Invest Like the Best with Patrick O'Shaughnessy
· Gavin Baker
[00:00:00] S1: This isn't just for chip design. To me, it's always been a fundamental question for venture. There are different ideas that are obvious to everyone on planet Earth as soon as they hear it. If that's where you are in venture — if it's not hard to do, if it becomes obvious to the world before you have built scale — scale is the ultimate advantage — you're in trouble. The great thing Amazon had was, I think it was obvious to a lot of people, but it wasn't obvious to the retail CEOs. Amazon, they were very smart. Any e-commerce company that VCs invested in, they would destroy. They'd be like, "Oh, that's so cute. We're going to take our margins in that to negative ten thousand percent." That's like the guys at Wayfair, they did something hard. Amazon tried to kill them and they failed. Those were tough, operationally really competent CEOs. For me in venture, I always look — is this going to be obvious to the world before this company could build scale? Or is this both not obvious, different, and really hard to do? A lot of founders are really struggling with this in AI. People are becoming worried today that in Jensen's five-layer cake of AI, the profits are accruing to energy, accruing to data centers, accruing to chips, accruing to models — not really accruing to the applications. Cursor and Cognition got to scale. They focused on coding. 18 months ago, OpenAI was doing everything under the sun. The people focusing on coding were Cursor, Cognition, Anthropic, and it was really right to focus on code. Amjad Masad, the founder of Replit, tweeted something I thought was so smart — something like, "Bitter lesson-adjacent is the fact that coding might be the shortest path to ASI and useful AI. Because if you're really good at coding, you can write yourself code to do anything. " So it was really smart of those companies to focus intensely on coding. They all probably got to a scale where they have a place. I think Cognition is doing something really, really different. But a lot of founders are really struggling. They're trying to get confidence that in "niche-ier" areas…
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: They won't get steamrolled. [00:00:00] S1: They can get to them and get a data moat before the model companies get to that niche, or that it's a small enough niche that the model companies won't do it themselves, but it can still produce a venture outcome. [00:00:00] S0: Is this related to what you would call the token path? I know you've used that phrase with me before. [00:00:00] S1: Yeah, I think it comes from a guy at Altimeter, Jamin Ball. He just said, "If you're a software company or an AI company of any kind, you have to be in the token path." So Databricks is in the token path. Comparable companies are in the token path. If you're not in the token path and you're not in some really niche thing, life may be hard. Even for these vertical niches, if you talk to the people at the model companies, they're skeptical of some of these, because all of the data that's being generated in these niches comes from humans. You're betting that you're able to use that proprietary data in this narrow vertical to train a model that's lower cost than the frontier labs can ever get to. Maybe that's a good bet, but you have to be very, very careful. On the other hand, if the returns to these frontier tokens relative to other tokens come down, there's going to be an explosion in value creation at the application layer. Another really important point is that I have a belief that whenever he wants, Jensen can probably get pretty close to the frontier. [00:00:00] S0: With his own model?
· Gavin Baker
[00:00:00] S1: With his own model. I don't think he wants to do that, but that is what OpenAI and Anthropic are trying to do to him, unsuccessfully. He's a very logical thinker. This is the logical countermove. You will see that open-source frontier — which today consists of Chinese models with stolen American tokens. Somebody told me that DeepSeek — the original one was only 150,000 reasoning traces. There are many ways to launder this if you're a Chinese company. You can hit all these different APIs, you can make it hard. The American labs are working really hard on anti-distillation technology. Chinese open source — they're doing really impressive things in a very resource-constrained way, but there's a lot of distillation. This is why, in addition to there not being enough compute to serve Mythos, they did not want it to be distilled. They wanted to use Mythos, distill it themselves, use it to RL their next model, whatever it is. And then what eventually OpenAI and anyone on the frontier will do — there's going to be some very interesting game theory because it's a new kind of prisoner's dilemma. The old prisoner's dilemma was around, "Hey, you're in a prisoner's dilemma where you have to spend." The new prisoner's dilemma is going to be, if you are at the frontier, do you release that model via API or not? If everyone at the frontier agrees not to do that, then Chinese open source — if one person defects, they're going to have the best model. They're going to have a lot of revenue and cash flow. And then of course, resources equal intelligence, so they'll start to pull ahead, and then that will lead to everybody else releasing it. So it's a new game theory. It's kind of the same game theory that you have with Taiwan Semi, Samsung, and Intel. The reality is, if a company like Nvidia or AMD were to ever really, really use one of these other foundries, that foundry would get better really quickly. So Jensen is going to keep open source a certain timeframe behind the frontier. That's going to be a very interesting thing to watch. And then by the way, open source gets monetized. There's this misnomer that open source is free. Open-source tokens cost energy to produce. You need to make a margin on GPUs, and the open-source model companies almost always get a revenue share.
· 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.