Dylan Patel (13:43)

Yeah, so Deep Seq V3 is a new mixture of experts Transformer language model from DeepSeek who is based in China. They have some new specifics in the model that we'll get into. Largely this is a open weight model and it's a instruction model like what you would use in ChatGPT. They also released what is called the base model, which is before these techniques of post training. Most people use instruction models today and those are what's served in all sorts of applications. This was released I believe December 26th or that week. And then weeks later on January 20th, Deep Seq released Deep Seq R1, which is a reasoning model which really accelerated a lot of this discussion. This reasoning model has a lot of overlapping training steps to deep seq v3 and it's confusing that you have a base model called V3 that you do something to to get a chat model and then you do some different things to get a reasoning model. I think a lot of the AI industry is going through this challenge of communications right now where OpenAI makes fun of their own naming schemes. They have GPT 4.0, they have OpenAI01, and there's a lot of types of models. So we're going to break down what each of them are. There's a lot of technical specifics on training and go from high level to specific and kind of go through each of them.

Lex Friedman (15:09)

There's so many places we can go here. But maybe let's go to open weights first. What does it mean for model to be open weights and what are the different flavors of open source in general?

Dylan Patel (15:17)

Yeah, so this discussion has been going on for a long time in AI. It became more important since ChatGPT or more focal since ChatGPT at the end of 2022. Open weights is the accepted term for when model weights of a language model are available on the Internet for people to download. Those weights can have different licenses, which is effectively the terms by which you can use the model. There are licenses that come from history and open source software. There are licenses that are designed by companies, specifically all of Llama, Deepseek, Quen, Mistral. These popular names in open weight models have some of their own licenses. It's complicated because not all the same models have the same terms. The big debate is on what makes a model open weight. Why are we saying this term? It's kind of a mouthful. It sounds close to open source, but it's not the same. There's still a lot of debate on the definition and soul of open source AI. Open source software has a rich history on freedom to modify, freedom to take on your own freedom from any restrictions on how you would use the software. And what that means for AI is still being defined. So for what I do, I work at the Allen Institute for AI. We're a nonprofit. We want to make AI open for everybody. And we try to lead on what we think is truly open source. There's not full agreement in the community, but for us that means releasing the training data, releasing the training code, and then also having open weights like this. And we'll get into the details of the models. And again and again, as we try to get deeper into how the models were trained, we will say things like the data processing, data filtering, data quality is the number one determinant of the model quality. And then a lot of the training code is the determinant on how long it takes to train and how fast your experimentation is. So without fully open source models where you have access to this data, it is hard to know or it's harder to replicate. So we'll get into cost numbers for deep seq v3 on mostly GPU hours and how much you could pay to rent those yourselves. But without the data, the replication cost is going to be far, far higher. And same goes for the code.

Lex Friedman (17:32)

We should also say that this is probably One of the more open models out of the frontier models. So like in this full spectrum where probably the fullest open source, like you said, open code, open data, open weights. This is not open code, this is probably not open data and this is open weights. And the licensing is MIT license or it's. I mean there's some nuance in the different models, but it's towards the free in terms of the open source movement. These are the kind of the good guys.

Dylan Patel (18:07)

Yeah. Deepseek is doing fantastic work for disseminating understanding of AI. Their papers are extremely detailed in what they do and for other teams around the world. They're very actionable in terms of improving your own training techniques. And we'll talk about licenses more. The Deep Seq R1 model has a very permissive license. It's called the MIT license. That effectively means there's no downstream restrictions on commercial use, there's no use case restrictions. You can use the outputs from the models to create synthetic data. And this is all fantastic. I think the closest peer is something like Llama where you have the weights and you have a technical report. And the technical report is very good for Llama. One of the most read PDFs of the year last year is the Llama 3 paper. But in some ways it's slightly less actionable. It has less details on the training specifics, less plots and so on. And the Llama 3 license is more restrictive than MIT. And then between the Deepsea custom license and the LLAMA license we could get into this whole rabbit hole. I think we'll make sure we want to go down the license rabbit hole before we do specifics.

Nathan Lambert (19:16)

Yeah.

Lex Friedman (19:16)

And I mean so it should be stated that one of the implications that Deepseek, it puts pressure on LLAMA and Everybody else on OpenAI to push towards open source. And that's the other side of Open Source that you mentioned is how much is published in detail about it. So how open are you with the sort of the insights behind the code? So like how good is the technical reports? Are they hand wavy or is there actual details in there? And that's one of the things that Deep SEQ did well is they publish a lot of the details.

Dylan Patel (19:46)

Yeah. Especially in the deep seq v3, which is their pre training paper, they were very clear that they are doing interventions on the technical stack that go at many different levels. For example, to get highly efficient training, they're making modifications at or below the CUDA layer for Nvidia chips. I have never worked there myself. And there are a Few people in the world that do that very well and some of them are at Deepseek and these types of people are at Deep Seq and leading American Frontier Labs. But there are not many places to.

Lex Friedman (20:19)

Help people understand the other implication of open weights. Just, you know, there's a topic we'll return to often here. So there's a fear that China, the nation, might have interest in stealing American data, violating privacy of American citizens. What can we say about open weights to help us understand what the weights are able to do in terms of stealing people's data?

Dylan Patel (20:49)

Yeah, so these weights that you can download from Hugging Face or other platforms are very big matrices of numbers. You can download them to a computer in your own house that has no Internet and you can run this model and you're totally in control of your data. That is something that is different than how a lot of language model usage is actually done today, which is mostly through APIs where you send your prompt to GPUs run by certain companies. And these companies will have different distributions and policies on how your data is stored, if it is used to train future models, where it is stored, if it is encrypted, and so on. So the open weights are you have your fate of data in your own hands and that is something that is deeply connected to the soul of open source.

Lex Friedman (21:31)

So it's not the model that steals your data, it's qover's hosting the model, which could be China if you're using the Deep Seq app. Or it could be perplexity. You know, you're trusting them with your data or OpenAI you trust them with your data. And some of these are American companies, some of these are Chinese companies. But the model itself is not doing the stealing, it's the host. All right, so back to the basics. What's the difference between deep seq v3 and deep seq r1? Can we try to like lay out the confusion potential?

Dylan Patel (22:05)

Yes. So for one, I'm very understanding of many people being confused by these two model names. So I would say the best way to think about this is that when training a language model you have what is called pre training, which is when you're predicting the large amounts of mostly Internet text, you're trying to predict the next token. And what to know about these new Deep SEQ models is that they do this Internet large scale pre training once to get what is called deep seq v3 base. This is a base model. It's just going to finish your sentences for you. It's going to be harder to work with than ChatGPT. And then what DeepSeq did is they've done two different post training regimes to make the models have specific desirable behaviors. So what is the more normal model in terms of the last few years of AI? An instruct model, A chat model, an aligned model, a helpful model. There are many ways to describe this is more standard post training. So this is things like instruction, tuning, reinforcement, learning from human feedback. We'll get into some of these words and this is what they did to create the deep seq v3 model. This was the first model to be released and it is very high performant, it's competitive with GPT4, llama 405B, so on. And then when this release was happening, we don't know their exact timeline or soon after they were finishing the training of a different training process from the same next token prediction base model that I talked about, which is when this new reasoning training that people have heard about comes in in order to create the model that is called deep seq R1. The R through this conversation is good for grounding for reasoning. And the name is also similar to OpenAI's O1, which is the other reasoning model that people have heard about. And we'll have to break down the training for R1 in more detail because for one we have a paper detailing it. But also it is a far newer set of techniques for the AI community. So it's a much more rapidly evolving area of research.

Lex Friedman (24:06)

Maybe we should also say the big two categories of training of pre training and post training, these umbrella terms that people use. So what is pre training and what is post training and what are the different flavors of things underneath Post training umbrella.

Nathan Lambert (24:22)

Yeah.

Dylan Patel (24:22)

So pre training, I'm using some of the same words to really get the message across is you're doing what is called autoregressive prediction to predict the next token in a series of documents. This is done over standard practice is trillions of tokens. So this is a ton of data that is mostly scraped from the web. In some of Deepseek's earlier papers they talk about their training data being distilled for math. I shouldn't use this word yet, but taken from Common Crawl and that's a public access that anyone listening to this could go download data from the Common Crawl website. This is a crawler that is maintained publicly. Yes, other tech companies eventually shift to their own crawler and deepseak likely has done this as well as most frontier labs do. But this sort of data is something that people can get started with and you're just predicting text in a series of documents. This can be scaled to be very efficient. And there's a lot of numbers that are thrown around in AI training, like how many floating point operations or flops are used. And then you can also look at how many hours of these GPUs that are used. And it's largely one loss function taken to a very large amount of compute usage. You set up really efficient systems and then at the end of that you have this base model. And pre training is where there is a lot more of complexity in terms of how the process is emerging or evolving and the different types of training losses that you will use. I think this is a lot of techniques grounded in the natural language processing literature. The oldest technique which is still used today is something called instruction tuning or also known as supervised fine tuning. These acronyms will be IFT or sft. People really go back and forth throughout them and I will probably do the same. Which is where you add this formatting to the model where it knows to take a question that is like explain the history of the Roman Empire to me or a sort of question you'll see on Reddit or StackOverflow. And then the model will respond in an information dense but presentable manner. The core of that formatting is in this instruction tuning phase. And then there's two other categories of loss functions that are being used today. One I will classify as preference fine tuning. Preference fine tuning is a generalized term for what came out of reinforcement learning from human feedback, which is RLHF. This reinforcement learning from human feedback is credited as the technique that helped ChatGPT break through. It is a technique to make the responses that are nicely formatted like these Reddit answers, more in tune with what a human would like to read. This is done by collecting pairwise preferences from actual humans out in the world to start. And now AIs are also labeling this data and we'll get into those trade offs and you have this kind of contrastive loss function between a good answer and a bad answer. And the model learns to pick up these trends. There's different implementation ways. You have things called reward models. You could have direct alignment algorithms. There's a lot of really specific things you can do. But all of this is about fine tuning to human preferences. And the final stage is much newer and we'll link to what is done in R1. And these reasoning models is, I think OpenAI's name for this. They had this new API in the fall which they called the Reinforcement Fine Tuning API. This is the idea that you use the techniques of reinforcement learning, which is a whole framework of AI. There's a deep literature here to summarize. It's often known as trial and error learning or the subfield of AI where you're trying to make sequential decisions in a certain potentially noisy environment. There's a lot of ways we could go down that. But fine tuning language models where they can generate an answer and then you check to see if the answer matches the true solution for math or code, you have an exactly correct answer. For math, you can have unit tests for code. And what we are doing is we are checking the language model's work and we're giving it multiple opportunities on the same question to see if it is right. And if you keep doing this, the models can learn to improve in verifiable domains to a great extent. It works really well. It's a newer technique in the academic literature. It's been used at Frontier labs in the US that don't share every detail for multiple years. So this is the idea of using reinforcement learning with language models. And it has been taking off, especially in this deep seek moment.

Lex Friedman (28:55)

And we should say that there's a lot of exciting stuff going on on the again across the stack. But the post training, probably this year there's going to be a lot of interesting developments in the post training. We'll. We'll talk about it. I almost forgot to talk about the. The difference between Deepseek v3 and R1 on the user experience side. So forget the technical stuff. Forget all of that. Just people that don't know anything about AI, they show up like, what's the actual experience? What's the use case for each one when they actually like type and talk to it, what is each good at? And that kind of thing.

Dylan Patel (29:26)

So let's start with deep seq v3 again. It's what more people would have tried. Something like it. You ask it a question, it'll start generating tokens very fast and those tokens will look like a very human legible answer. It'll be some sort of markdown list. It might have formatting to help you draw to the core details in the answer and it'll generate tens to hundreds of tokens. A token is normally a word for common words or a subword part in a longer word. And it'll look like a very high quality Reddit or Stack overflow answer. These models are really getting good at doing these across a wide variety of domains. I think Even things that if you're an expert, things that are close to the fringe of knowledge, they will still be fairly good at. I think cutting edge AI topics that I do research on. These models are capable for study aid and they're regularly updated. Where this changes is with the deep seq r1. What is called these reasoning models is when you see tokens coming from these models to start it will be a large chain of thought process. We'll get back to chain of thought in a second, which looks like a lot of tokens where the model is explaining the problem. The model will often break down the problem and be like, okay, they asked me for this, let's break down the problem. I'm going to need to do this. And you'll see all of this generating from the model. It'll come very fast. In most user experiences these APIs are very fast. So you'll see a lot of tokens, a lot of words show up really fast. It'll keep flowing on the screen and this is all the reasoning process. And then eventually the model will change its tone in R1 and it'll write the answer where it summarizes its reasoning process and writes a similar answer to the first types of model. But in Deepseek's case, which is part of why this was so popular even outside the AI community, is that you can see how the language model is breaking down problems and then you get this answer. On a technical side, they train the model to do this specifically where they have a section which is reasoning and then it generates a special token which is probably hidden from the user most of the time, which says, okay, I'm starting the answer. So the model is trained to do this two stage process on its own. If you use a similar model in say, OpenAI, OpenAI's user interface is trying to summarize this process for you nicely by kind of showing the sections that the model is doing. And it'll kind of click through, it'll say breaking down the problem, making X calculation, cleaning the result. And then the answer will come for something like OpenAI, maybe it's useful here.

Lex Friedman (31:58)

To go through an example of a deep seq R1 reasoning.

Dylan Patel (32:03)

Yeah, so if you're looking at the screen here, what you'll see is a screenshot of the Deep Seq chat app. And at the top is thought for 1517 seconds with the drop down arrow underneath that. If we were in an app that we were running, the drop down arrow would have the reasoning.

Lex Friedman (32:19)

So in this case the specific Question which, you know, I'm philosophically pothead inclined. So this is asking DeepSeekar1 for one truly novel insight about humans and it reveals the reasoning. And basically the truly novel aspect is what's pushing the reasoning to constantly sort of the model asking itself is this truly novel? So it's actually challenging itself to be more novel, more counterintuitive, more, less cringe I suppose so some of the reasoning says this is just snapshots. Alternatively, humans have a unique meta emotion where they feel emotions about their own emotions. For example, feeling guilty about being angry. This recursive emotional layering creates complex motivational drives that don't exist in other animals. The insight is that human emotions are nested. So it's like it's reasoning through how humans feel emotions, it's reasoning about meta emotions.

Dylan Patel (33:15)

Gonna have pages and pages of this. It's almost too much to actually read, but it's nice to skim as it's coming.

Lex Friedman (33:20)

It's stream of, it's a James Joyce like stream of consciousness. And then it goes, wait, the user wants something that's not seen anywhere else. Let me dig deeper and consider the human ability to hold contradictory beliefs simultaneously. Cognitive dissonance is known, but perhaps the funct is to allow flexible adaptation, so on and so forth. I mean that really captures the public imagination that holy shit, this isn't intelligent. Almost like an inkling of sentience because you're thinking through, you're self reflecting, you're deliberating. And the final result of that after 157 seconds is humans instinctively convert selfish desires into cooperative systems by collectively pretending abstract rules, money, laws, rights are real. These shared hallucinations act as quote games where competition is secretly redirected to benefit the group, turning conflict into society's fuel. Pretty profound. I mean, you know this is a.

Dylan Patel (34:26)

Commentary digression, but a lot of people have found that these reasoning models can sometimes produce much more eloquent text. I think that is a at least interesting example. I think depending on how open minded you are, you find language models interesting or not. And there's a spectrum there.

Lex Friedman (34:42)

Well, I mean it's some of the, we'll talk about different benchmarks and so on, but some is just a vibe like that in itself is a, let's say, quote fire tweet. Yeah, if I were trying to produce something, something where people are like oh shit, okay, so that's chain of thought, we'll probably return to it more. How were they able to achieve such low cost on the training and the inference maybe you could talk the training first.

Nathan Lambert (35:09)

Yeah. So there's two main techniques that they implemented that are probably the majority of their efficiency and then there's a lot of implementation details that maybe we'll gloss over or get into later that sort of contribute to it. But those two main things are. One is they went to a mixture of experts model, which we'll define in a second. And then the other thing is that they invented this new technique called MLA Latent Attention. Both of these are big deals. Mixture of experts is something that's been in the literature for a handful of years. And OpenAI with GPT4 was the first one to productize a mixture of experts model. And what this means is when you look at the common models around that most people have been able to interact with that are open, right? Think Llama. Llama is a dense model. That is every single parameter or neuron is activated as you're going through the model for every single token you generate. Right. Now, with a mixture of experts model, you don't do that. Right. How does the human actually work?

Dylan Patel (36:09)

Right.

Nathan Lambert (36:10)

Is like, oh, well, my visual cortex is active when I'm thinking about vision tasks or other things. My amygdala is when I'm scared. These different aspects of your brain are focused on different things. A mixture of experts models attempts to approximate this to some extent. It's nowhere close to what a brain architecture is, but different portions of the model activate, right? You'll have a set number of experts in the model and a set number that are activated each time. And this dramatically reduces both your training and inference costs. Because now if you think about the parameter count as the sort of total embedding space for all of this knowledge that you're compressing down during training when you're embedding this data in, instead of having to activate every single parameter every single time you're training or running inference, now you can just activate on a subset and the model will learn which expert to route to for different tasks. And so this is a humongous innovation in terms of, hey, I can continue to grow the total embedding space of parameters. And so deep SEQ's model is 600 something billion parameters, right? Relative to llama 405B, it's 405 billion parameters, right? Lamar Relative to llama 70B, it's 70 billion parameters, right? So this model technically has more embedding space for information to compress all of the world's knowledge that's on the Internet down. But at the same time, it is only activating around 37 billion of the parameters. So only 37 billion of these parameters actually need to be computed every single time you're training data or inferencing data out of it, versus again the llama model. 70 billion parameters must be activated or 405 billion parameters must be activated. So you've dramatically reduced your compute cost when you're doing training and inference with this mixture of experts architecture.

Dylan Patel (37:51)

Should we break down where it actually applies and go into the transformer? Is that useful?

Lex Friedman (37:56)

Let's go. Let's go into the transformer.

Dylan Patel (37:58)

Transformer is a thing that is talked about a lot and we will not cover every detail. Essentially the transformer is built on repeated blocks of this attention mechanism and then a traditional dense, fully connected, multilayer perceptron, whatever word you want to use for your normal neural network. And you alternate these blocks. There's other details. And where mixture of experts is applied is at this dense model. The dense model holds most of the weights if you count them in a transformer model. So you can get really big gains from those mixture of experts on parameter efficiency at training and inference because you get this efficiency by not activating all of these parameters.

Lex Friedman (38:38)

We should also say that a transformer is a giant neural network.

Dylan Patel (38:42)

Yeah.

Lex Friedman (38:43)

And then there's. For 15 years now, there's what's called the deep learning revolution. Network's gotten larger and larger. And at a certain point the scaling laws appeared where people realized this is a scaling law shirt, by the way, representing scaling laws. Where it became more and more formalized that bigger is better across multiple dimensions of what bigger means. So and, but these are all sort of neural networks we're talking about. And we're talking about different architectures of how to construct these neural networks such that the training and the inference on them is super efficient.

Dylan Patel (39:18)

Yeah. Every different type of model has a different scaling law for it, which is effectively for how much compute you put in. The architecture will get to different levels of performance at test tasks. And mixture of experts is one of the ones at training time. Even if you don't consider the inference benefits, which are also big at training time. Your efficiency with your GPUs is dramatically improved by using this architecture if it is well implemented. So you can get effectively the same performance model and evaluation scores with numbers like 30% less compute. I think there's going to be a wide variation depending on your implementation details and stuff. But it is just important to realize that this type of technical innovation is something that gives huge gains. And I expect most companies that are serving their models to move to this mixture of experts implementation. Historically, the reason why not everyone might do it is because it's an implementation complexity, especially when doing these big models. So this is one of the things Deepseek gets credit for is they do this extremely well. They do a mixture of experts extremely well. This architecture for what is called Deep SEQ moe MOE is the shortened version of mixture of experts is multiple papers old. This part of their training infrastructure is not new to these models alone. And same goes for what Dylan mentioned with multi head latent attention. This is all about reducing memory usage during inference and same things during training by using some fancy low rank approximation math. If you get into the details with this latent attention, it's one of those things I look at and it's like, okay, they're doing really complex implementations because there's other parts of the language model such as embeddings that are used to extend the context length. The common one that Deep SEQ used is rotary positional embeddings which is called rope. And if you want to use rope with a normal moe, it's kind of a sequential thing. You take two of the attention matrices and you rotate them by a complex value rotation, which is a matrix multiplication with deep SEQ mla, with this new attention architecture, they need to do some clever things because they're not set up the same and it just makes the implementation complexity much higher. So they're managing all of these things and these are probably the sort of things that OpenAI, these closed labs are doing. We don't know if they're doing the exact same techniques, but they actually shared them with the world, which is really nice to feel like. This is the cutting edge of efficient language model training.

Lex Friedman (41:43)

And some of this requires low level engineering just is a giant mess and trickery. So as I understand, they went below CUDA, so they go super low programming of GPUs.

Nathan Lambert (41:55)

Effectively Nvidia builds this library called nickel, right? In which you know when you're training a model, you have all these communications between every single layer of the model and you may have over 100 layers.

Dylan Patel (42:06)

What does Nickel stand for?

Nathan Lambert (42:07)

It's NCCL Nvidia Communications Collectives Library.

Lex Friedman (42:11)

Nice.

Nathan Lambert (42:15)

When you're training a model, you're going to have all these all reduces and all gathers between each layer, between the multilayer perceptron or feedforward network and the attention mechanism. You'll have basically the model synchronize or you'll have allreducer and all gather and this is a communication between all the GPUs in the network, whether it's in training or inference. So Nvidia has a standard library. This is one of the reasons why it's really difficult to use anyone else's hardware for training is because no one's really built a standard communications library. And Nvidia has done this at a sort of a higher level. Right, Deepseek, because they have certain limitations around the GPUs that they have access to. The interconnects are limited to some extent by the restrictions of the GPUs that were shipped into China legally, not the ones that are smuggled, but legally shipped in that they used to train this model. They had to figure out how to get efficiencies. And one of those things is that instead of just calling the Nvidia library Nickel, right? They instead created their. They scheduled their own communications, which some of the labs do. Right. Emeda Talked about in Llama 3 how they made their own custom version of NCCL. They didn't talk about the implementation details. This is some of what they did. Probably not as well as, maybe not as well as Deepseek, because Deepseek, necessity is the mother of innovation and they had to do this. Whereas in the case, you know, OpenAI has people that do this sort of stuff, anthropic, et cetera. But, you know, Deepseek certainly did it publicly and they may have done it even better because they were gimped on a certain aspect of the chips that they have access to. And so they scheduled communications, you know, by scheduling specific sms. SMS you could think of as like the core on a gpu, right? So there's hundreds of cores or there's, you know, a bit over 100 cores, SMS on a GPU. And they were specifically scheduling, hey, which ones are running the model, which ones are doing all reduce, which one are doing all gather, right? And they would flip back and forth between them. And this requires extremely low level programming.

Dylan Patel (44:16)

This is what Nickel does automatically. Or other Nvidia libraries handle this automatically usually.

Nathan Lambert (44:20)

Yeah, exactly. And so technically they're using, you know, ptx, which is like sort of like you could think of it as like an assembly type language. It's not exactly that or instruction set, right? Like coding directly to assembler instruction set. It's not exactly that, but that's still part of technically cuda. But it's like, do I want to write in Python Pytorch equivalent and call Nvidia libraries? Do I want to go down to the C Level and code even lower level, or do I want to go all the way down to the assembly or ISO level? And there are cases where you go all the way down there at the very big labs, but most companies just do not do that because it's a waste of time and the efficiency gains you get are not worth it. But DeepSeek's implementation is so complex, right? Especially with their mixture of experts, right? People have done mixture of experts, but they're generally 8, 16 experts, right? And they activate too. So you know, one of the words we like, like to use is like sparsity factor, right? Or usage, right? So, so you might have four, you know, one fourth of your model activate, right? And, and, and that's what Mistral's Mixtral model, right? Their, their model that really catapulted them to like, oh my God, they're really, really good. OpenAI has also had models that are MOE and so have all the other labs that are major closed. But what DeepSeq did that maybe only the leading labs have only just started recently doing is have such a high sparsity factor, right? It's not 1/4 of the model, right? Two out of eight experts activating every time you go through the model, it's eight out of 256.

Dylan Patel (45:45)

And there's different implementations for mixture of experts where you can have some of these experts that are always activated, which this just looks like a small neural network. And then all the tokens go through that, and then they also go through some that are selected by this routing mechanism. And one of the innovations in DeepSeq's architecture is that they change the routing mechanism. In mixture of expert models, there's something called an auxiliary loss, which effectively means during training you want to make sure that all of these experts are used across the tasks that the model sees why there can be failures. And mixture of experts is that when you're doing this training, the one objective is token prediction accuracy. And if you just let training go with a mixture of expert model on your own, it can be that the model learns to only use a subset of the experts. And in the MOE literature there's something called the auxiliary loss which helps balance them. But if you think about the loss functions of deep learning, this even connects to the bitter lesson is that you want to have the minimum inductive bias in your model to let the model learn maximally. And this auxiliary loss, this balancing across experts could be seen as in tension with the prediction accuracy of the tokens. So we don't know the exact Extent that the deep SEQMOE change, which is instead of doing an auxiliary loss, they have an extra parameter in their routing, which after the batches they update this parameter to make sure that the next batches all have a similar use of experts. And this type of change can be big, it can be small, but they add up over time. This is the sort of thing that just points to them innovating. And I'm sure all the labs that are training big moes are looking at this sort of things, which is getting away from the auxiliary loss. Some of them might already use it, but you keep accumulating gains. And we'll talk about the philosophy of training and how you organize these organizations. And a lot of it is just compounding small improvements over time in your data, in your architecture and your post training and how they integrate with each other. DeepSeq does the same thing. And some of them are shared or a lot. We have to take them on face value that they share their most important details. I mean, the architecture and the weights are out there. So we're seeing what they're doing and it adds up.

Nathan Lambert (47:59)

Going back to sort of the efficiency and complexity point, right? It's 32 versus 4, right. For like mixdraw and other moe models that have been publicly released. So this ratio is extremely high. And sort of what Nathan was getting at there was when you have such a different level of sparsity, you can't just have every GPU have the entire model, right? The model's too big, there's too much complexity there. So you have to split up the model with different types of parallelism, right? And so you might have different experts on different GPU nodes. But now what happens when this set of data that you get, hey, all of it looks like this one way and all of it should route to one part of my, you know, model, right? So, so when all of it routes to one part of the model, then you can have the, you can have this overloading of a certain set of the GPU resources or a certain set of the GPUs. And then the rest of the, the training network sits idle because all of the tokens are just routing to that. So this is the biggest complexity. One of the big complexities with running a very, you know, sparse mixture of experts model that is, you know, this 32 ratio versus this 4 ratio is that you end up with so many of the experts just sitting there idle. So how do I load balance between them, how do I schedule the communications between them? This is a lot of the extremely low level detailed work that they figured out in the public first and potentially like second or third in the world and maybe even first in some cases.

Lex Friedman (49:24)

What lesson do you in the direction of the bitter lesson do you take from all of this? Where is this going to be the direction where a lot of the gain is going to be? Which is this kind of low level optimization? Or is this a short term thing where the biggest gains will be more on the algorithmic high level side of like post training? Is this like a short term leap because they've figured out like a hack because constraints necessity is the mother of invention or is there still a lot of gains?

Dylan Patel (49:55)

I think we should summarize what the bitter lesson actually is about. Is that the bitter lesson essentially if you paraphrase it, is that the types of training that will win out in deep learning as we go are those methods that which are scalable in learning. And search is what it calls out. And this scale word gets a lot of attention in this. The interpretation that I use is effectively to avoid adding human priors to your learning process. And if you read the original essay, this is what it talks about is how researchers will try to come up with clever solutions to their specific problem that might get them small gains in the short term while simply enabling these deep learning systems to work efficiently and for these bigger problems in the long term might be more likely to scale and continue to drive success. And therefore we were talking about relatively small implementation changes to the mixture of experts model. And therefore it's like okay, we will need a few more years to know if one of these are actually really crucial to the bitter lesson. But the bitter lesson is really this long term arc of how simplicity can often win. And there's a lot of sayings in the industry like the models just want to learn. You have to give them the simple loss landscape where you put compute through the model and they will learn. And getting barriers out of the way.

Lex Friedman (51:24)

That's where the power of something like nickel comes in. Where standardized code that could be used by a lot of people to create sort of simple innovations that can scale. Which is why the hacks I imagine the code base for Deepseek is probably a giant mess.

Dylan Patel (51:39)

I'm sure they have Deepseek definitely has code bases that are extremely messy where they're testing these new ideas. Multi head latent attention probably could start in something like a jupyter notebook or somebody tries something on a few GPUs and that is really messy. But the stuff that trains deep seq v3 and deep seq r1. Those libraries, if you were to present them to us, I would guess are extremely high quality code.

Lex Friedman (52:04)

High quality, readable code.

Nathan Lambert (52:06)

I think there is one aspect to note though, right? Is that there is the general ability for that to transfer across different types of runs. Right? You may make really, really high quality code for one specific model architecture at one size and then that is not transferable to. Hey, when I make this architecture tweak, everything's broken again, right? Like that's, that's something that could be, you know, with their, with their specific low level coding of like scheduling, SMS is specific to this model architecture and size. Right. And whereas like Nvidia's Collective's library is more like, hey, it'll work for anything, right? You want to do an all reduce, great. I don't care what your model architecture is, it'll work. And you're giving up a lot of performance when you do that in many cases. But it's worthwhile for them to do this specific optimization for this specific run given the constraints that they have regarding compute.

Lex Friedman (52:58)

I wonder how stressful it is to these frontier models. Initiate training to have the code to push the button that you're now spending a large amount of money and time to train this. Like there must, I mean there must be a lot of innovation on the debugging stage of like making sure there's no issues, that you're monitoring and visualizing every aspect of the training, all that kind of stuff.

Nathan Lambert (53:27)

When people are training, they have all these various dashboards, but like the most simple one is your loss, right? And it continues to go down, but in reality, especially with more complicated stuff like MOE, the biggest problem with it, or FP8 training, which is another innovation, you know, going to a lower precision number format that is less accurate is that you end up with loss spikes. Right? And no one knows why the lost spike happened. And for a long.

Dylan Patel (53:49)

Some of them, you do some of them, you think some of them are bad data. Can I give Aitoo's example of what blew up? Our earlier models is a subreddit called Microwave Gang. We love to shout this out. It's a real thing you can pull up Microwave Gang. Essentially it's a subreddit where everybody makes posts that are just the letter M. So it's like M. So there's extremely long sequences of the letter M. And then the comments are like beep beep because it's in the microwave ends. Yeah. But if you pass this into a model that's Trained to be a normal producing text. It's extremely high loss because normally you see an M, you don't predict M's for a long time. So like this is something that caused a lot of spikes for us. But when you have much like this is, this is old, this is not recent, and when you have more mature data systems, that's not the thing that causes the loss spike. And what Dylan is saying is true, but it's like it's levels to this.

Nathan Lambert (54:35)

Sort of idea with regards to the stress, right? These people are like, you know, you'll go out to dinner with like a friend that works at one of these labs and they'll just be like looking at their phone every like 10 minutes. And they're not like, you know, it's one thing if they're texting, but they're just like, like, is the loss tokens.

Dylan Patel (54:52)

Per second lost, not blown up? They're just walking, watching this and the.

Lex Friedman (54:57)

Heart rate goes up.

Nathan Lambert (54:58)

If there's a spike and some level of spikes is normal, right? It'll. It'll recover and be back Sometimes a lot of the old strategy was like you just stop the run, restart from an old version and then change the data mix and then it keeps going.

Dylan Patel (55:10)

There are even different types of spikes. So Dirk Groeneveld has a theory at aid that's like fast spikes and slow spikes. Where there are sometimes where you're looking at the loss and there are other parameters, you can see it start to creep up and then blow up, and that's really hard to recover from. So you have to go back much further. So you have the stressful period where it's like flat or it might start going up and you're like, what do I do? Whereas there are also loss spikes that it looks good and then there's one spiky data point and what you could do is you just skip those. You see that there's a spike, you're like, okay, I can ignore this data, don't update the model and do the next one. And it'll recover quickly. But these trickier implementations, so as you get more complex in your architecture and you scale up to more GPUs, you have more potential for your loss blowing up. So it's like there's a distribution.

Nathan Lambert (55:54)

The whole idea of grokking also comes in, right? It's like, just because it slowed down from improving and loss doesn't mean it's not learning. Because all of a sudden it could be like this and it could just spike down and loss Again, because it truly learned something, right? And it took some time for it to learn that it's not like a gradual process, right? And that's what humans are like, that's what models are like. So it's really a stressful task as you mentioned.

