Invest Like the Best with Patrick O'Shaughnessy
00:24:12 · Cliff Sosin
[00:24:12] S1: hard. I've joked I've been willfully ignorant about them and probably I'm sort of worse off for it, but there's this big build out. And then you're trying to forecast inference demand. I can't forecast supply or demand within several orders of magnitude. But this idea of how much more inference will people use as intelligence gets better and cheaper is a really, really hard thing. And equally hard is how much better and cheaper inference is gonna get. And then even if you could somehow guess at those things, then there's the whole value chain, and you need to be at the bottleneck in order to capture the rents. And then, of course, finally, you get to your actual competition with your competitors in that particular piece of the value chain. It's just a super hard thing. As I think about my use of AI as a consumer, increasingly, I'm doing stuff like, instead of having the AI chatbot talk to my daughter or text with my daughter about how to do math, now I have it make a three blue, one brown style video explainer so that she can see a nice five minute or ten minute video on powers or area or circumference or something. It's super cool. I don't think I would do that if it was gonna be $5. And so there's this thing where there's real price elasticity. The point I'm trying to make is I'll film myself working out with my Muse app, and it'll show me tip pointers on my form. And I'll do that for very little money. But I won't do that if the price point is real, and I'm probably much less sensitive to the price than most people. AI demand is gonna be really sensitive to the price, which intrinsically, I think, does limit in some sense, the amount in total consumers are going to be willing to spend on this.
00:25:39 · Patrick O'Shaughnessy
[00:25:39] S0: Maybe this is a time to talk about capital cycles or something. It sure does seem like the smartest people in the world, and many or most of the best entrepreneurs in the world are heavily personally invested in this supply build out thing. If you watch SpaceX's Investor Day yesterday, this is basically the theme of it. Arguably, maybe the greatest entrepreneur of all time is dedicated to effectively doing this, building out the data center supply, etcetera. This is just a historical question. If that's all true, and there's so much uncertainty about supply and demand, and there are other ways to go build businesses, products, make money, why do you think so many of the best entrepreneurs, the smartest people are so completely dedicating their lives to the build out of the supply?
00:26:20 · Cliff Sosin
[00:26:20] S1: This is a huge infrastructure build out of a huge and important technology. And it would be impossible for there to be a huge infrastructure build out of a huge and important technology, if there weren't a strong price signal and a ton of talent going at it. So you may have your causality backwards. Obviously, these things work both ways. Could you imagine a telling of the story where nobody interesting who'd made no money was building out the AI? Right? That's probably the best answer. And to be clear, this is a huge and important project. It's only happening because super smart and talented people are focusing their lives on it. It's great that they are. They will have and deserve to make lots of money. It's just very, very hard from where I sit, and there are probably people who are smarter and figured it out. From where I sit, it is very, very hard to figure out where one would invest along this and whether or not you'll make any money doing it. It's not to say that there isn't plenty of opportunity potential there. I've just sort decided to focus my attention more on the longer term equilibrium results that come from what the world goes to. Instead of betting on which railroad tycoon is gonna make money and how many railroads we need, I'm trying to think about, okay, once you have railroads, I'd love to have the foresight to figure out CPG brands come next. That's the way I've been trying to think about it.
00:27:33 · Patrick O'Shaughnessy
[00:27:33] S0: Can you riff a little bit about what you've learned about capital cycles and innovation cycles like this? I would love your perspective on having studied so much market history. If we're in the middle of one of these, maybe the biggest one ever, what does history teach us about these cycles and how they work that's interesting to you?
00:27:49 · Cliff Sosin, Patrick O'Shaughnessy
[00:27:49] S1: You could think about American society right now as a household that's saving money for a house. We're building a house, and we're all kind of feeling pinched because all these resources are dedicated to building out this big AI infrastructure. All these electricians and plumbers and everything else are going to do these tasks. And as a consequence, there's higher costs elsewhere in the economy. And undoubtedly, we're gonna get a lot of value for it. And we're gonna keep pushing it because no one really knows. We're gonna keep pushing it until eventually we overdo it. And along the way, people have to make tons of money because otherwise people won't keep pushing it. And because things have lead times, and because it's a winner's curse, invariably, the last person to stop will regret it. And it's just so hard. It's just so hard. And then you combine that with the fact that there's just so much dynamism in the technology here, which I think was less true for other historic infrastructure build outs. There's just so much change in terms of how you do these things. I mean, other people have made the point... I don't think this is necessarily wrong that we talked about the models, but even at the chips level or whatever, the the potential for AI to commoditize even things like NVIDIA or... For cursive self improvement everywhere [00:28:58] S0: at the...
00:28:58 · Patrick O'Shaughnessy, Cliff Sosin
[00:28:58] S0: at the... [00:28:58] S1: Everywhere. And a lot of it ends up being just intelligence. Like NVIDIA quite literally makes flat files that they send to Taiwan. Their flat files are very hard to make. But what they have is a lot of people who think really hard about it. And so in theory, you could make them. And also, then the question becomes, what if you step back a [00:29:12] S0: little from the frontier? Then the cost of making something comes way down. In that world, would your model be that physical and scale are the things that you'd want in a business that you were to back on the supply side of this equation? TSMC?