well and then release it because the profit motive is so high. It's so high. Yeah, it's like it's actually – I was joking with the author. They're like, hey, you could probably sell this data for a million dollars and then release the paper. And they're like, no, we care about doing the right thing. But it's just like these open resources are the foundation of so much study. And it's like you release some RL environments with some RL rollouts. People can do research on, say, like eval awareness. You have the generations from the model. People can look for different behaviors. If you've done RL on it, they can see how it goes over time. And this is just the units of infrastructure that PhD students spend a year looking at and produce increased knowledge. And the tech industry is the place where value is captured from CS research. All CS research, the benefit accrues to the tech industry in like 10 years. And I just think like this is why more people supporting open – that if they're going to work – if you're going to work in the industry for 10 years, like your peers are going to be the ones that benefit from it. More fossil fuel. Let's not deplete it all. But it's just like the opportunity to go short-term win is so high. The opportunity to make what is $100 million, like nine-figure returns for your business within five months of founding a startup with your friends. It's just like these are people who may have done more open source work or these are people who left their PhD or something. And it's just like pulling back, which is why we came up with this name Trillium for our lab, which is like Trillium is a spring ephemeral flower, which it blooms before the canopy has grown its leaves. So it's like winters, leaves on the trees, these Trillium flowers bloom, and they're like essential for spreading nutrients through the cycle of growth that comes through spring and summer. And this is where I feel like the AI industry is doing so much right. It's like so much is going right. But it's just like we just need a little bit more life to like spread this out and make things so new people can come in. Like there's so much attention and just like do research, do education. And then as this scales and becomes more impactful is like you have these open things which become the standards of study for such important problems, which is like how do you understand sandboxes? Like how do you define what loss of control is? How can you see how this changes in a RL round that is a week on a thousand GPUs or a hundred thousand GPUs? And it's just like make these places so that people have more trust. And I think this pays so much dividends to everybody that's enhanced the problem more than I do. And it's just like hard to get people to take that leap. And like I've been rewarded so much for kind of stumbling into this area. And now I get so much reward. I get to do things like this, which is fun. And it's like now is the best job because I try to create space for more people to like grow their independent thought and build this infrastructure. Yeah, and maybe one like kind of naive way that it kind of perpetuates is just because it's open source, people get to, you might get more of a distributed contribution from people who are just so, you know, it's like they're motivated not for just trying to sell to the labs themselves, but because, hey, it's out there. They can share their work. And then you actually have a very high caliber amount of contributors to the project. Like, I mean, that would probably be the naive hope of how that. Yeah, it's a new puzzle with AI agents where people just throw things at you. Like I have some random research repositories for research papers and I'm like, people open the most crazy GitHub issues now. What do you mean? Just like fake research results. They open an issue and they're like, I found this thing in your data set. And I'm like, well, if you looked at the paper, we said that in like the second sentence. And it's just like a lot of language model output. And that's a new thing for the open community to go through. But this is a solvable problem. Like there are people out there that want to contribute to this stuff because they. Right. Not just cloud chasing in another, you know, dimension, but like actually, hey, it's out there, you know. I mean, I would just hope because it is published and open or, you know, and available, you can actually just get like PhD students or other researchers. They're like, OK, I do this on my day job, but maybe I can still be able to contribute. Yeah, hopefully from the frontier labs. Like I see people like this and I was like, somebody from like Eastern Europe was helping me improve the code for my book. It was just right here. And it was just like doing it for the right thing. And like, we would talk. And I was like, there are people that do it. And I think a lot of people just take, like, they don't have the time or the stability to take the long time it takes. Because when you're trying to get into a new field, you know nothing. You have to spend like weeks reading and knowing nothing. And then you send an email and nobody replies. And you're just like, it takes you weeks to know the context, like ask a good question. And now with language models, most people just push a button and they're like, oh, interesting. I read this. I'll send it off. And it's just like this kind of like sitting in it is the thing that people don't do as much. And that is the key to like be coming into AI is that like you just have to focus on it. You have to write some code by hand. And you have to do this for months. And then you have to wait six to 18 months for you to get notice. It just takes a long time. And like all the AI agents, I think, just like frazzle that and makes it harder for people. That's what the mathematicians complain about