Invest Like the Best with Patrick O'Shaughnessy
00:46:12 · Patrick O'Shaughnessy
[00:46:12] S0: Your finance team isn't losing money on big mistakes. It's leaking through a thousand tiny decisions nobody's watching. Ramp puts guardrails on spending before it happens. Real time limits, automatic rules, zero firefighting. Try it at ramp.com/invest. As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk. Learn more at vanta.com/invest. Ridgeline offers one unified platform that automates away the complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more all at scale. Schedule a demo at ridgeline.ai. Every investment firm is unique, and generic AI chatbots don't understand your process. Rogo is built by finance professionals. It's connected to your data, learns your process, and quickly produces client ready outputs. Check them out at rogo.com/invest. The best AI and software companies from OpenAI to Cursor to Perplexity use WorkOS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product. What do you think are the most underrated sources of future competitive advantage then? If ultimately good businesses need sustainable edge, sustainable competitive advantage to have great profits. Anything underrated? Underrated. We talked about attention and data and physical proximity. These are examples of things that might be super or more valuable in the future than they are today because of cheap intelligence. Is there any type of it? Maybe it's regulatory capture. Are there any types of edge that you think are underrated?
00:47:50 · Cliff Sosin, Patrick O'Shaughnessy
[00:47:50] S1: I get the sense that a lot of these are appropriately weighted. I guess one interesting question I've had, two other questions. One is, if you think of corporate margins in aggregate, do they go up or down? [00:47:59] S0: Down like this. [00:47:59] S1: Right. Do they go up or down? And I think everything we're talking about would argue down. There's many businesses where they make margin because... More consumer surplus. More consumer surplus. Everywhere. Everywhere. What's interesting is if you go back and read a lot of the research reports from the nineties when the Internet was coming along [00:48:15] S0: They said they
00:48:15 · Cliff Sosin
[00:48:15] S1: said the same thing. So the base rate here might not be great. Maybe we're wrong. But I think the main reason they were often wrong was this aggregation economics, the Ben Thompson aggregation economics. So maybe absent that, maybe it wouldn't have been so wrong. And I do think that a lot of aggregation theory breaks down because that basically relies on the idea that consumers only look at the first thing on Google. But it turns out if your agent looks at everything on search, then being ranked first doesn't matter. And all the core things like quality and price and everything else, service become more salient. But the other thing is that maybe another downstream effect of this is you'll be able to build more complicated businesses. Right now, a lot of industries are more horizontal in structure, because the complexity of running your slice is very high. And that makes it hard to subdue what's above and below you and not have undue complexity. With AI agents and basically cheap and ubiquitous code and intelligence, you could build the whole vertical stack. People are trying to do this, I think, in other countries, but you can have a full stack pet care business that does everything from make pet food all the way up through pet insurance and pet hospitals and pet vets and all these things that historically were multiple layers. But now because you can solve the coordination and reduce the principal agent problems by having AI do it, maybe those sorts of businesses are possible. And then maybe those businesses are higher margin in the end, more efficient and more consumer surplus. Carvana is an example of that built before this was easy to do, but maybe people can do it in other verticals, in which case maybe margins go up and there's consumer surplus. The other one is one of the things here is just how this affects wealth and income distribution. I can tell tell two stories. On the one hand, I think a lot of people have talked about this idea that it concentrates income distribution. We've got all these people who made money on various labs and whatever data, NVIDIA, and this idea that there are increasing returns to small groups of people. But the counter argument is if we have cheap and ubiquitous intelligence, a couple of things happen. One is there's all these rent takers in the economy.
00:50:14 · Cliff Sosin
Think you're the software company that charges bowling alleys, and those guys all get smoked, which sucks for them, but it does lower the cost of bowling. And so then that increases living standards for everyone who bowls. And that's flattening because my spending on bowling is small proportion to my income, but people who make less money spend more proportion to their income on bowling. And so that's a flattening effect on income. If all these little rent pieces go away, the decline in corporate profits, all the money that the average American is giving to Bank of America goes away from Bank of America, the capitalists lose, the average consumer wins, sets leveling. The other piece is there's a whole professional class who basically you could define their role as... Intelligence. Intelligence. They take big corpuses of information and apply them to certain circumstances in a fairly straightforward way. And if those all get commoditized, one, everyone gets the best ubiquitous, which is one, good for everybody, but two, particularly good for people who can't afford the best right now or any. But then three, those individuals, they earned these extra rents because they were smarter. They then have to drop to their next best alternative. And in theory, then that might flatten the curve. Now the counter argument to that might be something like, well, yes, but returns to initiative and ambition and intelligence could go up. Because now I can do so much more because these models are like a bicycle for the mind on steroids. They give people who are smart and have initiative, the ability to accomplish so much more. Maybe that actually causes increasing returns to that subset of people, and it steepens the curve. Truman asked for a one handed economist. I seem to very distinctly have two hands on everything. I think that's what makes......
00:51:49 · Patrick O'Shaughnessy, Cliff Sosin
[00:51:49] S0: Interesting questions. [00:51:50] S1: They're super interesting questions. And I'm fond of a lot of situations I look at. There's a lot of crosscurrent. We've started the conversation with the AI build. On the one hand, I can see all these incredible ways to use AI. And I use a gajillion tokens. And I can see how I could use a gajillion more. And obviously, I'm in the far right end of the distribution. Everyone uses as many tokens as me. We are so short. It's gonna be this huge thing that's gone forever. And everyone... And everyone who's in this build, that's gonna make a fortune. On the other hand, the amount I get per token is like going to the moon, and it turns out I'm a pretty unusual guy in a lot of senses, and people are gonna adapt more slowly, and the cost per token is gonna collapse. And so maybe, actually, we're totally overbuilt. I can tell both stories with equal certainty.
00:52:31 · Patrick O'Shaughnessy, Cliff Sosin
[00:52:31] S0: I have a good friend who has been running this very cool experiment where if you break investing down into picking what to own, and then how much to own of each thing that you've picked, overly simplified but interesting way of thinking about it, He'd been running this experiment where he continues to pick the things to own, but then runs his own portfolio construction as he would have in the past in one lane. And then the second lane is just completely handing that off to an AI harness thing that he's built and says, You do all the portfolio construction, but stick with the stuff that I've picked. And what he's found so far is that that is drastically outperforming his version of portfolio construction, which itself is quite sophisticated. And the simple argument that he would make is, well, AIs are just really good at optimization. And they've read every paper on portfolio construction and all this kind of stuff and bet sizing and Kelly criterion. They know it all. And that in the future, perhaps we make that transition where the investor's job is just picking what to own and not how much of each to own. And I ask you this question, what do think about this idea and the future? Because you have such an unusual portfolio where, again, 80% of it is in one specific asset. And my guess is if you were in lane two, AI would weight your portfolio very differently than you have chosen to do it. [00:53:46] S1: I'm underweight. [00:53:52] S0: So I'm just... I'm dying to know what you think about this idea. That's clever. Underweight kirvana. It should be 90.