Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: How are you preparing Atreides for the world of Mythos 3, Mythos 4? [00:00:00] S1: We're just trying to overinvest in cybersecurity. I really believe everybody needs to have a safe word. Everybody needs to leave your digital devices behind, literally go to the ocean, and have a family safe word or a company safe word. It can't be one that can be socially engineered. This is just to avoid cybercrime — where what looks like your son or your daughter or your grandparents or your parents FaceTime you. It's an utterly accurate simulation of them. They know everything and can extrapolate based on what they're likely to say, and they say, "Wire me a million bucks." So doing everything we can with cybersecurity. [00:00:00] S0: That's defensive. What about analytical or processing? What will you still be able to do that it won't be able to do, on the analytical side?
· Gavin Baker
[00:00:00] S1: It's a good question. I just watched The Last Samurai, and I asked people at my firm to watch it. If you haven't seen it, I highly recommend watching it. It's actually a movie that's aged really well — a Tom Cruise movie from 20 years ago. The conceit is, Tom Cruise is this bitter, washed-up Civil War veteran who's actually a very good soldier, but he's bitter and washed up because he feels he participated in negative actions against Native Americans. It's during the Meiji Restoration, and he's hired by the modern elements of the Japanese government to train an army of peasants how to fight the samurai. There's a first battle. Of course, the samurai win even though they don't have guns. He fights valiantly, so the samurai decide not to kill him and take him to their village. He becomes a samurai. It feels like the Civil War to him, so he fights on the side of the samurai. At the end of it, he's massacred by a peasant with a machine gun. The machine gun is here. If we do not all become masters of the machine gun, we're going to get mastered. So I am trying to become a master of the machine gun. I'm optimistic. There's a long period of time where, just like if you were a 50-year-old samurai veteran of many wars — "I fought many wars, Master Dwarf" — you will have advantages using the machine gun. I'm optimistic as a lifelong student of investing, that I'm going to be able to master the machine gun — this new technology — integrate it into my own process, integrate it into our firm's process in ways that let me contribute value as a human being for a long time. Like everyone, I have agents running all the time now.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: What's your most useful agent? [00:00:00] S1: My single most useful agent is a really good summary of the points that would be interesting to me from podcasts. There are 6 hours a day of stuff that I feel like it's in my job description to watch. Every time somebody from OpenAI, xAI, Google, Cursor, Fireworks, Baseten — to say nothing of Jensen, Elon, Dario — I feel compelled to watch, and I just don't have that much time. There are some real needles in haystacks. That is what I would say for me, is the most useful. There is a set of things I always like to see. I'm very sensitive to management compensation. What are they incentivized to do? Do they just have stupid RSUs, or do they have PSUs? And if they have PSUs, what do those PSUs incentivize them to do? We now have systems that do a very good first pass at that. That saves people a lot of time. It frees them up for more creative work than going through the proxy, pulling the PSU thing, looking at how it's changed versus all the proxies — because there's signal in that. And that's very labor intensive, and that's so good for an AI. There are obviously all sorts of similar things within investing. Pressuring the organization in those ways has been helpful. This is the most exciting, thrilling time to be an investor. I'm getting a little bit worried. [00:00:00] S0: The diversity breakdown thing? [00:00:00] S1: Yeah. [00:00:00] S0: Say just a little bit more about the kinds of people that are capitulating.
· 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.