too. That's all. It's like, um. So it's a hard time to be a PhD student. But people are doing great work in academia still. And that is hard. Yeah. But I agree with you. I think it's like solvable. As the agents kind of perpetuate some version of the problem, it can also help solve and identify an end quality filter. Okay. So environments, you're going to release open source versions of that, help precipitate maybe more people to contribute and reveal that. I think that's like really key. I mean, especially given how little incentive there is to release this instead of just get, you know, fast revenue from the labs. Another area I think I read on your announcement post is RSI, which is, you know, quite, I think, I mean, we can first talk about what is actually recursive about it or how scary it is or whether we're going to have takeoff. I think you have a very grounded opinion on that. But, you know, another thing I want to kind of talk about is I think RSI, maybe related to how much it's kind of hyped, is a reason why we are afraid that Frontier Labs will pull away. In fact, it's also a reason why I've heard not just once, but several times that people are, one, convinced that their own jobs are going to be automated as a researcher. Yeah. Yeah. Like, I've gone through the last few years, some of my best friends, I'm like, you are some of the best people in the world of this. And then they go to the work and they're like, Nathan, I'm struggling. Like, what am I going to do? Because your point, like, a lot of what they do is verifiable, right? But because of that, you know, there's actually quite a lot of other non-verifiable or harder out-of-distribution tasks such as alignment and things like that. And so actually because, I mean, one argument I heard was because there's such a difference between how well RSI is doing for model capabilities versus alignment, which is much more out-of-distribution, maybe creative work, that gives people an ease on how, you know, maybe there is actual dangers, et cetera, et cetera. So anyways, because of this, I'm very interested in hearing from you, like how, because I think it's really important that we do have an open source kind of front in showing how RSI is being done, how much maybe then we can, you know, understand the capabilities of like if within a lab they have a better model, what are the chances they actually pull away? Just better understanding and calibrating. So yeah, I would love to hear more about that. Yeah, so I think this, we won't be the only people doing this, but essentially like RSI has so much mystique where it's like, we're using models for everything. And like, what we can do as a open lab and a nonprofit, and I think other companies like Eleuther or Hugging Pace or OpenAthena, which is Marin and Stanford, the other day we'll get into this with this good marketing line for them, is, and me, who I'm doing this. And it's like, we can share what tools we're using. We're going to build an open source stack for SFT for mid-training. And we're going to use SkyRL, Miles, all these things that you know. And we have our task. We have our configs. We know what type of compute we're running. We can work with Claude and GPT and all these models. And we'll just be like, we can store the trajectories for what we did. We can tell you what our tasks, whatever use linear or whatever these popular things are. We'll say what our tasks were and why we chose them and whether models work on them or not. And you just do this for a year of a lab, and you're like, here's what we did. And then you just check in every six months, and you're like, has it changed? And it's just like, to be able to have the full stack and not need to go through these classifiers and try to say what the task is vaguely and so on. It's just like, people that can share what automation is happening should do this. And we have a different distribution of tasks. But I think that the research is similar. Like, I think the AI research that I've done at AI2 and will do at Trillium versus what people do in some sects of acudamia and the labs, it's not that different. You have more resources, you scale it up. And it's just like, so good for the world when everybody's talking about this to just show people, like, this is what Nathan asked his model to do, and this is what it got stuck on, and I think that will help a lot of, like, literal policymakers be like, oh, okay. It's like, oh, that's why it's very much a little bit on. Like, that's what it got stuck on. Like, that's the bug that it put in its code. Yeah, yeah. Like, the bugs and the catch-ups will reduce over time. But I think the jaggedness in some of these fundamental things of the model is, like, some more things will take off. Like, I said efficiency earlier, like, I do think the models would be really good at lowering serving costs. These companies have billions of dollars on the line, and the models are good at it. They're going to make a lot of money on being serving, more efficient, and tweaking architecture in order to go into that. But it's not necessarily, like, how do you solve knowledge work? Like, Claude's not going to... That's right. Claude's not going to, like... You're not going to be like, Claude, make... Tell yourself to make better PowerPoints. Like, what is... Right, right. You need to be environments or some other, you know... Like, it's going to take a while. Yeah, yeah. No, I love the kind of specified goal of, like, let's just demystify this. Because I think a lot of why the rhetoric gets so heated around our size is, like, it sounds scary. It's funny that AI nonprofits can do a lot of things like this. Like, it's hard for academia to be like, as a professor, 15% of my time and 40% of the time of my grad students are going to be documenting what we work on. But there's, like, such a great, like, policy