Invest Like the Best with Patrick O'Shaughnessy
· Gavin Baker
[00:00:00] S1: Complexity, and keeping the GPUs running at a high utilization rate in a big cluster, it's actually really hard. There are wild variations in how well companies run GPUs. If the most anybody ... because the laws of physics, maybe you can get two or three hundred thousand Blackwells coherent. We'll see. If you have 30% uptime on that cluster and you're competing with somebody who has 90% uptime, you're not even competing. One, there's a huge spectrum in how well people run GPUs. Two, then I think there is ... these AI researchers, they like to talk about taste. I find it very funny. "Why do you make so much money?" "I have very good taste." What taste means is you have a good intuitive sense for the experiments to perform. This is why you pay people a lot of money, because it actually turns out that as these models get bigger, you can no longer run an experiment on a thousand-GPU cluster and replicate it on 100,000 GPUs. You need to run that experiment on 50,000 GPUs and maybe it takes days, so there's a very high opportunity cost. You have to have a really good team that can make the right decisions about which experiments to run on this, and then you need to do all the reinforcement learning during post-training well and the test-time compute well. It's really hard to do and everybody thinks it's easy, but all those things. I used to have this saying like ... and I was retelling this long ago ... pick any vertical in America. If you can just run 1,000 stores in 50 states and have them clean, well-lit, stocked with relevant goods at good prices, and staffed by friendly employees who are not stealing from you, you're going to be a $20 billion company, a $30 billion company. Like 15 companies have been able to do that. It's really hard, and it's the same thing. Doing all of these things well is really hard. Then reasoning, with this flywheel, this is beginning to create barriers to entry. What's even more important, every one of those labs ... xAI, Gemini, OpenAI and Anthropic ... they have a more advanced checkpoint internally of the model. Checkpoint is just you're continuously working on these models and then you release a checkpoint, and then the reason these models get fast ...
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Yeah, the one they're using internally is further along. [00:00:00] S1: Is better, and they're using that model to train the next model. If you do not have that latest checkpoint, it's getting really hard to catch up. Chinese open source- [00:00:00] S0: Talk about that. [00:00:00] S1: Is a gift from God to Meta because you can use Chinese open source, that can be your checkpoint and you can use that as a way to kind of bootstrap this. And that's what I'm sure they're trying to do on everybody else. The big problem and the big giant swing factor, I think China's made a terrible mistake with this rare earth thing. So China, because they have Huawei Ascend, and it's a decent chip versus something like the deprecated Hoppers they’re based on, it looks okay. And so they're trying to force Chinese open source to use their Chinese chips, their domestically designed chips. The problem is Blackwell's going to come out now and the gap between these American frontier labs and Chinese open source is going to blow out because of Blackwell. And actually DeepSeek in their most recent technical paper V3.2 said, "One of the reasons we struggled to compete with the American frontier labs is we don't have enough compute." That was their very politically correct, still a little bit risky way of saying, because China said, "We don't want the Blackwells." And they're saying- [00:00:00] S0: "Would you please give us some Blackwells?"
