Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Yeah. [00:00:00] S1: Let's just say a truck goes from Chicago to Denver and then the trucker lives in Chicago, so it's going to go back from Denver to Chicago. There's an empty load, and C.H. Robinson has all these relationships with these truckers and trucking companies, and they match shippers, demand, with that empty load supply to make the trucking more efficient. They're a freight forwarder. There's actually lots of companies like this, but they're the biggest and most dominant. One of the most important things they do is they quote price and availability. Somebody, a customer, calls them up and says, "Hey, I urgently need three 18-wheelers from Chicago to Denver." In the past, they said it would take them 15 to 45 minutes, and they only quoted 60% of inbound requests. With AI, they're quoting 100% and doing it in seconds. They printed a great quarter and the stock went up 20%, and it was because of AI-driven productivity that's impacting the revenue line, the cost line, everything. I was actually very worried about the idea that we might have this Blackwell ROI air gap because we're spending so much money on Blackwell. Those Blackwells are being used for training, and there is no ROI on training. Training is you're making the model. The ROI comes from inference. I was really worried that we're going to have maybe this three-quarter period where the CapEx is unimaginably high. Those Blackwells are only being used for training. [00:00:00] S0: Right, R is staying flat, I's going up.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Right, R is staying flat, I's going up. [00:00:00] S1: Yeah. Yeah, exactly, so ROIC goes down, and you could see Meta. Meta, they printed ... because Meta has not been able to make a frontier model ... Meta printed a quarter where ROIC declined, and that was not good for the stock. I was really worried about that. I do think that those data points are important in terms of suggesting that maybe we'll be able to navigate this potential air gap in ROIC. [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?
· 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.