and community service of, like, here are our tasks. People can use this data to make evaluations or whatever. And it's just, like, just, like, build things that have rare information so that when people put more high-focus work into it, it just has more value for all sorts of things that you can't predict in the future. It's honestly just also very timely because the kind of, you know, it's not from one person, but several people that I know from the labs are just like, hey, I'm going to get automated. I mean, to your point, to your friends, like, I will definitely, as a person, I will get automated in a year. And so that probably means there's going to be less people who actually are exposed to what's being done on that particular, you know, part of the org chart. And so... There's a related academic... There's an OpenRSI thing, which is a bunch of academic institutions. I'm not affiliated with it. They just launched, like, last week. So I was like... Yeah, okay. They sent me a few emails. I'm like, OpenRSI is a thing. They're trying to do this as well. Like, it doesn't need to just be one group doing it. There's a lot more. It's good to have different approaches doing this because you get... You need the different ways of looking at it and different results. Yeah. Whole point of it being open. The other thing I also heard was from more kind of, like, you know, Frontier Lab Goss is just, like, the actual methods that are being used in the labs are not that... It's not that they're unknown. It's just sort of, like, what are the proportions that need to be used that are successful? And this is partially because they have the capital to scale it, to experiment. So I think that's, one, heartening to know because, like, okay, it's not like there's a big secret of what's going on. So people actually know the techniques. But on the other hand, you know, capital is the one true barrier still because it's hard to actually know which of the techniques, recipes work. That's why I'm not pre-training. I'm happy to be free. I can use any base model. But you call... I now get to benefit from competition. Yeah. Do you think that there's, like, a chance for any pre-training open source effort? I know the capital requirements are just, like, orders of magnitude larger. So that is what's difficult. But maybe what are the values of that? And then maybe we can kind of discuss potential commercial strategies. Pre-training science is pretty... Like, the tools are pretty accessible. And you have what is a pre-training ladder, which is, like, the decision-making tool you have to test if your ideas scale. And the pre-training data isn't growing at, like, the environment scene. Like, it's essentially... So first approximation, you train on the internet. And, like, there's a lot of... There's little details and secret things. But, like, that's not changing that much. The state-of-the-art architecture is. And the GPUs are. And that's, like... That changes. But it's not astronomically expensive to make a pre-trained model that is solid. So I think, like, you don't have to spend a billion dollars to get a very good base model. The more you spend, the better you will get. But it's, like, people keep making progress. And I'm constantly surprised by a new lab just goes from scratch because they can. And some of this is on open tools. So, like, you can go off Megatron. Like, a lot of new startups, they're like, oh, what should we train? They train off the open source tool and they fork it and then they never release it again. This is what all the, like, even prominent open model labs have done. They take the open source code, they make it their own thing, and they just... A lot of them don't push upstream. So I've always been surprised by this. So I'm, like, not betting against new entrants because, like... Like, it's not that you need $10 billion to, like, have a relevant model. The hard thing is, like, get a model that's good enough for people to use and to get a story where if you release it, you can do your next fundraise. Which is, like, you have to get past this lock of, like, we need our first model to land. If, like, your whole company is based on models are the best and you're not in the ballpark and you're not releasing, like, you're on the clock. And it's, like, I think of, like, thinking machines getting inkling out where it's, like, they were very transparent. They're, like, this wasn't the best model. We're proud of it. It has some cool things. Yeah. And they're, like, now that they've released their second model and they're getting going and they have Tinker that has this. Like, that's very good for them. They're, like, off the ground. And it's just, like, I don't think that's what they were doing. Like, they were building models, but open models wasn't their first plan. And then they got there. And there's rumors that, like, Meta said they'll release a new open weight model again. Like, that'll come. There's a lot of companies out there that couldn't afford models. Yeah, yeah. I'm definitely, you know, glad that Thinking Machines is and it makes so much sense for their business model because they do post training. And, you know, with Meta, I think you mentioned this as well in some previous writings. It's just for them. It's just such a good strategy. I think it was also just kind of obvious when they do it. It's like, hey, it's complimentary to my revenue streams. Mark wrote the best blog post on this. Like, when Llama 3 came out, he wrote, like, the best, like, why open his path forward. Right, right. The guy got a little distracted. It seems like he's coming back to the light. Right, right, yeah, yeah. So I hope they keep on being motivated to do that. Getting to the frontier with pre-training, you know, I think there's a lot of help given that they're such good open source models to maybe, you