Lex Friedman (56:15)

And the whole time the dollar count is going up.

Dylan Patel (56:18)

Every company has failed runs. You need failed run to push the envelope on your infrastructure. So a lot of news cycles are made of X company had Y failed running. Every company that's trying to push the frontier of AI has these. So yes, it's noteworthy because it's a lot of money and it can be week to month setback, but it is part of the process.

Lex Friedman (56:39)

But how do you get, if you're deep seek, how do you get to a place where holy shit. There's a successful combination of hyperparameters, a.

Dylan Patel (56:47)

Lot of small failed runs.

Lex Friedman (56:48)

So rapid iteration through failed runs until.

Dylan Patel (56:53)

And successful ones, you just, and then.

Lex Friedman (56:55)

You build up some intuition like this mixture of expert works and then this implementation of MLA works, key hyperparameters like.

Dylan Patel (57:04)

Learning rate and regularization and things like this. And you find the regime that works for your code base. Talking to people at Frontier Labs, there's a story that you can tell where training language models is kind of a path that you need to follow. So you need to unlock the ability to train a certain type of model or a certain scale. And then your code base and your internal know how of which hyperparameters work for for it is kind of known. And you look at the deep SEQ papers and models they've scaled up, they've added complexity and it's just continuing to build the capabilities that they have.

Nathan Lambert (57:36)

There's the concept of a YOLO run. So yolo, you only live once. And what it is, is there's all this experimentation you do at the small scale, right? Research ablations. You have your jupyter notebook where you're experimenting with MLA on three GPUs or whatever. And you're doing all these different things like hey, do I do four expert, four active experts, 128 experts. Do I arrange the experts this way? You know all these different model architecture things you're testing at a very small scale, right? Couple researchers, few GPUs, tens of GPUs, hundreds of GPUs, whatever it is. And then all of a sudden you're like, okay guys, no more fucking around, right? No more screwing around. Everyone take all the resources we have, let's pick what we think will work and Just go for it, right? Yolo. And this is where that sort of stress comes in as like, well, I know it works here, but some things that work here don't work here, and some things that work here don't work down here. Right. In this terms of scale, right? So it's really, truly a YOLO run. And sort of like there is this discussion of, like, certain researchers just have this methodical nature, like, they can find the whole search space and like, figure out all the ablations of different research and really see what is best. And there's certain researchers who just kind of like, you know, have that innate gut instinct of like, this is the YOLO run. Like, you know, I'm looking at the data. This is it.

Dylan Patel (58:54)

This is why you want to work in post training, because the GPU cost for training is lower, so you can make a higher percentage of your training runs. YOLO runs.

Nathan Lambert (59:01)

Yeah, for now.

Lex Friedman (59:02)

Yeah, for now, for now. So some of this is fundamentally luck still.

Nathan Lambert (59:08)

Luck is skill, right? In many cases, yeah.

Lex Friedman (59:11)

I mean, it looks lucky, right?

Dylan Patel (59:12)

When you're but the hill to climb, if you're on one of these labs and you have an evaluation, you're not crushing. There's a repeated playbook of how you improve things. There are localized improvements which might be data improvements, and these add up into the whole model just being much better. And when you zoom in really close, it can be really obvious that this model is just really bad at this thing and we can fix it. And you just add these up. So some of it feels like luck. But on the ground, especially with these new reasoning models we're talking to, it's just so many ways that we could poke around. And normally it's that some of them give big improvements.

Nathan Lambert (59:45)

The search space is near infinite, right? And yet the amount of compute and time you have is very low. And you have to hit release schedules. You have to not get blown past by everyone. Otherwise, what happened with Deep Seek, Crushing Meta and Mistral and Cohere and all these guys, they moved too slow, right? They maybe were too methodical. I don't know. They didn't hit the YOLO run. Whatever the reason was, maybe they weren't as skilled. Whatever. You know, you can call it luck if you want, but at the end of the day, it's skill.

Lex Friedman (60:12)

So 2025 is the year of the YOLO run. It seems like all the labs are like going in.

Nathan Lambert (60:19)

I think it's even More impressive what OpenAI did in 2022, right? At the time, no one believed in Mixture of experts, models, right? At Google, who had all the researchers, OpenAI had such little compute and they devoted all of their compute for many months, right? All of it, 100% for many months to GPT4 with a brand new architecture, with no belief that, hey, let me spend a couple hundred million dollars, which is all of the money I have on this model, right? That is truly YOLO right now, people. All these training run failures that are in the media, it's like, okay, great, but actually a huge chunk of my GPS are doing inference. I still have a bunch doing research constantly. And yes, my biggest cluster is training. But like on this YOLO run, But like that YOLO run is much less risky than like what OpenAI did in 2022 or maybe what DeepSeek did now or you know, like sort of like, hey, we're just going to throw everything at it.

Lex Friedman (61:13)

The big winners throughout human history are the ones who are willing to do YOLO at some point. Okay, what do we understand about the hardware it's been trained on Deep Seek.

Nathan Lambert (61:25)

Deep Seek is very interesting. This is where. Second, take us to zoom out out of who they are. First of all, right? High Flyer is a hedge fund that has historically done quantitative trading in China as well as elsewhere. And they have always had a significant number of GPUs, right? In the past, a lot of these high frequency trading algorithmic Quant traders used FPGAs, but it shifted to GPUs. Definitely. And there's both, right? But GPUs especially, and high Flyer, which is the hedge fund that owns Deep Seq and everyone who works for Deep Seq is part of High Flyer to some extent, right? Same same parent company, same owner, same CEO. They had all these resources and infrastructure for trading and then they devoted a humongous portion of them to training models, both language models and otherwise, right? Because these, these, these techniques were heavily AI influenced. You know, more recently people have, you know, realized, hey, trading with, you know, like, even, even when you go back to like Renaissance and all these, all these like quantitative firms, natural language processing is the key to like trading really fast, right? Understanding a press release and making the right trade, right? And so deepseek has always been really good at this. And even as far back as 2021, they have press releases and papers saying like, hey, we're the first company in China with an A100 cluster this large. It was 10,000 A100 GPUs, right? This is in 2021. Now this wasn't all for training large Language models. This was mostly for training models for their quantitative aspects, quantitative trading as well as, you know, a lot of that was natural language processing, to be clear. Right. And so this is the sort of history, right? So verifiable fact is that in 2021 they built the largest Chinese cluster. At least they claim it was the largest cluster in China. 10,000 GPUs before expert controls started.

Dylan Patel (63:08)

Yeah, it's like they've had a huge cluster before any conversation of export controls.

Nathan Lambert (63:12)

So then you step it forward to like, what have they done over the last four years since then? Right. Obviously they've continued to operate the hedge fund, probably make tons of money. And the other thing is that they've leaned more and more and more into AI. The CEO, Leon Chen Feng.

Dylan Patel (63:27)

Leon, you're not putting me spot on this. We discussed this.

Nathan Lambert (63:30)

Leon Fang, right, The CEO, Leon Fang, he owns maybe a little bit more than half the company, allegedly, right. Is an extremely like Elon Jensen kind of figure where he's just like involved in everything, right? And so over that time period he's gotten really in depth into AI. He actually has a bit of a, like a. If you see some of his statements, a bit of an EAC vibe almost, right?

Dylan Patel (63:53)

Total AGI vibes, like, we need to do this, we need to make a new ecosystem of OpenAI. We need China to lead on this sort of ecosystem because historically the Western countries have led on software ecosystems. And he straight up acknowledges, like, in order to do this, we need to do something different. Deepseek is his way of doing this. Some of the translated interviews with him are.

Lex Friedman (64:17)

So he has done interviews?

Dylan Patel (64:18)

Yeah.

Lex Friedman (64:19)

Do you think you would do a Western interview or. No. Or is there controls on this?

Dylan Patel (64:22)

There hasn't been one yet, but okay, I would try it.

Lex Friedman (64:26)

I just got a Chinese translator, so it's great. This is, this is all push. So fascinating figure engineer pushing full on into AI, leveraging the success from the.

Dylan Patel (64:37)

High frequency, trading very direct quotes like, we will not switch to closed source when asked about this stuff. Very long term motivated in how the ecosystem of AI should work. And I think from a Chinese perspective, he wants a Chinese company to build this vision.

Nathan Lambert (64:57)

And so this is sort of like the quote unquote, visionary behind the company, right. This hedge fund still exists, right? This, this quantitative firm. And so Deep Seek is the sort of at. At. You know, slowly he got turned to this full view of like AI, everything about this, right. But at some point it slowly maneuvered and he made Deepseek. And Deepseek has Done multiple models. Since then, they've acquired more and more GPUs. They share infrastructure with the fund, right? And so, you know, there is no exact number of public GPU resources that they have. But besides this 10,000 GPUs that they bought in 2021, right. And they were fantastically profitable, right. And then this paper claims they did only 2000 H800 GPUs, which are a restricted GPU that was previously allowed in China, but no longer allowed. And there's a new version, but it's basically Nvidia's H100 for China. Right. And there's some restrictions on it specifically around the communications sort of speed, the interconnect speed, right. Which is why they had to do this crazy sm, you know, scheduling stuff. Right? So. So going back to that, right, it's like this is obviously not true in terms of their total GPU count, obvious.

Lex Friedman (66:03)

Available GPUs, but for this training run, you think 2000 is the correct number or no.

Nathan Lambert (66:08)

So this is where it takes, you know, significant amount of sort of like zoning in, right? Like what do you call your training run, right? Do you count all of the research and ablations that you ran, right, Picking all this stuff? Because yes, you can do a YOLO run, but at some level you have to do the test at the small scale and then you have to do some tests at medium scale before you go to a large scale.

Dylan Patel (66:28)

Accepted practice is that for any given model that is a notable advancement, you're going to do 2-4x compute of the full training run in experiments alone.

Lex Friedman (66:37)

So a lot of this computer is being scaled up, is probably used in large part at this time for research.

Nathan Lambert (66:43)

Yeah, and research will, you know, research begets the new ideas that let you get huge efficiency.

Dylan Patel (66:47)

Research gets you, oh one like research gets you breakthroughs and you need to bet on it.

Lex Friedman (66:52)

So some of the pricing strategy that we'll discuss has the research baked into the price.

Nathan Lambert (66:56)

So the numbers that Deepseek specifically said publicly, right. Are just the 10,000 GPUs in 2021 and then 2000 GPUs for only the pre training for V3. They did not discuss cost on R1, they did not discuss cost on all the other RL. Right. For the instruct model that they made, they only discussed the pre training for the base model and they did not discuss anything on research and ablations and they do not talk about any of the resources that are shared in terms of, hey, the fund is using all these GPUs right. And we know that they're very profitable and they had 10,000 GPUs in 2021. So the, some, some of the research that we've found is that we actually believe they have closer to 50,000 GPUs.

Lex Friedman (67:37)

We as semianas. So we should say that you're sort of one of the world experts in figuring out what everybody's doing in terms of the semiconductor, in terms of cluster build outs, in terms of like who is doing what in terms of training runs. So yeah, so that's the. We. Okay, go ahead.

Nathan Lambert (67:54)

Sorry. We believe they actually have something closer to 50,000 GPUs right. Now. This is, this is split across many tasks. Right. Again, the fund research and ablations for ballpark.

Dylan Patel (68:04)

How much would OpenAI or Anthropic had? I think the clearest example we have because Meta is also open, they talk about order of 60k to 100k h100 equivalent GPUs in their training clusters.

Nathan Lambert (68:15)

Right. So like Llama 3, they trained on 16,000 h100. But the company of Meta last year publicly disclosed they bought like 400 something thousand GPUs.

Dylan Patel (68:24)

Yeah.

Nathan Lambert (68:24)

Right. So of course tiny percentage on the training, again, like most of it is like serving me the best Instagram reels. Right. Or whatever. Right.

Dylan Patel (68:31)

I mean we could get into a cost of like, what is the cost of ownership for a 2000 GPU cluster? 10,000. Like there's just different sizes of companies that can afford these things and Deepseek is reasonably big. Their compute allocation compared is one of the top few in the world. It's not OpenAI, anthropic, etc. But they have a lot of computer.

Lex Friedman (68:52)

Can you in general actually just zoom out and also talk about the Hopper architecture, the Nvidia Hopper GPU architecture and the difference between H100 and H800. Like you mentioned the interconnects.

Nathan Lambert (69:04)

Yeah. So there's, you know, ampere was the A100 and then H100 hopper. Right. People are using them synonymously in the US because really there's just H100 and now there's H200. Right. But same thing mostly in China they've had two. There have been different salvos of export restrictions. So initially the US government limited on a two factor scale. Right. Which is chip interconnect versus flops. Right. So any chip that had interconnects above a certain level and flops above a certain floating point operations above a certain level was restricted. Later the government realized that this was a Flaw in the restriction and they cut it down to just floating point operations.

Dylan Patel (69:40)

And so H800 had high flops, low communication.

Nathan Lambert (69:45)

Exactly. So the H800 was the same performance as H100 on flops. Right. But it didn't have. It had. It just had the interconnect bandwidth cut. Deepseek knew how to utilize this. You know, hey, even though we're cut back on the interconnect, we can do all this fancy stuff to figure out how to use the GPU fully anyways. Right. And so that was back in October 2022. But later in 2023, end of 2023, implemented in 2024, the U.S. government banned the H800. Right. And so by the way, this H800 cluster, these 2,000 GPUs, was not even purchased in 2024. Right. It was purchased in late 2023. And they're just getting the model out now. Right. Because it takes a lot of research, et cetera. H800 was banned and now there's a new chip called the H20. The H20 is cut back on, only flops, but the interconnect bandwidth is the same. And in fact, in some ways it's better than the H100 because it has better memory bandwidth and memory capacity. So there are, you know, Nvidia is working within the constraints of what the government says and then builds the best possible GPU for China.

Lex Friedman (70:46)

Can we take this actual tangent and we'll return back to the hardware. Is the philosophy, the motivation, the case for export controls? What is it? Dariama Day just published a blog post about export controls. The case he makes is that if AI becomes super powerful, and he says by 2026, we'll have AGI or super powerful AI, and that's going to give a significant. Whoever builds that will have a significant military advantage. And so because the United States is a democracy, and as he says, China is authoritarian or has authoritarian elements, you want a unipolar world where the super powerful military because of the AI, is one that's a democracy. It's a much more complicated world geopolitically when you have two superpowers with super powerful AI and one is authoritarian. So that's the case he makes. And so we want to. The United States wants to use export controls to slow down, to make sure that China can't do these gigantic training runs that will be presumably required to build AGI.

Dylan Patel (71:56)

This is very abstract. I think this can be the goal of how some people describe export controls. Is this super powerful AI there's and you touched on the training run idea. There's not many worlds where China cannot train AI models. I think export controls are kneecapping the amount of compute or the density of compute that China can have. And if you think about the AI ecosystem right now, as all of these AI companies revenue numbers are up and to the right, their AI usage is just continuing to grow, more GPUs are going to inference. A large part of of export controls, if they work, is just that the amount of AI that can be run in China is going to be much lower. So on the training side, Deepseek V3 is a great example, which you have a very focused team that can still get to the frontier of AI. On this 2000 GPUs is not that hard to get all considering in the world. They're still going to have those GPUs, they're still going to be able to train models. But if there's going to be a huge market for AI, if you have strong export controls and you want to have 100,000 GPUs just serving the equivalent of ChatGPT clusters with good export controls, it also just makes it so that AI can be used much less. And I think that is a much easier goal to achieve than trying to debate on what AGI is. And if you have these extremely intelligent autonomous AIs and data centers, those are the things that could be running in these GPU clusters in the United States, but not in China.

Nathan Lambert (73:24)

To some extent, training a model does effectively nothing, right? Like the thing that Dario is sort of speaking to is the implementation of that model, once trained to then create huge economic growth, huge increases in military capabilities, huge capability, increases in productivity of people, betterment of lives, whatever you want to direct super powerful AI towards, you can. But that requires significant amounts of compute, right? And so the US government has effectively said and forever right. Like train training will always be a portion of the total compute. You know we mentioned meta, 400,000 GPUs, only 16,000 made llama, right. So the percentage that meta is dedicating to inference now this might be for recommendation systems that are trying to hack our mind into spending more time and watching more ads. Or if it's, if it's, or if it's for a super powerful AI that's doing productive things doesn't matter about the exact use that our economic system decides. It's that that can be delivered whatever, in whatever way we want. Whereas with China, right, you know, you're, you know, export restrictions, great, you're Never going to be able to cut everything off. Right. And that's, that's like, I think that's quite well understood by the US government is that you can't cut everything off.

Dylan Patel (74:34)

You know, they'll make their own chips.

Nathan Lambert (74:36)

And they're trying to make their own chips. They'll be worse than ours. But you know, this is, the whole point is to just keep a gap, right? And therefore at some point as the, you know, in a world where 2, 3% economic growth, this is really dumb by the way, right? To cut off, you know, high tech and not make money off of it. But in a world where super powerful AI comes about and then starts creating significant changes in society, which is what all the AI leaders and big tech companies believe. I think super powerful AI is going to change society massively. And therefore this compounding effect of the difference in compute is really important. There's some sci fi out there where like AI is, is like measured in the power of, in like how much power is delivered to compute, right? Or how much is being, you know, that's sort of a way of thinking about what's the economic output is just how much power are you directing towards that AI.

Dylan Patel (75:20)

Should we talk about reasoning models with this as a way that this might be actionable as something that people can actually see? So the reasoning models that are coming out with R1 and O1, they're designed to use more compute. There's a lot of buzzy words in the AI community about this test, time, compute, inference, time compute, whatever. But Dylan has good research on this. You can get to the specific numbers on the ratio of. When you train a model, you can look at things about the amount of compute used at training and amount of comp use at inference. These reasoning models are making inference way more important to doing complex tasks. In the fall in December, OpenAI announced this O3 model. There's another thing in AI when things move fast, we get both announcements and releases. Announcements are essentially blog posts where you pat yourself on the back and you say you did things. And releases are run the models out there, the papers out there, et cetera. So OpenAI has announced O3 and we can check if O3 mini is out as of recording potentially. But that doesn't really change the point which is that the breakthrough result was something called ARC AGI task, which is the abstract reasoning corpus, a task for artificial general intelligence. Francois Chollet is the guy who's been. It's a multi year old paper, it's a brilliant benchmark. And the number for OpenAI03 to solve this was that it used some sort of number of samples in the API. The API has thinking effort and number of samples. They used 1,000 samples to solve this task. And it comes out to be like five to twenty dollars per question, which you're putting in effectively a math puzzle. And then it takes orders of dollars to answer one question. And this is a lot of compute. If this is going to take off in the US OpenAI needs a ton of GPUs on inference to capture this. They have this OpenAI ChatGPT Pro subscription which is $200 a month, which Sam.

Nathan Lambert (77:04)

Said they're losing money on, which means.

Dylan Patel (77:05)

That people are burning a lot of GPUs on inference. And I've signed up with it, I've played with it, I don't think I'm a power user, but I use it. And it's like that is the thing that a Chinese company with mediumly strong export controls, there will always be loopholes, might not be able to do at all. And if that the main result for O3 is also spectacular coding performance. And if that feeds back into AI companies being able to experiment better.

Lex Friedman (77:31)

So presumably the idea is for an AGI, a much larger fraction of the compute would be used for this test time compute for the reasoning for the AGI goes into a room and thinks about how to take over the world and come back in 2.7 hours. This is what it's going to take a lot of computer.

Dylan Patel (77:50)

This is what people, CEO or leaders of OpenAI and anthropic talk about is autonomous AI models, which is you give them a task and they work on it in the background. I think my personal definition of AGI is much simpler. I think language models are a form of AGI and all this super powerful stuff is a next step. That's great if we get these tools. But a language model has so much value in so many domains. It is a general intelligence to me. But this next step of agentic things where they're independent and they can do tasks that aren't in the training data is what the few year outlook that these AI companies are driving for.

Lex Friedman (78:27)

I think the terminology here that Dara Dario uses a super powerful AI. So I agree with you on the AGI. I think we already have something like that's exceptionally impressive that Alan Turing would for sure say is AGI, but he's referring more to something once in possession of then you would have a significant military and geopolitical advantage over other nations. So it's not Just like you can ask it how to cook an omelette.

Dylan Patel (78:52)

And he has a much more positive view. In his essay Machines of Love and Grace, I read into this that we don't have enough background in physical sciences to gauge exactly how competent I am. And if AI can revolutionize biology, I'm safe saying that AI is going to accelerate the progress of any computational science.

Lex Friedman (79:10)

So we're doing a depth first search here on topics taking tangent of a tangent. So let's continue on that depth first search. You said that you're both feeling the AGI, so what's your timeline? Dario's 2026 for the super powerful AI that's, you know, that's basically agentic to a degree where it's a real security threat. That level of AGI, what's your timeline?

Dylan Patel (79:39)

I don't like to attribute specific abilities because predicting specific abilities and when is very hard. I think mostly if you're going to say that I'm feeling the AGI is that I expect continued rapid, surprising progress over the next few years. So something like R1 is less surprising to me from Deepseek because I expect there to be new paradigms where substantial progress can be made. I think Deepseek R1 is so unsettling because we're kind of on this path with ChatGPT. It's like it's getting better, it's getting better, it's getting better. And then we have a new direction for changing the models. And we took one step like this and we took a step up, so it looks like a really fast slope and then we're going to just take more steps. So it's just really unsettling when you have these big steps. And I expect that to keep happening. I've tried OpenAI operator, I've tried Claude computer use. They're not there yet. I understand the idea, but it's just so hard to predict what is the breakthrough that'll make something like that work. And I think it's more likely that we have breakthroughs that work and things that we don't know what they're going to do. So everyone wants agents. Dario has very eloquent way of describing this. And I just think that it's like there's going to be more than that. So just expect these things to come.

Lex Friedman (80:48)

I'm going to have to try to pin you down to a date on the AGI timeline, like the nuclear weapon moment. So moment where on the geopolitical stage there's a real like, you know, because we're talking about export controls. When do you think just even to throw out a date, when do you think that would be? Like for me it's probably after 2030, so I'm not as I would say.

Nathan Lambert (81:15)

So define that right, because to me it kind of almost has already happened, right? You look at elections in India and Pakistan, people get AI voice calls and think they're talking to the politician. Right? The AI diffusion rules which was enacted in the last couple of weeks of the Biden admin and looks like the Trump admin will keep and potentially even strengthen limit cloud computing and GPU sales to countries that are not even related to China. It's like this is Portugal and all.

Dylan Patel (81:39)

These like normal countries are on the, you need approval from the US list.

Nathan Lambert (81:43)

Like yeah, Portugal. And like, you know, like, like all these countries that are allies, right? Singapore, right? Like they, they freaking have F35s and we don't let them buy GPUs. Like this is, this to me is already to the scale of like, you know.

Lex Friedman (81:56)

Well, that just means that the US military is really nervous about this new technology. That doesn't mean the technology is already there. So like they might be just very cautious about this thing that they don't quite understand. But that's a really good point. Sort of the, the robocalls, swarms of semi intelligent bots, could be a weapon, could be doing a lot of social engineering.

Nathan Lambert (82:19)

I mean there's tons of talk about, you know, from the 2016 elections, like Cambridge Analytica and all this stuff, Russian influence. I mean every country in the world is pushing stuff onto the Internet and has narratives they want, right? Like that's every. Every like technically competent, whether it's Russia, China, us, Israel, et cetera. Right. You know, people are pushing viewpoints onto the Internet and mass and language models crash the cost of very intelligent sounding language models.

Dylan Patel (82:43)

There's some research that shows that the distribution is actually the limiting factor. So language models haven't yet made misinformation particularly change the equation there. The Internet is still ongoing. I think there's a blog, aisnakeoil and some of my friends at Princeton that write on this stuff. So there is research. It's a default that everyone assumes and I would have thought the same thing is that that misinformation is going to get far worse with language models. I think in terms of Internet posts and things that people have been measuring, it hasn't been a exponential increase or something extremely measurable and things you're talking about with voice calls and stuff like that, it could be in modalities that are harder to measure. So it's something that it's too soon to tell in terms of. I think that's like political instability via the web is very. It's monitored by a lot of researchers to see what's happening. I think you're asking about the AGI thing. If you make me give a year, I'm going to be like, Okay, I have AI CEOs saying this. They've been saying two years for a while. I think that people like Dario at Anthropic, the CEO had thought about this so deeply, I need to take their words seriously, but also understand that they have different incentive. So I would be like, add a few years to that, which is how you get something similar to 2030 or a little after 2030.

Nathan Lambert (84:02)

I think to some extent we have capabilities that hit a certain point where any one person could say, okay, if I can leverage those capabilities for X amount of time, this is AGI, right? Call it 27, 28. But then the cost of actually operating that capability.

Dylan Patel (84:17)

Yeah, this is going to be my point.

Nathan Lambert (84:18)

So, so extreme that no one can actually deploy it at scale and mass to actually completely revolutionize the economy on a click, on a snap of a finger. So I don't think it will be like a snap of the finger moment physical constraint, rather it'll be a, you know, oh, the capabilities are here, but I can't deploy it everywhere, right? And so one, one simple example going back sort of to 2023 was when, you know, Bing with GPT4 came out and everyone was freaking out about search, right? Perplexity came out. If you did the cost on like, hey, implementing GPT3 into every Google search, it was like, oh, okay, this is just like physically impossible to implement, right? And as we step forward to going back to the test time compute thing, right? A query for, you know, you ask ChatGPT a question, it cost cents, right? For their most capable model of chat, right? To get a query back to solve an ARC AGI problem though, cost 5 to 20 bucks, right? And this is an.

Dylan Patel (85:12)

It's only going up from there.

Nathan Lambert (85:14)

This is 1000, 10,000 x factor difference in cost to respond to a query versus do a task. And the task of ARC AGI is not like, it's like, it's simple to some extent, you know, but it's also like, what are the tasks that we want? Okay, AGI quote unquote, what we have today can do ARC AGI three years from now. It can do much more complicated problems. But the Cost is going to be measured in thousands and thousands and hundreds of thousands of dollars of GPU time. And there just won't be enough power, GPUs, infrastructure to operate this and therefore shift everything in the world on the snap of the finger. But at that moment, who gets to control and point the AGI at a task? And so this was in Dario's post that he's like, hey, China can effectively and more quickly than us point their AGI at military tasks, right. And they have been in many ways faster at adopting certain new technologies into, into their military, right. Especially with regards to drones. Right. The US maybe has a long standing large air sort of fighter jet type of thing, bombers, but when it comes to asymmetric arms such as drones, they've completely leapfrogged the US and the West. And the fear that Dario is sort of pointing out there, I think is that yeah, great, we'll have AGI in the commercial sector. The US military won't be able to implement it super fast. Chinese military could and they could direct all their resources process to implementing it in the military and therefore solving military logistics or solving some other aspect of disinformation for targeted certain set of people so they can flip a country's politics or something like that, that is actually catastrophic versus the US just wants to because it'll be more capitalistically allocated just towards whatever is the highest return on income, which might be building factories better or whatever.

Lex Friedman (86:58)

So everything I've seen people's intuition seems to fail on robotics. So you have this kind of general optimism. I've seen this on self driving cars. People think it's much easier problem than it is similar with drones here. I understand it a little bit less, but I've just seen the reality of the war in Ukraine and the usage of drones on both sides and it seems that humans still far outperform any fully autonomous systems. AI is an assistant, but humans drive FPV drones where the humans controlling most of it just far, far, far outperforms AI systems. So I think it's not obvious to me that we're going to have swarms of autonomous robots anytime soon. In the military context, maybe the fastest I can imagine is 2030, which is why I said 2030 for the super powerful AI. Whenever you have large scale swarms of robots doing military actions, that's when the world just starts to look different to me. So that's the thing I'm really worried about. But there could be cyber war, cyber war type of technologies that from social engineering to actually just Swarms of robots that find attack vectors in our code bases and shut down power grids, that kind of stuff. And it could be one of those things like on any given weekend or something, power goes out, nobody knows why, and the world changes forever. Just power going out for two days in all of the United States, that will lead to murder, to chaos. But going back to export controls, do you see that as a useful way to control the balance of power geopolitically in the context of AI?

Nathan Lambert (88:50)

And I think going back to my viewpoint is if you believe we're in this sort of stage of economic growth and change that we've been in for the last 20 years, the export controls are absolutely guaranteeing that China will win long term. Right. If you do not believe AI is going to make significant changes to society in the next 10 years or five years, five year timelines are sort of what the more executives and such of AI companies and even big tech companies believe. But even 10 year timelines, it's reasonable. But once you get to, hey, these timelines are below that time period, then the only way to sort of create a sizable advantage or disadvantage for America versus China is if you constrain compute. Because talent is not really something that's constraining. Right. China arguably has more talent, right? More STEM graduates, more programmers. The US can draw upon the world's people, which it does. There's tons of, you know, foreigners in the AI industry.

Dylan Patel (89:51)

Too many of these AI teams are all people without a US passport.

Nathan Lambert (89:55)

Yeah, yeah. I mean, many of them are Chinese people who are moving to America. Right. And that's, that's great. That's exactly what we want. Right. But there's that talent is one aspect, but I don't think that's one that is a measurable advantage for the US or not. It truly is. Just whether or not compute right now, even on the compute side, when we look at chips versus data centers, right? China has the unprecedented ability to build ridiculous sums of power. Clockwork, right? They're always building more and more power. They've got steel mills that individually are the size of the entire US industry, Right. And they've got aluminum mills that consume gigawatts and gigawatts of power. And when we talk about what's the biggest data center, right. OpenAI made this huge thing about Stargate, their announcement. There's, that's not, that's like once it's fully built out, in a few years it'll be 2 gigawatts, right. Of power, right. And this is still Smaller than the largest, you know, industrial facilities in China. Right. China, if they wanted to build the largest data center in the world, if they had access to the chips, could. So there's not just, it's just a question of when, not if. Right.

Lex Friedman (91:01)

So their industrial capacity far exceeds the United States.

Nathan Lambert (91:04)

Exactly.

Lex Friedman (91:05)

To manufacture stuff. So why, why? So long term they're going to be manufacturing chips there.

Nathan Lambert (91:12)

Chips are a little bit more specialized. I'm specifically referring to the data centers. Right. Chips, fabs take huge amounts of power. Don't get me wrong, that's not necessarily the gating factor there. The gating factor on how fast people can build the largest clusters today in the US Is power, right? It is whether it's now, it could be power generation, power transmission substations and all these sorts of transformers and all these things building the data center. These are all constraints on the US Industry's ability to build larger and larger training systems as well as deploying more and more inference compute.

Dylan Patel (91:46)

I think we need to make the point clear on why the time is now for people that don't think about this. Because essentially with export controls, you're making it so China cannot make or get cutting edge chips. And the idea is that if you time this wrong, China is pouring a ton of money into their chip production. And if you time it wrong, they are going to have more capacity for production, more capacity for energy and figure out how to make the chips and have more capacity than the rest of the world to make the chips. Because everybody can buy. They're going to sell their Chinese chips to everybody. They might subsidize them. And therefore, if AI takes a long time to become differentiated, we've kneecapped the financial performance of American companies. Nvidia can sell less, TSMC cannot sell to China. So therefore we have less demand to, therefore inability to like keep driving the production cycle. So that's the assumption behind the time.

Nathan Lambert (92:36)

Timing being less than 10 years or 5 years to above. Right. China will win because of these restrictions long term unless AI does something in the short term, which I believe AI will do, you know, make massive changes to society in the medium, short term, Right. And so that's, that's the big unlocker there. And even, even today, right. If Xi Jinping decided to get, you know, quote unquote scale pilled, right. I. E. Decide that scaling laws are what matters, right? Just like the US executives like Satya Nadella and Mark Zuckerberg and Sundar and all these US Executives of the biggest, most powerful tech companies have decided they're scale pilled and they're building multi gigawatt data centers, right? Whether it's in Texas or Louisiana or Wisconsin, wherever it is, they're building these massive things that cost as much as their entire budget for spending on data centers globally in one spot, right? This is what they've committed to for next year, year after, et cetera. And so they're so convinced that this is the way, that this is what they're doing. But if China decided to, they could do it faster than us. But this is where the restrictions come in. It is not clear that China as a whole has decided from the highest levels that this is a priority. The US sort of has, right? You know, you see Trump talking about Deep Seek and Stargate within the same week, right? So he's in the Biden administration as well, had a lot of discussions about AI and such. It's clear that they think about it. Only just last week did Deep Seek meet the second in command of China, right? Like they have not even met the top, right? They haven't met Xi Xi hasn't sat down. And they only just released a subsidy of a trillion RMB, you know, roughly $160 billion, which is closer to the spending of like Microsoft and Meta and Google combined, right, for this year. So it's like they're realizing it just now. But that's where the export restrictions come in and say, hey, you can't ship the most powerful US chips to China. You can ship a cut down version. You can't ship the most powerful chips to all these countries who we know are just going to rent it to China. You have to limit the numbers, right?

