Invest Like the Best with Patrick O'Shaughnessy
· Gavin Baker
[00:00:00] S1: I don't know anyone like me who's not really bullish on DRAM. There are all these interesting things happening with AI right now. One is, cross-sectionally, the valuations do not make sense. They just flat-out do not make sense. They cannot all be true. You have semi-cap equipment companies trading at 40 times next quarter's annualized earnings and DRAM companies trading at mid-single digits. At the peak of the last cycle, that was 5 versus 12. At one point it was like 3 versus 45. Those can't both be true. Yes, semiconductor CapEx business models have improved more than the memory business models. We don't know how much HBM is going to improve memory business models yet. Yes, they have some element of recurring revenue with parts and maintenance, but it's not worth a 1,000% multiple gap. It's hard to square the valuation of something like Nvidia, which still in early April was essentially as cheap as it gets relative to the market — in the last 10 or 12 years or whatever it is — and very cheap absolute. It's very hard to square that valuation with something like GE Vernova's valuation, because it builds in an unfathomable amount of share loss for Nvidia. So valuations cross-sectionally are really different. Because we are in shortages, the lowest-quality companies are doing the best. If you're an oil and gas investor, a mining investor, natural resources investor, and you're well-versed in thinking of costs, this is very intuitive to you. In a real bull market for a commodity, the commodity suppliers with the highest costs go up the most because it's the most beneficial to them. They go from on the verge of bankruptcy to gushing cash. This is one reason commodity investing is really, really hard. Because quality outperforms during the cycles, but you get all of the outperformance during the downturns when the high-cost guys who mooned during the shortages and the commodity bull markets go bankrupt or whatever. You're seeing that happen in every industry. The lowest-quality players — companies that are hated and detested by the hyperscalers and the buyers because they have high costs, they're unreliable, the parts fail at a high rate — they're sold out and raising prices. And then that activity gets the interest of these retail accounts on X, and these stocks get bid to the moon.
· Gavin Baker
Whereas some of the higher-quality expressions have actually really underperformed. As an investor, it's hard because you know within a shadow of a doubt that the thing that's mooned 10x in 3 months or 6 months is going to go right back down, subject to what they do with all the cash. So it worries me a little bit that people who were very skeptical a year ago are no longer skeptical. But then I just contrast that with the valuations of these high-quality companies, which are just not extended, and it makes me feel better. I always thought it was funny in '24 and '25 that anyone asked about an AI bubble or talked about it. You have this nuclear bubble and this quantum bubble right here, right in front of you. What are we talking about? This is so real. Some of that nuclear and quantum silliness has maybe spread into more speculative, lower-quality, smaller-cap names — where if you have a big presence on X or Reddit, it's easy to move them. That frightens me a little bit. But I just wish there were more AI bears. I wish there were more memory bears. Astera is a stock I've been close to for a long time. There are a lot of bears on that. I love that. Great. I first invested in the Series C. Good luck thinking that's a copper loser. There's also — you can feel the baskets in the market, and the leverage baskets. And what baskets you're in is really important. Copper, optical, DRAM, NAND. A very interesting thing that's happened this year: in '24 and '25, the AI trade traded together. You could be long GPU compute, scale-up networking, and optical scale-across, and short power or whatever it was. That trade worked from a risk management sense, because I am very factor aware. That all blew out in January of this year. Scale-up networking would go crazy while scale-out was going down. Or DRAMs massively underperforming NAND and HDDs, which had not happened. These cross-sectional correlations within AI really fell apart, and you had to get very fine-grained. You couldn't hedge your memory anymore with some semi-cap equipment or NAND. Everything cross-sectionally really changed in a very interesting way in January. Maybe one reason for that was AI got to a quality where it was all of a sudden really easy for a bunch of people to get really smart on these different subsectors, start trading them, and then they get put into baskets, and those baskets influence…
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: AI creating price efficiency. [00:00:00] S1: Yeah, exactly. Some of the biggest opportunities outside of these higher-quality names that I think can compound for a long time and are safe — unlike these low-quality names, which are terrifying — are in names that are miscategorized. Astera was in a lot of copper-loser baskets. Astera's biggest product is going to be a switch. You use both copper and optics to connect switches to accelerators. Definitionally, if you're a switch company or an accelerator company, you cannot be a copper loser, because you're going to be on the other side of that connection. [00:00:00] S0: I wonder if you could riff just for a sentence or two on each of the major companies — Google, Microsoft, Amazon — the major players that are public. All the conversation is centered around these exciting new companies. Maybe run through them and riff.