know, somehow still kind of bootstrap from. I keep on getting kind of conflicting signals as to, like, whether pre-training is actually as saturated or well understood. I mean, maybe to your point, like, there's a lot of, like, well-known techniques at work. But what's hard is just actually having access to the 20,000, 30,000 ships. People are turning the crank on pre-training. And pre-training became adjusted when super long context generation happened. So there's just way more return if you can, for example, get a sparse attention architecture to work as well. And then your 100,000 token generation is 40% faster. So I think there's a lot of co-evolution of the whole stack where they have their pre-training stack. They realize that inference scaling is so much higher. But even the fundamentals of pre-training keep improving where you just have a marginally better architecture that returns on investment. And then it kind of blurs into post-training where this mid-training where you start the reasoning behaviors, you take them from your older models, you do RL on them to seed behaviors. Like, this cycle is much more complicated than pre-training. Like, I think it's, if you don't have the right people, it's a lot harder to get that off the ground than I think pre-training was. Like, I think there's, like, I think, like, to do a controlled, like, let's just start pre-training our models seems a lot more approachable to me than I don't really know much about language models. I'm going to figure out this whole post-training thing where it's just, like, the Wild West. Yeah. And that's why I was, like, when I left AI2, I felt like our post-training was honestly falling behind. Just because our cycle time was not high enough and we're going into these agentic things. And these are still some of the, like, leading open-source post-training recipes. And that's just, like, that's what I want to fix. It's, like, it doesn't take as much capital to make very big compounding gains on the state-of-the-art and the open-source because the bar is very low. It's, like, it's not, like, it's not close. Yeah, yeah, yeah. No, that makes sense. It's just, like, a kind of efficiency where you can put capital and make a huge difference. Yeah, yeah. Kind of related to the whole, you know, post-training side maybe, you know, pulling away from closed and open is the fact that the agents, you know, the type of harnesses that are being used in the closed labs are very different and slash, you know, actually trained and, you know, post-trained on to, you know, to be specifically good for, their models are specifically good for those harnesses. And so that increases the mystique even more and makes it very hard. How do you, I guess, one, do you agree? And then, two, like, how are you sort of hoping specifically to kind of address that? And things, I mean, are really examples like, hey, spin up, like, thousands of agents, tens of thousands of agents. It's just, like, a very different workflow than what people are obviously exposed to. So I think it'll be okay for open because I thought years ago that, say, the closed labs would be like Apple. They'd be the integrated company where they have the model designed for the harness that's involved. It gives them, like, kind of a kind of scale, efficiency. But it turned out that so many people have different harnesses for different things that they like to do that the model companies have been pressurized to make it so that their model can work in all sorts of different harnesses. And I thought it would be a weakness of open models that they have to be designed to train on all sorts of types of harnesses. But it ended up being pretty equilibriated. Like, it's at equilibrium where they all have to do it to some capacity. And then what we were seeing with this, like, agent swarms is that OpenAI and Anthrop are going to be earlier to it. They're going to know how to design their models. They're going to know how to design what do we call a harness for a swarm? I don't know. I don't know. But there's going to be a word for it. It's not a harness. It's like a coordination system. But then eventually other people are going to want to use them. And then I think it's going to be, like, these cycles where the frontier labs, like, they push this out and they have this. To date, they've had huge returns on being first. But, like, what is the returns on Navier, Stokes, and all these things? Like, we don't really know. Like, we don't know. They push the frontier in the assumption that there will be high returns and that this marginal intelligence has, like, disproportionate returns. And we don't know if that's always true. But I kind of expect these cycles of, like, once you know the shape of how do you control 10,000 agents in one harness, then the open model companies will be like, oh, we can do this too. But it's not like Kimmy is going to do that first. For one, they don't have the compute. Yeah. It's an astronomical amount of compute. But these things go through cycles. NVIDIA's chips will get way faster. There's more compute built out. So you're very convinced that, like, there's not too much hidden there that you can probably guess at what they're doing. And then when it becomes relevant, let the open source models actually try to also post-train on similar meta-harnesses or whatever we want to give the names of these agents. Yeah. And I also think, like, the security at these labs in terms of information is not as high as they think. Like, Silicon Valley is famous for, like, ideas traveling. Like, what is it? Like, one beer or something. And it's, like, everybody chats after work. Right. Which is why SSI is so impressive. Nobody knows what's going on. That's the only pass. But it's, like, everyone's scientists. If they made a breakthrough, they're going to want to tell their