· Gavin Baker
[00:00:00] S1: "That might be a big mistake." So if you just kind of play this out, these four American labs are going to start to widen their gap for Chinese open source, which then makes it harder for anyone else to catch up because that gap is growing, so you can't use Chinese open source to bootstrap. And then geopolitically, China thought they had the leverage. They're going to realize, "Oh, whoopsie-daisy, we do need the Blackwells." And unfortunately, the problem for them, they'll probably realize that in late '26. And at that point, there's an enormous effort underway DARPA has, there's all sorts of really cool DARPA and DOD programs to incentivize really clever technological solutions for rare earths. And then there's a lot of rare earth deposits in countries that are very friendly to America that don't mind actually refining it in the traditional way. So I think rare earths are going to be solved way faster than anyone thinks. They're obviously not that rare. They're just misnamed. They're rare because they're really messy to refine. And so geopolitically, I actually think Blackwell's pretty significant and it's going to give America a lot of leverage as this gap widens. And then in the context of all of that, going back to the dynamics between these companies, xAI will be out with the first Blackwell model and then they'll be the first ones probably using Blackwell for inference at scale. And I think that's an important moment for them. And by the way, it is funny, if you go on OpenRouter, you can just look. They have dominant share. Now OpenRouter is whatever it is. It's 1% of API tokens, but it's an indication. They processed 1.35 trillion tokens. Google did like eight or 900 billion. This is like whatever it is last seven days or last month. Anthropic was at 700 billion. xAI is doing really, really well. And the model is fantastic. I highly recommend it. But you'll see xAI come out with this. OpenAI will come out faster. OpenAI's issue that they're trying to solve with Stargate is because they pay a margin to people for compute. And maybe the people who run their compute are not the best at running GPUs. They're a high cost producer of tokens and I think this kind of explains a lot of their-
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Code red recently. [00:00:00] S1: Yeah. Well, the $1.4 trillion in spending commitments. And I think that was just like, hey, they know they're going to need to raise a lot of money, particularly if Google keeps its current strategy of sucking the economic oxygen out of the room. And you go from 1.4 trillion rough vibes, code red pretty fast. And the reason they have code red is because of all of these dynamics. So then they'll come out with a model, but they will not have fixed their per token cost disadvantage yet relative to both xAI and Google and Anthropic at that point. Anthropic is a good company. They're burning dramatically less cash than OpenAI and growing faster. So I think you have to give Anthropic a lot of credit. And a lot of that is their relationship with Google and Amazon for the TPUs and the Trainiums. And so Anthropic has been able to benefit from the same dynamics that Google has. I think it's very indicative in this great game of chess. You can look at Dario and Jensen maybe have taken a few public comments that were between them. [00:00:00] S0: Jousting.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Jousting. [00:00:00] S1: A little bit of jousting. Well, Anthropic just signed the $5 billion deal with Nvidia. That is because Dario is a smart man and he understands these dynamics about Blackwell and Rubin relative to TPU. So Nvidia now goes from having two fighters, xAI and OpenAI, to three fighters. That helps in this Nvidia versus Google battle. And then if Meta can catch up, that's really important. I am sure Nvidia is doing whatever they can to help Meta. "You're running those GPUs this way, maybe we should twist the screw this way or turn the dial that way." And then it will be also, if Blackwell comes back to China, which it seems like it'll probably happen, that will also be very good because then Chinese open source will be back. [00:00:00] S0: I'm always so curious about the pulls of things. One pull would be the other breakthroughs that you have your mind on, things in the data center that aren't chips that we've talked about before as one example. [00:00:00] S1: I think the most important thing that's going to happen in the world, in this world, in the next three to four years is data centers in space. And this has really profound implications for everyone building a power plant or a data center on planet earth. And there is a giant gold rush into this. [00:00:00] S0: I haven't heard anything about this, so please educate.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: I haven't heard anything about this, so please educate. [00:00:00] S1: Yeah. It's like everybody thinks like, "Hey, AI is risky, but you know what? I'm going to build a data center. I'm going to build a power plant that's going to do a data center." We will need that, but if you think about it from first principles, data centers should be in space. What are the fundamental inputs to running a data center? They're power and they're cooling, and then there are the chips. [00:00:00] S0: If you think about it from a total cost perspective. [00:00:00] S1: Yeah. And just the inputs to making the tokens come out of the magic machines. In space, you can keep a satellite in the sun 24 hours a day, and the sun is 30% more intense. You can have the satellite always kind of catching the light. The sun is 30% more intense, and this results in six times more irradiance in outer space than on planet earth. So you're getting a lot of solar energy. Point number two, because you're in the sun 24 hours a day, you don't need a battery. And this is a giant percentage of the cost. So the lowest cost energy available in our solar system is solar energy and space. Second, for cooling. In one of these racks, a majority of the mass and the weight is cooling. The cooling in these data centers is incredibly complicated. The HVAC, the CDUs, the liquid cooling- [00:00:00] S0: It's very cool to see. Yeah, very cool to see. [00:00:00] S1: It's amazing to see. In space, cooling is free. You just put a radiator on the dark side of the satellite. [00:00:00] S0: It's fucking cold.