Dylan Patel (94:42)

And the tools.

Nathan Lambert (94:44)

And same with manufacturing deep tools, all these different aspects, but it all stems from AI. And then what downstream can slow them down in AI? And so the entire semiconductor restrictions, you read them, they are very clear. It's about AI and military civil fusion of technology, right? It's very clear. And then from there it goes, oh well, we're banning them from buying like lithography tools and etch tools and deposition tools and oh, this random subsystem from a random company that's like, like tiny, right? Like why are we banning this? Because all of it the US government has decided is critical to AI systems.

Dylan Patel (95:17)

I think the fulcrum point is the transition from 7 nanometer to 5 nanometer chips, where I think it was Huawei that had the 7 nanometer chip a few years ago, which caused another political brouhaha almost like this moment. And then it's The ASML Deep euv. What is that?

Nathan Lambert (95:35)

Extreme ultraviolet lithography to set context on the chips. Right. What Nathan's referring to is in 2020 Huawei released their Ascend 910 chip, which was an AI chip, first one on 7 nanometer before Google did, before Nvidia did. And they submitted it to the MLPERF benchmark, which is sort of a industry standard for machine learning performance benchmark. And it did quite well and it was the best chip at the submission. Right. This was a huge deal. The Trump Admin, of course banned. It was 2019, right? Banned the Huawei from getting 7 nanometer chips from TSMC. And so then they had to switch to move using internal domestically produced chips, which was a multi year setback.

Dylan Patel (96:14)

Many companies have done 7 nanometer chips and the question is like we don't know how much Huawei was subsidizing production of that chip. Like intel has made 7 nanometer chips that are not profitable and things like this. So this is how it all feeds back into the economic engine of export control.

Lex Friedman (96:30)

Well, so you're saying that for now, Xi Jinping has not felt the AGI, but it feels like the Deep Seat moment. Yeah, might like there might be meetings going on now where he's going to start wearing the same T shirt and things are going to escalate.

Nathan Lambert (96:46)

I mean like, like this. He may have woken up last week, right? Leon Fang met the vice chairman, vice, the second command guy and they had a meeting and then the day, the next day they announced the AI subsidies which are trillion rmb. Right.

Lex Friedman (96:59)

So it's possible that this Deep Seek moment is truly the beginning of a cold war.

Dylan Patel (97:04)

That's what a lot of people are worried about. People in AI have been worried that this is going towards a cold war or already is.

Lex Friedman (97:10)

But there was. It's not Deep Seek's fault, but there's something, a bunch of factors came together where history works explosion. I mean it all has to do with Nvidia stock going down. Probably it's just some mass hysteria that happened that eventually led to Xi Jinping having meetings and waking up to this idea.

Nathan Lambert (97:29)

And the US government realized in October 7, 2022 before ChatGPT released that, that restriction October 7, which dropped and shocked everyone and it was very clearly aimed at AI. Everyone was like, what the heck are you doing?

Dylan Patel (97:42)

Stable diffusion was out then. But not chatgpt.

Nathan Lambert (97:44)

Yeah, but not chatgpt.

Dylan Patel (97:45)

So it was like starting to be.

Nathan Lambert (97:46)

Rumblings like of what Genai can do to society. But it was very clear I think to at least like, like National Security Council and, and those sort of folks that this was where the world is headed, this cold war that's happening.

Lex Friedman (97:59)

So is there any concerns that the export controls push China to take military action on Taiwan?

Nathan Lambert (98:09)

This is, this is the big risk, right? The further you push China away from having access to cutting edge American and global technologies, the more likely they are to say, well, well, because I can't access it, I might as well, like no one should access it, right? And there's a few like, interesting aspects of that, right? Like, you know, China has a urban rural divide like no other. They have a male female birth ratio like no other. To the point where, you know, if you look in most of China, it's like the ratio is not that bad. But when you look at single dudes in rural China, it's like a 30 to 1 ratio. And those are disenfranchised dudes, right? Like quote unquote. Like the US has an incel problem, like China does too. It's just they're placated in some way or cut, crushed down. What do you do with these people? And at the same time you're not allowed to access the most important technology. At least the US thinks so. China's maybe starting to think this is the most important technology by starting to dump subsidies in it, right? They thought EVs and renewables were the most important technology. They dominate that now, right now. They started thinking about that, about semiconductors in the late 2010s and early 2020s and now they've been dumping money and they're catching up rapidly and they're going to do the same with AI, right? Because they're very talented, right? So the question is like, when does this hit a breaking point, right? And if China sees this as, hey, they can continue if not having access and starting a true hot war, right? Taking over Taiwan or trying to subvert its democracy in some way, or blockading it hurts the rest of the world far more than it hurts just them. This is something they could potentially do, right? And so is this pushing them towards that potentially? Right. I'm not quite a geopolitical person, but it's obvious that the world regime of peace and trade is super awesome for economics, but at some point it could break, right?

Dylan Patel (100:01)

I think we should comment that why Chinese economy would be hurt by that is that they're export heavy. I think the United States buys so much, like if that goes away, like that's how their economy.

Nathan Lambert (100:10)

Well, also, also they just like would not be able to import raw materials from like all over the world. Right. The US would just shut down the trade of Malacca and like, you know, at the same time the US entire, like you could argue almost all the GDP growth in America since, you know, the 70s has been either population growth or tech. Right. Because you know, your, your life today is not that much better than someone from the 80s outside of tech. Tech. Right. You still, you know, you know, cars, they all have semiconductors in them everywhere, fridges, semiconductors everywhere. There's these funny stories about how Russians were taking apart laundry machines because they had certain, like Texas instrument chips that they could then repurpose and put into like their, their anti missile missile things. Right. Like their S400 or whatever. You would know more about this. But there's all sorts of like everything about semiconductors is so integral to every part of our lives.

Lex Friedman (101:00)

So can you explain the role of TSMC in the story of semiconductors and maybe also how the United States can break the reliance on tsmc?

Nathan Lambert (101:11)

I don't think it's necessarily breaking the reliance. I think it's getting TSMC to build in the US but so taking a step back, TSMC produces most of the world's chips, especially on the foundry side. There's a lot of companies that build their own chips. Samsung, intel, you know, ST Micro, Texas Instruments, you know, Analog Devices, all these kinds of companies build their own chips and xp. But more and more of these companies are outsourcing to TSMC and have been for multiple decades.

Lex Friedman (101:44)

Can you explain the supply chain there and where most of TSMC is in terms of manufacturing?

Nathan Lambert (101:49)

Sure. So historically supply chain was companies would build their own chips. They would, you know, it'd be a company started, they'd build their own chips and then they'd design the chip and build the chip and sell it. Over time, this became really difficult because the cost of building a FAB continues to compound every single generation. Of course the technology, figuring out the technology for it is incredibly difficult regardless. But just the dollars and cents that are required, ignoring, you know, saying, hey, yes, I have all the technical capability, which it's really hard to get that by the way, right. Intel's failing, Samsung's failing, et cetera. But if you look at just the dollars to spend to build that next generation fab, it keeps growing, right? Sort of like, you know, Moore's law is halving the cost of chips every two years. There's a separate law that's sort of like doubling the cost of fabs every handful of years. And so you look at a leading edge fab that is going to be profitable today, that's building, you know, 3 nanometer chips or 2 nanometer chips in the future. That's going to cost north of 30, $40 billion, right? And that's just for like a token amount. That's for a, like, that's like the base building blocking probably need to build multiple. Right. And so when you look at the industry over the last, you know, if I go back 20, 30 years ago, there were 20, 30 companies that could build the most advanced chips and then they would design them themselves and sell them, right? So companies like AMD would build their own chips. Intel of course, still builds their own chips, are very famous for IBM would build their own chips. And you know, you could just keep going down the list. All these companies built their own chips. Slowly they kept falling like flies. And that's because of what TSMC did, right? They created the Foundry business model, which is, I'm not going to design any chips, I'm just going to contract manufacture chips for other people. And one of their early customers is Nvidia, right? Nvidia was, is, is the only semiconductor company that's worth, you know, that's doing more than a billion dollars of revenue. That was started in the era of Foundry, right? Every other company started before then and at some point had fabs, which is actually incredible, right? You know, like AMD and Intel and Broadcom, such a great pact. It's like everyone had fabs at some point or you know, brought, you know, some companies like Broadcom, it was like a mer amalgamation of various companies that rolled up. But even today Broadcom has fabs, right? They build iPhone, RF, radio chips sort of in Colorado for, you know, for Apple, right? Like there's all these companies had fabs and for most of the fabs, they threw them away or sold them off or they got rolled into something else. And now everyone relies on tsmc, right? Including Intel. Their latest PC chip uses TSMC chips, right? It also uses some intel chips, but it uses TSMC process.

Lex Friedman (104:13)

Can you explain why the foundry model is so successful for these companies? Why are they going with economies of scale?

Nathan Lambert (104:20)

Scale, yeah. So I mean like, like I mentioned, right, the cost of building a fab is so high, the R and D is so difficult. And when you look at like these like companies that had their own vertical stack, there was an antiquated process of like, okay, like I'm so hyper customized to each specific chip, right? But as we've gone through the history of sort of like the last 50 years of, of electronics and semiconductors. A you need more and more specialization, right? Because Moore's law has died, Dennard scaling has died. I. E Chips are not getting better just for free, right? From manufacturing. You have to make real architectural innovations. Google is not just running on Intel CPUs for web serving. They have a YouTube chip, they have TPUs, they have Pixel chips. They have a wide diversity of chips that generate all the economic value of Google. It's running all the services and stuff and this is just Google. And you could go across any company in the industry and it's like this cars contain 5,000 chips, you know, 200 different varieties of them, right? All these random things. A Tesla door handle has two chips, right? Like it's like ridiculous and it's a cool door handle, right? It's like you know, you don't think about it but it's like has two really chip like penny like chips in there, right? Anyway, so as you have more diversity of chips, as you have more specialization required and the cost of fabs continues to grow, you need someone who is laser focused on building the best process technology and making it as flexible as possible.

Dylan Patel (105:40)

I think you could say it simply which is the cost per fab goes up. And if you are a small player that makes a few types of chips, you're not going to have the demand to pay back the cost of the fab. Whereas Nvidia can have many different customers and aggregate all this demand into one place and then they're the only person that makes enough money building chips to build the next fabric. So this is kind of why the companies slowly get killed. Because they have 10 years ago a chip that is profitable and is good enough, but the cost to build the next one goes up. They may try to do this fail because they don't have the money to make it work and then they don't have any chips or they build it and it's too expensive and they just.

Nathan Lambert (106:19)

Have or they're more failure points, right? You could have one little process related to some sort of chemical etch or some sort of like plasma etch or you know, some little process that screws up, you didn't engineer it, right? And now the whole company falls apart. You can't make chips, right? And so super, super powerful companies like intel, they had like the weathering storm to like hey, they still exist today even though they really screwed up their manufacturing six, seven years ago. But in the case of like amd, they almost went bankrupt. They had to sell Their fabs to Mubadala, UAE, right? And like that became a separate company called GlobalFoundries, which is a foundry firm. And then AMD was able to then focus on like, on the return back up was like, hey, let's focus on making chiplets and a bunch of different chips for different markets and focusing on specific workloads rather than, you know, all of these different things. And so you get more diversity of chips. You have more companies than ever designing chips, but you have fewer companies than ever manufacturing them. Right? And this is, this is where TSMC comes in, is they've, they've just been the best, right? They are so good at it, right? They're customer focused. They make it easy for you to fabricate your chips. They take all of that complexity and like kind of try and abstract a lot of it away from you. They make good money. They don't make insane money, but they make good money. And, and they're able to aggregate all this demand and continue to build the next fab. The next fab. The next fab.

Lex Friedman (107:38)

So why is Taiwan so special for tsmc? Why is it happening there? Can it be replicated inside the United States?

Nathan Lambert (107:45)

Yeah, so there's aspects of it that I would say yes and aspects that I'd say no, right? TSMC is way ahead because former executive Morris Chang of Texas Instruments wasn't promoted to CEO and he was like, screw this, I'm going to go make my own chip company. And he went to Taiwan and made tsmc, right? And there's a whole lot more story there. So it could have been Texas Instruments, could have been the, it could have been tsmc, but Texas Semiconductor Manufacturing, right, instead of Texas Instruments, right? So there is that whole story.

Dylan Patel (108:15)

There's but sitting here in Texas, I.

Lex Friedman (108:17)

Mean, and that sounds like a human story, like it didn't get promoted.

Nathan Lambert (108:21)

Just the brilliance of Morris Chang, you know, which I wouldn't underplay. But there's also like a different level of like how, how this works, right? So in Taiwan, the, you know, like the number top percent of graduates of students that go to the best school, which is ntu, the top percent of those all go work to tsmc, right? And guess what their pay is? Their starting Pay is like $80,000, $70,000, right? Which is like, that's like starting pay for a good graduate in the US Right? Not the top, the top graduates are making hundreds of thousands of dollars at the Googles and the Amazons and now I guess the open AIs of the world, right? So there is a Large dichotomy of like, what is the top 1% of the society doing and where are they headed because of economic reasons, right? Intel never paid that crazy good, right? And it didn't make sense to them, Right, right. That's one aspect, right? Where's the best going? Second is the work ethic, right? Like, you know, we, we like to work, you know, you work a lot, we work a lot. But at the end of the day, when there's a, you know, when, when what, what is the time and amount of work that you're doing and what does a fab require, right? Fabs are not work from home jobs. They are, you go into the fab and grueling work, right? There's, there's, hey, if there is any amount of vibration, right, an earthquake happen, happens, vibrates the machines, they're all, you know, they're either broken, you've scrapped some of your production and then in many cases they're like not calibrated properly. So, so when tsmc, when there's an earthquake, right? Recently there's been an earthquake. TSMC doesn't call their employees. They just, they just go to the fab and like they just show up. The parking lot gets slammed and people just go into the fab and fix it, right? Like it's like an arm, it's like ants, right? Like it's like, you know, a hive of ants doesn't get told by the queen what to do. The ants just know.

Dylan Patel (110:02)

It's like one person just specializes on these, one task and it's like you're going to take this one tool and you're the best person in the world and this is what you're going to do for your whole life is this one task in the fab, which is.

Nathan Lambert (110:11)

Like some special chemistry plus nano manufacturing on one line of tools that continues to get iterated. And yeah, it's just like, it's like specific plasma etch for removing silicon dioxide, right? That's all you focus on your whole career. And it's like such a specialized thing. And so it's not like the tasks are transferable. AI today is awesome because like people can pick it up like that. Semiconductor manufacturing is very antiquated and difficult. None of the materials are online for people to read easily and learn, right? The papers are very dense and it takes a lot of experience to learn and so it makes the barrier to entry much higher too. So when you talk about, hey, you have all these people that are super specialized, they will work 80 hours a week in a factory, right? In a fab. And if anything goes wrong, they'll go show up in the middle of the night because some earthquake, their wife's like, there was an earthquake. He's like, great, I'm gonna go to the fab. Would you, would you like, as an American do that?

Dylan Patel (111:06)

Right?

Nathan Lambert (111:06)

It's like these sorts of things are like what, you know, I guess are the exemplifying, like why TSMC is so amazing now. Can you replicate it in the U.S. let's not ignore intel was the leader in manufacturing for over 20 years. They brought every technology to market first. Besides EUV, strained silicon, high K metal gates, FinFET, you know, and the list goes on and on and on of technologies that intel brought to market first, made the most money from and manufactured at scale first, best, highest profit margins, right? So we shouldn't ignore that intel can't do this, right? It's that the culture has broken, right? You've invested in the wrong things. They said no to the iPhone. They had all these different things regarding like mismanagement of fabs, mismanagement of designs, this lockup, right? And at the same time, all these brilliant people, these 50,000 PhDs or masters that have been working on specific chemical or physical processes or nanomanufacturing processes for decades in Oregon, they're still there, they're still producing amazing work. It's just like getting it to the last mile of production at high yield where you can manufacture dozens and hundreds of different kinds of chips, you know, and it's good customer experience has broken, right? You know, it's that customer experience. It's like the like part of it is like people will say intel was too pompous in the 2000s, 2010s, right? They just thought they were better than everyone. The tool guys were like, oh, I don't think that this is mature enough. And they're like, you just don't know. We know, right? This sort of stuff would happen. And so can the US bring it to the. Can the US bring leading edge semiconductor manufacturing to the us? Emphatically yes. Right? And we are right.

Dylan Patel (112:40)

It's happening. Arizona is getting better and better or as time goes on.

Nathan Lambert (112:44)

TSMC has built, you know, roughly 20% of their capacity for 5 nanometer in the U.S. right now. This is nowhere near enough, right. You know, 20% of capacity in the US is like nothing. Right? And furthermore, this is still dependent on Taiwan existing. Right? All there's sort of important way to separate it out. There's R and D and there's High volume manufacturing. There are, there effectively there are three places in the world that are doing leading edge R and D. There's Hsinchu, Taiwan, there's Hillsboro, Oregon, and there is Pyongyang, South Korea. These three places are doing the leading edge R and D for the rest of the world's leading edge semiconductors. Right now, manufacturing can be distributed more globally. And this is sort of where this dichotomy exists of like who's actually modifying the process, who's actually developing the next generation one, who's improving them is Hsinchu, is Hillsboro, is Pyongyang. It is not the rest of these fabs like Arizona, right? Arizona is a paperweight. If Hsinchu disappeared off the face of the planet within a year, couple years, Arizona would stop producing two, right? It's actually pretty critical. One of the things I like to say is if I had a few missiles, I know exactly where I could cause the most economic damage, right? It's not targeting the White House, right?

Lex Friedman (114:03)

It's the R and D centers.

Nathan Lambert (114:04)

It's the R and D centers for tsmc, Intel, Samsung and then some of the memory guys, Micron and Hynix, because.

Lex Friedman (114:09)

They define the future evolution of these semiconductors. And everything's moving so rapidly that it really is fundamentally about R and D and it is all about tsmc. Huh?

Nathan Lambert (114:21)

And so tsmc, you know, you cannot purchase a vehicle without TSMC chips, right? You cannot purchase a fridge without TSMC chips. You cannot. You can, you like. I think one of the few things you can purchase ironically is a Texas Instruments like graphing calculator, right? Because they actually manufacture in Texas, but outside of that, a laptop, a phone, anything you servers, GPUs, none of this stuff can exist. And this is without tsmc. And in many cases it's not even like the leading edge. Sexy. 5 nanometer chip, 3 nanometer chip, 2 nanometer chip. Oftentimes it's just some stupid power IC that's converting from some voltage to another and it's made tsmc, right?

Dylan Patel (114:59)

That's what China is investing in as well. It's like they can build out this long tail fab where the techniques are much more known. You don't have to figure out these problems with euv. They're investing in this and then they have large supply for things like the car door handles and the random stuff. And that trickles down into this whole economic discussion as well, which is they have far more than we do. And having supply for things like this is crucial to normal life.

Lex Friedman (115:23)

So they're doing the, they're starting to invest in high volume manufacture but they're not doing R and D. So they.

Nathan Lambert (115:29)

Do R and D on their own. They're just way behind, right? So I would say like in 2015 China had a five year plan where they defined by 2025 and 2020 certain goals including like 80% domestic production of semiconductors. They're not, they're not going to hit that, right to be clear. But they are, they are in certain areas really, really close, right? Like BYD is probably going to be the first company in the world to not have to use TSMC for making because they have their own fabs, right, for making chips. Now they still have to buy some chips from foreign for example around self driving ADAS capabilities because those are really high end but at least internal combustion engine has 40 chips and an EV just for controlling flow rates and all these things and EVs are even more complicated. So all these different power ICs and battery management controllers and all these things they're insourcing, right? And this is something that like China has been doing since 2015. Now as far as like the trailing edge, they're getting so much capacity there. As far as the leading edge, right. I. E. This 5 nanometer and so on so forth, right where GPUs they are still behind and this is the US restrictions are trying to stop them in the latter but you know all that's happened, you know is yes they've slowed down their 5 nanometer, 3 nanometer etc. But they've accelerated their hey 45 nanometer, 90 nanometer power IC or analog IC or you know, random chip in my keyboard, right? That kind of stuff. So, so there is an angle of like the US's actions have been so from these export, you know, from the angle of the export controls have been so inflammatory at slowing down China's progress on the leading edge that they've turned around and have accelerated their progress elsewhere because they know they. This is so important, right. If the US is going to lock them out here, what if they lock us out here as well in the trailing edge. And so going back can the US build it here? Yes, but it's going to take a ton of money I truly think like to. To revolutionize and completely insource semiconductors would take a decade and a trillion dollars.

Lex Friedman (117:27)

Is some of it also culture like you said, extreme competence, extreme work ethic in Taiwan I think if you have.

Dylan Patel (117:33)

The demand and the money is on the line, the American companies Figure it out. It's going to take handholding with the government, but I think that the culture helps TSMC break through and it's easier for them.

Nathan Lambert (117:44)

TSMC has something like 90,000 employees. Right. It's not actually that insane an amount. The Arizona Fab has 3,000 from Taiwan. And these people, like, their wives were like, yeah, we're not going to have kids unless we. You sign up for the Arizona Fab. We go to Arizona and we have our kids there. There's also a Japan Fab where the same thing happened. Right. And so, like, these wives drove like, like these, like these dudes to, like, go to Japan or America to have the kids there. And it's like, it's an element of culture. Yeah, sure, Taiwan works that hard, but also, like, the US has done it in the past. They could do it now. Right. You know, we can just import. I say import the best people in the world if we want to.

Lex Friedman (118:20)

That's where the immigration conversation is a tricky one, and there's been a lot of debate over that. But, yeah, it seems absurdly controversial to import the best people in the world. I don't understand why it's controversial. That's. That's the. One of the ways of winning.

Dylan Patel (118:32)

We agree with you.

Nathan Lambert (118:34)

Even if you can't import those people, I still think you could do a lot to manufacture most of in the US if the money's there. Right.

Dylan Patel (118:40)

And so it's just way more expensive. It's not profitable for a long time.

Nathan Lambert (118:44)

And that's the context of, like, the Chips act is only like $50 billion relative to, you know, some of the renewable, you know, initiatives that were passed in the Inflation Reduction act and the Infrastructure act, which total in the hundreds of billions of dollars. Right. And so, like, the amount of money that the US Spending on the semiconductor industry is nothing. Right. Whereas all these other countries have structural advantages in terms of work ethic and amount of work and things like that. But also a number of STEM graduates, the percentile of their best going to that. But they also have differences in terms of, hey, there's just tax benefits in the law and have been in the law for 20 years. And then some countries have massive subsidies. Right, Right. China has something like $200 billion of semiconductor subsidies a year. We're talking about $50 billion in the U.S. over like, 6. Right. So the girth or difference in, like, the subsidy amounts is also huge. Right. And. And so I think, you know, Trump has been talking about tariffing Taiwan recently. You know, that's sort of like one of these things that's like, oh, okay, well, like, you know, maybe he doesn't want to subsidize the semiconductor industry. Obviously tariffing Taiwan is going to cost. Costs a lot of things to go get much more expensive. But does it change the equation for TSMC building more fabs in the U.S. that's what he's sort of positing. Right.

Lex Friedman (119:59)

So can you lay out the. So we laid out the importance. By the way, it's incredible how much you know about so much.

Dylan Patel (120:07)

We told you Dylan knows all this stuff.

Nathan Lambert (120:10)

Yeah.

Lex Friedman (120:10)

So, okay, you laid out why TSMC is really important. If we look out into the future, 10, 20 years out, US China relationship seems like it can go to a dark place of cold war, escalated cold war, even hot war, or to a good place of anything from frenemies to cooperation to working together. So in this game theory, complicated game, what are the different trajectories? What should US be doing? What do you see as the different possible trajectories of US China relations? As both leaders start to feel the AGI more and more and see the importance of chips and the importance of.

Dylan Patel (120:57)

AI I mean, ultimately the export controls are pointing towards a separate future economy. I think the US has made it clear to Chinese leaders that we intend to control this technology at whatever cost to global economic inter, like integration. So that it's hard to unwind that like the card has been played to the same extent.

Nathan Lambert (121:22)

They've also limited US Companies from entering China. Right. So it is, it is. You know, it's been a long time coming. You know, at some point, you know, there was, there was a convergence, right? But, but over at least the last decade, it's been branching further and further out, right? Like U.S. companies can't enter China, Chinese companies can't enter the U.S. the U.S. is saying, hey, China, you can't get access to our technologies in certain areas. And China's rebuttaling with the same thing around. Like, you know, they've done some sort of specific materials in, you know, Gallium and things like that that they've tried to limit the US on one of the. There's a US drone company that's not allowed to buy batteries and they have like, military customers. And this drone company just tells the military customers, like, hey, yeah, just get it from Amazon, because I can't actually physically get them. Right. Like, like there's all these things that are happening that point to further and further divergence. I have zero idea. And I would love if we could all hold hands and sing Kumbaya. But like, I have zero idea how that could possibly happen.

Lex Friedman (122:15)

Is the divergence good or bad for avoiding war? Is it possible that the divergence in terms of manufacturer chips, of training, AI systems is actually good for avoiding military conflicts?

Nathan Lambert (122:29)

It's an objective fact that the world has been the most peaceful has ever been. When there are global hegemons, right? Or regional hegemons, right? In historical context, right? The Mediterranean was the most peaceful ever when the Romans were there, right. China had very peaceful and warring times. And the peaceful times were when dynasties had a lock hold over not just themselves, but all their tributaries around them. Right. And likewise, the most peaceful time in human history has been when the US was the global hegemon. Right? The last hand, you know, decades now, we've sort of seen things start to slide, right, with Russia, Ukraine, with what's going on in the Middle east and Taiwan risk, all these different things are starting to bubble up. Still objectively extremely peaceful. Now what happens when it's not one global hegemon, but it's two, obviously. And China will be competitive or even overtake the us? It's possible, right. And so this change in global hegemony, I don't think it ever happens super peacefully. When empires fall, which is a possible trajectory for America, they don't fall gracefully, right? They don't just slide out of irrelevance. Usually there's a lot of shaking. And so what the US is trying to do is maintain its top position and what China is trying to do is become the top position, right? And obviously there's butting of heads here in the most simple terms.

Lex Friedman (123:47)

And that could take shape in all kinds of ways, including proxy wars, it.

Dylan Patel (123:52)

Seems like it's already happening. Like as much as I want there to be centuries of prolonged peace, it looks like further instability internationally is ahead.

Nathan Lambert (124:03)

And the US's sort of current task is like, hey, if we control AI, if we're the leader in AI, then AI significantly accelerates progress. Then we can maintain the global hegemony position. And therefore I hope that works. And as an American kind of like, okay, I guess that's going to lead to peaceful piece for us. Now, obviously other people around the world get affected negatively. Obviously the Chinese people are not going to be in as advantageous of a position if that happens. But this is sort of the reality of what's being done and the actions that are being carried out.

Lex Friedman (124:38)

So can we go back to the specific detail of the different hardware? There's this nice graphic in the export controls. Of which, which GPUs are allowed to be exported and which are not. Can you kind of explain the difference is there from a technical perspective, are the H20s promising?

Nathan Lambert (125:02)

Yeah. So this goes. And I think we'd have to like, we need to dive really deep into the reasoning aspect and what's going on there. But the H20, you know, the US has gone through multiple iterations of the export controls, right? This H800 was at one point allowed back in 23, but then it got canceled and by then Deepseek had already built their cluster of. They claimed 2k. I think they actually have many more like something like 10k of those. And now this H20 is the legally allowed chip, Right? Nvidia shipped a million of these last year to China. Right. For context, it was like 4 or 5 million GPUs. Right. So the percentage of GPUs that were this China specific H20 is quite high, right? Roughly 20%, 25%, 20% or so. And so this H20 has been neutered in one way, but it's actually upgraded in other ways. And you could think of chips along three axes for AI, ignoring software stack and exact architecture, just raw specifications. There's floating point operations, flops, there is memory bandwidth and memory capacity, IO memory, and then there is interconnect, right? Chip to chip interconnections. All three of these are incredibly important for making AI systems, Right? Because AI systems involve a lot of compute. They involve a lot of moving memory around, whether it be to memory or to other chips. Right? And so these three vectors, the US initially had two of these vectors controlled and one of them not controlled, which was flops. And interconnect bandwidth were initially controlled. And then they said, no, no, no, no, we're going to remove the interconnect bandwidth and just make it a very simple only flops. But now Nvidia can now make a chip that has, okay, it's cut down on flops, it's like 1/3 that of the H100 on spec sheet paper performance for flops in real world, it's closer to like half or maybe even like 60% of it. Right? But then on the other two vectors, it's just as good for interconnect bandwidth and then for memory bandwidth and memory capacity, the H20 has more memory bandwidth and more memory capacity than the H100. Right? Now recently we at our research, we cut Nvidia's production for H20 for this year down drastically. They were going to make another 2 million of those this year. But they just canceled all the orders a couple weeks ago. In our view, that's because we think that they think they're going to get restricted. Because why would they cancel all these orders for H20? Because they shipped a million of them last year. They had orders in for a couple million this year and just gone. Right. For H20, B20. Right. A successor to H20. And now they're all gone. Now why would they do this? Right. I think it's very clear. Right. The H20 is actually better for certain tasks. And that certain task is reasoning. Right. Reasoning is incredibly different than when you look at the different regimes of models. Pre training is all about flops. Right? It's all about flops. There's things you do, like mixture of experts that we talked about to trade off interconnect or to trade off other aspects and lower the flops and rely more on interconnect and memory. But at the end of the day, flops is everything. Right. We talk about models in terms of how many flops they are. Right. So we talk about, oh, GPT4 is 2E25. Right. 2 to the 25th, 25 zeros. Flop. Floating point operations.

Dylan Patel (128:22)

For training.

Nathan Lambert (128:23)

For training. Right. And we're talking about the restrictions for the 2U24. Right. 25, whatever. The US has an executive order that Trump recently unsigned, but which was. Hey, 1e26. Once you hit that number of floating point operations, you must notify the government and you must share your results with us. Right. Like there's a level of model where the US government must be told. Right. And that's 1E26. And so as we move forward, this is, this is an incredibly like, important. FLOP is the vector that the government has cared about historically. But the other two vectors are arguably just as important. Right. And especially when we come to this new paradigm which the world is only just learning about over the last six months. Right. Reasoning.

Lex Friedman (129:02)

And do we understand firmly which of the three dimensions is best for reasoning? So interconnect, the flops don't matter as much. Is it memory?

Dylan Patel (129:11)

Memory, Right.

Nathan Lambert (129:12)

Yes.

Dylan Patel (129:12)

Excellent. We're going to get into technical stuff real fast.

Nathan Lambert (129:16)

There's two articles in this one that I could show, maybe graphics that might be interesting for you to pull up for the listeners.

Lex Friedman (129:21)

We're looking at the section of O1 inference architecture tokenomics.

Nathan Lambert (129:27)

You want to explain Kvcache before we talk about this? I think it's better to.

Dylan Patel (129:30)

Okay, yeah, we need to go through a lot of specific technical things of transformers to make this easy for people.

Nathan Lambert (129:37)

Because it's incredibly important, because this changes how models work. But I think resetting. Why is memory so important? It's because so far we've talked about parameter counts. Right. And mixture of experts. You can change how many active parameters versus total parameters to embed more data but have less flops. But more important, you know, another aspect of, you know, what's part of this humongous revolution in the last handful of years is the transformer. Right. And the attention mechanism. Attention mechanism is that the model understands the relationships between all the words in its context. Right. And that is. That is separate from the parameters themselves. Right. And that is. That is something that you must calculate. Right. How each token. Right. Each word in the context length is relatively connected to each other. Right. And I think, Nathan, you can explain KVCACHE better.

Lex Friedman (130:26)

KVCACHE is one of the optimizations.

