Invest Like the Best with Patrick O'Shaughnessy
· Dylan Patel
[00:00:00] S1: Yeah, exactly. There's people who have argued GPU useful lives are less than five years. Complete nonsense. There are clusters now re-signing. Three or four-year-old Hopper clusters re-signing for three or four more years. There's A100 clusters that are re-signing for another couple years. So the useful life is clearly not five years. It's maybe even seven or eight years, arguably. We don't know yet. We'll see when Hopper gets there, but it's clearly not five years. So useful life is extending and the prices are going up on that renewal. In effect, the gross margin was not 35% on a cluster. It's beyond that. So margins are expanding in the cloud layer. Margins are extremely healthy on the hardware layer with NVIDIA still charging 75 or whatever percent gross margin. As we move down the stack memory, obviously margins have skyrocketed there. Places like Optics and Logic, there are large prepayments and margins are growing slowly. More so, the companies that are making chips like NVIDIA are paying huge prepayments. So in effect, the cast of capital or timing of cash flows or return on invested capital is going up even if the gross margin isn't. And you see this across the whole supply chain. You see ASML is completely sold out and they need Carl Zeiss to expand faster. Everyone's either sold out and margins are going up or they're getting prepayments, which increases the return on invested capital because the invested capital is lower. And so this is a consistent trend across any part. It's even like – to make a PCB requires copper foil, and that copper foil is sold out and people are making prepayments for it. Anything and everything that has a pulse and is sold out, people are jumping to get more incremental supply and fighting over the supply for the years after.
· Patrick O'Shaughnessy
[00:00:00] S0: What do you think are the most important bottlenecks? Typically in economic history, when there's this kind of demand, supply reorients and rises very, very quickly to meet the demand. It seems like it's almost impossible for supply right now in this moment to keep up. Famous last words, every shortage is followed by a glut historically. But what are the most interesting bottlenecks to you across the supply side?
· Dylan Patel
[00:00:00] S1: Supply chains are usually very fast to react. One unique thing is that our supply chains now are more complex than ever. And the things we're building are more complex than ever. And therefore the lead times are longer. And it's not like we haven't seen 18 month long lead times in other industries. It's just building incremental supply didn't take years. And this is the case with memory. Memory can only grow capacity low double digit percentages a year, right? 20, 30% a year, even less for NAND, a little bit higher for DRAM, but whatever. Even though the demand signal was very strong at the end of 2025, the memory companies immediately started reacting. None of that incremental capacity really gets here until the second – they've decided to do in addition to the typical 20 to 30% – they can stretch a little bit, but really the true incremental supply doesn't come until 28, which is a very unique thing. Even if they wanted to build as fast as possible, it doesn't come till 28, late 27 at best. So the result is memory prices have gone through the roof. And guess what? They're going to double and triple again. At least on DRAM, especially. People are like, oh, the memory story is overplayed. Everyone gets it. And it's like, no, no, no, you don't get it. DRAM will double or triple from here still, because that's how much capacity is required. And they have to steal capacity from somewhere else. And the only way to steal capacity from somewhere else in a capitalist economy is demand destruction via higher pricing. We're not rationing stuff here. And so ultimately, that's what's going to happen. And so margins continue to go up. I think Logic also has humongous capacity problems. TSMC just had their earnings. They keep upping CapEx. Ultimately, it takes them quite some time to build fabs. And they're trying to do everything they can to squeeze every little output out of every fab that they have. But ultimately, they're not raising prices fast because they're good people, it seems like. Single-digit price increases instead of triple-digit price increases like the memory guys have had. So you ultimately have this market where, yeah, TSMC is a great company, but are they actually going to extract all the value? I mentioned things like copper foil, glass fibers for PCBs, lasers.
· 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.