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Odd Lots Podcast Episode Summary
Episode Title Bridgewater's Greg Jensen on AI, Inflation and What Markets Are Getting Wrong
Episode Description In this episode, Bloomberg's Joe Weisenthal and Tracy Alloway speak with Greg Jensen, co-CIO of Bridgewater Associates, about the intersection of AI technology and financial markets. They discuss how Bridgewater has been utilizing AI and adjacent technologies in investment strategies and examine the potential limitations of such technologies. The conversation also delves into current market conditions and inflation, highlighting Jensen's view that many investors remain overly optimistic about the Federal Reserve's ability to tame inflation.
Key Discussions
AI in Investment
- Understanding AI Applications: The episode begins with a discussion on the excitement surrounding AI, particularly in finance. Jensen explains the different ways Bridgewater uses AI, machine learning, and quantitative strategies in their investment processes.
- Historical Context: Jensen shares his background in the evolution of AI at Bridgewater, emphasizing the shift from expert systems relying on human intuition to more sophisticated algorithms capable of mimicking human reasoning.
Limitations of AI
- Data Challenges: Jensen points out that traditional statistical methods struggle due to limited data in financial markets, which contrasts with more static environments like chess.
- Hallucination in AI: The phenomenon where AI models generate inaccurate or fictional outputs is highlighted as a significant limitation. This reinforces the necessity for rigorous fact-checking and the integration of human oversight.
AI Strategy and Structure at Bridgewater
- Team Structure: Jensen outlines how Bridgewater has restructured to prioritize AI development, employing a dedicated team focused on machine learning applications in their investment strategies.
- Theoretical Generation: AI is seen as a tool for generating investment theories rather than making direct stock picks. This allows for rapid testing and refinement of ideas.
Market Analysis and Macro Insights
- Current Economic Environment: Jensen provides insights into the current inflation and growth dynamics. He notes that the market's expectations for the Federal Reserve's effectiveness in controlling inflation may be overly optimistic.
- Challenges Ahead: He discusses the potential for a recession, highlighting weak growth and persistent inflation as key factors that will challenge market stability.
Reflexivity of AI and Markets
- Adverse Selection: Jensen uses the Zillow example to illustrate how AI can inadvertently influence market conditions, emphasizing the need for understanding the broader implications of deploying AI in trading contexts.
- Human vs. AI Analysis: The conversation concludes with a reflection on the necessity of human intuition in investment management, even as AI becomes more integrated into investment processes.
Conclusion The episode underscores the complexities of incorporating AI into investment strategies, emphasizing the need for a balanced approach that combines human judgment with the advanced capabilities of machine learning. Jensen's insights on current market conditions and inflation provide a cautionary perspective for investors navigating an increasingly volatile economic landscape.
Key Takeaways
- AI Implementation: Bridgewater is leveraging AI for investment theory generation, but human oversight remains crucial for accuracy and precision.
- Market Optimism: Investors may be too optimistic about the Fed's ability to control inflation, with structural financial dynamics suggesting more challenges ahead.
- Reflexivity: The interaction between AI models and market behavior is complex and requires careful consideration to avoid unintended consequences.
Additional Notes
- For more insights and discussions, listeners are encouraged to follow the podcast and its hosts on social media or check out their website for supplementary materials.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
Automatic transcript. May contain errors.0:00You're being sold an AI future where you're obsolete or irrelevant. That vision is wrong. At Palantir, they're building AI that helps workers and unlocks their full potential. American workers are our nation's greatest strength. AI shouldn't eliminate them. It should elevate them. Palantir is here to tell their stories. From factories to hospitals, AI is freeing people from drudgery, letting them do what humans do best. Create. Solve. Build. Palantir, making Americans irreplaceable.