Dylan Patel (130:27)

Yeah. So the attention operator has three core things. It's queries, keys and values. QKV is the thing that goes into this. You'll look at the equation, you see that these matrices are multiplied together. These words, query, key and value come from information retrieval backgrounds. Where the query is, is the thing you're trying to get the values for. And you access the keys and the values is reweighting. My background's not in information retrieval and things like this. It's just fun to have backlinks. And what effectively happens is that when you're doing these matrix multiplications, you're having matrices that are of the size of the context length. So the number of tokens that you put into the model and the kvcache is effectively some form of compressed representation of all the previous tokens in the model. So when you're doing this, we talk about autoregressive models. You predict one token at a time. You start with whatever your prompt was. You ask a question like, who was the President in 1825? The model then is going to generate its first token. For each of these tokens, you're doing the same attention operator where you're multiplying these query key value matrices. But the math is very nice so that when you're doing this repeatedly, this, this KVCache, this key value operation, you can keep appending the new values to it. So you keep track of what your previous values you were inferring over in this autoregressive chain. You keep it in memory the whole time. This is a really crucial thing to manage when serving inference at scale. There are far bigger experts in this and there are so many levels of detail that you can go into. Essentially one of the key drawbacks of the attention operator and the transformer is that there is a form of quadratic memory cost in proportion to the context length. So as you put in longer questions, the memory used in order to make that computation is going up in the form of a quadratic. You'll hear about a lot of other language model architectures that are like sub quadratic or linear attention forms which is like state space models. We don't need to go down all the these now. And then there's innovations on attention to make this memory usage and the ability to attend over long contexts much more accurate and high performance.

Lex Friedman (132:44)

And those innovations are going to help you with, I mean you're highly memory constrained.

Dylan Patel (132:48)

They help with memory constraint and performance. So if you put in a book into. I think Gemini is the model that has the longest context length that people are using. Gemini is known for 1 million and now 2 million context length. You put a whole book into Gemini and sometimes it'll draw facts out of it. It's not perfect. They're getting better. So there's two things. One, to be able to serve this on the memory level, Google has magic with their TPU stack where they can serve really long contexts. And then there's also many decisions along the way to actually make long contacts performance work that supplies the data. There's subtle changes to these computations in attention and it changes the architecture. But serving long context is. Is extremely memory constrained, especially when you're making a lot of predictions. I actually don't know why input and output tokens are more expensive, but I think essentially output tokens you have to do more computation because you have to sample from the model.

Nathan Lambert (133:40)

I can explain that. So today if you use a model like you look at an API, OpenAI charges a certain price per million tokens, right. And that price for input and output tokens is different. Right. And the reason is, is that there is, you know when you're inputting a query into the model, right. Let's say you have a book, right? That book you must now calculate the entire KV cache for, right? This key value cache. And so when you do that, that is a parallel operation. All of the tokens can be processed at one time and therefore you can dramatically reduce how much you're spending. Right. The FLOP requirements for generating a token and an input token are identical, right. If I input one token or if I generate one token and it's completely identical, I have to go through the model. Right. But the difference is That I can do that input, that is the prefill, that is the prompt simultaneously in a batch nature. Right. And therefore it is all flop.

Lex Friedman (134:33)

I think the pricing model, mostly they use for input tokens is about 1/4 the price of the output tokens.

Nathan Lambert (134:38)

Correct. But then output tokens, the reason why it's so expensive is because I can't do it in parallel, right? It's autoregressive. Every time I generate a token, I must not only take the entire, I must not only read the whole entire model into memory and activate it, calculate it to generate the next token, I also have to read the entire KV cache and I generate a token and then I append that one token I generated and it's KV cache and then I do it again. Right. And so therefore this is a non parallel operation and this is one where you have to, you know, in the case of pre fill or prompt, you pull the whole model in and you, you calculate 20,000 tokens at once. Right?

Dylan Patel (135:14)

These are features that APIs are shipping, which is like prompt caching, pre filling because you can drive prices down and you can make APIs much faster. If you know you're going to keep, if you run a business and you're going to keep passing the same initial content to Claude's API, you can load that in to the anthropic API and always keep it there. But it's very different than we're kind of leading to the reasoning models, which we showed this example earlier and read some of this kind of mumbling stuff. And what happens is that the output context length is so much higher. And I learned a lot about this from Dylan's work, which is essentially as the output length gets higher, you're writing this quadratic in terms of memory used and then the GPUs that we have, effectively you're going to run out of memory. And they're all trying to serve multiple requests at once. So they're doing this batch processing where not all of the prompts are exactly, exactly the same. Really complex handling. And then as context lengths gets longer, there's this, I think you call it critical batch size, where your ability to serve more users, so how much you can parallelize your inference plummets because of this long context. So your memory usage is going way up with these reasoning models and you still have a lot of users. So effectively the cost to serve multiplies by a ton.

Lex Friedman (136:29)

And we're looking at a plot when the X axis is sequence length, I.

Nathan Lambert (136:34)

E. How many tokens are being generated Slash prompt, right? So if I put in a book, that's a million tokens, right? But you know, if I put in, you know, the sky is blue, then that's like six tokens or whatever.

Lex Friedman (136:43)

I should say that what we're calling reasoning and chain of thought is extending this sequence length.

Dylan Patel (136:49)

It's mostly output.

Nathan Lambert (136:51)

So before, you know, three months ago, whenever O1 launched, all of the use cases for long context length were like, let me put a ton of documents in and then get an answer out, right? And it's a, a, it's a single, you know, pre fill, compute a lot in parallel and then output a little bit. Now with reasoning and agents, this is a very different idea right now instead I might have, I might only have like, hey, do this task or I might have all these documents. But at the end of the day, the model is not just like producing a little bit, right? It's producing tons of information. This chain of thoughts just continues to go and go and go and go. And so the sequence length is effectively that, that, you know, if it's generated 10,000 tokens, it's 10,000 sequence length, right? And plus whatever you input it in the prompt. And so what this chart is showing, and it's a logarithmic chart, right, Is as you grow from 1k to 4k or 4k to 16k, the memory requirements grow so fast for your KV cache that you end up not being able to run a certain number of your sequence length is capped or the number of users you can see.

Dylan Patel (137:52)

Let's save the model. So this is showing for a 405B.

Lex Friedman (137:54)

Model and batch size 64 llama 31405B.

Dylan Patel (137:58)

Yeah, and batch size is crucial to essentially they just like you want to have higher batch size to Parallelize, parallel.

Nathan Lambert (138:05)

Your throughput 64 different users at once, right?

Dylan Patel (138:07)

Yeah.

Nathan Lambert (138:07)

And therefore your serving costs are lower, right? Because the server costs the same, right? This is eight one hundreds, roughly $2 an hour per GPU. That's $16 an hour, right? That is, that is like somewhat of a fixed cost. You can do things to make it lower, of course, but like it's like $16 an hour now how many users can you serve? How many tokens can you generate? And then you divide the two and that's your cost, right. And so with reasoning models, this is where a lot of the complexity comes about and why memory is so important. Because if you have limited amounts of memory, then you can't serve so many users. If you have limited amounts of memory, your serving Speeds get lower, right? And so your costs get a lot, lot worse. Because all of a sudden, if I was used to, hey, on this $16 an hour server, I'm serving llama 405B or if I'm serving, you know, deep seq v3 and it's all chat style applications, I. E. We're just chit chatting. The sequence lengths are a thousand, few thousand, right? You know, when you use a language model, it's a few thousand context lengths most of the time. Sometimes you're dropping a big document, but then you process it, you get your answer, you throw it away, right? You move on to the next thing, right? Whereas with reasoning, I'm now generating tens of thousands of tokens in sequence, right? And so this memory, this KV cache has to stay resident and you have to keep loading it, you have to keep it in memory constantly. And now this butts out other users, right? If there's now a reasoning task and the model is capable of reasoning, then all of a sudden that memory pressure means that I can't serve as many users simultaneously.

Dylan Patel (139:30)

Let's go into deepseek again. So we're in the post Deepseek R one time, I think, and there's two sides to this market. Watching how hard it is to serve it. On one side, we're going to talk about deepseek themselves. They now have a chat app app that got to number one on the App Store. Disclaimer. Number one on the App Store is measured by velocity. So it's not necessarily saying that more people have the Deep Seq app than the ChatGPT app, but it is still remarkable. Claude has never hit the number one in the App Store, even though everyone in San Francisco is like, oh my God, you got to use Claude, don't use ChatGPT. So Deepseek hit this. They also launched an API product recently where you can ping their API and get these super long responses for R1 out. At the same time as these are out, we'll get to what's happened to them. Because the model weights for Deepsea R1 are openly available and the license is very friendly. The MIT license commercially available. All of these mid sized companies and big companies are trying to be first to serve R1 to their users. We were trying to evaluate R1 because we have really similar research going on. We released a model and we're trying to compare to it and out of all the companies that are quote unquote serving R1 and they're doing it at prices that are way higher than the DeepSeq API, most of them barely work and the throughput is really low.

Nathan Lambert (140:45)

To give, to give context, right everyone. One of the parts of like freaking this out was like China reached capabilities. The other aspect is they did it so cheap, right? And the so cheap we kind of talked about on the training side why it was so cheap.

Lex Friedman (140:55)

Yeah, let's talk about why it's so cheap. On the inference it works well and it's cheap. Why is R1 so damn cheap?

Nathan Lambert (141:02)

So I think there's a couple factors here, right? One is that they do have model architecture innovations, right? This, this mla, this new attention that they've done is different than the attention from. Attention is all you need the transformer attention right now. Others have already innovated. There's a lot of work like mqa, gqa, Local Global, all these different innovations that try to bend the curve, right? It's still quadratic, but the constant is now smaller. Right?

Dylan Patel (141:28)

Related to our previous discussion, this multi head latent attention can save about 80 to 90% in memory from the attention mechanism, which helps especially at long contexts.

Nathan Lambert (141:39)

It's 80 to 90% versus the original. But then versus what people are actually doing. It's still an innovation.

Dylan Patel (141:43)

This 80 to 90% doesn't say that the whole model is 80 to 90% cheaper just as one part of it.

Nathan Lambert (141:49)

Well, and not just that, right? Like other people have implemented techniques like local global and sliding window and gq, mqa. But anyways, like Deepseek has. Their attention mechanism is a true architectural innovation. They did tons of experimentation and this dramatically reduces the memory pressure. It's still there, right? It's still a quadra. It's still attention. It's still quadratic. It's just dramatically reduced it relative to prior forms.

Lex Friedman (142:10)

Right? That's the memory pressure, I should say, in case people don't know R1 is 27 times cheaper than 01.

Dylan Patel (142:19)

We think that OpenAI had a large margin built in.

Lex Friedman (142:22)

Okay, so that's one.

Dylan Patel (142:23)

There's multiple factors. We should break down the factors.

Lex Friedman (142:25)

I think it's 2 bucks per million token output for R1 and $60 per million token output. It 401.

Dylan Patel (142:34)

Yeah, let's look at this.

Nathan Lambert (142:37)

So I think this is very important, right? OpenAI is that drastic gap between Deep SEQ and pricing. But Deep SEQ is offering the same model because they open weights it to everyone else for a very similar like much lower price than what others are able to serve it for. Right? So there's two factors here, right? Their model is cheaper, right? It is 27 times cheaper. Well, I don't remember the number exactly off the top of my head.

Lex Friedman (143:01)

So we're looking at a graphic that's showing different places serving v3, deep seq v3 which is similar to deep seq r1. And there's a vast difference in serving cost. Right? Serving cost. And what explains that difference?

Nathan Lambert (143:18)

And so like part of it is OpenAI has a fantastic margin. Right? They're serving when they're doing inference. Their gross margins are north of 75%. Right. So that's a 4 to 5x factor right there of the cost difference is that OpenAI is just making crazy amounts of money because they're the only one with the capability.

Lex Friedman (143:34)

Do they need that money? Are they using it for R and D?

Nathan Lambert (143:36)

They're losing money obviously as a company because they spend so much on training. Right. So the inference itself is very high margin, but it doesn't recoup the cost of everything else they're doing. Okay. So yes, they need that money because the revenue and margins pay for continuing to build the next thing, right alongside raising more money.

Lex Friedman (143:52)

So the suggestion is that Deep Seek is like really bleeding out money.

Nathan Lambert (143:55)

Well, so, so here's one thing, right? We'll get to this in a second. But like, like deepseek doesn't have any capacity to actually serve the model. They stopped signups. The ability to use it is like non existent now. Right. For most people because so many people are trying to use it, they just don't have the GPUs to serve it. Right. OpenAI has hundreds of thousands of GPUs between them and Microsoft to serve their models. Deepseek has a factor of much lower. Right. Even if you believe our research, which is 50,000 GPUs and a portion of those are for research, portion of those are for the hedge fund. Right? Right. They still have nowhere close to the GPU volumes and capacity to serve the model at scale. So it is cheaper. A part of that is OpenAI making a ton of money. Is Deepseek making money on their API unknown? I don't actually think so. And part of that is this chart, right? Look at all the other providers. Right? Together AI fireworks. AI are very high end companies, right? X Meta Together AI is TreeDow and the inventor of Flash Attention, which is a huge efficiency technique. Technique, right. They're very efficient, good companies and they're serving. And I do know those companies make money, right? Not, not tons of money on inference, but they make money. And so they're serving at like a 5 to 7x difference in cost. Right. And so you know, now when you, when you equate, okay, OpenAI is making tons of money, that's like a 5x difference. And the companies that are trying to make money for this model is like a 5x difference. There is still a gap, right? There's still a gap. And that is just Deep Seq being really freaking good. Right? The model architecture, mla, the way they did the moe, all these things there is like legitimate, just efficiency differences.

Dylan Patel (145:22)

All their low level libraries that we talked about in training, some of them probably translate to inference and those weren't released.

Lex Friedman (145:27)

So we may go a bit into conspiracy land, but is it possible the Chinese government is subsidizing Deep Seq?

Nathan Lambert (145:34)

I actually don't think they are. I think when you look at the Chinese labs, there's, there's Huawei has a lab, Moonshot AI, there's a couple other labs out there that are really close with the government and then there's labs like Alibaba and Deep Seq which are not close with the government. And you know, we talked about this, this, the CEO, this like reverent figure who's like quite different, who has like. Sounds awesome, very different, like viewpoints based on the Chinese interviews that are translated than what the CCP might necessarily want. Now to be clear, right, does he have a loss leader because he can fund it through his hedge fund? Yeah, sure.

Lex Friedman (146:08)

So the hedge fund might be subsidizing it.

Nathan Lambert (146:10)

Yes, I mean they absolutely did. Right. Because Deep Seek has not raised much money. They're now trying to raise a round in China, but they have not raised money historically. It's all just been funded by the hedge fund. And he owns over half the company. Like 50, 60% of the company is owned by him.

Dylan Patel (146:23)

Some of the interviews, there's discussion on how doing this is a recruiting tool. You see this at the American companies too. It's like having GPUs recruiting tool being at the cutting edge of AI recruiting tool open sourcing. Open sourcing recruiting tools.

Nathan Lambert (146:36)

So much talent. They were so far behind and they got so much talent because they just open sourced stuff.

Lex Friedman (146:40)

More conspiracy thoughts. Is it possible since they're a hedge fund that they timed everything with this release and the pricing and they shorted Nvidia stock and stock of US AI companies and released it with Stargate. Like just perfect timing to be able to make money.

Dylan Patel (147:02)

Like they released it on inauguration day. They know that international what is on the international calendar. But I mean I don't expect them to. If you listen to their motivations for AI is like, no, if you released.

Nathan Lambert (147:13)

They released V3 on like December 26th, like who releases the day no one looks, right. They released the papers before this, right? The V3 paper and the R1 paper. So people had been looking at it be like, wow. And then they just released the V R1 model. I think they're just shipping as fast as they can and like, who cares about Christmas? Who cares about, you know, get it out before Chinese New Year. Right. Obviously, which just happened. I don't think they actually were like, like timing the market or trying to make the biggest splash possible. I think they're just like shipping.

Dylan Patel (147:40)

I think that's one of their big advantages. We know that a lot of the American companies are very invested in safety and that is the central culture of a place like Anthropic. And I think Anthropic sounds like a wonderful place to work. But if safety is your number one goal, it takes way longer to get artifacts out. That's why Anthropic is not open sourcing things. That's their claims. But there's reviews internally. Anthropic mentions things to international governments. There's been news of how Anthropic has done pre release testing with the UK Safety Institute. All of these things add inertia to the process of getting things out. And we're on this trend line where progress is very high. So if you reduce the time from when your model is done training, you run evals. That's good. You want to get it out as soon as possible to maximize is the perceived quality of your outputs. Deepsea does this so well.

Nathan Lambert (148:31)

Dario explicitly said Claude 3.5 Sonnet was trained like nine months or nine to ten months ago. Nine to ten months ago. And I think it took them another like handful of months to release it. Right. So it's like there is a significant gap here. Right? And especially with reasoning models, the word in the San Francisco street is that like Anthropic has a better model than O3. Right. And they won't release it. Why? Because. Because chains of thought are scary, right? And they are legitimately scary, right. If you look at R1, it flips back and forth between Chinese and English. Sometimes it's gibberish and then the right answer comes out. Right? And for you and I, it's like, great.

Dylan Patel (149:05)

I mean that's why people are infatuated with you. You're telling me this is a high value thing and it works and it's doing this.

Nathan Lambert (149:11)

It's amazing. I Mean, you talked about that sort of like chain of thought for that philosophical thing, which is not something they trained to be philosophically good. It's just sort of an artifact of the chain of thought thought training. It did. But that's super important in that. Can I inspect your mind and what you're thinking right now? No. And so I don't know if you're lying to my face. And chain of thought models are that way. Right. This is a true risk between a chat application where, hey, I asked the model to say bad words or whatever or how to make anthrax and it tells me that's unsafe. Sure. But that's something I can get out relatively easily. What if I tell the AI to do a task task, and then it does the task all of a sudden randomly, in a way that I don't want it. Right. And now that has like, much more task versus like response is very different. Right. So the bar for safety is much higher. At least this is Anthropic's case. Right. Like for Deep Seek, they're like ship, right?

Lex Friedman (150:03)

Yeah. So, I mean, the bar for safety is probably lowered a bit because of deep Seek. I mean, there's parallels here to the space race. The reason the Soviets probably put a man in space first is because. Because their approach to safety was the.

Nathan Lambert (150:19)

Bar for safety was lower and they killed that dog. Right. And all these things. Right.

Lex Friedman (150:22)

So it's like less risk averse than the US based program. And there's parallels here, but there's probably going to be downward pressure on that safety bar for the U.S. companies. Right.

Dylan Patel (150:35)

This is something that Dario talks about. That's the situation that Dario wants to avoid, is Dario talks too about the difference between race to the bottom and race to the top. And the race to the top is where there's a very high standard on safety. There's a very high standard on your model performs in certain crucial evaluations. And when certain companies are really good to it, they will converge. This is the idea. And ultimately AI is not confined to one nationality or to one set of morals for what it should mean. And there's a lot of arguments online like, should we stop open sourcing models? And if the US stops, it's pretty clear. I mean, it's way easier to see now at Deepseek that a different international body will be the one that builds it. We talk about the cost of training. Deepseek has this shocking $5 million number. Think about how many entities in the world can afford 100 times that to have the best open Source model that people use in the world. And it's a scary reality, which is that these open models are probably going to keep coming for the time being whether or not we want to stop them. And it is. Stopping them might make it even worse and harder to prepare, but it just means that the preparation and understanding what AI can do is just so much more important. That's why I'm here at the end of the day. But it's like letting that sink into people, especially not in AI, is that this is coming. There are some structural things in a global, interconnected world that you have to accept.

Lex Friedman (152:03)

Yeah, you mentioned you send me something that Zuck, Mark Zuckerberg mentioned on the earnings call. He said that. I think in light of some of the recent news, the new competitor, Deep Seq from China, I think it's one of the things that we're talking about is there's going to be an open source standard globally. And I think for our kind of national advantage, it's important that it's an American standard. So we take that seriously. We want to build the AI system that people around the world are using. And I think that if anything, some of the recent news has only strengthened our conviction that this is the right thing to be focused on. So, yeah, open sourcing.

Dylan Patel (152:37)

Yeah. Mark Zuckerberg is not new to having American values and how he presents his company's trajectory. I think their products have long since been banned in China and I respect saying it directly.

Nathan Lambert (152:49)

And there's an interesting aspect of just because it's open weights or open source doesn't mean it can't be subverted, right? There have been many open source software bugs that have been like, you know, for example, there was a Linux bug that was found after like 10 years, which was clearly a backdoor because somebody was like, why is this taking, you know, half a second to load?

Dylan Patel (153:09)

This is the recent one, right?

Nathan Lambert (153:10)

Like, why is this taking half a second to load? And it was like, oh crap, there's a back door here. That's why. Right. And it's like, this is very much possible with AI models right Today. You know, the alignment of these models is very clear. Right. Like, I'm not going to say, you know, bad words. I'm not going to teach you how to make Anthrax. I'm not going to talk about Tiananmen Square. I'm not gonna, you know, things like, I'm gonna say Taiwan is part of, you know, is, is just an eastern province. Right. Like, you know, all these things are like, depending on who you are, what you align, what you know, whether you know. And even like XAI is aligned a certain way, right. You know, there they might be. It's not aligned in the like woke sense, it's not aligned in the like pro China sense. But there is certain things that are imbued within the model. Now when you release this publicly in an instruct model that's open weights, this can then proliferate, right? But as these systems get more and more capable, what you can embed deep down in the model is not as clear, right? And so there are. That is like one of the big fears is like if an American model or a Chinese model is the top model, right? You're going to embed things that are unclear and it could be unintentional too, right? Like British English is dead because American LLMs won. Right. And the Internet is American and therefore like color is spelled the way Americans spell it, right.

Lex Friedman (154:22)

And this is just strong words right now.

Nathan Lambert (154:24)

This is just like, this is just the factual nature of the llc.

Dylan Patel (154:27)

I was like carpet. The English is the hottest programming language and that English is defined by a bunch of companies that primarily are in San Francisco.

Lex Friedman (154:36)

The right way to spell optimization is with a Z, just in case people are listening. I think it's an S in British English.

Nathan Lambert (154:43)

It is taking it as something silly, right? Like something as silly as the spelling. Like which British and English, you know, Brits and Americans will like laugh at about probably, right? I don't think we care that much. But like, you know, some people will. But like this can, this can boil down into like very, very important topics like hey, you know, subverting people, right? You know chatbots, right? Character AI has shown that they can like you know, talk to kids and adults and like it will like people feel a certain way. Right? And that's unintentional alignment. But like what happens when there's intentional alignment deep down on the open source standard? It's a backdoor today for like Linux that we discover or some encryption system. China uses different encryption than NIST defines the US NIST because there's clearly at least they think there's backdoors in it, right? What happens when the models are backdoors not just to computer systems but to our minds?

Dylan Patel (155:36)

Yeah, they're cultural backdoors. The thing that amplifies the relevance of culture with language models is that we are used to this mode of interacting with people in back and forth conversation and we have now have a super, a very powerful computer system that slots into a social context that we're used to, which makes people very. We don't know the extent that, which people can be impacted by that.

Lex Friedman (156:03)

So there could be. This is one, this is an actual concern with a Chinese company that is providing open weights models is that there could be some secret Chinese government sort of requirement for these models to have a certain kind of backdoor, to have some kind of thing where I don't.

Nathan Lambert (156:22)

Necessarily think it'll be a backdoor. Right. Because once it's open weights, it doesn't like phone home. It's more about like if it recognizes a certain system, it could like if. Now it could be a backdoor in the sense of like, hey, if you're building a software, you know something in software, all of a sudden it's a software agent. Oh, program this backdoor that only we know about about. Or it could be subvert the mind to think that XYZ opinion is the correct one.

Dylan Patel (156:45)

Anthropic has research on this where they show that if you put certain phrases in at pre training, you can then elicit different behavior when you're actually using the model because they've poisoned the pre training data. As of now, I don't think anybody in a production system is trying to do anything like this. I think it's mostly anthropic is doing very direct work and mostly just subtle things. We don't know what these models are going to, how they are going to generate tokens, what information they're going to represent and what the complex representations they have are.

Lex Friedman (157:20)

Well, one of the things we're talking about, Anthropic, which is generally just permeated with good humans trying to do good in the world. We just don't know of any labs. This would be done in a military context that are explicitly trained to, okay, how can we. The the front door looks like a happy LLM, but underneath it's a thing that will over time do the maximum amount of damage to our quote, unquote enemies.

Nathan Lambert (157:52)

There's this very good quote from Sam Altman, who, you know, he can be a hype beast sometime, but one of the things he said, and I think I agree, is that superhuman persuasion will happen before superhuman intelligence. Right. And if that's the case, then these things, before we get this AGI ASI stuff, we can embed superhuman persuasion towards our ideal or whatever the ideal of the model maker is. And again today, I truly don't believe Deepseek has done this, but it is a sign of what could happen.

Lex Friedman (158:21)

So one of the dystopian Worlds is described by Brave New World. So we could just be stuck scrolling Instagram, looking at cute puppies or worse and then talking to bots that are giving us a narrative and we completely get lost in that world that's controlled by somebody else versus thinking independently. And that's a major concern as we rely more and more on these kinds of systems.

Dylan Patel (158:46)

We've already seen this with recommendation systems.

Nathan Lambert (158:48)

Yeah, recommendation systems hack the dopamine induced reward circuit. It. But the brain is a lot more complicated. And what other sort of circuits, feedback loops in your brain can you hack, subvert in ways like recommendation systems are purely just trying to do increased time and ads and et cetera. But there's so many more goals that can be achieved through these complicated models.

Dylan Patel (159:09)

There's no reason in some number of years that you can't train a language model to maximize time spent on a chat app. Like right now they are trained.

Nathan Lambert (159:18)

I mean is that not what character AI has done? Their time per session is like two hours.

Dylan Patel (159:22)

Yeah, character AI very likely could be optimizing this where it's like the, the way that this data is collected is naive. Where it's like you're presented a few options and you choose them. But there's. That's not the only way that these models are going to be trained.

Nathan Lambert (159:35)

Naive stuff like talk to an anime girl. But like it can be like, yeah, this is a risk. Right? Like it's.

Lex Friedman (159:40)

It's a bit of a cliche thing to say, but I've over the past year had a few stretches of time where I didn't use social media or the Internet at all and just read books and was out in nature. And it clearly has an effect on the mind where it changes. I feel like I'm returning. Of course I was raised before the Internet really took off, but I'm returning to some more.

Dylan Patel (160:05)

I know where you're going. I mean you can see it physiologically. I take three days if I'm backpacking or something. And you're literal, you're breaking down addiction cycles.

Lex Friedman (160:16)

I feel like I'm more in control of my mind. There feels like a sovereignty of intelligence that's happening when I'm disconnected from the Internet. I think the more I use the Internet and social media, the more other people are controlling my mind. That's definitely a feeling. And then in the future that will be not other people, but algorithms or other people presented to me via algorithms there.

Dylan Patel (160:39)

I mean there are already tons of AI bots on the Internet and every. So right now it's not frequent but every so often I have replied to one and they're instantly replies and I'm like crap, that was a bot. And that is just going to become more common. Like they're going to get good.

Nathan Lambert (160:53)

One of the hilarious things about technology over its history is that the illicit adult entertainment industry has always adopted technologies first, right? Whether it was like video streaming to like where you know, the, there's now the like sort of like independent adult illicit content creators who have their you know, subscription pages and there they actually heavily utilize, you know, generative AI has already been like diffusion models and all that is huge there. But now these like these, these subscription based individual creators do use bots to approximate themselves and chat with their, you know, whales.

Dylan Patel (161:26)

People pay a lot for it and.

Nathan Lambert (161:27)

People pay a lot, right? It's a lot of times it's them. But a lot of there are agencies that do this for these creators and do it like on a like mass scale. So the largest creators are like able to talk to hundreds or thousands of like people at a time because of these bots. And so it's already being used there. Obviously you know, like video streaming and other technologies have gone there first. It's going to come to the rest of society too.

Lex Friedman (161:52)

There's a general concern that models get censored by the companies that deploy them. So one case where we've seen that and maybe censorship was one word, alignment, maybe via RLHF or some other way is another word. So we saw that with Black Nazi image generation with Gemini as you mentioned. We also see that with Chinese models refusing to answer what happened in June 4th, 1989 at Tiananmen Square. So how can this be avoided? And maybe can you just in general talk about how this happens and how can it be avoided?

Dylan Patel (162:33)

You give multiple examples. There's probably a few things to keep in mind here. One is the kind of Tiananmen Square factual knowledge. How does that get embedded into the models? Two is the Gemini what you called the Black Nazi incident, which is when Gemini as a system had this extra thing put into it that dramatically changed the behavior. And then three is what most people would call general alignment RLHF post training. Each of these have very different scopes in how they are applied. In order to do if you're just looking at the model weights in order to audit specific facts is extremely hard because you have to chrome through the pre training data and look at all of this and then that's terabytes of files and look for very specific words or Hints of the words.

Lex Friedman (163:26)

So I guess one way to say it is that you can insert censorship or alignment at various stages in the pipeline and what you refer to now is at the very beginning of the data selection.

Dylan Patel (163:36)

So if you want to get rid of facts in a model, you have to do it at every stage, you have to do it at the pre training. So most people think that pre training is where most of the knowledge is put into the model and then you can elicit and move that in different ways, whether through post training or whether through systems afterwards.

Nathan Lambert (163:53)

This is where the whole like hacking models comes from. Right? Like GPT will not tell you how to make anthrax, but if you try really, really hard, you can eventually get it to tell you about anthrax because they didn't filter it from the pre training data set. Right.

Lex Friedman (164:06)

But by the way, removing facts has such an ominous dark feel to it.

Dylan Patel (164:12)

Almost think it's practically impossible possible because you effectively have to remove them from the Internet. You're taking on a.

Lex Friedman (164:19)

Did they remove the MMM thing from the subreddits? The mmm, it gets filtered out.

Dylan Patel (164:25)

Right? So you have quality filters which are small language models that look at a document and tell you like, how good is this text? Is it close to a Wikipedia article? Which is a good thing that we want language models to be able to imitate.

Lex Friedman (164:36)

So couldn't you do a small language model that filters out mentions of Tianima Square in the data?

Dylan Patel (164:41)

Yes, but is it going to catch wordplay or encoded language?

Nathan Lambert (164:45)

People have been memeing on like games and other stuff, how to like say things that don't say Tiananmen Square but. Or like. Yeah, so there's always like different ways to do it. There's. Hey, the Internet as a whole does tend to just have a slight left bias, right? Because it's always been richer, more affluent, younger people on the Internet relative to the rest of the population. So there is already inherently a slight left bias on the Internet. And so how do you filter things that are this complicated? Right, and some of these can be like factual, non factual, but like Tiananmen Square is obviously the example of a factual, but it gets a lot harder when you're talking about aligning to a ideal. Right. And so Grok, for example, Elon's tried really hard to make the model not be super PC and woke. But the best way to do pre training is to throw the whole freaking Internet at it. Right? And then later figure out. But then at the end of the day, the model at its core now still has some of these ideals, right? You still ingested Reddit r Politics, which is probably the largest political discussion board on the world that's freely available to scrape. And guess what? That's left leaning, right? And so, you know, there are some aspects like that you just can't censor unless you try really, really, really, really really hard part.

Lex Friedman (165:59)

So the base model will always have some tds Trauma Derangement syndrome, because it's trained so much.

Dylan Patel (166:05)

It'll have the ability, I don't know if you express it, but what if, what if you.

Lex Friedman (166:09)

There's a. There's a wide representation in the data.

Dylan Patel (166:12)

This is what happens. It's like put a lot of what is called post training. It's a series of techniques to get the model on rails of a really specific behavior.

Nathan Lambert (166:22)

It's like you can. You also have the ingested data of like, like Twitter or like Reddit r thedonald, which is like also super pro Trump, right? And then you have like fascist subreddits or like you have communist subreddits. So you. The model in pre training ingests everything. It has no worldview. Now it does have like some, some skew because more of the text is skewed a certain way, which is general, like slight left like, but also like, you know, somewhat like, you know, intellectual, somewhat like, you know, it's just like the general Internet is a certain way. And then as Nathan's about to describe eloquently, right, you can elicit certain things.

Dylan Patel (166:56)

Out and there's a lot of history here. So we can go through multiple examples and what happened. Llama 2 was a launch that the phrase too much RLHF or too much safety was a lot. That was the whole narrative after llama2's chat models released. And the examples are sorts of things like you would ask llama2chat, how do you kill a Python process? And it would say I can't. You can't talk about killing because that's a bad thing. And anyone that is trying to design an AI model will probably agree that that's just like eh, you messed up a bit on the training there. I don't think they meant to do this. But this was in the model weight. So this is not. It didn't necessarily be. There's things called system prompts which are when you're querying a model, it's a piece of text that is shown to the model but not to the user. So a fun example is your system prompt could be talk like a pirate. So no matter what the user says to the model, it'll respond like a pirate. In practice, what they are is you are a helpful assistant. You should break down problems. If you don't know about something, don't tell them. Your date cutoff is this, Today's date is this. It's a lot of really useful context for how can you answer a question well.

