Invest Like the Best with Patrick O'Shaughnessy
· Dylan Patel, Patrick O'Shaughnessy
These are things that are well understood and niche supply chains, but they're very, very tight. And ultimately, upstream, the semiconductor wafer fabrication equipment supply chain is one that's gone up a lot, but it's still very underappreciated. TSMC CapEx this year, they say $56 billion. We've had $57.4 billion since January, and we may up it slightly more just because we see some ways that they can get incremental CapEx. But what people aren't focusing on is, what does that mean next year? And what does that mean the year after? And it turns out, three years from now, TSMC is going to spend $100 billion on CapEx. Maybe two years from now, it might be '28. Sincerely, they may spend $100 billion on CapEx in 2028. And people just can't fathom that. But what does that mean for their downstream supply chains? Companies like LAM Research or Applied Materials or ASML, or their further downstream supply chains like MKSI and all these other companies. [00:00:00] S0: Everyone's just gotta keep up. [00:00:00] S1: The tailwhip, it just gets whipped harder and harder and harder. And that's a shortage if TSMC wants to spend $100 billion in 2028, which is a real possibility. I think people would think that's insane, but that's a real, real possibility. [00:00:00] S0: What about other parts of the chip ecosystem where GPUs have been completely dominant? What about like CPUs or ASICs or things that start to pop out as both opportunities and bottlenecks beyond just NVIDIA's GPU dominance?
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: What about other parts of the chip ecosystem where GPUs have been completely dominant? What about like CPUs or ASICs or things that start to pop out as both opportunities and bottlenecks beyond just NVIDIA's GPU dominance? [00:00:00] S1: Yeah, I mean, ASICs are obviously taking off, but I'll pivot away from AI chips to talk about these other things. There's a project we did on FPGAs, and it turns out there's 120 FPGAs per next-generation rack, AI rack. And then what about all the FPGA names? CPU-wise, all these reinforcement learning environments, plus all the slop code you and I are generating that is now running on some Vercel instance or whatever it is, or some AWS instance or some bucket that we've spun up. All of that requires CPU. And so CPUs are completely sold out and demand is skyrocketing there. [00:00:00] S0: How do people understand the role that CPU plays in everything?
· Dylan Patel
[00:00:00] S1: There's two main reasons why you need tons of CPUs. One is when you're doing reinforcement learning, the CPU is very critical to that. So before you would throw all the internet's data into the model, train it, and it spits some stuff out. Now you train all the world's internet, you put all the internet data into the model, then you put it in this environment. This environment is like, hey, model, try this out. And it tries stuff out, tries a bunch of different things. And in the end, there is an environment which scores whether or not what it tried out is successful, and it grades it. And these environments can be anything. It can be, hey, check if the text was output in the right way, structured outputs. It can be very simple stuff. It can be very complex stuff. And people are starting to get into very complex things. Like, hey, I want you to open this file, change it, edit it, update it, submit it to this website, I want you to open up this physics simulation from Siemens, and edit this CAD model. So the environments can get more and more complex. And those environments run on CPUs, they don't run on GPUs, they don't run on ASICs. The ASICs run the model that takes the input data from the environment, runs it through the model, the model creates outputs of various different trajectories, ways that it think it could solve it in different instances. Those trajectories are graded slash scored. And the ones that are successful, you train on and you update and you iterate, iterate, iterate. And so CPUs are very useful for that one. And then once you have these great models, and you're deploying them, those models are generating code, they're generating useful output. That useful output, it doesn't go from a GPU straight to the human brain, it goes from a GPU or an ASIC through to a deployed app that you're deploying somewhere that actually just runs on CPUs. So that's another area where there's a lot of demand, and things are sold out in a large, large way.
· Patrick O'Shaughnessy
[00:00:00] S0: As you continue to assess and try to be the world's best informed person on both the trajectory of supply and demand, what are things that you wish you knew to make that understanding that you don't know?
· Dylan Patel
[00:00:00] S1: I think the hardest area for us and for everyone is understanding tokenomics – the economics of tokens. I think we have a really tremendously good insight into how much it costs to run infrastructure, what the cost of tokens are, what the cost of models are, what the margins of these labs are. But the usage and adoption is what's really difficult to model. Continuously, right? January, we had crazy estimates for February. Anthropic smashed them. How do we calibrate this model? What are the data sources for this? February, we had crazy assumptions for March. I know people were like, you're crazy, Dylan. And then they smashed them. Everyone sees the number of 10 billion, and they're like, what the fuck? How do they add 10 billion of revenue? Who's using all these tokens? Why are they using them? What are they building with them? And then more importantly, with what they're building with these tokens, how is that actually diffusing into the economy, and what value is that generating? Because it's not really something that you can capture in any GDP statistic. All of the value of the tokens that I use get transformed into better information, which I then sell at a discount to what people used to sell information for relatively. And therefore, that information is now making its way throughout the economy, and people are making better investment decisions or better competitive decisions if they're a semiconductor company or a data center company or hyperscaler. What is the value of this? What has that done to the economy? It's clearly, by every subjective metric, amazing. But where is the phantom GDP? What is the phantom GDP? How do we track the real economic value? Because the GDP metrics are not accurate if you were to say, what is the GDP that Dylan Patel is making? It's tiny, compared to the value that I think is being created. And I think you would say the same for Patrick. What is the value being created by these tokens? Not on a basis of simple, what is the knock-on effect of all the things that these things are doing? I think that's the real question and challenge. It's hard to measure. I think we've got a tremendous reading on the supply side of things. I think we've got a tremendous reading on even a lot of the demand side signals. But it's, what is the value these tokens are generating? That's hard to quantify and measure.
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: I hope we get a chance to do this every three months because this changes so quickly. What do you think is going to happen next? When I come back three months from now and we're in San Francisco together again, what do you expect? [00:00:00] S1: Large-scale protests. [00:00:00] S0: Really? [00:00:00] S1: Yeah, I think there will be a large-scale protest against Anthropic and OpenAI. People hate AI. AI is less popular than ICE, less popular than politicians. With Anthropic adding so much revenue, that's going to start causing business changes downstream. People are going to get more and more scared of AI. They'll start blaming more and more of their own problems and things that are global, have been deep-seated problems for a long time. Those will bubble up and be blamed on AI. Probably some politician or some influencer will be able to start taking and weaponizing AI against people. You look at the comments of news articles where Sam Altman had a Molotov cocktail thrown at his house twice in two weeks. People were cheering it on. And this is just the beginning. So I think we'll see large-scale protests against AI in three months. [00:00:00] S0: What is the counterweight to that? How should the AI industry head that off?