Invest Like the Best with Patrick O'Shaughnessy
00:00:00 · Patrick O'Shaughnessy
[00:00:00] S0: Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp just opened for business in The United Kingdom. So if you're running a business in The UK, you can now use Ramp's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on Ramp and so should you. Learn more at ramp.com/invest. OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common. They all use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. That's where WorkOS comes in. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise ready and stay focused on what matters most: your product. Visit workos.com to get started. Rogo is the AI platform purpose built for financial institutions serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft sims, run buyer outreach, and due diligence on data rooms. All with full security and compliance handled. Every action feeds Rogo intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest. You called me saying it's just intelligence was sort of the theme of our conversation. Describe that simple idea.
00:01:53 · Cliff Sosin
[00:01:53] S1: There's this idea that if intelligence starts in amoeba and it gets better and better, and then it's like a monkey, and then it's like a person, and then it's like Einstein, and then it zoom, and it becomes God. I think that that's something of an intellectual mistake. What people have wrong there is they're missing what are the real limits of what intelligence can do. And maybe another way to put it, if you think about intelligence as like an unbounded scaler at the height of a tree, isn't correct. What you should really think about intelligence as is more like a ratio, like the roundness of a ball. And intelligence is a gadget, and we use it to make predictions about the universe. And so we start with this thin scattering of facts about the universe where we have relatively few of them, and the universe is complex. And we use intelligence to interpolate between them. So as your intelligence goes up, your ability to color in the spaces gets better. And some domains are really amenable to that. In mathematics, you can solve it, those sorts of things. And in those areas, get to perfect. But for most of the interesting stuff that is most of the questions in the world, the mathematical term is they're a complex dynamic system. So what that basically means is they're kind of like weather in the sense that you can't just guess your way. You can't make a closed form answer. You can try to simulate it, but if you simulate it, invariably your simulation will have some degree of error in your initial conditions or in each step going forward, because at a minimum your steps, for example, are gonna be truncated. And so what happens is your error will build, and invariably, after the simulation runs for some period of time, you'll be off by some amount. And so for mixing cream into your coffee or predicting the weather or thinking about how groups of people who are all very complicated are gonna interact with each other, intelligence just isn't that powerful. And so my sense is that what you get is this increasing intelligence to the point where you are making all the predictions that something that's super smart could make, which it turns out is a relatively small set of all the interesting things you'd wanna know about the world. To get past that, you then need to go out and get more information about the world, and that just takes time.
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.