Invest Like the Best with Patrick O'Shaughnessy
00:06:48 · Cliff Sosin
[00:06:48] S1: We're all gonna have a team of 200 IQ people working full time in our pocket. And that team will have the team that it can deploy many more. And they're gonna be trying to figure out all the little optimizations that we're too lazy to do. And every company will make similar teams available to their employees in order to do their jobs. There's just a lot of businesses where that's not good for them. Consumers are boundedly rational at present. They only have so much cognitive space. And so you go to a consumer and you say, look, we'll take your money, we won't steal it, we'll pay you a decent interest rate. You don't have think about it, and you can go about your life. And the consumer doesn't say, look, if I think about this every day, I could save $400 a year, so therefore I'm gonna think about this every day. The consumer says something like, sounds good enough, and we move on. But with an agent, you would think the agent will continuously optimize for them. Then there's also companies where what they produce fundamentally is some sort of artifact that is just some sort of crystallized intelligence. I'm by far not the first person to realize that AI could be bad for certain software companies, But just this idea that what an AI model is is it's kind of like this general purpose intelligence tool. And what many software tools are crystallized and hardened pieces like projections of intelligence. And so as I've been thinking about companies and investment issues, I found it helpful to assume everyone has really great intelligence and ask what's left. The pushback is often some version of, but you don't understand how hard this is, you don't understand how complicated this is, you don't understand how many smart people have had to think for so long about this, and my hand wavy and approximately right response is, okay, sure, but that's all just intelligence. And to follow this logic all the way through to the end is if intelligence becomes cheap and ubiquitous, what you want to own are the complements to intelligence. And so the complements to intelligence are, their data, attention, love, affection, whatever sort of emotional attachment you want. It's physical proximity, it's scale. It's a little cynical, but it's regulatory capture.
00:08:41 · Cliff Sosin, Patrick O'Shaughnessy
It's these sorts of things where for some businesses, what happens is their finite ability to deploy intelligence, be it economics, just the cost of it, they're only a certain size company, and they don't have access to 10,000 of Google's best engineers for ten years. Or be it just that the organization is behind or whatever, the ability to deploy way more intelligence to your business and your competitors to deploy more intelligence services and your customers to use lots of intelligence. It can work out to be better for you, or if intelligence is a barrier to you serving your customers, the lack of it. Or it can be bad for you if your customers' lethargy is the fundamental element of your economic value. [00:09:16] S0: Maybe pick Meta or something as the perfect example of a company that I think you believe owns perfect complements to Sure. Ubiquitous cheap intelligence, just to bring the example to life. [00:09:27] S1: I'm actually gonna set aside for a moment, muse and agents and that whole thing and focus on business. And the core business at Meta is personalized TV. [00:09:35] S0: Personalized TV.
00:09:36 · Cliff Sosin
[00:09:36] S1: It used to be social media, but now it's personalized TV. And it turns out that people really like watching Instagram or whatnot, discovered that I love dark British humor, and it's great. People talk about the 10,000 foot or whatever. Let's talk about Meta from space. The highest view. They have 250,000,000,000 in revenue, and they spend $250,000,000,000 a year. Some of it gets accounted as first cost, some of it's accounted for as CapEx. It doesn't really matter. Just they spend it all. The question you might ask is, what do they spend it on? And a really reductionist view is that they spend it all on intelligence. What do I mean by that? Well, they're buying data centers. What is a data center? Well, it's kind of an intelligent producing tool. What else they're doing? Well, they're actually literally buying inference. That's just intelligence. What else they're doing? Well, they're paying really smart people to write algorithms. That's intelligence. They're writing code, also intelligence. They're surveying videos. That's intelligence adjacent. Sure. It's intelligence. And so in some highly reductionist sense, and you shouldn't take these things literally, but I'm just trying to highlight the really big directions here. All they buy is intelligence. And if you think forward with the fact that the price of intelligence is falling by a factor of a 100 or something over the next five years, first principles, their costs kind of go away. And that's just thinking about the cost side of things. On the revenue side of things, there are all of the creators. You are a creator. How much more quality creative can every creator that makes content for Meta make? If they have access to intelligence, that's one one hundred the cost and 10,000 times more effective. The point is probably better. And then meta in turn, maybe they don't take their costs to zero. Maybe they spend more or more than zero. And they do a far better job of picking the videos to show people next, which should make the content better targeted and more enjoyable. And then, of course, on the ad side, advertisers could make far better designed, far better targeted personalized ads that then meta in turn can target much better. And advertisers can also build much more slippery funnels or whatever to convert.
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.