Invest Like the Best with Patrick O'Shaughnessy
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.
00:19:16 · Cliff Sosin
And if somebody had read all three papers and thought about it, they would have realized this, but nobody had. But it turns out the models read all the papers, and they'll figure that out. There's gonna be all kinds of great stuff that falls out of that. But at some point, we're gonna kinda finish that. And then it's gonna be about like, hey, there's some way to think about things that are outside of what we know. And we have a hypothesis. But a model, even a super smart model, won't be better at guessing that necessarily... Will be better at guessing that than the smartest person. But the smartest person's odds are super low anyway. So it'll be able to come up with great ideas for experiments to do, and they'll be worth doing. But that'll limit our ability to expand. The other thing I just think about is my own use of AI on a lot of levels. One, just to go back to the commoditization thing. I've totally commoditized my use of models for its worth. It's because I'm lazy about how I do a lot of my building for stuff. I'll tell your Grok bot to build things. And because I don't wanna use up all my Grok bot Weed tokens, I then have it run all of them. I subscribed to all the things. And I'll have it build using those subscriptions on a server. And so I actually don't know what model I'm using anymore. I used to know this model is better, this one's worse. I no longer know. All of this is being optimized on the back end for me and all I know is that stuff is getting built and it's not using up my subscriptions and so we're good.
00:20:31 · Patrick O'Shaughnessy
[00:20:31] S0: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work, so you can close deals faster and with less friction. Customers report a 520% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest. Ridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI and they fall roughly into two camps, with some unsure where to even start and others convinced they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data. And no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation, and you can request a demo at ridgeline.ai. There's this batch of companies that I'm curious what you think about, which is effectively real world experimentation companies that create a loop that's automatic. There's this happening in robotics. There's this happening in several places in bio labs and automated wet labs, automated testing labs. Any commentary on that brand of company?
00:22:16 · Cliff Sosin
[00:22:16] S1: Yeah. Super great idea. Closing the loop in the physical world is gonna be great, and it's gonna accelerate the process of learning. And this is a great idea. But again, there are speed limits. The thing I keep coming back to is some version of, this is amazing. And I'm super, in a sense, AI pilled in the sense of what I think these are things we're gonna do and they're useful for. But I'm also cognizant of the idea that things bump up against... The way I like to tell this to people is I said, look, you're really smart, so you must understand. Being smart has limits. Being smart has limits. And so the ability to just think your way to solutions is limited. And so if you're gonna build an automatic biology testing laboratory, and you're gonna connect it to a computer, and the computer's gonna come up with an idea, and then it's gonna run an experiment, and it's gonna iterate, that's awesome. But if that experiment requires breeding mice, and mice need a year to grow up and die, that experiment will take time. And it might be a great experiment, you might learn a lot, and that's great, still gonna take time. And the lab's only so big, and so you might have 5,000 great ideas, you're gonna pick 20, And the model may be better. Your odds of that experiment marketing might even be 1%. Turns out that might be 10 times better than the average researcher. And that's amazing. It's still 1%. None of this is to be pessimistic. This is super amazing. Discovering all the things that we should know because it's already in our corpus that we forgot is awesome. Discovering all the things and better directing our research is awesome. Automating that research to move faster is awesome. We will learn that will push the frontier. But this is not like suddenly the model looks around and says, you guys missed it. Here's some new world that you guys can't comprehend or whatnot. I don't think that's where
00:23:50 · Patrick O'Shaughnessy
[00:23:50] S0: we're headed. It seems like the market has been, especially in private markets and the leading edge of investors thinking about AI, very focused on investing in supply related stuff, like how are we gonna build and serve all the intelligence versus what I'll call demand related stuff. Someone watching their personalized TV on their meta app or whatever. What do you think about that first batch of companies as investments? Super