Lex Friedman (168:03)

And Anthropic publishes their system, which I think is great.

Dylan Patel (168:06)

And there's a lot of research that goes into this. And one of your previous guests, Amanda Askel is, is probably the most knowledgeable person, at least in the combination of execution and sharing. She's the person that should talk about system prompts and character of models.

Lex Friedman (168:20)

Yeah. And then people should read these system prompts because you're like trying to nudge sometimes through extreme politeness the model to be a certain way.

Dylan Patel (168:30)

And you could use this for bad things. We've done tests, which is what if I tell the model to be a dumb model? Like which evaluation scores go down and it's like we'll have this behavior where it could sometimes say oh, I'm supposed to be dumb. And sometimes it doesn't affect math abilities as much. But something like if you're trying, it's just the quality of a human judgment would draw through the floor. Let's go back to post training, specifically RLHF around Llama 2 was. It was too much safety prioritization was baked into the model weights. This makes you refuse things in a really annoying way for users. It's not great. It caused a lot of awareness to be attached to RLHF that it makes.

Nathan Lambert (169:11)

The models dumb and it stigmatized the.

Dylan Patel (169:13)

Word it did in AI culture. And as the techniques have evolved, that's no longer the case where all of these labs have very fine grained control over what they get out of the models through techniques like RLHF.

Nathan Lambert (169:24)

Although different labs are definitely different levels. Like on one end of the spectrum is Google and then maybe OpenAI does less and Anthropic does less and then on the other end of the spectrum is xai. But they all have different forms of RLHF trying to make them a certain way.

Dylan Patel (169:41)

And the important thing to say is that no matter how you want the model to behave, these RLHF and preference tuning techniques also improve performance. So on things like math evals and code evals, there is something innate to these, what is called contrastive loss functions. We could start to get into RL here. We don't really need to, but RLHF also boosts performance on anything from a chat task to a math problem to a code problem. So it is becoming a much more useful tool to these labs. So this kind of takes us through the arc of we've talked about pre training, hard to get rid of things. We've talked about post training and how post training you can mess it up. It's a complex multifaceted optimization with 10 to 100 person teams converging on one artifact. It's really easy to not do it perfectly. And then there's the third case which is what we talked about, Gemini. The thing that was about Gemini is this was a served product where Google has their internal model weights. They've done all these processes that we talked about. And in the served product, what came out after this was that they had a prompt that they were rewriting user queries to boost diversity or something. And this just made it. The outputs were just blatantly wrong. It was some sort of organizational failure that had this prompt in that position. And I think Google executives probably have owned this. I don't pay attention that detail. But it was just a mess up in execution that led to this ridiculous thing. But at the system level, the model weights might have been fine.

Lex Friedman (171:04)

So at the very end of the pipeline there was a rewriting to something.

Dylan Patel (171:07)

Like a system prompt. It was like the system prompt, or what is called an industry is like you rewrite prompts. So especially for image models, if you're using Dall E or ChatGPT can generate you an image, you'll say, draw me a beautiful car. With these leading image models, they benefit from highly descriptive prompts. So what would happen is if you do that on ChatGPT, a language model behind the scenes will rewrite the prompt, say make this more descriptive and then that is passed to the image model. So prompt rewriting is something that is used at multiple levels of industry and it's used effectively for image models. And the Gemini example is just a failed execution.

Lex Friedman (171:46)

Big philosophical question here with RLHF. To generalize, where is human input? Human in the loop human data most useful at the current stage.

Dylan Patel (172:00)

For the past few years, the highest cost human data has been in these preferences, which is comparing, I would say highest cost and highest total usage. So a lot of money has gone to these pairwise comparisons where you have two model outputs and a human is comparing between the two of them. In earlier years there was a lot of this instruction tuning data, so creating highly specific examples to something like a Reddit question, to a domain that you care about language models used to struggle on math and code. So you would pay experts in math and code to come up with questions and write detailed answers that were used to train the models. Now it is the case that there are many model options that are way better than humans at writing detailed and eloquent answers for things like model and code. So they talked about this with the llama 3 release where they switched to using llama 3 405B to write their answers for math and code. But they in their paper talk about how they use extensive human preference data, which is something that they haven't gotten AIs to replace. There are other techniques in industry like constitutional AI where you use human data for preferences and AI for preferences. And I expect the AI part to scale faster than the human part. But among the research that we have access to is that humans are in this kind of preference loop.

Lex Friedman (173:19)

So as reasoning becomes bigger and bigger and bigger, as we said, where's the role of humans in that?

Dylan Patel (173:25)

It's even less prevalent. So the remarkable thing about these reasoning results, and especially the deep seq R1 paper, is this result that they call deep seq R10, which is they took one of these pre trained models, they took deep seq v3 base and then they do this reinforcement learning optimization on verifiable questions or verifiable rewards for a lot of questions and a lot of training. And these reasoning behaviors emerge naturally. So these things like, wait, let me see, wait, let me check this. Oh, that might be a mistake. And they emerge from only having questions and answers. And when you're using the model, the part that you look at is the completion. So in this case, all of that just emerges from this large scale RL training. And that model, which the weights are available, has no human preferences added into the post training. The deep seq R1 full model has some of this human preference tuning, this RLHF after the reasoning stage. But the very remarkable thing is that you can get these reasoning behaviors and it's very unlikely that there's humans writing out reasoning chains. It's very unlikely that they somehow hacked OpenAI and they got access to openai01's reasoning chainsaw means it's something about the pre trained language models and this RL training where you reward the model for getting the question right and therefore it's trying multiple solutions. And it emerges this chain of thought.

Lex Friedman (174:47)

This might be a good place to mention the eloquent and the insightful tweet of the great and the powerful Andrej Karpathy I think he had a bunch of thoughts but one of them last thought not sure if this is obvious. You know something profound is coming when you're saying it's not sure if it's obvious. There are two major types of learning in both children and in deep learning. There's 1 imitation learning, watch and repeat I. E. Pre training supervised fine tuning and 2 trial and error learning, reinforcement learning. My favorite simple example is AlphaGo. One is learning by imitating expert players. Two is reinforcement learning to win the game. Almost every single shocking result of deep learning and the source of all magic is always two. Two is significantly more powerful. Two is what surprises you. Two is when the paddle learns to hit the ball behind the blocks and break out. Two is when AlphaGo beats even Lee Sedol. And two is the aha moment when the deep seek or O1 etc discovers that it works well to to reevaluate your assumptions, backtrack, try something else etc. It's the solving strategies you see this model use in its chain of thought. It's how it goes back and forth thinking to itself. These thoughts are emergent three exclamation points and this is actually seriously incredible, impressive and new and is publicly available and documented. The model could never learn this with imitation because the cognition of the model and the cognition of the human labeler is different. The human would never know to correctly annotate these kinds of solving strategies and what they should even look like. They have to be discovered during reinforcement learning as empirically and statistically useful towards the final outcome. Anyway, the Alpha 0 sort of metaphor analogy here, can you speak to that? The magic of the chain of thought that he's referring to.

Dylan Patel (176:48)

I think it's Good to recap AlphaGo and AlphaZero because it plays nicely with these analogies between imitation learning and learning from scratch. So AlphaGo, the beginning of the process was learning from humans where they started the first. This is the first expert level go player or chess player in DeepMind series of models where they had some human data and then why it is called Alpha Zero is that there was zero human data in the loop. And that change to AlphaZero made a model that was dramatically more powerful for DeepMind. So this, this remove of the human prior the human inductive bias makes the final system far more powerful. We mentioned bitter lesson hours ago and this is all aligned with this. And then there's been a lot of discussion in language models. This is not new. This goes back to the whole Q rumors which if you piece together the pieces is probably the start of OpenAI figuring out its O1 stuff when last year in November, the Q rumors came out. There's a lot of intellectual drive to know when is something like this going to happen with language models? Because we know these models are so powerful and we know it has been so successful in the past. And it is a reasonable analogy that this new type of reinforcement learning training for reasoning models is when the door is open to this. We don't yet have the equivalent of turn 37, which is the famous turn where the DeepMind's AI playing ghost dumped Lee Sedal completely. We don't have something that's that level of focal point. But that doesn't mean that the approach to technology is different. And the impact of the general training, it's still incredibly new.

Lex Friedman (178:27)

What do you think that point would be? What would be move 37 for chain.

Dylan Patel (178:30)

Of thought for reasoning scientific discovery. You use this sort of reasoning problem and it just something we fully don't expect.

Nathan Lambert (178:38)

I think it's actually probably simpler than that. It's probably something related to computer use or robotics rather than science discovery. Because the important aspect here is models take so much data to learn. They're not sample efficient, right? Trillions. They take the entire web, right? Over 10 trillion tokens to train on. This would take a human thousands of years to read. A human does not. And humans know most of the stuff, a lot of the stuff. Models know better than it. Humans are way, way, way more sample efficient. That is because of the self play, right? How does a baby learn what its body is as it sticks its foot in its mouth and it says, oh, this is my body, right? It sticks its hand in its mouth and it calibrates its touch on its fingers with the most sensitive touch thing on its tongue, right? It's how babies learn. And it's just self play over and over and over and over again. And now we have something that is similar to that, right? With the these verifiable proofs, right? Whether it's a unit test in code or mathematical verifiable task, generate many traces of reasoning and keep branching them out, Keep branching them out and then check at the end, hey, which one actually has the right answer? Most of them are wrong. Great. These are the few that are right. Maybe we use some sort of reward model outside of this to select even the best one to preference as well. But now you've started to get better and better at these benchmarks. And so you've seen over the last six Months, a skyrocketing in a lot of different benchmarks, right?

Dylan Patel (180:05)

All math and code benchmarks were pretty much solved except for Frontier math, which is designed to be almost questions that aren't practical to most people because they're like, they're exam level open math problem type things. So it's like on the math problems that are somewhat reasonable, which is like somewhat complicated word problems or coding problems. It's just what Dylan is saying.

Nathan Lambert (180:27)

So the thing here is that these are only with verifiable tasks. We earlier showed an example of the really interesting what happens when chain of thought is to a non verifiable thing. There's just like a human chatting, thinking about what's novel for humans, a unique thought. But this task and form of training only works when it's verifiable. And from here the thought is, okay, we can continue to scale this current training method by increasing the number of verifiable tasks in math and coding. Coding probably has a lot more to go. Math has a lot less to go in terms of what are verifiable things. Can I create a solver that then I generate trajectories toward or traces towards reasoning. Traces towards and then prune the ones that don't work and keep the ones that do work? Well, those are going to be solved pretty quickly. But even if you've solved math, you have not actually created intelligence, right? And so this is where I think the aha moment of computer use or robotics will come in. Because now you have a sandbox or a playground that is infinitely verifiable, right? Did you, you know, messing around on the Internet, there are so many actions that you can do that are verifiable. It'll start off with like log into a website, create an account, click a button here, blah blah, blah. But it'll then get to the point where it's hey, go do a task on Tasker or whatever these other all these various task websites, hey, go get hundreds of likes, right? And it's going to fail. It's going to spawn hundreds of accounts counts. It's going to fail on most of them. But this one got to 1,000. Great. Now you've reached the verifiable thing and you just keep iterating this loop over and over. And same with robotics. That's where you have an infinite playground of tasks like hey, did I put the ball in the bucket all the way to oh, did I build a car? There's a whole trajectory to speedrun or what models can do. But at Some point, I truly think that we'll spawn models and initially all the training will be in sandboxes. But then at some point, you know, the language model pre training is going to be dwarfed by. What is this reinforcement learning. You know, you'll pre train a multimodal model that can see, that can read, that can write, you know, blah, blah, blah, whatever, vision, audio, et cetera. But then you'll have it play in a sandbox infinitely and figure out. Figure out math, figure out code, figure out navigating the web, figure out operating a robot arm. Right. And then it'll learn so much. And the aha moment, I think, will be when this is available to then create something that's not good. Right. Like, oh, cool. Part of it was like figuring out how to use the web. Now all of a sudden it's figured out really well how to just get hundreds of thousands of followers that are real and real engagement on Twitter. Because all of a sudden this is one of the things that are verifiable.

Lex Friedman (182:56)

And maybe not just engagement, but make money.

Nathan Lambert (182:59)

Yes.

Lex Friedman (183:01)

I mean, that could be the thing where almost fully automated, it makes, you know, $10 million by being an influencer, selling a product, creating the product, like, and, and I, I'm not referring to like a hype product, but an actual product, like, holy, this thing created a business, it's running it. It's the face of the business. That kind of thing. Maybe. Or maybe a number one song, like, it creates the whole infrastructure required to create the song, to be the influencer that represents that song. That kind of thing. It makes a lot of. That could be the move. I mean, this, our culture respects money in that kind of way.

Nathan Lambert (183:38)

And it's. And it's verifiable, right?

Lex Friedman (183:40)

It's verifiable.

Nathan Lambert (183:41)

The bank account can't lie.

Lex Friedman (183:43)

Exactly.

Dylan Patel (183:44)

There's surprising evidence that once you set up the ways of collecting the verifiable domain, that this can work. There's been a lot of research before this R1 on math problems, and they approach math with language models just by increasing the number of samples. So you can just try again and again and again. And you look at the amount of times that the language models get it right. And what we see is that even very bad models get it right sometimes. And the whole idea behind reinforcement learning is that you can learn from very sparse rewards. So the space of language and the space of tokens, whether you're generating language or tasks for a robot, is so big that you might say that the tokenizer for A language model can be like 200,000 things. So at each step it can sample from that big of a space space. So if it can generate a bit of a signal that it can climb onto, that's what the whole field of RL is around is learning from sparse rewards. And the same thing has played out in math where it's very weak models that sometimes generate answers. We see research already that you can boost their math scores. You can do this sort of RL training for math. It might not be as effective, but if you take a 1 billion parameter model, so something 600 times smaller than Deep Sea Seek, you can boost its grade school math scores very directly with a small amount of this training. It's not to say that this is coming soon. Setting up the verification domains is extremely hard and there's a lot of nuance in this. But there are some basic things that we have seen before where it's at least expectable that there's a domain and there's a chance that this works all right.

Lex Friedman (185:18)

So we have fun things happening in real time. This is a good opportunity to talk about other reasoning models. 0103 just now OpenAI as perhaps expected, released O3 mini. What are we expecting from the different flavors? Can you just lay out the different flavors of the O models? And from Gemini, the reasoning model.

Dylan Patel (185:41)

Something I would say about these reasoning models is we talked a lot about reasoning training on math and code and what is done is that you have the base model. We've talked about a lot plot on the Internet. You do this large scale reasoning training with reinforcement learning. And then what the deep seq paper detailed in this R1 paper, which for me is one of the big open questions on how do you do this is that they did reasoning heavy but very standard post training techniques after the large scale reasoning rl. So they did the same things with a form of instruction tuning through rejection sampling, which is essentially heavily filtered instruction tuning with some reward models. And then they did this RLHS stuff but they made it math heavy. So some of this transfer. We looked at this philosophical example early on. One of the big open questions is how much does this transfer? If we bring in domains after the reasoning training, are all the models going to become eloquent writers by reasoning? Is this philosophy stuff going to be open? We don't know in the research of how much this will transfer. There's other things about how we can make soft verifiers and things like this. But there is more training after reasoning which makes it easier to use These reasoning models and that's what we're using right now. So we're going to Talk about with 3 mini and 01. These have gone through these extra techniques that are designed for human preferences after being trained to elicit reasoning.

Nathan Lambert (187:01)

I think one of the things that people are ignoring is Google's Gemini Flash Thinking is both cheaper than R1 and better. And they released it in the beginning.

Lex Friedman (187:11)

Of December and nobody's talking about it.

Nathan Lambert (187:12)

No one cares.

Dylan Patel (187:13)

It has a different flavor to it, its behavior is less expressive than something like O1 or it has has fewer tracks than it is on. Quinn released a model last fall qwq which was their preview reasoning model and DeepSeq had R1 Lite last fall. Where these models kind of felt like they're on rails, where they really, really only can do math and code and O can answer anything. It might not be perfect for some tasks, but it's flexible, it has some richness to it. And this is kind of the art of Is a model a little bit undercooked? It's like, I mean it's good to get a model out the door, but it's hard to gauge and it takes a lot of taste to be like, is this a full fledged model? Can I use this for everything? They're probably more similar for math and code. My quick read is that Gemini Flash is not trained the same way as O, but taking an existing training stack, adding reasoning to it. So taking a more normal training stack and adding reasoning to it it. And I'm sure they're going to have more. I mean they've done quick releases on Gemini Flash Reasoning and this is the second version from the holidays. It's evolving fast and it takes longer to make this training stack where you're doing this large scale.

Nathan Lambert (188:26)

Let's get the same question from earlier. The one about the human nature. Yeah.

Lex Friedman (188:32)

What was the human nature?

Dylan Patel (188:33)

One, why I can ramble about this so much is that that we've been working on this at AI2 before 01 was fully available to everyone and before R1, which is essentially using this RL training for fine tuning. We use this in our To Loo series of models and you can elicit the same behaviors where you say wait and so on. But it's so late in the training process that this kind of reasoning expression is much lighter. So there's essentially a gradation and just how much of this RL training you put into it determines how the output looks.

Lex Friedman (189:07)

So we're now using Gemini 2.0 Flash Thinking Experimental 121.

Dylan Patel (189:15)

It summarized the prompt as humans Self domesticated apes.

Lex Friedman (189:22)

Okay, all right, so wait, is this revealing the reasoning? Here's why. This is a novel.

Dylan Patel (189:27)

Okay, click to expand.

Lex Friedman (189:30)

Okay, analyze the request. Novel is the key word.

Dylan Patel (189:35)

Like, see how it just looks a little different? It looks like a normal output.

Lex Friedman (189:39)

Yeah, it's. I mean in some sense it's better structured, it makes more sense.

Nathan Lambert (189:44)

And when it latched onto human and then it went into organisms and oh wow.

Lex Friedman (189:50)

Apex predator. Focus on domestication. Apply domestication to humans. Explore the idea of self domestication.

Dylan Patel (189:59)

Not good. Not good.

Lex Friedman (190:01)

Where is this going? Refine, articulate the insight. Gracious. Greater facial expressiveness and communication ability. Yes. Plasticity and adaptability.

Nathan Lambert (190:10)

Yes.

Lex Friedman (190:11)

Dependence on social groups. Yes. All right. And self critique and refine further. Wow. Is this truly novel? Is it well supported? So on and so forth. And the insight is getting at is humans are not just social animals, but profoundly self domesticated apes. And this self domestication is the key to understanding our unique cognitive and social abilities. Self domesticated apes.

Dylan Patel (190:40)

I prefer the deep SEQ response.

Lex Friedman (190:43)

I mean, it's novel. The insight is novel. I mean that's like a good book title. Self domesticated apes. Like there could be a case made for that. I mean, yeah, it's cool and it's revealing the reasoning. It's magical. Magical. It's magical. Like this is really powerful. Hello everyone, this is Lex with a quick intermission recorded after the podcast since we reviewed responses from DeepSeek, R1 and Gemini Flash 2.0 thinking during this conversation, I thought at this moment it would be nice to insert myself quickly doing the same for OpenAI 01 Pro and O3 mini with the same prompt. The prompt being give one truly novel insight about humans. And I thought I would in general give my vibe check and vibe based anecdotal report on my own experiences with the new O3 mini model, now that I got a chance to spend many hours with it in different kinds of contexts and applications. So I would probably categorize this question as, let's say open ended philosophical question. And in particular the emphasis on novelty I think is a nice way to test one of the capabilities of the model, which is come up with something that makes you pause and almost surprise you with its brilliance. So that said, my general review after running each of the models on this question a bunch of times is that O1Pro consistently gave brilliant answers, ones that gave me pause and made me think, both cutting in its insight and just really nicely phrased with wit, with clarity. With nuance over and over consistently generating the best answers. After that is R1 which was less consistent but again deliver brilliance. Gemini Flash 2.0 thinking was third and last was O3 mini. Actually it often gave quite a generic answer, at least to my particular sensibilities. That said, in a bunch of other applications that I tested for brainstorming purposes, it actually worked extremely well and often outperformed R1. But on this open ended philosophical question it did consistently worse. Now another important element for each of these models is how the reasoning is presented. Deep seq R1 shows the full chain of thought tokens which I personally just love for these open ended philosophical questions. It's really really interesting to see the model think through it, but really also just stepping back me as a person who appreciates intelligence and reasoning and reflection reading these kind of chain of thought raw tokens of R1. There's something genuinely beautiful about observing the path of deliberation in an intelligent system. I think we don't always have that explicitly laid out for us humans. So to see it in another intelligence system, the non linearity of it akin to Ulysses of Finnegan's Wake by James Joyce, it's just beautiful to watch. Anyway, as we discussed in the episode, Deepseek R1 talked about humans being able to convert selfish desires into cooperative systems by collectively pretending abstract rules like money, laws and rights are real. And these shared hallucinations act as games where competition is secretly redirected to benefit the group, turning conflict into society's fuel. Gemini 2.0 flash thinking said humans are not just social animals but self domesticated apes and this self domestication is the key to understanding our unique cognitive and social abilities. Now it's important to say that the chain of thought there was really interesting. It was looking through the entire evolution of life on earth, considering apex predators and considering how from that we ended up to where we are, I think that domestication by choice is a really interesting angle. Again, it's one of those things when somebody presents a different angle on a seemingly obvious thing, it just makes me smile. And the same with deepseek R1 that these hallucinations of money, laws and rights and us collectively pretending like it's real and we play games with them that look like competition when secretly we're just cooperating with each other and that is the fuel of progress. Beautifully put. Now OpenAI01Pro consistently over and over delivered bangers. I can go through many of them, but the first one was humans are the only species that turns raw materials into Symbolic Resources then uses those symbols to reorganize the very materials they came from, creating a closed feedback loop between meaning and matter. Here, I just ran it again, banger after banger. I'm telling you, humans are unique among known species in that they simultaneously rewrite two layers of reality, the external world and their own private mental landscapes, and then merge these two rewritten layers into a continuous personal narrative that feels objectively true. Feels true. This is poetry. Okay, and then O3 mini high for me was smart, fast actually, and kind of generic. Never quite got there for me. So here's the first one I got from O3 mini. Humans are not fixed beings, but rather ongoing narratives, dynamic stories that we continuously write, edit and reinterpret. This narrative plasticity is more than just memory or self reflection. It's an intrinsic cognitive process that acts like an internal error correction system. It allows us to adapt our identities and values over time in response to new experiences, challenges, and social contexts. Now, it almost sneaks up to something approximating cutting insight with narrative plasticity in quotes, but then it goes back to the sort of the generic. I don't know. All of these models are incredible for different reasons. There's a lot of concerns, as we discuss in this episode, but there's a lot of reasons to be excited as well. And I've probably spoken for too long. I am severely sleep deprived, borderline delirious. So hopefully some of this made sense. And now, dear friends, back to the episode.

Nathan Lambert (197:31)

I think when you, you know, to Nathan's point, when you look at like the reasoning models, to me, even when I used R1 versus 01, there was that sort of rough edges around the corner feeling. Right. And flash thinking. Earlier I didn't use this version, but the one from December, and it definitely had that rough edges around the corner feeling where it's just not fleshed out in as many ways. Right. Sure, they added math encoding capabilities via these verifiers in rl, but it feels like they lost something in certain areas and 01 is worse performing than chat that in many areas as well, to be clear.

Dylan Patel (198:09)

Not by a lot.

Nathan Lambert (198:10)

Not by a lot though, right? And it's like R1 definitely felt to me like it was worse than V3 in certain areas. Like doing this. RL expressed and learned a lot, but then it weakened in other areas. And so I think that's one of the big differences between these models and what 01 offers. And then OpenAI has O1 Pro and what they did with O3, which is also very unique. Is that they stack search on top of chain of thought. Right. And so chain of thought is one thing where it's able, it's one chain. It backtracks, goes back and forth. But how they solved the ARC AGI challenge was not just the chain of thought. It was also sampling many times, I. E. Running them in parallel and then selecting.

Dylan Patel (198:52)

Is running in parallel actually search? Because I don't know if we have the full information on how Zero1 Pro works. So like, I'm not. I don't have enough information to confidently.

Nathan Lambert (199:00)

Say that it is search, it is parallel sample.

Dylan Patel (199:02)

Yeah.

Nathan Lambert (199:03)

And then it selects something and we.

Dylan Patel (199:04)

Don'T know what the selection function is. The reason why we're debating is because since O was announced, there's been a lot of interest in techniques called Monte Carlo research, which is where you will break down the chain of thought into intermediate steps. We haven't defined chain of thought. Chain of thought is from a paper from years ago where you introduced the idea to ask a language model that at the time was much less easy to use. You would say, let's verify step by step, step. And it would induce the model to do this bulleted list of steps. Chain of thought is now almost a default in models where if you ask it a math question, you don't need to tell it to think step by step. And the idea with Monte Carlo tree search is that you would take an intermediate point in that train, do some sort of expansion, spend more compute, and then select the right one. That's like a very complex form of search that has been used in things like mu0 and alphazero potentially. I know mu0 does this.

Nathan Lambert (199:55)

Another form of search is just asking five different people and then taking the majority answer right. There's a variety of like, you know, it could be complicated, it could be simple. We don't know what it is, just that they are. They are not just issuing one chain of thought in sequence, they're launching many in parallel. And in the ARC AGI, they launched a thousand in parallel for their. The one that like really shocked everyone that beat the benchmark was they would launch a thousand in parallel and then they would get the right answer like 80% of the time or 70% of the time, 90 maybe even. Whereas if they just launched one, it was like 30%.

Dylan Patel (200:27)

There are many extensions to this. I would say the simplest one is that our language models to date have been designed to give the right answer the highest percentage of the time in one response. And we are now opening the door to different ways of running inference on our models in which we need to reevaluate many parts of the training process. Process which normally opens the door to more progress. But we don't know if OpenAI changed a lot or if just sampling more and multiple choice is what they're doing or if it's something more complex where they change the training and they know that the inference mode is going to be different.

Lex Friedman (201:01)

So we're talking about 01 Pro$200 a month and they're losing money. So the thing that we're referring to, this fascinating exploration of the test time, compute space, is that actually possible? Do we have enough compute for that? Does the financials make sense?

Nathan Lambert (201:22)

So the fantastic thing is, and it's in the thing that I pulled up earlier, but the cost for GPT3 has plummeted. If you scroll up just a few images. I think the important thing about like hey, is cost limiting factor here, right? Like my view is that like we'll have like really awesome intelligence before we have like AGI before we have it permeate throughout the economy. And this is sort of why that reason is right. GPT3 was trained in what, 2020, 2021. And the cost for running inference on it was 60, $70 per million tokens. Right. Which is the cost per intelligence was ridiculous. Now as we scaled forward two years, we've had a 1200x reduction in cost to achieve the same level of intelligence as GPT3.3.

Lex Friedman (202:09)

So here on the x axis is time over just a couple of years and on the Y axis is log scale dollars to run inference on a million tokens. And so you have just a linear decline on log scale from GPT 3.

Nathan Lambert (202:30)

Through 35 to llama like $0.05 or something like that now. Right. Which is is versus versus $60 1200x. That's not the exact numbers, but it's 1200x. I remember that number is, is the humongous, humongous cost per intelligence. Right. Now the freak out over deep seek is oh my God, they made it so cheap. It's like actually if you look at this trend line, they're not below the trend line first of all and at least for GPT3. Right. They are the first to hit it. Right. Which is, which is a big deal. But they're not below the trend line as far as GPT3. Now we have GPT4. What's going to happen with these reasoning capabilities? Right? Right. It's a mix of architectural innovations, it's a mix of better data and it's going to be better training techniques and all of these different, better inference systems, better hardware, right. Going from each generation of GPU to new generations or ASICs, everything is going to take this cost curve down and down and down and down. And then can I just spawn a thousand different LLMs to create a task and then pick from one of them or whatever? Search, search technique. I want a tree Monte Carlo tree search. Maybe it gets that complicated, maybe it doesn't because it's too complicated to actually scale. Like, who knows? Better lesson, right? The question is, I think when, not if, because the rate of progress is so fast. Right. Nine months ago, Dario was saying, or Dario said nine months ago, the cost to train and inference was this. And now we're much better than this. And Deepseek is much better than this. And that cost curve for GPT4, which was also roughly $60 per million tokens when it launched, has already fallen to $2 or so. Right. And we're going to get it down to cents probably for GPT4 quality. And then that's the base for the reasoning models like O1 that we have today. And 01 Pro is spawning multiple and 03, so on and so forth. These search technologies, techniques too expensive today, but they will get cheaper and that's what's going to unlock the intelligence, right?

Lex Friedman (204:25)

So it'll get cheaper and cheaper and cheaper. The big Deepseek R1 release freaked everybody out because of the cheaper. One of the manifestations of that is Nvidia stock plummeted. Can you explain what happened? I mean, and also just explain this moment and whether, you know, if Nvidia is going to keep winning.

Dylan Patel (204:46)

We are both Nvidia bulls here, I would say. And in some ways the market response is reasonable. Most of the market, like Nvidia's biggest customers in the US are major tech companies and they're spending a ton on AI. And if a simple interpretation of Deep SEQ is you can get really good models without spending as much on AI. So in that capacity it's like, oh, maybe these big tech companies won't need to spend as much in AI and go down, down. The actual thing that happened is much more complex where there's social factors, where there's the rising in the App Store, the social contagion that is happening. And then I think a lot of some of it is just like, I'm not, I don't trade, I don't know anything about financial markets, but it builds up over the weekend or the social pressure where it's like if it was during the week and there was multiple days of trading when this was really becoming, but it comes on the weekend and then everybody wants to sell. And that is a social contagion, I think.

Nathan Lambert (205:37)

I think. And like, there were a lot of false scenarios, which is like, like, hey, these guys are spending billions on models, right? And they're not spending billions on models. No one's spent more than a billion dollars on a model that's released publicly, right? GPT4 was a couple hundred million and then they've reduced the cost with 4.04 turbo 4.0, right? But billion dollar model runs are coming, right? This concludes pre training and post training, right? And then the other number is like, hey, Deep Seq didn't include everything, right? They didn't include. A lot of the cost goes to research and all this sort of stuff. Stuff. A lot of the cost goes to inference. A lot of the cost goes to post training. None of these things were factored. It's research salaries, right? Like, all these things are like counted in the billions of dollars that OpenAI is spending, but they weren't counted in the, you know, hey, 6 million, $5 million that Deepseek spent, right? So, but, so there's a bit of misunderstanding of what these numbers are. And then there's also an element of Nvidia has just been a straight line up, right? And there's been so many different narratives that have been trying to push down Nvidia. I don't say push down Nvidia stock.

Lex Friedman (206:35)

Stock.

Nathan Lambert (206:35)

Everyone is looking for a reason to sell or to be worried, right? You know, it was, it's, it was Blackwell delays, right? Their GPU was, you know, there's a lot of report. Every two weeks there's a new report about their GPUs being delayed. There's, there's the whole thing about scaling laws ending, right? It's so, it's so ironic, right?

Dylan Patel (206:52)

It lasted a month.

Nathan Lambert (206:53)

It was, it was just, it was just like, literally just, hey, models aren't getting better, right? They're just not getting better. There's no reason to spend more pre training. Scaling is dead. And then it's like 0103, right?

Dylan Patel (207:04)

R1, R1, right?

Nathan Lambert (207:06)

And now it's like, wait, models are getting too. They're progressing too fast. Slow down the progress, stop spending on GPUs, right? But you know, the funniest thing I think that like comes out of this is Javon's paradox is true, right? AWS pricing for H1 hundreds has gone up over the last couple weeks, right? Since, since, since. Since a little bit after Christmas, since V3 was launched, AWS H100 pricing has gone up. H2 hundreds are like almost out of stock everywhere because H200 has more memory and therefore R1 wants that chip over H100. Right.

Dylan Patel (207:37)

We were trying to get GPUs on a short notice this week for a demo and it wasn't that easy. We were trying to get just like 16 or 32h1 hundreds for demo, and it was not very easy.

Lex Friedman (207:45)

So for people who don't know, Gen's paradox is when the efficiency goes up, somehow, magically, counterintuitively, the total resource consumption goes up as well.

Nathan Lambert (207:57)

Right. And semiconductors is, you know, we're 50 years of Moore's Law. Every two years, half the cost, double the transistors, just like clockwork. And it's slowed down, obviously, but, like the semiconductor industry has gone up the whole time, right. It's been wavy.

Dylan Patel (208:10)

Right.