· Gavin Baker
[00:00:00] S1: Google was incredible last year because they had that TPU advantage, which is now gone. The reason I think they're still in a great position is they have the most compute of everyone. We talked about the value of installed bases being higher as a result of shortages — they have the biggest installed base of compute. Google I/O is this week. If they don't release something that even slightly leapfrogs OpenAI and/or Claude, that's interesting. It's not a disaster for Google, it's just interesting. It just means this Nvidia effect we discussed is even more powerful than maybe I'd imagined. But I'm very curious to see what the Pareto frontier looks like literally in 5 days after Google's announced its new stuff. This is a big card for them. But Google, between the amount of data they have — and the YouTube data is actually really genuinely valuable, and is valuable in a world of robotics. The amount of compute they have, the search business they have — Google's never not going to be in a good position. You see that with GCP going crazy. You've got to give Zuckerberg immense credit, for what he's done in terms of making Meta an AI-first company internally. He is the only one of those true internet giants to have done that. I give him a lot of credit for that. I give him a lot of credit for paying up when he did for contracts, that talent. And Muse was a really big upside surprise. It was the first model from MSL, and it's not on the Pareto frontier with xAI, Google's one entrant, and then OpenAI and Claude, but it's pretty close. That was very impressive to me. So Meta is in a better position — still not as strong of an absolute position as Google, but a better position. And rates of change matter more than level in markets, particularly over short 3-year timeframes. Over long timeframes, level of competitive advantage tends to dominate. But even within that, changes really matter. Amazon is in a really strong position because of Trainium. You're going to see real P&L efficiencies from robotics over the next 18 months in their retail business. I actually think Nova — their internal models are not where Muse is, but they're better than they get credit for. Then Microsoft — I like Satya, I admire him. He's an exceptional CEO, and I give him a lot of credit for the decisions he's made.
· Gavin Baker
But he did go from, "We're going to make Google dance," to being the product manager of Copilot in 3 years. I would love to know — during the coup attempt against OpenAI, does Satya regret his decisions? Does Satya wish he had supported Ilya instead of Sam, and that Ilya and Mira were really running OpenAI today? In his heart of hearts, I would love to know. Because the Microsoft-OpenAI partnership might look very different in that world. That's a very interesting question that we'll never know the answer to. But I give him a lot of credit. What he is doing now — he's taking risk. This goes to the decisions you have to make in that cone of uncertainty — not only how much you spend, but what you're going to spend it on. Microsoft flinched for a moment in early '25. They have this algorithm: "We spend this much CapEx dollars, we get this return." That algorithm was kind of off. If you flinch, you lose position. You lose all these allocations, and it's difficult to get them back. So they flinched. The decision Satya is making now, which the market has punished him for, but I think is the right decision — who knows how fast Azure could be growing if they were willing to just sell GPUs to OpenAI. "We're going to use our compute internally to make our own products better." One reason Copilot was so bad, or has been so bad, is that there wasn't enough compute available. They're fixing that. He's the product manager at Copilot. I do think he's a great CEO. They're trying to use their compute to train their own models. I am a little skeptical that they have the right team to succeed there, but just like Meta, they can afford to hire a different team. But he's making good decisions that are risky decisions, to position Microsoft for this world where frontier models are no longer API-accessible. It's a really courageous decision that I give him a lot of credit for. He is foregoing — Microsoft probably would be an $800 stock today if they were using their GPUs to serve solely OpenAI and Anthropic's capacity instead of using them for their own products. So I give him a lot of credit for making a great decision. What's really interesting is the degree to which these companies are outward-facing in their decisions. The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile.
· Gavin Baker, Patrick O'Shaughnessy
Then there's a really intense engagement with Google — they're next most intense. Broadcom is engaged in a different way; they're just everybody's favorite ASIC supplier. If you're a startup, it's considered a level up if you get to work with Broadcom for your second-gen chip, and it's considered manna from heaven if Broadcom works with you for your first-gen chip. And then you see essentially zero engagement with startups from AMD, Microsoft, and Meta. When I say zero, it's a little. But I just wonder about that decision, because some of the best teams are no longer at big public companies. They're at these smaller startups. It's going to end up being a pretty big advantage for Nvidia and Amazon — with Google right behind them — to have this engagement that you just don't see from these other hyperscalers. [00:00:00] S0: As we wrap up, I'm curious for you to riff on any other out-there knock-on effects you've started to think about for this giant trend. We've talked about the specific companies in a lot of detail that this most impacts. We talked a little bit about the application layer and what would have to happen for there to be more value accruing to that layer of the stack. I'm curious about any other just fun knock-on things you've been thinking about as this world changes so quickly.