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1:16Hello and welcome to another episode of the Odd Lots podcast. I'm Tracy Alloway. And I'm Joe Weisenthal. Joe, I think it's fair to say there is a lot of excitement about investing in AI. There is also a lot of excitement about using AI to invest. Yes. I mean, I think there's like a new like chat ETF I saw an ad for and there's like, oh, we're getting now. I think I saw another like project. It was like, we're going to have chat GPT pick the stocks for us. And I, you know, I get it. It's kind of exciting. And maybe there's some new way of like these super advanced digital brains that can beat the market, et cetera.
1:55But like, I don't totally get it. Well, I also feel like there's a tendency nowadays for people to talk about artificial intelligence in a sort of abstract manner. You hear people bring up AI almost as a synonym for just software at this point. I think you pointed out recently that the Kroger CEO mentioned AI like eight times on the earnings call. So a supermarket chain, right? Yeah. And, you know, it's like machine learning, tech, algebra, algorithms. It's all existed for a long time, quantitative investing. But it feels like because of the excitement around a few specific consumer facing products that have been unveiled over the last six months and the way they've captured people's attention, people like, you know, suddenly there's a lot of interest in like, how are companies using this tech to do something?
2:47Yeah. Well, I'm glad you mentioned that because today we really do have the perfect guest. This is someone we've actually spoken to about AI before last year. In fact, someone who is at a firm that has a lot of experience using machine learning and AI of different types, and we're going to get into the differences between all those technologies. I am very pleased to say we are going to be speaking once again with Greg Jensen, the co-chief investment officer at Bridgewater Associates. So, Greg, thank you so much for coming back on All Thoughts. Yeah, it's great to be here. Exciting topic. Yeah.
3:25So I actually revisited our conversation from last year. I think it was in May of 2022. And you said two things that stuck out in retrospect. So, number one, you said that markets had further to fall, which turned out to be correct. And two, you brought up artificial intelligence as a major point of interest for Bridgewater. And this was all before chat GPT really became a thing. Everyone started talking about AI at every single conference and earnings call and so on. So I guess just to begin with, maybe you could lay the scene and going back to Joe's point in the intro, we are used to hearing these terms.
4:07So Bridgewater does machine learning and systematic strategies and quantitative trading strategies and AI and things like that. What's the difference between all of these things and how do they relate to each other at a firm like Bridgewater? Yeah, great question. So I think to answer that, let me take a step back for a second and give you a little bit of my background because it all kind of comes together in a way to connect these different pieces. So, you know, even as a kid or whatever, I was certainly interested in kind of translating and predicting things using some mix of my thinking and technology.
4:48So I can think back to in the late 80s using Stratomatic baseball cards, I don't even know what they are, but programming them into computers to try to calculate the way to create the best baseball lineup and use that in fantasy baseball type situations. and similar things with poker and whatever, and trying to learn how to kind of use technology to combine with human intuition to get at what was different ways to create edges. And then in college, when I heard about Bridgewater, Bridgewater was a tiny place at the time, but the basic idea that there was a place where we were trying to understand the world, trying to predict what was next, but doing that by taking human intuition and translating that into algorithms to predict what was next, kind of mixed two things that I loved.
5:36I love to try to understand the world. And I love the idea of having the discipline to write down what you believed and stress test what you believed and utilize that, right? So if you go back, and this is now in the 90s, kind of where artificial intelligence was at the time, most of the focus was still on expert systems, was still on the notion that you could take human intuition, you could translate that into algorithms. And if you did enough of that, if you kept kind of representing things in symbolic algorithms, that you could build enough human knowledge to get kind of a superpowered human.
6:11And Bridgewater was a rare example of where that worked, where given the focus of trying to predict what was next in markets, given the incredible investment that we made into creating the technology to take human intuition and translate that into algorithms and stress test that, it's an incredibly successful expert system, essentially, that was built over the years. I'd say probably the most profitable expert system out there. And that's really what Bridgewater has been about, which is building this great technology to help us take human intuition out of the brain, get it into technology where it's both then readable by, let's say, investment experts, but also runs on a technology basis.