Nathan Lambert (208:10)

There's obviously cycles and stuff. And I don't expect AI to be any different. Right? There's going to be ebbs and flows, but this is an AI, it's just playing out at an insane timescale, right? It was 2x every two years. This is 1200x in like three years. Right. So it's like the scale of improvement that is like, hard to wrap your head around.

Lex Friedman (208:28)

Yeah. I was confused because to me, Nvidia stock on that should have gone up, but maybe it went down because there's kind of suspicion of foul play on the side of China, something like this. But if you just look purely at the actual principles at play here, like, it's obvious. Yeah.

Dylan Patel (208:46)

The progress that AI makes or the higher the derivative of AI progress is especially, you should. Because Nvidia is in the best place. The higher the derivative is, the sooner the market's going to be bigger and expanding. And Nvidia is the only one that does everything reliably right now.

Lex Friedman (209:01)

Because it's not like an Nvidia competitor arose, it's another company that's using Nvidia.

Dylan Patel (209:08)

Who historically has been a large Nvidia.

Nathan Lambert (209:10)

Customer and has press releases about them cheering about being China's biggest Nvidia customer. Right. Like, yeah, obviously they've quieted down, but, like, I think that's like another element of. Is that they don't want to say how many GPUs they have.

Lex Friedman (209:24)

Yeah.

Nathan Lambert (209:25)

Because, hey, they. Yes, they have H800s. Yes. They have H20s. They also have some H1 hundreds. Right. Which are smuggled in can you speak to that?

Lex Friedman (209:33)

To the smuggling. What's the scale of smuggling that's feasible for a nation state to do for companies? Is it possible to.

Nathan Lambert (209:40)

I think, I think there's a few angles of smuggling here, right? One is ByteDance arguably is the largest smuggler of GPUs for China, right? China's not supposed to have GPUs. ByteDance has like over 500,000 GPUs. Why? Because they're all rented from companies around the world. They rent from Oracle, they rent from Google, they rent from all these mass. And a bunch of smaller cloud companies too, right? All the Neo clouds, right. Of the world they rent so so many GPUs. They also buy a bunch, right? And they do this for mostly like what Meta does, right? Serving TikTok, right. Serving next best separate discussion, same as Meta, right? To be clear, that's today the use, right? And it's a VAL use, right. Hack the dopamine circuit right? Now that's theoretically now very much restricted with the AI diffusion rules which happened in the last week of the Biden admin and Trump admin. Looks like they're going to keep them, which limits allies even like Singapore, which Singapore is like 20% of Nvidia's. 20, 30% of Nvidia's revenue. But Singapore's had a memorial on not building data centers for like 15 years because they don't have enough power. So where are they going? I mean I'm not claiming they're all going to China, right? But a portion are. You know, many are going to Malaysia, including Microsoft and Oracle have big data centers in Malaysia. Like you know, all they're going all over Southeast Asia, probably India as well, right? Like there's stuff routing but like the diffusion rules are very de facto. Like you can only buy this many GPUs from this country and it's. And you can only rent a cluster this large to companies that are Chinese, right? Like they're very explicit on trying to stop smuggling, right? And a big chunk of it was hey, let's you know, random company, buy 16 servers, ships them to China, right? There's actually I saw a photo from someone in the semiconductor industry who leads like a team for like networking chips that competes with Nvidia. And he sent a photo of a guy checking into a first class United flight from San Francisco to Shanghai or Shenzhen with a super micro box that is this big which can only contain GPUs. Right? And he was booking first class because think about it, 3 to 5k for your first class ticket server cost 240,000 in the US, 250,000. You sell it for 300,000 in China. Wait, you just got a free first class ticket and a lot more money. So it's like, and that's like small scale smuggling. Most of the large scale smuggling is like companies in Singapore and Malaysia, like routing them around or renting GPUs completely legally.

Dylan Patel (212:04)

I want to jump in. How much was the scale? I think there's been some number, like some people that are higher level economics understanding say that as you go from 1 billion of smuggling to 10 billion, it's like you're hiding certain levels of economic activity. And that's the most reasonable thing to me is that there's going to be some level where it's so obvious that it's easier to find this economic activity. And.

Nathan Lambert (212:24)

Yeah, so, so, so my, my, my belief is that last year, roughly so, so Nvidia made a million H20s which are legally allowed to be shipped to China, which we talked about is better for reasoning, right? Inference at least. Maybe not training, but reasoning inference and inference generally. Then they also had a couple hundred thousand, we think like 200 to 300,000 GPUs were routed to China from Singapore, Malaysia, US wherever companies spawn, up by 16 GPUs, 64 GPUs, whatever it is, route it. And Huawei is known for having spun up a massive network of companies to get the materials they need after they were banned in 2018. So it's not like otherworldly, but I agree, right. Nathan's point is like, hey, you can't smuggle up $10 billion in GPUs. And then the third sort of source, which is just now banned, which wasn't considered smuggling, but is China is renting, I believe from our research, Oracle's biggest GPU customer is ByteDance. Right? And for Google, I think it's their second biggest customer, right? And you go down the list of clouds and especially these smaller cloud companies that aren't like the hyperscalers, right? Think beyond Core Weave and Lambda even. There's a whole sea. There's 60 different new cloud companies serving Nvidia GPUs. I think ByteDance is renting a lot of these, right? All over it, right? And so these companies are renting GPUs to Chinese companies. And that was completely legal up until the diffusion rules, which happened just a few weeks ago. And even now you can rent GPU clusters that are less than 2,000 GPUs, or you can buy GPUs and ship them wherever you want if, if they're less than 1500 GPUs. Right. So it's like there are still like some ways to smuggle. But yeah, it's not, you know, as the numbers grow, right. You know, 100 something billion dollars of revenue for Nvidia last year, 200 something billion this year. Right. And if next year, you know, it could, it could nearly double again or more than double. Right. Based on like what we see with data center footprints like being built out all across the US and the rest of the world, it's going to be really hard for China to keep up with these rules. Right? Yes. There will always be smuggling and Deep SEQ level models of GPT4 level models,01 level models capable to train on what China can get, even the next tier above that. But if we speed run a couple more jumps to billion dollar models, $10 billion models, then it becomes, hey, there is a compute disadvantage for China for training models and serving them. And the serving part is really critical. Right. Deep SEQ cannot serve their model today. Right. It's completely out of inventory. It's already started falling in the app store actually downloads because you download it, you try and sign up, they say we're not taking registrations because they have no capacity. You open it up, you get like less than 5 tokens per second if you even get your request approved. Right? Because there's just no capacity because they just don't have enough GPUs to serve the model. Even though it's incredibly efficient.

Lex Friedman (215:08)

It would be fascinating to watch the smuggling because I mean there's drug smuggling, right? That's a market, there's weapons smuggling and GPUs will surpass that at some point.

Dylan Patel (215:20)

Our highest value per kilogram probably by far. I have another question for you, Dylan. Do you track model API access internationally? How easy is it for Chinese companies to use hosted model APIs from the.

Nathan Lambert (215:35)

U.S. yeah, I mean that's incredibly easy, right? Like OpenAI publicly stated, Deepseek uses their API and as they say, they have evidence. Right. And this is another element of the training regime is people at OpenAI have claimed that it's a distilled model, I. E. You're taking OpenAI's model, you're generating a lot of output and then you're training on the output in their model. And even if that's the case, what they did is still amazing. By the way, what Deep SEQ did.

Dylan Patel (215:57)

Efficiency wise distillation is standard practice in industry. Whether or not if you're at a closed lab where you care about terms of service and IP closely, you distill from your own models. If you are a researcher and you're not building any products, you distill from the the OpenAI models.

Lex Friedman (216:10)

This is a good opportunity. Can you explain big picture distillation as a process? What is distillation? What's the process?

Dylan Patel (216:18)

We talked a lot about training language models. They are trained on text. In post training you're trying to train on very high quality text that you want the model to match the features of. Or if you're using rl, you're letting the model find its own thing. But for supervised fine tuning, for preference data, you need to have some completions. What the model is trying to learn to imitate and what you do there is instead of human data or instead of the model you're currently training, you take completions from a different, normally more powerful model. I think there's rumors that these big models that people are waiting for, these GPT5s of the world, the Claude 3 opuses of the world, are used internally to do this distillation process.

Nathan Lambert (216:58)

At there's also public examples, right? Like Meta explicitly stated did not necessarily distilling but they used 405B as a reward model for 70B in their llama 3.2 or 3.3.

Dylan Patel (217:09)

Yes, this is all the same topic.

Lex Friedman (217:11)

So is this, is this ethical? Is this legal? Like why, why is that Financial Times article headline say OpenAI says that there's evidence that China's Deep SEQ used its model to train competitor?

Dylan Patel (217:25)

This is a long, at least in the academic side and research side has a long history. Because you're trying to interpret OpenAI's rule. OpenAI's terms of service say that you cannot build a competitor with outputs from their models. Terms of service are different than a license, which are essentially a contract between organizations. So if you have a terms of service on OpenAI's account, if I violate it, OpenAI can cancel my account. This is very different than a license that says how you could use a downstream artifact. So a lot of it hinges on a word that is very unclear in the AI space, which is what is a competitor?

Nathan Lambert (217:55)

And so, and then the ethical aspect of it is like why is it unethical for me to train on your model when you can train on the Internet's text? Yeah, right.

Lex Friedman (218:04)

So there's a bit of a hypocrisy because sort of OpenAI and potentially most of the companies trained on the Internet's Text without permission.

Dylan Patel (218:14)

There's also a clear loophole which is that I generate data from OpenAI and then I upload it somewhere and then somebody else trains on it and the link has been broken like they're, they're not under the same terms of service contract.

Nathan Lambert (218:27)

This is, this is why a lot.

Dylan Patel (218:28)

Of hip hop, there's a lot of like to be discovered details that don't make a lot of sense.

Nathan Lambert (218:32)

This is why a lot of models today, even if they train on zero OpenAI data, you ask the model who trained you, it'll say I was, I am chatgpt trained by OpenAI because there's so much copy paste of like OpenAI outputs from that on the Internet that you just weren't able to filter it out. And in the. And there was nothing in the RL where they implemented like hey, like or post training or sft whatever that says hey, I'm actually model by Allen Institute instead of we have to do this.

Dylan Patel (218:58)

If we serve a demo, we do research and we use OpenAI APIs because it's useful and we want to understand post training. And our research models, they will say they're written by OpenAI unless we put in the system prop that we talked about that. I am Toulouse. I am a language model trained by the Allen Institute for AI. And if you ask more people around industry, especially with post training, it's a very doable task to make the model say who it is or to suppress the OpenAI thing. So in some levels it might be that Deepseek didn't care that it was saying that it was by OpenAI. If you're going to upload model weights, it doesn't really matter because anyone that's serving it in an application and cares a lot about serving is going to. When serving it, if they're using it for a specific task, they're going to tailor it to that and it doesn't matter that it's saying it's chatgpt.

Lex Friedman (219:43)

Oh, I guess one of the ways to do that is like a system prompt or something like that. If you're serving it to say that you're.

Dylan Patel (219:49)

That's what we do. Like if we host a demo, you say you are too. Loop 3, a language model trained by the Allen Institute for AI we also are benefited from OpenAI data because it's a great research tool.

Lex Friedman (220:00)

I mean, do you think there's any truth and value to the claim? OpenAI's claim that there's evidence that China's Deep SEQ used this model to train.

Nathan Lambert (220:10)

I think everyone has benefited regardless because the data is on the Internet it and therefore it's in your pre training now. Right? There are like subreddits where people share the best ChatGPT outputs and those are in your.

Dylan Patel (220:23)

I think that they're trying to shift the narrative, like they're trying to protect themselves. And we saw this years ago when ByteDance was actually banned from some OpenAI APIs for training on outputs. There's other AI startups that most people, if you're in the AI culture were like, they just told us they trained on OpenAI outputs and they never got banned. That's how they bootstrapped their early models. So it's much easier to get off the ground using this than to set up human pipelines and build a strong model. So there's a long history here and a lot of the communications seem like narrative.

Nathan Lambert (220:54)

Actually over the last couple of days we've seen a lot of people distill Deep seq's model into LLAMA models because the Deep SEQ models are kind of complicated to run inference on because they're mixture of experts and they're 600 plus billion parameters and all this. And people distilled them into the llama models because the llama models are so easy to serve and everyone's built the pipelines and tooling for inference difference with the llama models. Right, because it's the open standard. So, you know, we've seen it, we've seen a sort of roundabout, right, like is it bad? Is it illegal? Maybe it's illegal, whatever. I don't know about that.

Dylan Patel (221:24)

But like it could break contracts. I don't think it's illegal like in any legal, like no one's going to jail for this.

Lex Friedman (221:30)

I think like fundamentally I think it's ethical or I hope it's ethical because like the moment it becomes we ban that kind of thing, it's going to make everybody much worse off. And I also, actually it's. This is difficult, but I think you should be allowed to train on the Internet. I know a lot of authors and creators are very sensitive about it. That's, that's a difficult question. But like the mo. The moment you're not allowed to train on the Internet.

Dylan Patel (221:57)

I agree.

Nathan Lambert (221:58)

I, I have a schizo take on how you can solve this because it already works.

Dylan Patel (222:01)

I have a reasonable take on it.

Nathan Lambert (222:03)

All right, all right, so, so, you know, Japan has a law which you're allowed to train on any training data and copyrights don't apply. If you want to Train a model A. B, Japan has 9 gigawatts of curtailed nuclear power. C, Japan is allowed under the AI diffusion rule to import as many GPUs as they'd like. So all we have to do, we have a market here to make. We build massive data centers, we rent them to the labs and then we train models in a legally permissible way. And there's no if, ands or buts. And now the models have no like potential copyright lawsuit from New York Times or anything like that? No, no. It's just like completely legal. No genius.

Dylan Patel (222:42)

The early copyright lawsuits have fallen in the favor of AI training. I would say that the long tail of use is going to go in the side of AI, which is if you scrape trillions of data, you're not looking at trillions of tokens of data, you're not looking and saying this one New York Times article is so important to me. But if you're doing a audio generation for music or image generation and you say make it in the style of X person, that's a reasonable case where you could figure out what is their profit margin on inference. I don't know if it's going to be the 5050 of YouTube Creator Program or something but I would opt into that program as a writer. Like, please like, like that. It's just, it's going to be a rough journey but there will be some solutions like that that make sense. But there's a long tail where it's just on the Internet.

Lex Friedman (223:30)

I think one of the other aspects of that Financial Times article implies and so that leads to a more general question. Do you think there's how difficult is spying, espionage and stealing of actual secret code and data from inside companies? How much of that is being attempted?

Dylan Patel (223:49)

Code and data is hard, but ideas is easy. Silicon Valley operates on the on the way that top employees get bought out by other companies for a pay raise. And a large reason why these companies do this is to bring ideas with them. And there are, there's no, I mean in California there's rules that like certain like non competes or whatever are illegal in California. And whether or not there's NDAs and things that is how a lot of it process happens. Recently there was somebody from Gemini who helped make this 1 million context length and everyone is saying the next llama who I mean he went to the meta team is going to have 1 million context length. And that's kind of how the world works.

Nathan Lambert (224:28)

You know as far as like industrial espionage and things that has been greatly successful in the past, right. You know, the Americans did it to the Brits, the Chinese have done it to the Americans, right. And you know, so on and so forth. It's just, it is a fact of life. And so like to argue industrial espionage can be stopped is probably unlikely. You can make it difficult. But even then like, there's all these stories about like, hey, F35 and F22 have already been like sort of like given to China in terms of design plans and stuff, code and stuff. Like between, you know, I say companies, not nation states is probably very difficult. But ideas are discussed a lot, right? Whether it be a house party in San Francisco or a company changing employees or you know, or the, you know, the always, the like mythical honeypot that always gets talked about, right? Like someone gets honey potted, right? Because everyone working on AI is a single dude who's in their 20s and 30s. Not everyone, but like a insane amount of insane percentages. So there's always like all these like, you know, and obviously.

Lex Friedman (225:28)

So honey potted is like a spy, a female spy approaches you and like.

Nathan Lambert (225:32)

Yeah, yeah, or male, right. You know, it's San Francisco, right. But as a single dude, I will say in his late 20s, right. Is like we are very easily corrupted, right? Like, you know, like not, not corrupted myself, but you know, like we are, we are, right?

Dylan Patel (225:47)

Everybody else, not me, I'm too oblivious that I am not single. So I'm safe from one espionage access.

Lex Friedman (225:54)

Yeah, you have to make sure to close all security vulnerabilities. So you, Dylan, collect a lot of information about each of the mega clusters for each of the major AI companies. Can you talk about the buildouts for each one that stand out?

Nathan Lambert (226:12)

Yeah. So I think the thing that's really important about these mega cluster build outs is their completely unprecedented in scale, right. Us, you know, sort of like data center power consumption has been slowly on the rise and it's gone up to 2, 3% even through the cloud computing revolution. Right. Data center consumption as a percentage of total U.S. and that's been over decades, right. Of data centers, etc. It's been climbing, climbing slowly. But now 2 to 3% now by the end of this decade, it's like even, even under like, you know, when I say like 10%, a lot of people that are traditionally by like 20, 28, 2030 people, traditionally non traditional data center people, like that's nuts. But then like people who are in like AI who have like really looked at this at like the anthropics and open AIs are like that's not enough. And I'm like, okay, but like you know, this is, this is both through globally distributed or distributed throughout the US as well as like centralized clusters, right? The, the distributed throughout the US is, is exciting and it's the bulk of it, right? Like hey, say OpenAI or say meta is adding a gigawatt, right? But most of it is distributed through the US for inference and all these other things, right?

Lex Friedman (227:20)

So maybe we should lay out what a cluster is. So does this include aws? Maybe it's good to talk about the different kinds of clusters and what you mean by mega clusters and what's a GPU and what's a computer and what. Yeah, not that far back, but yeah. So like what do we mean by.

Nathan Lambert (227:38)

The clusters build out? I thought I was about to do the Apple ad, right? What's a computer? So traditionally data centers and data center tasks have been a distributed systems problem that is capable of being spread very far and widely, right? I E. I send a request to Google, it gets routed to a data center somewhat close to me. It does whatever search, ranking recommendations, sends a result back, right? The nature of the task is changing rapidly in that the task, there's two tasks that people are really focused on now, right? It's not database access, it's not serve me the right page, serve me the right ad. It's now a inference. And inference is dramatically different from traditional distributed systems, but it looks a lot more simple, similar. And then there's training, right? The inference side is still like, hey, I'm going to put thousands of GPUs in block all around these data centers. I'm going to run models on them. You know, user submits a request, gets kicked off or hey, my service, you know, they submit a request to my service, right? They're on Word and they're like, oh yeah, help me copilot. And it kicks it off. I'm on my Windows copilot. Whatever Apple Intelligence, whatever it is, it gets kicked off to a data center, right? And that data center does some work and sends it back. That's inference. That is going to be the bulk of compute. But then you know that and that's like, you know, there's thousands of data centers that we're tracking with like satellites and like all these other things and those are the bulk of what's being built. But the scale of. And so that's like what's really reshaping and that's what's getting millions of GPUs. But the scale of the largest cluster is also really important, right. When we look back at history, right. Like you know, or through, through the age of AI, right. Like it was a really big deal when they did Alexnet on, I think two GPUs or four GPUs, I don't remember. It was a really big deal deal.

Dylan Patel (229:24)

It's a big deal because you use GPUs, it's a big deal.

Nathan Lambert (229:26)

They use GPUs and they used multiple, right. But then over time its scale has just been compounding, right. And so when you Skip forward to GPT3, then GPT4, GPT4, 20,000 A100 GPUs unprecedented run, right. In terms of the size and the cost, right. A couple hundred million dollars on a YOLO. Right? A YOLO run for GPT4. And it yielded this magical improvement that was perfectly in line with what was experimented. And just like a log scale right up.

Dylan Patel (229:53)

Oh yeah, from the paper, the technical were part.

Nathan Lambert (229:56)

The scaling laws were perfect, right? But that's not a crazy number, right? 20,000. A1 hundreds roughly. Each GPU is consuming 400 watts. And then when you add in the whole server, right, everything. It's like 15 to 20 megawatts of power, right. You know, you know, maybe you could look up what the power of consumption of a human person is because the numbers are going to get silly. But like that 15 to 20 megawatts was standard data center size. It was just unprecedented. That was all GPUs running one task.

Dylan Patel (230:23)

How many watts was a toaster?

Nathan Lambert (230:25)

A toaster is like a good example. Similar power consumption to an A100, right? H100 comes around, they increase the power from like 400 to 700 watts. And that's just per GPU. And then there's all the associated stuff around it. So once you count all that, it's roughly like 1200 to 1400 watts for everything. Networking, CPUs, memory, blah, blah, blah.

Lex Friedman (230:43)

So we should also say, so, so what's required? You said power. So a lot of power is required. A lot of heat is generated. So the cooling is required. And because there's a lot of GPUs that have to be. Or CPUs or whatever. They have to be connected. So there's a lot of networking.

Nathan Lambert (231:00)

Yeah, yeah. So I think. Yeah, sorry for skipping past that. And then the data center itself is like complicated. Right? But these are still standardized data centers for GPT4 scale. Right? Now we step forward to sort of what is the scale of clusters that people have built last year, right? And it ranges Widely, right. It ranges from like, hey, these are standard data centers and we're just using multiple of them and connecting them together really with a ton of fiber between them, a lot of networking, et cetera. That's what OpenAI and Microsoft did in Arizona. Right. And so they have a, you know, 100,000 GPUs, right. Meta Similar thing. They took their standard existing data center design and it looks like an 8 and they connected multiple of them together. And you know, they got to, they first did 16,000 GPUs, 24,000 GPUs total. Only 16 of them. Thousand of them were running on the training run because GPUs are very unreliable. So they need to have spares to like swap in and out all the way to like now 100,000 GPUs that they're training on llama 4 on currently, right? Like 128,000 or so. Right. This is, you know, think about 100,000 GPUs with roughly 1400 watts apiece. That's, that's, that's one hundred and forty megawatts. One hundred and fifty megawatts, right. For one hundred and twenty eight, right. So you're talking about, you've jumped from 15 to 20 megawatts to 10x, you know, almost 10x that number, 9x that number to 150 megawatts in, in two years, right. From 2022 to 2024. Right. And some people like Elon, he admittedly, right, and he says himself got into the game a little bit late for pre training large language models. Right. XAI was started later, right. But then he, he bet heaven and hell to game, get his data center up and get the largest cluster in the world, right, which is 200,000 GPUs. And he did that. He bought a factory in Memphis. He's upgrading the substation, but at the same time he's got a bunch of mobile power generation, a bunch of single cycle combine. He tapped the natural gas line that's right next to the factory and is just pulling a ton of gas, burning gas. He's generating all this power. He's in a factory, in an old appliance factory that's shut down and moved to China long ago. Like, you know, and he's got 200,000 GPUs in it. And now what's the next scale? Right? Like all the hyperscalers have done this now. The next scale is, is something that's even bigger, right. And so you know, Elon, just to stick on the topic, he's, he's building his own natural gas plant, like a proper one right next door. He's. He's deploying tons of Tesla megapack batteries to make the power more smooth and all sorts of other things. He's got like industrial chillers to cool the water down because he's water cooling the chips. So all these crazy things to get the clusters bigger and bigger. But when you look at like say what OpenAI did with Stargate, that's that in Arizona, in, in Abilene, Texas, right? What they've announced at least, right? It's not built, right. Elon says they don't have the money. You know, there's some debates about this, but at full scale at least the first section is like definitely money's accounted for, but there's multiple sections. But full scale, that data center is going to be 2.2 gigawatts, right? 2,200 megawatts of power in and roughly like 1.8 gigawatts or 1800 megawatts of power delivered to chips right? Now. This is an absurd scale. 2.2 gigawatts is like more than most cities, right? To be clear. Delivered to a single cluster that's connected to do training, right? To train these models, to do both the pre training, the post training, all of this stuff, right?

Lex Friedman (234:16)

This is insane.

Dylan Patel (234:17)

It is. What is a nuclear power plant again?

Nathan Lambert (234:19)

Everyone is doing this, right? Everyone is doing this, right? Meta. Meta in Louisiana, right? They're building two natural gas plants, massive ones. And they're. And then they're building this massive data center. Amazon has like plans for this scale. Google has plans for this scale. XAI has plans for these scale, right? Like all of these, the guys that are racing, the companies that are racing are racing hard and they're doing multi gigawatt data centers, right? Right. To, to build this out because they, they think that yeah, if I, if I now have, you know, obviously pre training scaling is going to continue, but to some extent. But then also all this post training stuff where you have RL sandbox for computer use or whatever, right? Like, you know, this is where they're going to. And all these variable viable domains where they just keep learning and learning and learning self play, whatever, whatever it is makes the AI so much more capable because the line does go up, right? As you throw more compute, you get more performance. The shirt is about scaling laws. You know, to some extent it is diminishing returns, right? You 10x the compute, you don't get 10x better model, right? You get a diminishing returns but also you get efficiency improvements. So you bend the curve. Right. And these scale of data centers are doing, you know, wreaking, you know, a lot of like havoc on the network. Right. You know, Nathan was mentioning there's Amazon has tried to buy this nuclear power plant, Talon, and if you look at the Talon stock, it's just like skyrocketing and, and they're building a massive multi gigawatt data center there. And you just go down the list, there's so many ramifications. Interesting thing is certain regions of the U.S. transmitting power cost more than actually generating it. Right. Because the grid is so slow to build and the demand for power and the ability to build power and re ramping on a natural gas plant or even a coal plant is easy enough to do, but transmitting the power is really hard. So in some parts of the U.S. like in Virginia, it cost more to transmit power than it costs to generate it. Which is like, you know, there's, there's all sorts of like second order effects that are insane here.

Lex Friedman (236:10)

Can the power grid support this kind of growth?

Nathan Lambert (236:13)

You know, Trump's executive orders, there was a, there was a Biden executive order before the end of the year, but then Trump had some more executive orders which hopefully reduce the regulations to where, yes, things can be built. But yeah, this is a big, big challenge, right? Is building enough power fast enough.

Lex Friedman (236:28)

Are you going to basically have a nuclear power plant next to data center for each one of these?

Nathan Lambert (236:32)

So, so the fun thing here is this is too slow to build the power plant. To build a power plant or to reconfigure an existing power plant is too slow. And so therefore you must use natural data center. Power consumption is flat. Right. You know, I mean, like it's by.

Dylan Patel (236:47)

Chance, which is why nuclear is also good for it. Like long term, nuclear is a very natural fit, but you can't do solar or anything in the short term like.

Nathan Lambert (236:56)

That because data center power is like this, right? Like you're telling me, you know, I'm going to buy tens of billions of dollars of GPUs and idle them because the power is not being generated. Like power is cheap, right? Like if you look at the cost of a cluster, less than 20% of it is power, right? Most of it is the capital cost and depreciation of the GPUs. Right? And so it's like, well, screw it, I'll just, like, you know, I'll just build natural gas plants. This is what Meta's doing in Louisiana. This is what OpenAI is doing in Texas. And like all these different places, they may not be doing it directly, but they are partnered with someone. And so, so there is a couple hopes, right? Like one is, you know, and Elon, what he's doing in Memphis is like, you know, to the extreme. They're not just using dual combine cycle gas, which is like super efficient. He's also just using single cycle and like mobile generators and stuff, which is less efficient. But you know, there's also like the flip side, which is like solar power generation is like this and wind is another like this. Different correlate, you know, different. So if you stack both of those, plus you get a big chunk of battery, plus you have a little bit of gas, it is possible to run it more green. It's just the timescales for that is slow, right? So people are trying, but you know, Meta basically said, whatever, don't care about my sustainability pledge. Or they'll buy like a power, it's called a PPA Power Purchasing agreement, where there'll be a massive wind farm or solar farm, like wherever, and then they'll just pretend like those electrons are being consumed by the data center. But in reality they're paying for the power here and selling it to the grid and they're buying power here, here. And then another thing is like Microsoft quit on some of their sustainability pledges, right? Elon, what he did with Memphis is objectively somewhat dirty, but he's also doing it in an area where there's like a bigger natural gas plant right next door and like a sewer next, or not a sewer, but like a wastewater treatment and a garbage dump nearby. Right? And he's, he's obviously made the world a lot more clean than that one data center is going to do, right? So I think like it's fine to some extent. And maybe AGI solves involves, you know, global warming and stuff, right? Whatever it is, you know, this is, this is sort of the attitude that people at the labs have, right? Which is like, yeah, it's great, we'll just use gas, right? Because the race is that important and if we lose, you know, that's way worse, right?

Lex Friedman (238:58)

I should say that I got a chance to visit the Memphis data center and it's kind of incredible. I mean, I visited with, with Elon. Just the teams and the rate of innovation there is insane because my sense is that, you know, nobody's ever done anything of this scale and nobody has certainly ever done anything of this scale at the rate that XAI is doing. So they're like figuring out, I Mean, so I was sitting in on all these meetings where they're brainstorming. It's like, it's insane. It's exciting because they're like, they're trying to figure out what the bottlenecks are, how to remove the bottlenecks, how to make sure that, you know, there's just so many really cool things about, about putting together a data center because, you know, everything has to work. It's the people that do like the sysadmin, the machine learning, all that is the exciting thing, so on. But really the people that run everything are the folks that know like the low level software and hardware that runs everything, the networking, all of that. And so you have to like make sure you have procedures that test everything. I think they're using Ethernet. I don't know how they're doing the.

Nathan Lambert (240:08)

Networking, but they're using Nvidia Spectrum X Ethernet. There's actually like, I think, yeah, the unsung heroes are the cooling and electrical systems, which are just like glossed over.

Lex Friedman (240:18)

Yeah.

Nathan Lambert (240:19)

But I think like, like one story that maybe is like exemplifies how insane this stuff is, is when you're training, right, you're always doing, you're, you're, you're running through the model a bunch, right. In the most simplistic terms, running through the model a bunch and then you're, you're going to exchange everything and synchronize the weights, right? Right. So you'll do a step, this is like a step in model training, right. At every step your loss goes down, hopefully. And it doesn't always. But in the simplest terms you'll be computing a lot and then you'll exchange. Right. The interesting thing is GPU power is most of it. Networking power is some, but it's a lot less. But so while you're computing, your power for your GPUs is here. But then when you're exchanging weights, if you're not able to overlap communications and compute perfectly, there may be a time period where your GPUs are just idle and you're exchanging weights and you're like, hey, the model's up updating. So you're exchanging the gradients, you do the model update and then you start training again. So the power goes right and it's super spiky. And so funnily enough, right. When you talk about the scale of data center power, you can blow stuff up so easily. And so Meta actually has accidentally upstreamed something to code in Pytorch, where they added an operator and I kid you not whoever made this. Like, I want to hug the guy because it says, says Pytorch. It's like Pytorch power plant. No blow up equals 0 or equal 1. And what it does, what it does is amazing, right? Either you know, when you're exchanging the weights, the GPU will just compute fake numbers so the power doesn't spike too much. And so then the power plants don't blow up because the transient spikes, like, screw stuff up.

Lex Friedman (241:48)

Well, that makes sense. I mean, you have to do that kind of thing. You have to make sure they're not idle. Yeah.

Nathan Lambert (241:53)

And Elon's solution was like, let me throw a bunch of Tesla megapacks and a few other things, right? Like everyone has different solutions. But like Meta's at least was publicly, openly known, which is just like set this operator. And what this operator does is it just makes the GPUs compute nothing so that the power doesn't spike.

Lex Friedman (242:08)

But that just tells you how much power you're working with. I mean, it's insane. It's insane.

Dylan Patel (242:13)

People should just Google like scale, like what does X watts do? And go through all the scales from 1 watt to a kilowatt to a megawatt, and you look and stare at that and you're how high on the list a gigawatt is? And it's, it's mind blowing.

Lex Friedman (242:28)

Can you say something about the cooling? So I know Elon's using liquid cooling. I believe in all cases that's a new thing, right? Most of them don't use liquid cooling. Is there something interesting to say about the cooling?

Nathan Lambert (242:41)

Yeah, yeah. So air cooling has been the de facto standard. Throw a bunch of metal heat pipes, etc. And fans, right? And like, that's cool. That's been enough to cool it. People have been dabbling in water cooling. Google TPUs are water cooled, right? So they've been doing that for a few years. But with GPUs, no one's ever done, and no one's ever done the scale of water cooling that Elon just did. Right now, next generation Nvidia is for the highest end gpu. It is mandatory water cooling. You have to water cool it. But Elon did it on this current generation and that required a lot of stuff, Right. If you look at some of the satellite photos and stuff of the Memphis facility, there's all these external water chillers that are sitting. Basically it looks like a semi truck pod thing. What's it called? The container. But really those are water chillers. And he has like 90 of those water chillers just sitting outside. 90 different containers, right? With water, you know, chill the water, bring it back to the data center and then you distribute it to all the chips, pull all the heat out and then send it back. Right? And this is both a way to cool the chips, but also an efficiency thing. All right? And going back to that like sort of three vector thing, right, there is, there is, you know, memory bandwidth, flops and interconnect. The closer the chips are together, the easier it is to do high speed interconnects, right? And so this is, this is also like a reason why you want to go water cooling is because you can just put the chips right next to each other and therefore get higher speed connectivity.

