Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Yeah. It makes me wonder about, in this market, unlike everybody else, it's the 10 companies at the top that are all the market cap, more than all of the attention. There's 490 other companies in the S&P 500. You studied those too. What do you think about that group? What is interesting to you about the group that now nobody seems to talk about and no one really seems to care about, because they haven't driven returns and they're a smaller percent of the overall index? [00:00:00] S1: I think that people are going to start to care if you have more and more companies print these C.H. Robinson-like quarters. I think the companies that have historically been really well run, the reason they have a long track record of success, you cannot succeed without using technology well. If you have a internal culture of experimentation and innovation, I think you will do well with AI. I would bet on the best investment banks to be earlier and better adopters of AI than maybe some of the trailing banks. Just sometimes past is prologue, and I think it's likely to be in this case. One strong opinion I have, all these VCs are setting up these holding companies and we're going to use AI to make traditional businesses better. They're really smart VCs and great track records, but that's what private equity's been doing for 50 years. You're just not going to beat private equity at their game. [00:00:00] S0: This is what Vista did in the early days, right?
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: This is what Vista did in the early days, right? [00:00:00] S1: Yeah. I do think this is actually private equities maybe had a little bit of a tough run. Just multiples have gone up. Now private assets are more expensive. The cost of financing has gone up. It's tough to take a company public, because the public valuation is 30% lower than the private valuation, so PE's had a tough run. I actually think these private equity firms are going to be pretty good at systematically applying AI. [00:00:00] S0: We haven't spent much time talking about Meta, Anthropic or OpenAI. I'd love your impression on everything that's going on in this infrastructure side that we talked about. These are three really important players in this grand game. How does all of this development that we've discussed so far impact those players specifically? [00:00:00] S1: First thing, let me just say about frontier models broadly. [00:00:00] S0: Yeah.
· Gavin Baker
[00:00:00] S1: In 2023 and '24, I was fond of quoting Eric Vishria and Eric Vishria's statement ... our friend, brilliant man ... and Eric would always say, "Foundation models are the fastest-appreciating assets in history." I would say he was 90% right. I modified the statement. I said, "Foundation models without unique data and internet scale distribution are the fastest-appreciating assets in history." Reasoning fundamentally changed that in a really profound way. There was a loop ... a flywheel, to quote Jeff Bezos ... that was at the heart of every great internet company. It was you made a good product, you got users, those users using the product generated data that could be fed back into the product to make it better, and that flywheel has been spinning at Netflix, at Amazon, at Meta, at Google, for over a decade. That's an incredibly powerful flywheel, and it's why those internet businesses were so tough to compete with. It's why there were increasing returns to scale. Everybody talks about network effects. They were important for social networks. I don't know to what extent Meta is a social network anymore. It's more like a content distribution, but they just had increasing returns to scale because of that flywheel. That dynamic was not present in the pre-reasoning world of AI. You pre-trained a model, you let it out in the world, and it was what it was. It was actually pretty hard to do RLHF, reinforcement learning with human feedback. You'd try and make the bot model better, and maybe you'd get a sense from Twitter vibes that people didn't like this and so you'd tweak it. There are the little up-and-down arrows, but it was actually pretty hard to feed that back into the model. With reasoning, it's early, but that flywheel has started to spin, and that is really profound for these frontier labs. One, reasoning fundamentally changed the industry dynamics of frontier labs.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Just explain why specifically that is. What is going on? [00:00:00] S1: Because if a lot of people are asking a similar question, they're consistently either liking or not liking the answer, then you can use that as a verifiable reward, that's a good outcome. Then you can feed those good answers back into the model, and we're very early at this flywheel spinning. [00:00:00] S0: Yeah, got it.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Yeah, got it. [00:00:00] S1: It's hard to do now, but you can see it beginning to spin, so this is important fact number one for all of those dynamics. Second, I think it's really important that Meta, Mark Zuckerberg at the beginning of this year in January, said ... I'm highly confident I'm going to get the quote wrong ... that at some point in 2025, we're going to have the best and most performant AI. I don't know if he's in the top hundred, so he was as wrong as it was possible to be. I think that is a really important fact, because it suggests that what these four companies have done is really hard to do, because Meta threw a lot of money at it and they failed. Yann LeCun had to leave. They had to have the famous billion-dollar for AI researchers. By the way, Microsoft also failed. They did not make such an unequivocal prediction, but they bought Inflection AI. There were a lot of comments from them that, "We anticipate our internal models quickly getting better, and we're going to run more and more of our AI on our internal models." Nope. Amazon, they bought a company called Adept AI. They have their models called Nova. I don't think they're in the top 20. Clearly, it's much harder to do than people thought a year ago, and there's many, many reasons for that. It's actually really hard to keep a big cluster of GPUs coherent. A lot of these companies were used to running their infrastructure to optimize for cost instead of performance. [00:00:00] S0: Complexity and performance.
· Gavin Baker
[00:00:00] S1: Complexity, and keeping the GPUs running at a high utilization rate in a big cluster, it's actually really hard. There are wild variations in how well companies run GPUs. If the most anybody ... because the laws of physics, maybe you can get two or three hundred thousand Blackwells coherent. We'll see. If you have 30% uptime on that cluster and you're competing with somebody who has 90% uptime, you're not even competing. One, there's a huge spectrum in how well people run GPUs. Two, then I think there is ... these AI researchers, they like to talk about taste. I find it very funny. "Why do you make so much money?" "I have very good taste." What taste means is you have a good intuitive sense for the experiments to perform. This is why you pay people a lot of money, because it actually turns out that as these models get bigger, you can no longer run an experiment on a thousand-GPU cluster and replicate it on 100,000 GPUs. You need to run that experiment on 50,000 GPUs and maybe it takes days, so there's a very high opportunity cost. You have to have a really good team that can make the right decisions about which experiments to run on this, and then you need to do all the reinforcement learning during post-training well and the test-time compute well. It's really hard to do and everybody thinks it's easy, but all those things. I used to have this saying like ... and I was retelling this long ago ... pick any vertical in America. If you can just run 1,000 stores in 50 states and have them clean, well-lit, stocked with relevant goods at good prices, and staffed by friendly employees who are not stealing from you, you're going to be a $20 billion company, a $30 billion company. Like 15 companies have been able to do that. It's really hard, and it's the same thing. Doing all of these things well is really hard. Then reasoning, with this flywheel, this is beginning to create barriers to entry. What's even more important, every one of those labs ... xAI, Gemini, OpenAI and Anthropic ... they have a more advanced checkpoint internally of the model. Checkpoint is just you're continuously working on these models and then you release a checkpoint, and then the reason these models get fast ...