Invest Like the Best with Patrick O'Shaughnessy
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?
00:29:12 · Patrick O'Shaughnessy, Cliff Sosin
[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? [00:29:26] S1: I don't want to carry myself around as some sort of TSMC expert. I mean, certainly, what they do has better attributes, I think, that regard. They've also got this sort of interesting problem where they did this super hard thing that requires all this process power built over many years, and it's super hard to replicate, and much less amenable to AI just because it is so physical and because it's all these little things, many of which aren't even legible to an AI. It's like some guy who is in the system kinda knows that you have to do this. Because there's so much demand and because they have no immediate, they've basically caused everyone in the world to seek to copy their moat. That's not a great fact. But I think more generally, stepping outside the AI build out, if you think about businesses... We talked about the meta example. Just the idea of businesses that have physical proximity or physical scale might benefit if they can use intelligence to serve their customers better or have lower cost as a consequence.
00:30:14 · Patrick O'Shaughnessy
[00:30:14] S0: I want to go back through the natural complement to intelligence. Just have you explain why it's a complement to intelligence so that people can go away and think about, okay, what companies have this feature that might be interesting? Good example to go talk about physical proximity. How would you think about DoorDash or Uber or these businesses that have local density effects that matter a lot? Does intelligence being ubiquitous sort of naturally magnify all of the scale winners of this type where physical proximity is some sort of key advantage?