Invest Like the Best with Patrick O'Shaughnessy
00:11:34 · Cliff Sosin
The whole point of all this is that maybe instead of taking their cost to zero, with much better intelligence and much lower prices, maybe they can sell a few more apps. All of this is downstream of what they possess, which is not destroyed by ubiquitous intelligence, which is data and attention. They just have this enormous amounts of data about all these people and how they view videos and whatnot. And they have an enormous amount of attention, and these things compound each other. And so I don't think that the availability of cheap and ubiquitous intelligence is gonna cost people not to be interested in watching funny twenty second videos. And so if anything, they can win some time on the margin from other things people do with their time. I don't actually own it, but a company that benefits, in theory, from having a lot more intelligence is Grindr. Basically, Grindr is the gay dating app, but it has been constrained over its life by the fact that it's just not that big a company. And it turns out they've got lots of attention, and they've got lots of data. But their ability to build all the things that you could imagine building on what ultimately amounts to a finite size team and budget and ability to create things has meant in some ways that Grindr can't be all the things you could imagine it could be. And again, I don't actually own any Grindr. I study lots of companies and that just sort of occurred to me that this is another example of something where here, this mental model is what if you had 10,000 of Google's best engineers to go work on your problem for five years, what could you do? And Grindr is one where maybe they could do more.
00:13:01 · Patrick O'Shaughnessy
[00:13:01] S0: I want to talk about the labs. So back to the original point, I think lots of great investors would say intelligence, specifically frontier intelligence, is not a commodity. Historically, the conversation has been about the gap in intelligence between closed source and open source. Increasingly, the conversation is about the gap between what's publicly available from the labs and what's privately used by them inside their own businesses. They seem to have figured out how to stop or slow distillation. Lots of people would say that there's this escape velocity thing happening, and that the frontier token is going be way more valuable and useful, and that the labs themselves can then distill their own models and provide the best version of the cheap intelligence too. I'm curious for you to riff on the labs as companies through this mind of an investor.
00:13:46 · Cliff Sosin
[00:13:46] S1: If they have models that are better and the ability to produce adequate intelligence at way lower cost than everybody else, and nobody can copy it, then it's gonna be a great business. I guess I have a couple of thoughts. One is, as I've watched models get deployed, feels like one way to think about it is you start out with these very inadequate models that are basically able to do a very small subset of jobs. And what happens is the models get better and they work their way up the stack. And with each successive step up, the frontier model has unlocked a larger call addressable market. But behind it, what's happening is that once the eVals are ever get saturated, you would never use a frontier model for chatbot in a customer service application. You'd be an idiot. And so what's happening is those areas, they clearly commoditize. And so there's this wave of hard tasks where you really need a frontier model, and then behind that, you don't. So the friends I have who are big frontier labs companies, bulls or whatever, obviously, these companies initials are public, and they might have an information advantage over me, but would say something like, don't worry. We can also, along the whole efficient frontier, produce cheaper tokens. And maybe they can right now. Maybe they can't right now. Maybe they will in a few years. Maybe they won't. I find these to be very hard to think about. Certainly, the trend has been rapid commoditization of these things if you were to look at the rise of Chinese models in every step. And at a minimum, the cost has been collapsing. So even if they do have a position where the token's necessary to do tasks that are well behind the frontier, it might not be a very big market because it just doesn't cost that much in the scheme of things to deliver. And it is competitive even if they are a cost leader in a shrinking commodity market. And then the other thing is that I think that programming was this huge, use of models. As you push forward, the question becomes for the next next next layers of intelligence, do you still have bigger and bigger places to go after? And the bull story would be some version of, yes, people will pay anything for a model that can figure out how to cure cancer. Maybe that's true, but to go to our prior conversation, think figuring out how to cure cancer requires touching reality.
00:15:41 · Cliff Sosin
And I'm not sure that more IQ gets you that. If you don't believe that there's some super incredible use cases where you just turn an enormous amount for more intelligence, Then you start getting this point where, like, the next step up in terms of, like, you go coding and then maybe something else. But you get into smaller niches. It's like this diamond shaped demand frontier where initially you get wider and wider uses. And then actually, starts to get more and more rarefied. How often do you really need a 300 IQ? One might start to ask. And look, we're gonna find out, and I don't wanna be too dogmatic. But it strikes me as totally reasonable that what we end up with is some version of... There are very few use cases for super elite intelligence. There are lots of use cases for very good intelligence. That very good intelligence is highly competitive and commoditized. And if these frontier labs can somehow become cost leaders in doing that, there's a margin to make in being a cost leader. But then there's also just rampant deflation. And then this question becomes a lot like, how big is the market if good enough intelligence is very, very cheap, then how much good enough intelligence are people gonna buy? I'll just keep playing the devil's advocate.
00:16:44 · Patrick O'Shaughnessy
[00:16:44] S0: So the two things people might say in this case are Javon's paradox that sure it gets cheaper, we're just gonna want to use more of it. So I'm curious what you think about that. The second would be, historically, the new uses of intelligence have typically been discovered by highly intelligent people. My good friend says intelligence creates its own demand. What about that? What about the fact that maybe it's diamond shaped for humans if humans are driving the new questions that the intelligence is set after to answer. But what if the thing itself is creating the new questions? And we train it to do that really effectively and just explore in an open ended way, in the way that all of humanity would be doing, led by the people with 150 plus IQ or something historically.
00:17:25 · Cliff Sosin
[00:17:25] S1: Jevan's paradox is just a narrow example of price elasticity demand. In general, all markets are u shape. So initially, when the product is terrible, there's no demand. And then the product gets better, and people will pay a lot, but they won't buy very much. And then as the price goes down, the size of the market will rise because it becomes Jevan's paradox essentially. But then you get to a point where further price decreases don't actually drive enough demand and the market shrinks. And eventually, of course, if you take the price to zero, then the size of the market goes to zero. One example that I've thought about is the cost of fiber optic communication or just communication. The cost of moving information used to be super high, and people moved very little of it. And then through a whole series of innovations, the cost of moving information went down a lot, and the amount of information moved went up a ton, and the market grew a lot for a long time. And then the market stopped growing. And the cost of moving information kept falling, and the market kept shrinking. And nowadays, Cogent Communications, I think, routes 30 or 40% of all IP traffic across the Internet. And it's through one of its divisions, which is not even its biggest division, and Cogent Communications is a few billion dollar market cap company. Just not a very big industry. Now I'm not saying that we're gonna get quite that small in terms of intelligence, but Jevan's paradox is a statement about a particular spot on a curve, which doesn't necessarily have to be true. And as your other question about can more intelligence create more desire for intelligence? I mean, I have a couple of thoughts on that. Certainly, maybe. I started out by saying you need to touch reality to answer questions. And so if you had a model and it was gonna think more about physics, it could certainly think lots of interesting thoughts about physics. But eventually, it's gonna need to do experiments. And that's not to say we couldn't also do experiments, but now we're bound by our ability to physically do experiments. And so I think there's gonna be this incredible time where there's gonna be so many cool discoveries, which we're already starting to read about, where this paper was written, and this paper was written, and this paper was written.