Bridgewater is testing an AI-first investment fund

Transcript

From the transcript Odd Lots publishes with the episode.

1. 16:12

   Can you give us a few numbers? Like when you say those benchmarks, what do you think specifically?

2. 16:15

   So I've been for 30 years, one of the jobs I've been doing is training investors, right? And so we've been now setting up AI tests along the different dimensions of what investors at Bridgewater have done for 30 years. And, you know, probably back in 2023, we might've talked about this, but in any event, like on like answering a question about economics or whatever, it was kind of like second year analyst type work. I mean, now it's a hyperproductive super analyst, you know? Now it's still not capable of everything that you need to do to be an investor, but super capable. We set up two, we have two factories running, right? One, which is human intuition translated algorithm supported by AI. AI is helping us move quicker on different kinds of indicators about what's going to happen in the future than we ever had before. I'll talk a

3. 17:04

   little bit about that. But we have a second factory, where we put the AI first. All the people in that factory are training the AI. That's their goal. And we have two funds. We have Pure Alpha, that's the human intuition with AI helping move that human intuition along against this other laboratory where we're doing, where the AI is making the decisions. Should we buy the yen, sell the yen? What's going to happen next in Japanese GDP, et cetera, et cetera. We have both those, right? And human intuition is, is still the bigger of it and works better. But the acceleration of how close what we call AIA is to Pure Alpha is happening incredibly fast.

4. 17:43

   And in fact, that's why we're more and more merging those things. But we set it up that way, right? Like put the AI at the center and see what you can do to build a investment management firm with the AI as the core, right? Now we have human risk controls around it. We have human data controlling the data acquisition for safety reasons and other. But the AI is making the investment decisions and doing that in a better and better way, such that now we've got these two intelligences, this human intuition system that we've worked on for 50 years, compounding all of our understanding, this AI system that's now been at it for two and a half years. And when you look at those outputs, you're like, wow, this is happening, that you can build that. I think we are a couple years from it being significantly better

5. 18:32

   than the group of all humans at Bridgewater. We'll see. That's a bit of a forecast, but that's how fast it's coming.

6. 18:39

   Wait, how proactive are the models right now in terms of generating ideas or coming up with their own new tasks? Because again, when we look back to 2023, I think the idea was like a lot of these things would sit alongside an investor or an analyst, and they would be the ones generating ideas and then using the models to rigorously stress test those ideas. Is it different now? Do you see more, I guess, originality maybe from the models?

7. 19:02

   Yeah, I think a tremendous amount of originality. Now you still, there's like a good argument that there's certain type of breakthroughs that they're not getting to, but if you think about the math proofs, et cetera, and then you think about our business, it's not like the fact that we have 50 years of reasoning proofs of humans gives us the kind of raw material to help train AI. How do you reason about these things? We've been systemizing for a very long time, writing down all our reasoning. We have all of that that helps our AI learn how to learn. And I would say because of harnesses too, if you basically take two things that have obviously evolved a lot since we last talked about this a lot is how harnesses can work to create that generation. Like, what do you actually do? Wake up in the morning,

8. 19:47

   think about what's going on. It said, what are all the things you do? You can just harness an AI to do all of those things and assess how it's doing it. And when I watch it and when I see it and when I see how creative and differentiated it is, it was sort of interesting to do this whole AI thing, doing the same thing we're doing and making predictions about the future, winning in markets at about similar rate as Pure Alpha in totally different ways. Super interesting, a different intelligence doing that. And, and when you put the wrapper around it, right, this is where we're getting close to closing that whole loop. So you have kind of a Claude Claude loop for investing, right? How do you like wake up in the morning? Think about what's going on. Think about how you, what you would do about that stress test, whether that's a good idea or

9. 20:31

   not go through that whole loop. You know, that's like our hope is we've closed that full loop as through a lot of that right now, but close that full loop in the next six to 12 months. And that we have our own. version of that, which is a little clunky at the moment, but coming together such that you could do everything that I think about that I do that investors need to do to predict the future in a full AI loop. And so that's where it's headed, I think. And I think that it's hard. Like one of the reasons you kind of mentioned before, I think in the intro, why don't you see a 6% or 7% productivity growth of all this stuff? It is hard, right?

10. 21:10

    And obviously the It doesn't just flow through to every company. Like you guys, we put a lot of effort in, we have a, I believe the best AI science lab in New York here. We have great scientists working with great investors hard, and it is hard to build this to be as productive as I'm describing, but it's possible. And it's just going to get easier. You know, those things like the harnesses to build harnesses will come, you know, so that then you do, how do I harness podcasts or whatever? And the harness to build harnesses will come.

11. 21:43

    And that'll just make it easier and easier to do these things.