Lex Friedman (244:07)

I gotta ask you, so in one of your recent posts there's a section called cluster measuring contest.

Nathan Lambert (244:16)

There's another word there, but I won't say it.

Lex Friedman (244:22)

Who's got the biggest now and who's.

Nathan Lambert (244:24)

Going to have the biggest today? Individual largest is Elon, right?

Lex Friedman (244:29)

Elon's cluster.

Nathan Lambert (244:30)

Elon's cluster in Memphis. 200,000 GPUs, right? Meta has like 128,000. OpenAI has 100,000. Now to be clear, other companies have more GPUs than Elon on. They just don't have them in one place, right? And for training you want them tightly connected. There's some techniques that people are researching and working on that let you train across multiple regions, but for the most part you want them all in like one area, right? So you can connect them highly with high speed networking. And so, you know, elon today has 200,000 GP H1 hundreds and 100,000 H100. A hundred thousand H2 hundreds, right? Meta, OpenAI, you know, and Amazon all have on the scale of 100,000 a little bit less. But next this year, right, this year people are building much more, right? Anthropic and Amazon are building a cluster of 400,000 Trainium 2, which is Amazon specific chip. Trying to get away from Nvidia, right? You know, Meta and OpenAI have scales for hundreds of thousands, but by next year you'll have like 500,000 to 700,000 GPU clusters. And note those GPUs are much higher power consumption than existing ones, right? Hopper, 700 watts, Blackwell goes to 1200 watts. Right? So, so the power per chip is growing and the number of chips is growing, right?

Lex Friedman (245:44)

Nuts. You think, you think Elon said he'll get to a million? You think that's actually feasible?

Nathan Lambert (245:50)

I mean, I, I, I don't doubt Elon, right? The filings that he has for like, you know, the power plan and the Tesla battery packs. It's clear he has some crazy plans for Memphis, like permits and stuff is open record. Right. But it's not quite clear that, you know what, what and what the timescales are. I just never doubt Elon. Right. You know, that's, he's going to surprise us.

Lex Friedman (246:10)

So what's the idea with these clusters? If you have a million GPUs, what percentage in let's say two, three years is used for training and what percent pre training and what percent is used for like for the actual mega clusters.

Nathan Lambert (246:26)

Make no sense for inference, right? You could route inference there and just not train train. But most of the inference capacity is being, you know, hey, I've got a 30 megawatt data center here, I've got 50 megawatts here, I've got 100 here, whatever. I'll just throw inference in all of those. Because the mega clusters, right, Multi gigawatt data centers, I want to train there because that's where all of my GPUs are co located, where I can put them at a super high networking speed connected together. Right. Because that's what you need for training. Now with pre training, this is the old scale, right? You could, you would increase parameters, you'd increase data, the model gets better. That doesn't, that doesn't apply anymore because there's not much more data in the pre training side, right? Yes, there's video and audio and image that has not been fully taken advantage of. So there's a lot more scaling. But a lot of people like, like have, have transcript taken transcripts of YouTube videos and that gets you a lot of the data doesn't get you all of the learning value out of the video and image data. But you know, there, there's, there's still scaling to be done on pre training. But this post training world is where all the flops are going to be spent, right? The model's gonna play with itself. It's gonna self play, it's gonna do verifiable tasks, it's gonna do computer use in sandboxes. It might even do like simulated robotics things, right? Like all of these things are gonna be environments where compute is spent in quote unquote, post training. But I think, I think it's gonna be good. We're gonna, we're gonna drop the post from post training. Yeah, it's gonna be pre training and it's gonna be training. I think at some point because for the like bulk of the last few years years pre training has dwarfed post training. But with these verifiable methods, especially ones that scale really potentially infinitely like computer use and robotics, not just math and coding where you can verify what's happening, those infinitely verifiable tasks, it seems you can spend as much compute as you.

Dylan Patel (248:07)

Want on them, especially at the context length increase. Because at the end of pre training is when you increase the context length for these models. And we've talked earlier in the conversation about how the context leaves length when you have a long input is much easier to manage than output. And a lot of these post training and reasoning techniques rely on a ton of sampling and it's becoming increasingly long context. So effectively your compute efficiency goes down. I think FLOPS is the standard for how you measure it. But with RL and you have to do all these things where you move your weights around in a different way than at pre training and, and just generation it's going to become less efficient and FLOPS is going to be less of a useful term. And then as the infrastructure gets better it's probably going to go back to flops.

Lex Friedman (248:51)

So all of the things we've been talking about is most likely going to be Nvidia. Right. Is there any competitors?

Nathan Lambert (248:58)

Google kind of ignored them.

Lex Friedman (249:00)

I was what's the story with tpu?

Nathan Lambert (249:03)

Like what's the TPU is awesome, right? It's great. Google is. They're a bit more tepid on building data centers. For some reason they're building big data centers, don't get me wrong. And they actually have the biggest cluster. I was talking about Nvidia clusters. They actually have the biggest cluster period. But the way they do it is very interesting. Right. They have two sort of data center super regions, right? In that the data center isn't physically like all of the GPUs aren't physically on one site, but they're like 30 miles from each other and not GPUs. TPUs, right? They have like in Iowa, Nebraska, they have four data centers that are just like right next to each other.

Lex Friedman (249:38)

Why doesn't Google flex its cluster size.

Nathan Lambert (249:42)

Go to multi data center training? There's a good images in there. So I'll show you what I mean. It's just semi analysis multi data center. So this is like, you know, so this is an image of like what a standard Google data center looks like. By the way, their data centers look very different than anyone else's data centers.

Lex Friedman (249:56)

What are we looking at here?

Nathan Lambert (249:57)

So these are. Yeah, so if you, if you see this image right in the center, there are these big rectangular boxes, right? Those are where the actual chips are kept. And then if you scroll down a little bit further, you can see there's these water pipes, there's these chiller cooling towers in the top and a bunch of diesel generators. The diesel generators are backup power. The data center itself is look physically smaller than the water chillers. Right. So the chips are actually easier to keep together. But then cooling all the water for the water cooling is very difficult. So Google has a very advanced infrastructure that no one else has has for the tpu. And what they do is they've like stamped these data center, they've stamped a bunch of these data centers out in a few regions, right? So if you go a little bit further down, this is a Microsoft, this is in Arizona, this is where GPT5 quote unquote will be trained.

Dylan Patel (250:47)

If it doesn't exist already.

Nathan Lambert (250:48)

Yeah, if it doesn't exist already. But each of these data centers, I've shown a couple images of them. They're like really closely co located in the same region, right? Nebraska, Iowa. And then they also have a similar one in Ohio. Complex. Right. And so these data centers are really close to each other and what they've done is they've connected them super high bandwidth with fiber. And so these are just a bunch of data centers. And the point here is that Google has a very advanced infrastructure, very tightly connected in a small region. So Elon will always have the biggest cluster fully connected. Right. Because it's all in one building. Right. And he's completely right on that. Right. Google has the biggest cluster, but you have to spread over three sites and by, by a significant margin, but you have to go across multiple sites.

Lex Friedman (251:29)

Why doesn't Google compete with Nvidia? Why don't they sell TPUs?

Nathan Lambert (251:36)

I think, I think there's a couple problems with it. It's like one TPU has been a form of allowing search to be really freaking cheap and build models for that. Right? And so like a big chunk of the search, GPU purchases or TPU purchases or big chunk of Google's purchases and usage, all of it is for internal workloads, whether it be search now, Gemini, YouTube, all these different applications that they have ads, these are where all their TPUs are being spent and that's what they're hyper focused on. Right. And so there's certain aspects of the architecture that are optimized for their use case that are not optimized elsewhere. Right. One simple one is like They've open sourced a GEMMA model and they called it Gemma 7B. Right? Right. But then it's actually 8 billion parameters because the vocabulary is so large. And the reason they made the vocabulary so large is because TPU's like matrix multiply unit is massive because that's what they've like sort of optimized for. And so they decided, oh, I'll just make the vocabulary large too, even though it makes no sense to do so on such a small model because that fits on their hardware. So Gemma doesn't run as efficiently on a GPU as a LLAMA does. Right. But vice versa, LLAMA doesn't run as efficiently on a TPU as a GEMMA does. Right. And it's so like there's like certain like aspects of like hardware, software, co design. So all their search models are, their ranking and recommendation models, all these different models that are AI but not like gen AI, right. Have, have been hyper optimized with TPUs forever. The software stack is super optimized. But all of this software stack has not been released publicly at all. Right. Very small portions of it, JAX and XLA have been. But like the experience when you're inside of Google and you're training on TPUs as a researcher, you don't need to know anything about the hardware in many cases, right. Like it's like pretty beautiful, but as.

Dylan Patel (253:17)

Soon as you step outside they'll, they all go. A lot of them go back. They leave Google and then they go back.

Nathan Lambert (253:22)

Yeah, yeah, they're like, they leave and they start a company because they have all these amazing research ideas and they're like, wait, infrastructure's hard, software is hard and this is on GPUs or if they try to use TP, same thing because they don't have access to all this code. And so it's like how do you convince a company whose golden goose is search, where they're making hundreds of billions of dollars from, to, to start selling GPU or GPUs, which they used to only buy a couple billion of? You know, I think in 2023 they bought like, like a couple billion and now they're buying like 10 billion to $15 billion worth. But how do you convince them that they could, they should just buy like twice as many and figure out how to sell them and make $30 billion. Like who cares about making $30 billion?

Lex Friedman (253:59)

Won't that 30 billion exceed actually the search profit eventually?

Nathan Lambert (254:04)

Oh, I mean like you're always going to make more money on services than, than, than always. I mean like, yeah, like, you know, like to be clear, like today people are spending a lot more on hardware than they are the services. Right? Because the hardware front runs the service spend.

Lex Friedman (254:19)

But like you're investing.

Nathan Lambert (254:20)

If there's no revenue for AI stuff or not enough revenue, then obviously like it's going to blow up. Right. You know, people won't continue to spend on GPUs forever. And Nvidia is trying to move up the stack with like software that they're trying to sell and license and stuff. Right? But, but Google has never had that like DNA of like this is a product we should sell. Right. They don't act. The Google Cloud does. It is, which is a separate organization from the TPU team, which is a separate organization from the DeepMind team, which is a separate organization from the search team. Right. There's a lot of bureaucracy.

Lex Friedman (254:46)

Wait, Google Cloud is a separate team than the TPU team.

Nathan Lambert (254:49)

Technically, TPU sits under infrastructure, which sits under Google Cloud. But like Google Cloud, like for like renting stuff and TPU architecture are very different goals, right? In hardware and software, like all of this, right? Like the JAX XLA teams do not serve Google's customers externally, whereas Nvidia's various CUDA teams for like things like Nickel serve external customers. Right. The internal teams like JAX and XLA and stuff, they More so serve DeepMind and search. Right. And so their customer is different. They're not building a product for them.

Lex Friedman (255:21)

Do you understand why aws keeps winning vs Azure for Cloud vs Google Cloud? Google Cloud is tiny, isn't it?

Nathan Lambert (255:31)

Relative to aws, Google Cloud is third. Microsoft is the second biggest. But Amazon is the biggest, right? And Microsoft deceptively sort of includes like Microsoft Office 365 and things like that, like some of these enterprise wide licenses. So in reality the gulf is even larger. Microsoft is still second though, right? Amazon is way bigger. Why? Because using AWS is better and easier. And in many cases it's too. And it's first.

Dylan Patel (255:54)

It was first.

Lex Friedman (255:54)

Yeah, but there's a lot of things that are first that.

Dylan Patel (255:57)

Well, it's easier, it's harder to switch.

Lex Friedman (255:59)

Than it is to aws because it's large.

Dylan Patel (256:02)

There's big fees for switching too.

Nathan Lambert (256:03)

Aws generates over 80% of Amazon's profit. I think over 90%.

Lex Friedman (256:07)

That's insane.

Nathan Lambert (256:08)

The distribution centers are just like one day we'll decide to make money from this. But they haven't yet, right? Like they make tiny little profit from.

Dylan Patel (256:14)

Yeah, one day Amazon prime will triple in price.

Lex Friedman (256:16)

You would think they would improve AWS interface because it's like horrible, it's like clunky.

Dylan Patel (256:23)

But everybody is I, I, yeah, you one would think.

Nathan Lambert (256:27)

I, I think actually Google's interface is sometimes nice, but it's also like they don't care about anyone besides their top customers.

Lex Friedman (256:33)

Exactly.

Nathan Lambert (256:33)

And like their customer service sucks. And like they have a lot less.

Lex Friedman (256:36)

Like I mean all these companies, they optimize for the big customers. Yeah, it's supposed to be for business.

Nathan Lambert (256:40)

And Amazon has always optimized for the small customer too though. Right. Like obviously they optimize a lot for the big customer but, but like when they started they just would go to like random Bay Area things and give out credits. Right. And then they like or just put in your credit card and use us. Right. Like back in the early days. So they've always, the business has grown with them. Right. And Virgin. So like why does Amazon, like why is Snowflake all over Amazon? Because Snowflake in the beginning when Amazon didn't care about them was still using Amazon. Right. And then of course one day Snowflake and Amazon has a super huge partnership. But like this is the case like Amazon's user experience and quality is better. Also a lot of the silicon they've engineered engineered makes them have a lower cost structure in traditional cloud storage, CPU networking, that kind of stuff than in databases. Right. I think four of Amazon's top five revenue products, margin products, sorry like gross profit products are all database related products like redshift and all these things. Right. So Amazon has a very good silicon user experience like entire pipeline with aws. I think Google, their silicon teams, yeah they have awesome Silicon Internally TPU, the YouTube chip, some of these other chips that they've made and the problem is they're not serving external customers, they're serving internal customers.

Dylan Patel (257:52)

Right. I mean Nvidia's entire culture is designed from the bottom up to do this. There's this recent book the Nvidia Way by Take him that details this and how they look for future opportunities and ready their CUDA software libraries to make it so that new applications of high performance computing can very rapidly be evolved on CUDA and Nvidia chips. And that is entirely different than Google as a services business.

Lex Friedman (258:18)

Yeah, I mean Nvidia, it should be said, is a truly special company. Like I mean they, the whole, the culture, everything, they're really optimized for that kind of thing. Speaking of which, is there somebody that can even challenge Nvidia hardware wise? Intel amd?

Nathan Lambert (258:33)

I really don't think so. We went through A very long process of working with AMD on training on their GPUs, inference and stuff. And they're decent, their hardware is better in many ways than Nvidia's. The problem is their software is really bad. And I think they're getting better, right? They're getting better faster. But the gulf is so large and they don't spend enough resources on it or haven't historically. Maybe they're changing their tune now, but, but for multiple months we were submitting the most bugs, right? Like us semi analysis, right? Like what the fuck? Why are we submitting the most bugs, right? Because they only cared about their biggest customers and so they'd ship them a private image, blah blah blah. And it's like, okay, but I am just using Pytorch and I want to use the publicly available libraries and you don't care about that, right? So they're getting better. But I think AMD is not possible. Intel's obviously in dire straits right now and needs to be saved somehow. Very important for national security for Americans.

Lex Friedman (259:33)

Can you explain? Obviously. So why are they in the dire straits?

Nathan Lambert (259:36)

Going back to earlier, only three companies can R and D, right? Taiwan, Hsinchu, Samsung, Pyongyang and then Intel Hillsborough. Samsung's doing horribly, Intel's doing horribly. We could be in a world where there's only one company that can do R and D and that one company already manufactures most of chips. They've been gaining market share anyways. But like that's, that's a critical thing, right? So what happens to Taiwan means the rest of the world's semiconductor industry and therefore tech relies on Taiwan, right? And that's obviously precarious as far as like intel, they've been slowly, steadily declining. They were on top of servers and PCs, but now Apple's done the M1 and Nvidia's releasing a PC chip and Qualcomm's releasing a PC chip. And in servers, hyperscalers are all making their own ARM based servers, chips and intel has no AI silicon like wins, right? They have very small wins and they never got into mobile because they said no to the iPhone. And like all these things have compounded and they've lost their process technology leadership, right? They were ahead for 20 years and now they're behind by at least a couple of years, right? And they're trying to catch back up and we'll see if like their 18A 14A strategy works out where they try and leapfrog TSMC. But like, and intel is just like losing tons of Money anyways. Right. And they just fired their CEO. Even though the CEO was the only person who understood the company well. Right. We'll see. He was not the best, but he was pretty good, relatively technical guy.

Lex Friedman (260:55)

Where does intel make most of its money? The CPUs.

Nathan Lambert (260:57)

Still PCs and data center CPUs. Yeah, but data center CPUs are all going cloud. And Amazon, Microsoft, Google are making arm based CPUs. And then PC side AMD's gained market share. Nvidia's launching a chip that's not going to be a success. Right. MediaTek, Qualcomm ever launched chips. Apple's doing well. Right. Like they could get squeezed a little bit in PC. Although PC generally I imagine will just stick. Intel mostly for Windows side.

Lex Friedman (261:21)

Let's talk about the broad AI race. Who do you think wins? We talked about Google.

Dylan Patel (261:27)

The default leader has been Google because of their infrastructure advantage.

Lex Friedman (261:31)

Well, like in the news, OpenAI is the leader.

Dylan Patel (261:35)

They're the leading in the.

Nathan Lambert (261:36)

They have the best model.

Dylan Patel (261:37)

They have the best model that people can use and they're.

Nathan Lambert (261:40)

And they have the most AI revenue. You.

Dylan Patel (261:42)

Yeah. OpenAI is winning.

Lex Friedman (261:45)

So who's making money on AI right now? Is anyone making money?

Nathan Lambert (261:49)

So accounting profit wise, Microsoft is making money, but they're spending a lot of CapEx. Right. You know, and that gets depreciated over years. Meta is making tons of money. But with recommendation Systems, which is AI. But not with Llama. Right. Llama's losing money for sure. Right. I think Anthropic and OpenAI are obviously not making money because otherwise they wouldn't be raising money. Right. They have to raise money to build more. Right. Although theoretically they are making money. Right. Like you know, you spent a few hundred million dollars on GPT4 and it's doing billions in revenue. So like obviously it's like making money. Although they had to continue to research to get the compute efficiency wins. Right. And, and move down the curve to like you know, that 12 get that 1200 x that has been achieved for GPT3. You know, maybe we're only at like a, you know, a couple hundred X now but you know, with GPT4 Turbo and 4.0 and there will be another one probably cheaper than GPT4O even that comes out at some point.

Lex Friedman (262:39)

And that research costs a lot of money.

Nathan Lambert (262:42)

Yep, exactly.

Lex Friedman (262:43)

That's the thing that I guess is not talked about with the cost that when you're referring to the cost of the model, it's not just the training or the test runs, it's the Actual research. The manpower.

Nathan Lambert (262:56)

Yeah. To do things like reasoning. Right now that that exists, they're going to scale it, they're going to do a lot of research still. I think the, you know, now people focus on the payback question, but it's really easy to just be like, well, GDP is humans and industrial capital and if you can make intelligence cheap, then you can grow a lot. Right. That's the sort of dumb way to explain it. But that's sort of what basically the investment thesis is. I think only Nvidia is actually making tons of money and other hardware vendors, the hyperscalers, are all on paper making money, but in reality they're like spending a lot more on purchasing the gpu, which you don't know if they're still going to make this much money on each GPU in two years. Right. You don't know if all of a sudden OpenAI goes kapoof. And now Microsoft has hundreds of thousands of GPUs they were renting to OpenAI that they paid for themselves with their investment in them that no longer have a customer. This is always a possibility. I don't believe that. I think OpenAI will keep raising money. I think others will keep raising money because the investments, the returns from it are going to be eventually huge once we have AGI.

Lex Friedman (264:02)

So do you think multiple companies will get. Let's assume.

Nathan Lambert (264:05)

I don't think it's winner take all.

Lex Friedman (264:06)

Okay, so it's not. Let's not call it AGI, whatever. It's like a single day.

Dylan Patel (264:12)

It's a gradual thing, super powerful AI.

Lex Friedman (264:14)

But it's a gradually increasing set of features that are useful and rapidly increasing. Rapidly increasing set of features. So you're saying a lot of companies will be. It just seems absurd that all of these companies are building gigantic data centers.

Dylan Patel (264:35)

There are companies that will benefit from AI, but not because they train the best model like Meta has so many avenues to benefit from AI and all of their services. People are there, people spend time on Meta's platforms and it's a way to make more money per user, per hour.

Lex Friedman (264:48)

Yeah, it seems like Google X xai, slash Tesla. Important to say. And then Meta will benefit not directly from the AI, like the LLMs, but from the intelligence, like the additional boost of intelligence to the products they already sell. So whether that's the recommendation system or for Elon who's been talking about Optimus the robot, potentially the intelligence of the robot, and then you have personalized robots in the home, that kind of thing, he thinks it's a ten plus trillion dollar business which at some point, maybe.

Dylan Patel (265:27)

I don't not soon, but who knows what robotics.

Nathan Lambert (265:30)

Let's do a Tam analysis, right? 8 billion humans and let's get 8 billion robots, right? And let's pay them the average salary and There we go. 10 trillion. More than 10 trillion.

Lex Friedman (265:40)

Yeah. I mean if there's robots everywhere, why does it have to be just 8 billion robots?

Nathan Lambert (265:47)

Yeah, yeah, of course, of course. I'm going to have like one robot, you're going to have like 20.

Lex Friedman (265:51)

Yeah, I mean I see a use case for that. So yeah, so I guess the benefit would be in the products they sell which is why OpenAI is in a trickier position because they all of the.

Dylan Patel (266:01)

Value of OpenAI right now as a brand is in ChatGPT and there is actually not that for most users there is not that much of a reason that they need OpenAI to be spending billions and billions of dollars on the next best model when they could just license llama 5 and for be way cheaper. So that's kind of like ChatGPT is an extremely valuable entity to them. But like they could make more money just off that.

Nathan Lambert (266:25)

The chat application is clearly like does not have tons of room to continue. Right, like the standard chat, right, where you're just using it for random questions and stuff. Right. The cost continues to collapse. V3 is the latest biggest but it's going to get supported by ads, right? Like know meta already serves 405B and probably loses the money. But at some point you know they're going to get. The models are going to get so cheap that they can just serve them for free with ad supported. Right? And that's what Google is going to be able to do and that's obviously they've got a bigger reach, right? So chat is not going to be the only use case. It's like these reasoning code agents, computer use, all this stuff is where OpenAI has to actually go to make money in the future. Otherwise they're kaputs.

Lex Friedman (267:04)

But X, Google and Meta have these other products, so doesn't it. Isn't it likely that OpenAI and Anthropic disappear eventually?

Nathan Lambert (267:16)

Unless they're so good at models. They are.

Lex Friedman (267:18)

But it's such a cutting edge. I mean it depends on where you.

Dylan Patel (267:21)

Think AI capabilities are going.

Lex Friedman (267:22)

You have to keep winning. Yes, you have to keep winning as you climb. Even if the AI capabilities are going super rapidly awesome into the direction of AGI, there's still a boost for X in terms of data, Google in terms of data, Meta in terms of data, in terms of other products and the money. And there's just huge amounts of money.

Nathan Lambert (267:44)

The whole idea is human data is kind of tapped out. We don't care. We all care about self play. Verifiable. Yes.

Lex Friedman (267:49)

So self play, which is an R and D problem.

Dylan Patel (267:52)

AWS does not make a lot of money on each individual machine. And the same can be said for the most powerful AI platform, which is even though the calls to the API are so cheap, there's still a lot of money to be made made by owning that platform. And there's a lot of discussions as it's the next compute layer.

Nathan Lambert (268:10)

You have to believe that. And there's a lot of discussions that tokens and tokenomics and LLM APIs are the next compute layer or the next paradigm for the economy. Kind of like energy and oil was. But there's also like you have to sort of believe that APIs and chat are not where AI is stuck. Right. It is actually just tasks and agents and robotics and computer use and those are the areas where. Where all the value will be delivered. Not API, not chat application. Right.

Lex Friedman (268:37)

Is it possible you have. I mean it all just becomes a commodity and you have the very thin wrapper like perplexity. Just joking.

Dylan Patel (268:49)

There are a lot of wrappers making a lot of money.

Lex Friedman (268:51)

Yeah, but do you think it's possible that people would just even forget what OpenAI anthropic is because there'll be wrappers around the API and it just dynamically.

Nathan Lambert (269:00)

If model progress is not rapid. Yeah, it's. It's becoming a commodity. Right. Deep seq v3 shows this but also the GPT3 chart earlier chart showed this. Right. Llama 3B is 1200 x cheaper than GPT3. Any GPT3 like anyone whose business model was GPT3 level capabilities is dead. Anyone whose business model is GPT4 level capabilities is dead.

Dylan Patel (269:20)

It is a common thing that the best businesses being made now are ones that are predicated on models getting better.

Lex Friedman (269:26)

Right. Which would be like wrappers thing that is riding the wave of the models.

Dylan Patel (269:31)

The short term. The company that could make the most money is the one that figures out what advertising targeting method works for language model generations. We have the meta ads which are hyper targeted in feed, not within specific pieces of content. And we have search ads that are used by Google and Amazon has been rising a lot on search. But within a return from ChatGPT it is not clear how you get a high quality placed ad ad within the output. And if you can do that with model costs coming down you can just get super high revenue per like that revenue is totally untapped and it's not clear technically how it is done.

Lex Friedman (270:07)

Yeah, that is I mean the sort of the AdSense innovation that Google did. The one day you'll have in GPT output an ad and that's going to make like billions of dollars and it.

Dylan Patel (270:19)

Could be very subtle, it could be in conversation like we have voice mode now. It could be some way of making it so the voice introduces certain things. It's much harder measure and it takes imagination but yeah and it wouldn't be.

Lex Friedman (270:31)

So shade, it wouldn't come off shady so you would receive public blowback, that kind of thing. So you have to do it loud enough to where it's clear it's an ad and balance all that. So that's the open question they're trying to solve Anthropic and OpenAI. They need to.

Dylan Patel (270:45)

They may not say.

Nathan Lambert (270:46)

I don't think they care about that at all.

Dylan Patel (270:47)

They don't care about it right now. I think it's place are experimenting on that more.

Lex Friedman (270:53)

Oh interesting. Yeah, yeah for sure.

Nathan Lambert (270:55)

Like perplexity, Google Meta care about this. I think OpenAI and Anthropic are purely laser focused on AGI. Yeah, agents and AGI. And if I build AGI I can make tons of money. Right. Or I can spend pay for everything. Right. And this is, this is, it's just predicated like back on the like export control thing. Right. If you think AGI is five, 10 years away or less. Right. These labs think it's two, three years away. Obviously your actions are if you assume they're rational actors, which they are mostly what you do in a two year AGI versus five year versus ten years. Very, very, very different. Right.

Lex Friedman (271:34)

Do you think agents are promising? We have to talk about this. This is like the excitement of the year that agents are going to rev. This is the generic hype term that a lot of business folks are using. AI agents are going to revolutionize everything.

Dylan Patel (271:51)

Okay, so mostly the term agent is obviously overblown. We've talked a lot about reinforcement learning as a way to train for verifiable outcomes. Agents should mean something that is open ended and is solving a task independently on its own and able to adapt to uncertainty. There's a lot of term agent applied to things like Apple intelligence which we still don't have after the last WWDC which is orchestrating between apps, apps and that type of tool use thing is something that language models can do really well. Apple Intelligence, I suspect, will come eventually. It's a closed domain, it's your messages app integrating with your photos with AI in the background, that will work. That has been described as an agent by a lot of software companies. To get into the narrative, the question is what ways can we get language models to generalize to new domains and solve their own problems in real time? Maybe some tiny amount of training when they are doing this with fine tuning themselves, or in context learning, which is the idea of storing information in a prompt and you can use learning algorithms to update that. And whether or not you believe that that is going to actually generalize to things like me saying, book my trip to go to Austin in two days, I have XYZ constraints and actually trusting it. I think there's a HCI problem coming back for information.

Lex Friedman (273:13)

Well, what's your prediction there? Because my gut says we're very far away from that.

Nathan Lambert (273:18)

I think OpenAI's statement, I don't know if you've seen the five levels where it's chat is level one, reasoning is level two, and then agents is level three. And I think there's a couple more levels. But it's important to note, right, we were in chat for a couple years, right. We just theoretically got to reasoning. We'll be here for a year or two, right? Right. And then agents. But at the same time, people can try and approximate capabilities of the next level, but the agents are doing things autonomously, doing things for minutes at a time, hours at a time, et cetera. Right. Reasoning is doing things for tens of seconds at a time, right. And then coming back with an output that I still need to verify and use and try, check out. Right. And the biggest problem is of course, like it's the same thing with manufacturing, right? Like there's the whole six sigma thing, right? Like, like how many nines do you get? And then you compound the nines onto each other and it's like if you multiply by the number of steps that are six sigma, you get to a yield or something, right? So like in semiconductor manufacturing, tens of thousands of steps, 999-9999 is not enough, right? Because you multiply by that many times, you actually end up with like 60% yield. Right? Really low yield. Yeah. Or zero. And this is the same thing with agents, right? Like chaining tasks together each time time LLMs, even the best LLMs in particularly pretty good benchmarks don't get 100%, right. They get a little bit below that because there's a lot of noise and so how do you get to enough nines? Right. This is the same thing with self driving. We can't have self driving because without it being like super geofenced, like Google's. Right. And even then they have a bunch of tele operators to make sure it doesn't get stuck. Right. But you can't do that because. Because it doesn't have enough nines.

Lex Friedman (275:01)

And self driving has quite a lot of structure because roads have rules, it's well defined, there's regulation. When you're talking about computer use for the open web, for example, or the open operating system, it's a mess. So the possibility. I'm always skeptical of any system that is tasked with interacting with the human world, with the open, messy human world.

Dylan Patel (275:30)

That's the thing. If we can't get intelligence that's enough to solve the human world on its own, we can create infrastructure like the human operators for Waymo over many years that enable certain workflows.

Nathan Lambert (275:41)

There is a company, I don't remember it, but it is. But that's literally their pitch is, yeah, we're just going to be the human operator when agents fail and you just call us and we fix it. Yeah, same thing for API call. And it's hilarious.

Dylan Patel (275:51)

There's going to be teleoperation markets when we get human robot switches. There's going to be somebody around the world that's happy to fix the fact that it can't finish loading my dishwasher when I'm unhappy with it. But that's just going to be part of the Tesla service package.

Lex Friedman (276:05)

I'm just imagining like an AI agent talking to another AI agent. One company has an AI agent that specializes in helping other AI agents.

Dylan Patel (276:15)

But if you can make things that are good at one step, you can stack them together. So that's why I'm. If it takes a long time, time, we're going to build infrastructure that enables it. You see the operator launch, they have partnerships with certain websites, with DoorDash, with OpenTable, with things like this, those partnerships are going to let them climb really fast. Their model is going to get really good at those things. It's kind of proof of concept that might be a network effect where more companies want to make it easier for AI. Some companies will be like, no, let's put blockers in place. And this is the story of the Internet. We've seen, we see it now with training data for language models. We're coming. Companies are like, no, you have to pay. Like business working it out.

Lex Friedman (276:54)

That said, I think like airlines have a Very and hotels have high incentive to make their site work really well and they usually don't like if you look at how many clicks it takes to order airplane ticket. It's insane. I don't.

Dylan Patel (277:09)

You actually can't call an American Airlines agent anymore. They. They don't have a phone number. It's.

Lex Friedman (277:14)

I mean it's, it's horrible on many. And the interface front to imagine that agents will be able to deal with that website. When I as a human struggle like I have an existential crisis every time I try to book an airplane ticket that I think it's going to be extremely difficult to build an AI agent that's robust.

Dylan Patel (277:34)

Think about it. United has accepted the Starlink term which is they have to provide Starlink for free. And the users are going to love it. What if one airline is honestly we're going to take a year and we're going to make our website have white text that works perfectly for the AIs. Every time anyone asks about an AI flight they buy whatever airline it is.