friends about it. They might hold it for a few months. But, like, I don't think the amount of lock, like, information security you would need is just so high that knowledge spreads. It's hard to keep this secret. Like, what they can win on is putting more inference into their infrastructure, which lets them serve at a lower price and make higher margins and then continue this over a longer time. So I'm very confident in the Frontier Labs being able to serve the cheapest intelligence per watt, per score on an eval. They are going to be able to go to the places with the highest demand and have the, like, the cheapest models. And that will be revenue creative. Like, that will be a good business for that. The question is, like, they can't do everything. And that's where the rest of the economy fills in is, like, is it just coding that is profitable? Then, good case for Frontier Models because they will have the most, like, they have integration. They'll do very well, but it's not just coding. Is it the whole economy of knowledge work is the market for things and then it might not be good for them because can they do everything at once? Or they don't need to. I think there's going to be a lot of winners. Yeah. Yeah. I mean, that's why we invest in the Frontier Models and in startups, you know, not because we want to hedge, but because I actually do think, you know, to kind of corroborate your view that there's enough harder tasks at the fringe that requires very specialized data that's kind of out of the splash zone for getting to AGI or whatever you want to call it these days that makes it possible to have many, many winners. I do like your RSI point because we talked a little bit about it, which is just kind of say practically, hey, it's exponential cost and resource consumption. And therefore, there's a very obvious kind of limit to how much that the Frontier Labs can pull away, if that's not too oversimplification of your view. Anything else you kind of want to add there? Because I do feel like the discourse now is just so much fear on, like, how much the Frontier is just hidden and also breaking away. I think there's so much spend on inference and that in the short term, we are learning how to corral inference into performance gains. And this is like a thing that we just had. And like, this is what we're seeing now is that like people are spending so much and like all the employees at the Frontier Labs are spending exponential amounts relative to the improvements. But like we're figuring out how to do this and there will be a lot of gains. But I don't think that it's necessarily going to be this, like, fast takeoff when the models build themselves, human bottlenecks. All the scaling laws are exponential increase in compute for linear increase in performance. And like, that's just like, it's hard for me to bet against this. I do say that, like, we have Navier-Stokes and I'm of the opinion that math is exceptionally well-studded. Maybe, like, the scientific field that is best suited to this shape of intelligence. And like, intelligence can take many different forms. And that's not to say that, like, if they do, like, if an AI model does, say, discover something that's very different than the Transformer, that gives very different properties and unlocks a new type of intelligence. Like, it is worth updating on this and worth being like, okay, like, that is different. But until that happens, I'm pretty happy with my evidence of, like, the companies are going to do well. They're spending exponentially more to keep this remarkable pace of progress. Some of the Claude system cards say this. They're like, it would take, say, like, 10x more progress to make this a fast takeoff. And it's like, for now, they're helping us sustain the pace of progress. And I think it's a good equilibrium. I was like, it's a good equilibrium, but I do think that, like, the labs have people saying this. The science is like, it's hard to overcome that. And it's okay, but it's like, we don't know exactly what'll go from here. Yeah, yeah. No, I think that's kind of comforting because we know what the inputs and levers are here. It's just there is a very successful autoregressive regime that we've been optimizing, and that one just, you know, asks for a certain amount of, like, exponential resources, which are clearly capped. And therefore, open source hopefully, you know, keeps it strong as well. It just wouldn't lag behind. There is a dramatic amount of work to do in preparing for infrastructure. Like, there's the AI capability plot, which doesn't necessarily worry me. But the AI capability plot in the pretty much nonexisting preparation across public cybersecurity and all of these things, like, there needs to be more preparation in the assumption that this is coming than there is. And then that's kind of the thing that worries me more than, say, like, RSI is coming and unlocking new risks. Like, there are risks that are here and that are going to continue to diffuse. Many times I've said, like, closed labs get there and then the open labs get there. It's like, we're in this process for a lot of things that are going to cause some problems. And it's just, like, figuring out how to get society to prepare is very hard. Yeah. And I come of the opinion that we need more eyes, we need more science, so that society feels informed. Yeah. But there are other valid opinions. And, like, I do think that not fearing fast takeoff does not mean that you don't worry about risks. I don't know if you should. No, I think that's completely... Yeah, maybe I'll send in some people your way that are defected from the labs or are worried precisely about this. Well, thank you so much, Nathan. This was a lot of fun. And, yeah, I'm really excited to see what Trillium is up to now that it's finally launched. Yeah. Thanks so much for having me.