Invest Like the Best with Patrick O'Shaughnessy
00:03:45 · Cliff Sosin, Patrick O'Shaughnessy
You have to do experiments. Have to touch reality. One interesting corollary to that is that the Zoom actually becomes... One of the things you can turn your intelligence to is reducing the cost of intelligence, which we're seeing. So what you actually end up getting is everyone gets unlimited, really great, very round ball, so to speak, intelligence for essentially free. And obviously, that's a limit statement. It won't actually be free. But the point is that what we're seeing is that really good enough intelligence is rapidly falling in price. And it turns out that really good enough intelligence will solve the vast majority of questions where people need to use intelligence. Yeah. [00:04:17] S0: Can you say more about why this notion of a complex adaptive or dynamic system means that, actually even perfect intelligence or whatever intelligence we have in a bottle ten years from now or pick your horizon will still be limited in its applications?
00:04:31 · Cliff Sosin
[00:04:31] S1: Yes. Nonlinear dynamic systems. The famous story here and how these were discovered, this came into the branch of mathematics that became chaos theory, is back in the day, there was a guy who was building a weather forecasting model back on big computers that took up rooms and that sort of thing. So he runs the model, and it makes predictions of pressure and temperature over some span of some forecasting period. And he's like, cool. And so then, just to make sure it's still working, they reset it and they run it again. And it starts out basically the same results. But then the results start to diverge, and by the third, fourth, fifth day of the forecast, the results were entirely different. And the guy thought some version of, darn, I must have a bug in my system, and begins the process of trying to figure out why the computer isn't producing the same results in repeated runs. And it would turned out that the technician who had loaded the data into the computer in the second run had rounded off at some decimal, like one decimal less, all of the initial conditions for pressure and temperature and such. And you would have thought that, okay, maybe that would have produced a small but consistent error over the whole forecast period. But what had actually happened was that small error through all of the repeated steps. You take your current conditions, you calculate your next set of conditions, they compound, and you had an entirely different forecast by some small distance into the forecasting period in the unrecognizable weather. One model said it was 70 degrees and sunny, and the other model said it was 20 degrees in Portland. People called it the butterfly effect because at least in principle, a butterfly could flap its wings in Africa, and that could lead to a hurricane in North America or whatnot. And it just turns out that many systems that we're interested in have this characteristic. You can know everything about pressure and temperature, but you can't necessarily write down an equation that you can solve easily that would tell you that there's gonna be a hurricane. You have to model it. And the model in turn is gonna be green at some level, unless you actually go to the step of actually having the whole universe. And so ultimately, in the limit, if you wanna simulate the whole universe, what you actually need is the whole universe.
00:06:25 · Cliff Sosin, Patrick O'Shaughnessy
If you don't actually do that. There are many, many circumstances where simulation is super useful for approximations. And in those circumstances, you can use intelligence to solve them. But there are many more where it doesn't work. Yeah. And so intelligence is limited in that fundamental unknowableness, I guess, about the universe. [00:06:42] S0: So back to this notion of it's just intelligence. [00:06:44] S1: Yeah. [00:06:45] S0: What does that mean about where you think we're going from a more practical standpoint?
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.