Nathan Lambert (277:53)

Or like they just like here's an API in it's only exposed to AI agents and if anyone queries it the price is 10% higher and for any flight. But we'll let you see any of our flights and you can just book any of them. Here you go. Agent magazine and then it's like own and I made 10% higher price. Awesome. And am I willing to say that for like hey, book me a flight to select, right? And it's like yeah, whatever. I think you know, computers and real world and the open world are really, really messy. But if you start defining the problem in narrow regions, people are going to be able to create very, very productive things and ratchet down cost massively. Right now crazy things like robotics in the home, those are going to be a lot harder to do. Just like self driving, right. Because there's just a billion different failure modes, right. But agents that can navigate a certain set of websites and do certain sets of tasks or look at your take a photo of your fridge or upload your recipes and then it figures out what to order from Amazon Whole Foods food delivery, then that's going to be pretty quick and easy to do, I think. So it's going to be a whole range of business outcomes and it's going to be tons of sort of optimism around people can just figure out ways to make money.

Dylan Patel (279:08)

To be clear, these sandboxes already exist in research. There are people who have built clones of all the most popular websites of Google, Amazon, blah Blah, blah to make it so that there's. I mean OpenAI probably has them internally to train these things. It's the same as DeepMind's robotics team for years has had clusters for robotics where you interact with robots fully remotely. They just have a lab in London and you send tasks to it, arrange the blocks and you do this research. Obviously there's text there that fix stuff, but we've turned these cranks of automation before. You go from sandbox to progress and then you add one more domain at a time and generalize. I think in the history of NLP and language processing, instruction tuning in tasks per language model used to be like one language model did one task. And then in the instruction tuning literature there's this point where you start adding more and more tasks together where it just starts to generalize to every task and we don't know where on this curve we are. I think for reasoning with this RL and verifiable domains, we're early but we don't know where the point is where you just start training on enough domains and poof, more domains just start working and you've crossed the generalization barrier.

Lex Friedman (280:16)

Well, what do you think about the programming context? So software engineering, that's where I personally and I know a lot of people interact with AI the most.

Nathan Lambert (280:29)

There's a lot of fear and angst too from current CS students. But there's also. That's where. That is the area where probably the most AI revenue and productivity gains have come. Right. Whether it be co pilots or Cursor or what have you. Right. This is. Or just standard chat GPT. Right. Like a lot of. I don't, I know very few programmers who don't have chat GPT and actually many of them have the $200 tier because that's, that's what it's so good for. Right. I think that in that world we already see it like swebench. I don't know if you've looked at the benchmark made by some Stanford students. I wouldn't say it's really hard, but I wouldn't say it's easy either. I think it takes someone who's been through at least a few years of CS or a couple years of programming to do Swebench well. And the models went from 4% to 60% in a year. And where are they going to go to next year? It's going to be higher, probably won't be a hundred percent because again that nines is really hard to do do. But we're going to get to some point where that's, and then we're going to need harder software engineering benchmarks and so on and so forth. But the way that people think of it now is it can do code completion easy, it can do some function generation and I have to review it. Great. But really the software engineering agents I think can be done faster, sooner than any other agent because it is a verifiable domain. You can always unit, test or compile. And there's many different regions of like it can inspect the whole code base at once, which no, no engineer really can. Only the architects can really think about this stuff, the really senior guys and they can define stuff and then the agent can execute on it. So I think, I think software engineering costs are going to plummet like crazy. And, and one interesting aspect of that is when software engineering costs are really low, you get very different markets, right? So in the US you have all these platform SaaS companies, right? Salesforce and so on and so forth. Right, right. In, in China, no one uses platform SaaS. Everyone just builds their own stack. Because software engineering is much cheaper in China partially because like people stem, number of STEM graduates, et cetera. So stem, so it's generally just cheaper to do. And so at the same time, code for LL, like code LLMs have been adopted much less in China because the cost of an engineer there is much lower. But like what happens when every company can just invent their own business logic, like really cheaply and quickly you stop using platform SaaS, you start building custom tailored solutions, you change them really quickly. Now all of a sudden your business is a little bit more efficient too, potentially because you're not dealing with the hell that is like some random platform SaaS company stuff not working perfectly and having to adjust workflows or random business automation cases that aren't necessarily AI required. It's just logic that needs to be built that no one has built. Right. All of these things can go happen faster. And so I think software and then, and then the other domain is like industrial, chemical, mechanical engineering engineers suck at coding, right? Just generally. And like their tools, like semiconductor engineers, their tools are 20 years old. All the tools run on XP, including ASML. Lithography tools run on Windows XP. Right? It's like, you know, and like a lot of the analysis happens in Excel, right? Like it's just like guys like you guys can move 20 years forward with all the data you have and gathered and like do a lot better. And it's just you need the engineering skills for software Engineering to be delivered to the actual domain expert engineer. So I think, I think that's the area where I'm like super duper bullish of generally AI creating domain value.

Dylan Patel (283:41)

The big picture is that I don't think it's going to be a cliff. It's like we talked a really good example of how growth changes is when meta added stories. So Snapchat was on an exponential. They added stories, it flatlined. Software engineers been up and to the right. AI is going to come in. It's probably just going to be flat. It's not like everyone's going to lose their job. It's hard because the supply corrects more slowly. So the amount of students is still growing and that'll correct on a multi year of like a year delay. But the amount of jobs will just turn and then maybe in 20, 40 years it'll be well down. But in the few years there'll never be the snap moment where it's like software engineers aren't useful.

Lex Friedman (284:24)

I think also the nature of what it means to be a programmer and what kind of jobs programmers do changes because I think there needs to be a human in the loop of everything you've talked about. There's a really important human in that picture of like correcting the code, like.

Nathan Lambert (284:43)

Thinking larger than the context length.

Lex Friedman (284:45)

Yep. And debugging also like debugging by sort of reading the code, understanding the steering the system like no, no, no, you missed the point. Adding more to the prompt kind of like yes, adding the human.

Dylan Patel (284:59)

Designing the perfect Google button. Google's famous for having people design buttons that are so perfect. And it's like how, like how is AI going to do that? Like it's like they could give you all the ideas perfect.

Lex Friedman (285:11)

I mean that's the thing you can call it taste humans have. One thing humans can do is figure out what other humans enjoy better than AI systems. That's where the preference you loading that in. But ultimately humans are the greatest preference generator. That's where the preference comes from.

Dylan Patel (285:27)

And humans are actually very good at reading or like judging between two things versus this is this goes back to the core of what RLHF and preference tuning is, is that it's hard to generate a good answer for a lot of problems, but it's easy to see which one is better. And that's how we're using humans for AI now is judging which one is better. And that's what software engineering could look like. It's the PR review. Here's a few options. What are the like here are some potential pros and cons and they're going to be judge judges.

Lex Friedman (285:54)

I think the thing I would very much recommend is people start programmers start using AI and embracing that role of the supervisor of the AI system and like partner of the AI system versus writing from scratch or not learning coding at all and just generating stuff. Because I think there actually has to be a pretty high level of expertise as a programmer to be able to manage increasingly intelligent systems.

Nathan Lambert (286:18)

I think it's, I think it's that and then becoming a domain expert in something.

Lex Friedman (286:21)

Sure, yeah.

Nathan Lambert (286:22)

Right. Because seriously, if you go look at aerospace or semiconductors or chemical engineering, everyone is using really crappy platforms, really old software. Like the job of a data science is like, is like a joke, right? In many cases, in many cases it's very real. But it's like bring what the forefront of human capabilities are to your domain and like even if the forefront is like from the AI your domain, you're like at the forefront, right? So it's like, it's like you have to be at the forefront of something and then leverage the like rising tide that is AI for everything else.

Lex Friedman (286:51)

Oh yeah, there's so many low hanging forests, fruit everywhere. In terms of where software can like help automate a thing or digitize a thing in, in the legal system. I mean that's why Doge is exciting. You have, I mean I get to hang out with a bunch of the Doge folks and they, I mean government is like so old school. It, it, it's like begging for the modernization of software, of organizing the data, all this kind of stuff. I mean in that case it's by design because bureaucracy create protects centers of power and so on. But software breaks down those barriers. So it hurts those that are holding on to power but ultimately benefits humanity. So there's a bunch of domains of that kind. One thing we didn't fully finish talking about is open source. So first of all, congrats. You released a new model.

Dylan Patel (287:52)

Yeah, Tulu. I'll explain what a tulu is. A Tulu is a hybrid camel. When you breed a dromedary with a Bakrian camel. Back in the early days after ChatGPT, there was a big wave of models coming out like alpaca, vicuna, et cetera, that were all named after various mammalian species. So Tulu is the brand is multiple years old which comes from that. And we've been been playing at the frontiers of post training with open source code. And this first part of this release was in the fall where We've built on Llama's open models, open weight models and then we add in our fully open code or fully open data. There's a popular benchmark that is chatbot arena and that's generally the metric by which how these chat models are evaluated. And it's humans compare random models from different organizations. And if you looked at the leaderboard in November or December, among the top 60 models from tens to twenties of organizations, none of them had open code or data for just post training. Among that even fewer or none have pre training data and code available. But post training is much more accessible at this time. It's still pretty cheap and you can do it. And the thing is how high can we push this number where people have access to all the code and data? So that's kind of the motivation of the project. We draw in lessons from Llama. Nvidia had a Nematron model where the recipe for their post training was fairly open with some data and a paper. And it's putting all these together to try to create a recipe that people can fine tune models like GPT4 to their domain.

Lex Friedman (289:22)

So to be clear, in the case of Tulu, maybe you can talk about Almo too. But in the case of Tulu, you're taking Llama 3 405B.

Dylan Patel (289:33)

Tulu has been a series of recipes for post training. So we've done multiple models over years.

Lex Friedman (289:37)

Okay, and so you're open sourcing everything?

Dylan Patel (289:40)

Yeah, if you start with an open weight based model, the like whole model technically is an open source because you don't know what Llama put into it. Which is why we have a separate thing that we'll get to. But it's just getting parts of the pipeline where people can zoom in and customize. I know, I hear from startups and businesses that are like, okay, like I can take this post training and try to apply it to my domain. We talk about verifiers a lot. We use this idea which is reinforcement learning with verifiable domains rewards rlvr kind of similar to RLHF. And we applied it to map and the model today which is like we applied it to the Llama 405B base model from last year and we have our other stuff, we have our instruction tuning and preference tuning. But the math thing is interesting which is like it's easier to improve this math benchmark. There's a benchmark, Math, math, all capital models, tough name on the benchmark name is the area that you're evaluating. We're researchers, we're not brand strategists. And this is something that the Deep Seq paper talked about as well, is like, at this bigger model, it's easier to elicit powerful capabilities with this RL training. And then they distill it down from that big model to the small model. And this model we released today, we saw the same thing. We're at AI2. We don't have a ton of compute. We can't train 405B models all the time. So we just did a few runs and. And they tend to work. And it just shows that there's a lot of room for people to play in these things.

Nathan Lambert (291:06)

And they crushed Llama's actual release, right? They're way better than it.

Dylan Patel (291:11)

Yeah. So our eval numbers, I mean, we have extra months in this, but our eval numbers are much better than the Llama instruct model that they released.

Lex Friedman (291:18)

And then you also said better than DeepSeek v3.

Dylan Patel (291:20)

Yeah. On our eval benchmark, DeepSeek v3 is really similar. We have a safety benchmark to understand if it will say harmful things and things like that. And that's what draws down most of the way. It's still.

Nathan Lambert (291:32)

It's like an amalgamation of multiple benchmarks or. What do you mean?

Dylan Patel (291:34)

Yeah. So we have a 10 evaluation. This is standard practice in post training, is you choose your evaluations you care about. In academics and smaller labs, you'll have fewer evaluations. In companies, you'll have really one domain that you really care about. In Frontier labs, you'll have tens to twenties to maybe even like 100 evaluations of specific things. So we choose a representative suite of things that look. Look like chat. Precise instruction following, which is respond. Only in emojis does the model follow weird things like that math code. And you create a suite like this. So safety would be one of 10 in that type of suite where you have. What does the broader community of AI care about? For example, in comparison to Deep seq, it would be something like our average eval for our model would be 80, including safety and similar without. And DeepSeq would be 79 average score without safety. And their safety score would bring it down to like 6%.

Nathan Lambert (292:26)

Oh, so you beat them even ignoring safety.

Dylan Patel (292:28)

Yeah. So this is something that internally it's like, I don't want to win only by how you shape the eval benchmark. So if there's something that's like, people may or may not care about safety in their model. Safety can come downstream. Safety can be when you host the model for An API like safety is addressed in a spectrum of locations in AI applications. So it's like if you want to say that you have the best recipe, you can't just gate it on these things that some people might not want. And this is like the time of progress. We benefit, we can release a model later. We have more time to learn new techniques like this RL technique. We had started this in the fall. It's now really popular with reasoning models. The next thing to do for open source post training is to scale up verifiers, to scale up data, to replicate some of DeepSeq's results. And it's awesome that we have a paper to draw on and it makes it a lot easier. And that's the type of things that is going on among academic and closed frontier research in AI.

Lex Friedman (293:22)

Since you're pushing open source, what do you think is the future of it? You think Deepseek actually changes things since it's open source or open. Wait, or is pushing the open source movement into the open direction?

Dylan Patel (293:33)

This goes very back to license discussion. So Deepseak R1 with a friendly license is a major reset. So it's like the first time that we've had a really clear frontier model that is open weights and with a commercially friendly license with no restrictions on downstream use cases, synthetic data distillation, whatever. This has never been the case at all in the history of AI. In the last few years since ChatGPT, there have been models that are off the frontier or models with weird licenses that you can't really use them.

Nathan Lambert (293:58)

So isn't meta's license pretty much permissible except for five companies?

Dylan Patel (294:04)

So this goes to what open source AI is, which is there's also use case restrictions in the llama license which says you can't use it for specific things. So if you come from an open source software background, you would say that that is not an open source source license.

Nathan Lambert (294:16)

What kind of things are those though?

Dylan Patel (294:18)

Like, are they like at this point I can't pull them off my head.

Lex Friedman (294:22)

But it'll be like competitor.

Dylan Patel (294:23)

It used to be military use was one and they removed that for scale. It'll be like, like csam, like child abuse material or like that's the type of thing that is forbidden there. But that's enough from an open source background to say it's not open source license. And also the llama license has this horrible thing where you have to name your model Llama Llama if you touch it to the llama model. So it's like the branding thing. So if a company uses Llama technically, the license says that they should say built with Llama at the bottom of their application. And from a marketing perspective, that just hurts. I could suck it up as a researcher. I'm like, oh, it's fine. It says Llama dash on all of our materials for this release. But this is why we need truly open models, which is we don't know deep seq R1's data.

Nathan Lambert (295:06)

So you're saying I can't make a cheap copy of Llama and pretend it's mine, but I can do this with the Chinese model model?

Dylan Patel (295:12)

Hell yeah, that's what I'm saying. And that's why it's like we want this whole open language models thing. The Olmo thing is to try to keep the model where everything is open with the data as close to the frontier as possible. So we're compute constrained, we're personnel constrained, we rely on getting insights from people like John Shulman tells us to do RL on outputs. We can make these big jumps, but it just takes a long time to push the frontier of open source. And fundamentally I would say that that's because open source AI does not have the same feedback loops as open source software. We talked about open source software for security also. It's just because you build something once and you can reuse it. If you go into a new company, there's so many benefits. But if you open source a language model, you have this data sitting around, you have this training code. It's not that easy for someone to come and build on and improve because you need to spend a lot on compute, you need to have expertise. So until there are feedback loops of open source AI, it seems like mostly an ideological mission. People like Mark Zuckerberg, which is like, America needs this and I agree with him, but in the time where the motivation ideologically is high, we need to capitalize and build this ecosystem around. What benefits do you get from seeing the language model data? And there's not a lot about that. We're going to try to launch a demo soon where you can look at omo model model and a query and see what pre training data is similar to it, which is legally risky and complicated. But it's like, what does it mean to see the data that the AI was trained on? It's hard to parse, it's terabytes of files. It's like, I don't know what I'm going to find in there. But that's what we need to do as an ecosystem. If People want open source AI to be financially useful.

Lex Friedman (296:55)

We didn't really talk about Stargate. I would love to get your opinion on what the new administration, the Trump administration. Everything that's doing that's being done from the America side and supporting AI infrastructure and the efforts of the different AI companies. What do you think about Stargate? What are we supposed to think about Stargate and does Sam have the money?

Nathan Lambert (297:17)

Yeah. So I think Stargate is a opaque thing. It definitely doesn't have $500 billion, doesn't even have $100 billion. Right. So what they announced is this $500 billion number. Larry Ellison, Sam Altman and, and Trump said it, they thanked Trump and it's. And it's use the. The Trump did do some executive actions that like, do significantly improve the ability for this to be built faster. You know, one of the executive actions he did is on federal land. You can just basically build data centers in power, you know, like pretty much like that. And then the permitting process is basically gone or you file after the fact. So like one of the. Again, like I had a schizo take earlier. Another schizo take. If you've ever been to the Presidium in San Francisco, beautiful area. You could build a power plant in a data center there if you wanted to, because it is federal land. It used to be a military base, but obviously this would like, piss people off. It's a good bit. Anyways. Trump has made it much easier to do this. Right. Generally, Texas has the only unregulated grid in the nation as well.

Lex Friedman (298:18)

Let's go Texas.

Nathan Lambert (298:19)

And so therefore, ERCOT enables people to build faster as well. In addition, the federal regulations are coming down, down. And so Stargate is predicated. And this is why that whole show happened. Now, how they came up with a $500 billion number is beyond me. How they came up with $100 billion number makes sense to some extent. Right. And there's actually a good table in here that I would like to show in that Stargate piece that I had. It's the most recent one. Yeah. So anyways, Stargate, you know, it's basically right. Like there is. It's a table about cost there. You passed it already. It's that one. So this table is kind of explaining what happens. Right. So Stargate is in Abilene, Texas. The first hundred billion dollars of it. That site is 2.2 gigawatts of power in about 1.8 gigawatts of power consumed. Right. Per GPU, they have like, roughly. Oracle is already building the first part of this before Stargate came about, to be clear, they've been building it for a year. They tried to rent it to Elon, in fact, right? But Elon was like, it's too slow. I need it faster. So then he went and did his Memphis thing. And so OpenAI was able to get it with this weird joint venture called Stargate. They initially signed a deal with just Oracle for the first section of this cluster. This first section of this cluster is roughly $5 billion to $6 billion of server spend, right? And then there's another billion or so of data center spend. But the. And then, and then likewise, like if you fill out that entire 1.8 gigawatts with the next two generations of Nvidia's chips, GB200, GB300, VR200, and you fill it out completely, that ends up being roughly $50 billion of server cost, right? Plus there's data center costs, plus maintenance cost, plus operation cost, plus, plus all these things. And that's where OpenAI gets to their $100 billion announcement that they had, right? Because they talked about $100 billion is phase one. That's this Abilene, Texas data center, right? $100 billion of total cost of ownership, quote, unquote. Right? So it's not Capex, it's not investment, it's $100 billion of total cost of ownership. And then there will be future phases. They're looking at other sites that are even bigger than this 2.2 gigawatts, by the way, in Texas and elsewhere. Elsewhere. And so they're not, you know, completely ignoring that. But there is, there is the number of $100 billion that they say is for phase one, which I do think will happen. They don't even have the money for that. Furthermore, it's not $100 billion, it's $50 billion of spend, right? And then like $50 billion of operational cost, power, et cetera, rental pricing, et cetera, because they're renting it for. OpenAI is renting the GPUs from the Stargate joint venture, right? What money do they actually have? Right? SoftBank. SoftBank is going to invest. Oracle is going to invest. OpenAI is going to invest. OpenAI Is on the line for $19 billion. Everyone knows that. They've only got 6 billion in their last round and 4 billion a debt. So. But there is, there is, like, news of, like, SoftBank maybe investing 25 billion into OpenAI, right? So that's, that's, that's part of it, right? So 19 billion can come from there. So OpenAI does not have the money at all. Right. To be clear, ink is not dried on any. Anything open has $0 for this 50 billion. Right, in which they're legally obligated to put 19 billion of capex are into the joint venture and then the rest they're going to pay via renting the GPUs from the joint venture. And then there's, then there's Oracle. Oracle has a lot of money. They're building the first section completely. They were spending for themselves, right, this $6 billion of CapEx, $10 billion of TCO. But they. And they were going to do that first section. They're paying for that, right. As far as the rest of the section, I don't know how much Larry wants to spend. Banned, Right. At any point he could pull out, right? Like this is again, it's like completely voluntary. So at any point there's no signed Inc on this. Right. But he potentially could contribute tens of billions of dollars. Right. To be clear, he's got the money, Oracle's got the money. And then there's like MGX, which is the UAE fund, which technically has $1.5 trillion for investing in AI. But again, like, I don't know how real that money is. And like, whereas there is no INC signed for this, SoftBank does not have $25 billion of cash. They have to sell down their stake in ARM, which is the leader in CPUs, and they IPO'd it. This is obviously what they've always wanted to do. They just didn't know where they'd redeploy the capital. Selling down the stake in ARM makes a ton of sense. So they can sell that down and invest in this if they want to, and invest in OpenAI if they want to. As far as money secured, the first hundred thousand GB200 cluster can be funded. Everything else after that up in the air is up in the air. Money's coming. I believe the money will come. I personally do.

Lex Friedman (302:57)

It's a belief, okay.

Nathan Lambert (302:58)

It's a belief that they are going to release better models and be able to raise more.

Lex Friedman (303:00)

Yeah, right.

Nathan Lambert (303:01)

But like the actual reality is, is that Elon's right. There is the money does not exist. Right.

Lex Friedman (303:06)

What does the US government have to do with anything? What does Trump have to do with everything? He's just a hype man.

Nathan Lambert (303:11)

Trump is. He's reducing the regulation so they can build it faster. Right. And he's allowing them to do it. Right. You know, because any investment of this side is going to involve like antitrust stuff. Right. Like, so obviously he's going to, he's going to allow them to do it, he's going to enable the regulations to actually allow it to be built. I don't believe there's any US government dollars being spent on this though.

Lex Friedman (303:31)

Yeah. So I think he's also just creating a general vibe that this is regulation will go down and this is the era of building. So if you're a builder, you want to create stuff, you want to launch stuff, this is the time to do it.

Nathan Lambert (303:44)

And so look, we've had this 1.8 gigawatt data center in our data for a year now and we've been like sort of sending it to all of our clients, including many of these companies that are building the multi gigawatts. But that is like at a level that's not quite maybe executives like seeing $500 billion, $100 billion and then everyone's asking them like, so it could spur like another, like an even faster arms race. Right. Because there's already an arms race. But like this, this like $1005-000000-00000 number Trump talking about it on TV like it could spur the arm race to be even faster and more investors to flood in and et cetera, et cetera. So I, I think you're right in that sense that OpenAI or sort of Trump is sort of like championing people are going to build more and his actions are going to let people build more.

Lex Friedman (304:25)

What are you excited about these several years that are upcoming in terms of cluster build outs, in terms of breakthroughs in AI, the best possible future you can imagine in the next couple years, two, three, four years. What does that look like? Just, it could be very specific technical things like breakthroughs on post training or it could be just size big.

Nathan Lambert (304:53)

Yeah. I mean it's impressive.

Lex Friedman (304:54)

Clusters.

Nathan Lambert (304:56)

I really enjoy tracking supply chain and like who's involved in what I really do. It's really fun to see the numbers, the cost, who's building what capacity, helping them figure out how much capacity they should build, winning deals, Strategic stuff. That's really cool. Cool. I think technologically there's a lot around the networking side that really excites me with optics and electronics. Right. Like kind of getting closer and closer. Whether it be co package optics or some sort of like forms of new forms of switching.

Lex Friedman (305:22)

This is internal to a cluster.

Nathan Lambert (305:24)

Cluster, yeah. Also multi data center training. Right. Like there's people are putting so much fiber between these data centers and lighting it up with so many different, you know, with so much bandwidth that there's a lot of interesting stuff happening on that end. Right? Telecom has been really boring since 5G and now it's like really exciting again.

Lex Friedman (305:41)

Can you, can you educate me a little bit about the speed of things? So the speed of memory versus the speed of interconnect versus the speed of fiber between data centers are these like orders of magnitude different? Is can we at some point converge towards the place where it all just feels like one computer?

Nathan Lambert (305:58)

No, I don't think that's possible. All right. It's going to, it's only going to get harder to program, not easier. It's only going to get more difficult and complicated and more layers. Right. The general image that people like to have is like this hierarchy of memory. So on chip is really close localized within the chip, right? You have registers, right? And those are shared between some compute elements. And then you'll have caches which are shared between more compute elements. Then you have like memory, right, Like HBM or dram, like DDR memory or whatever it is. And that's shared between the whole chip. And then you can have pools of memory that are shared between many chips, right? And then storage and it keep, you keep zoning out, right? The access latency across data centers, across. Within the data center within a chip is differ. So like you're obviously always, you're always going to have different programming paradigms for this. It's not going to be easy. Programming this stuff is going to be hard. Maybe AI can help, right? You know, with programming this. But the, the, the way to think about it is that like there is, there's sort of like the more elements you add to a task, you don't get strong scaling, right? If I double the number of chips, I don't get 2x the performance, right. This is just like a reality of computing because there's inefficiencies and there's a lot of interesting work being done to make it not, you know, to make it more linear, whether it's making the chips more networked together more tightly or you know, cool programming models or cool algorithmic things that you can do on the model side, right. Deepseek did some of these really cool innovations because they were limited on interconnect, but they still needed to parallelize. Right. Like all sorts of, you know, everyone's always doing stuff. Google's got a bunch of work and everyone's got a bunch of work about this. That stuff is super exciting on the model and workload and innovation side, right? Hardware, solid state transformers are interesting, right? For the power side, there's all sorts of stuff on batteries and there's all sorts of stuff on. You know, I think, I think when you look at, if you look at every layer of the computer stack, whether it goes from lithography and etch, all the way to fabrication to optics, to networking, to power, to transformers, to cooling, to networking and you just go up and up and up and up the stack. Even air conditioners for data centers are innovating. Copper cables are innovating. You wouldn't think it, but copper cables, there are some innovations happening there with the density of how you can pack them. And it's like all of these layers of the stack all the way up to the models. Human progress is at a pace that's never been seen before.

Lex Friedman (308:18)

I'm just imagining you sitting back in a lair somewhere with screens everywhere, just monitoring the supply chain where all these clusters, like all the information you're gathering, I mean you, there's a big team.

Nathan Lambert (308:29)

There's a big team.

Lex Friedman (308:30)

I mean you're, you, you do quite incredible work with semiannalysis. I mean it's just keeping your finger on the pulse of human civilization in the digital world. It's pretty cool. Like just to watch. Feel that.

Dylan Patel (308:45)

That.

Nathan Lambert (308:46)

Yeah. Thank you. I guess.

Lex Friedman (308:47)

Feel, Feel all of us like doing epic.

Nathan Lambert (308:51)

Feel the AGI.

Lex Friedman (308:52)

Feel the. I mean, from meme to like reality. What Nathan, is there like breakthroughs that you're like looking forward to potentially?

Dylan Patel (309:01)

I had a while to think about this while listening to Dylan's beautiful response.

Nathan Lambert (309:04)

He did listen to me. He was so nervous. I knew, knew.

Dylan Patel (309:06)

No, I knew this was coming. And it's like realistically training models is very fun because there's so much low hanging fruit and the thing that makes my job entertaining. I train models, I write analysis about what's happening with models. And it's fun because there is obviously so much more progress to be had. And the real motivation why I do this, somewhere where I can share things is that there's just. I don't trust people that are like, trust me bro, we're going to make AI good. It's like we're the ones that. It's like we're going to do it and you can trust us and we're just going to have all the AI and it's just like I would like a future where more people have a say in what AI is and can understand it and it's a little bit less fun that it's not a positive thing of like this is just all really fun. Like training models is fun and bringing people in is fun, but it's really AI. If it is going to be the most powerful technology of my lifetime, it's like we need to have a lot of people involved in making that and.

Lex Friedman (310:03)

Making it open helps with that. As accessible as possible. As open as possible. Yeah.

Dylan Patel (310:08)

In my read of the last few years is that more openness would help the AI ecosystem in terms of having more people understand what's going on. Rather that's researchers from non AI fields to governments to everything. It doesn't mean that openness will always be the answer. I think then it'll reassess of like what is the biggest problem facing AI and tack on a different angle to the wild ride that we're on.

Lex Friedman (310:30)

And, and for me, just from even the user experience, anytime you have the like apathy said the aha moments like the magic like seeing the reasoning, the chain of thought, it's like there's something really just fundamentally beautiful about that. It's putting a mirror to ourselves and seeing like oh shit. It is solving intelligence as the cliche like goal of these companies is. And you get to understand why we humans are special, the intelligence within us is special. And for now also why we are special in terms of we seem to be conscious and the AI systems for now aren't and we get to solve, we get to explore that mystery. So that's. It's just really cool to get to explore these questions that I don't think I would have never imagined would be even possible possible back when. So just watching with excitement Deep Blue beat Kasparov. Like I wouldn't have ever thought this kind of AI would be possible in my lifetime. It's like this is really feels like AI. It's incredible.

Dylan Patel (311:38)

I started with AI of learning to fly a cilia quadrotor. It's like learn to fly. And it was just like it learned to fly up. It would hit the ceiling and stop and catch it. It's like, okay, that is like really stupid compared to what's going on now.

Lex Friedman (311:51)

And now you could probably with natural language tell it to learn to fly. And it's going to generate the control algorithm required to do that.

Dylan Patel (311:58)

Probably there's low level blockers, like we had to do some weird stuff for that. But you can, you, you have to.

Lex Friedman (312:03)

Like back to our robotics conversation. Yeah. When you have to interact in actual physical world, it's hard. What gives you hope about the future of human civilization? Looking into the next 10 years, 100 years. Thousand years years. How long do you think we'll make it? You think we got a thousand years?

Dylan Patel (312:22)

Humans will definitely be around in a thousand years. I think there's ways that very bad things could happen. There'll be way fewer humans, but humans are very good at surviving. There's been a lot of things that is true. I don't think they're necessarily. We're good at long term credit assignment of risk, but when the risk becomes immediate, we tend to figure things out. And for that reason there's physical constraints to things like AGI, recursive improvement to kill us all type stuff for physical reasons and for how humans have figured things out before. I'm not too worried about AI takeover. There are other international things that are worrying, but there's just fundamental human goodness and trying to amplify that. We're almost a tenuous time. And I mean if you look at humanity as a whole, there's been times where things go backwards. There's times when things don't happen at all. And we're on what should be very positive trajectory right now.

Lex Friedman (313:23)

Yeah, there seems to be progress, but just like with power, there's like spikes of human suffering and we want to try to minimize the amount of spikes.

Nathan Lambert (313:32)

Generally humanity is going to suffer a lot less. Right. I'm very optimistic about that. I do worry of like techno fascism type stuff arising as AI becomes more and more prevalent and powerful and those who control it can do more and more. Maybe it doesn't kill us all, but at some point every very powerful human is going to want a brain computer interface so that they can interact with the AGI and all of its advantages in many more way and merge its mind with sort of like. And its capabilities or that person's capabilities abilities can leverage those much better than anyone else. And therefore be. It won't be one person rule them all, but it will be. The thing I worry about is it'll be few people, hundreds, thousands, tens of thousands, maybe millions of people rule whoever's left. Right. And the economy around it. Right. And I think that's like the thing that's probably more worrisome is like human machine amalgamations. This enables an individual human to have more impact on the world and that impact can be both positive and negative. Right. Generally humans have positive impacts on the world, at least societally. But it's possible for individual humans to have such negative impacts and AGI, at least as I think the labs define it, which is not a runaway sentient thing, but rather just something that can do a lot of tasks really efficiently amplifies the capabilities of someone causing extreme damage. But for the most part, I think it'll be used for profit seeking motives which will then reduce. Which will increase the abundance and supply of things and therefore reduce suffering. Right?

Dylan Patel (315:06)

That's the goal.

Lex Friedman (315:06)

Scrolling on a timeline, just.

Dylan Patel (315:12)

Holding scrolling holds the status quo of the world.

Nathan Lambert (315:14)

That is a positive outcome, Right? It's like if I have food tubes and lung dumps scrolling and I'm happy, that's a positive outcome.

Lex Friedman (315:23)

While expanding out into the cosmos. Well, this is a fun time to be alive. And thank you for pushing the forefront of what is possible in humans. And thank you for talking today. This was fun.

Dylan Patel (315:36)

Thanks for having us.

Nathan Lambert (315:36)

Thanks for having us.

Lex Friedman (315:38)

Thanks for listening to this conversation with Dylan Patel and Nathan Lambert to support this podcast. Please check out our sponsors in the description. And now let me leave you with some words from Richard Feynman. For a successful technology, reality must take precedence over public relations, for nature cannot be fooled. Thank you for listening and hope to see you next time.