6:53And that's kind of where algorithms, let's say, the mix of algorithms and human intuition. It was really important. If you go through the history of our competitors, they're littered by people that tried to do something more statistical, meaning that they would take the data, run regressions, and then after regressions, let's say basic machine learning techniques to predict the future. And the problem that always had is that there wasn't enough data. The truth is that market data isn't like the data in the physical world in the sense that, A, you only have one run through human history. You don't have very many cycles, even cycles that cycles could take 70 years to play out.
7:31Economic cycles tend to play around for seven years. There's just not enough data to represent the world. And secondly, that the game changes as participants learn. So the existence of algorithms, as an example, change the nature of markets such that the history that preceded it was less and less relevant to the world you're living in. So those are big problems with, let's say, a more pure statistical technique to market. So you had to get to a world where statistical techniques or machine learning could substitute for human intuition. And that's really where kind of the exciting leaps are now, that you're getting closer.
8:13It's not totally there, but you're much closer than you've ever been, where large language models actually allow a path to something that at least mimics human intuition, if not is human intuition. And that you can then combine that with other techniques. And suddenly you have a much more powerful set of tools that can deal, at least take a big leap forward on dealing with the problem of very small data sets and the fact that the world changes as people learn in a way that up until the big breakthroughs in large language models, I think we're much further away. So that's a huge change in the limits of ways that statistical machine learning could affect something with small amounts of data, something where the future varies from the past.
9:02All of those problems were closer to having at least ways to take on more and more of what humans have done at Bridgewater and what humans generally do in investment management firms. And that's a huge leap forward that's going on now. I have one very short, quick question. I realize just now that not long after we talked to you last year, last spring, like a month later, you won your first World Series of Poker bracelet. So congratulations on that. At least say that because you mentioned poker. Did you play the World Series this year? I'm heading out actually after this. because i know there's okay congrats congrats and good luck yeah and it kind of connects to this because i never get to i don't get to play very much poker but i am i really studied what machines were learning about poker so much has been learned in the last five years ten years and um and one of the you know basically trying to translate that into intuitions that i could use you know that basically can't actually replicate a computer-play spoke right in a very complex way, but you can pull the concepts out, right?
10:09And this actually mirrors to part of what we're doing at Bridgewater, which is that as you get to computer-generated theories, that if you can pull the concepts out of these complex algorithms, you can make more of an assessment, a human assessment of whether they make sense and what the problems might be. And that's really a big deal. So there's actually a link between what I'm doing in poker, imperfectly for sure, and many of the concepts that we're trying to apply at Bridgewater. And like you said, we had talked kind of before the LLMs had really hit the public scene. But I mean, just to give you a little bit of background for me, if you go back to 2012, first off, we brought Dave Ferrucci, who had run the Watson project at IBM, that had beat Jeopardy into Bridgewater.
10:59And that was a time when I was trying to experiment with, okay, what can we do with more machine learning techniques? And Dave was trying to take what he had done to win at Jeopardy, but actually put in more of a reasoning engine. Because while what happened on Jeopardy was impressive, it was pure data. It had no idea why it was doing what it was doing. And therefore, really, a lot of the path with Watson or whatever was going to be very hard to move forward with because at its end, it was just statistical. and it didn't really have any reasoning capability. So Dave came to Bridgewater and later partnered with Bridgewater to roll out a company, Elemental Cognition, that's focused on using large language models, et cetera, but overlaying a reasoning engine that essentially helps with things like hallucinations that large language models have and focus on what is human reasoning and how does it work and how does that limit views that are unlikely to be true.
11:56So that's one thing. And then in 2016 or 17, I was introduced to OpenAI. And actually, as they transitioned from a charity to a company, I was in that first round and met a lot of the people and looked hard at their vision using scale and technical scale to build general intelligence and build reasoning. So I both worked with Dave Ferrucci and sort of understood many of the people at OpenAI at the time and moving forward with those things. And then I was literally the first check for Anthropic, another large language model, kind of people that had been at OpenAI. And so I've been passionate about this, trying to take different paths to how will we build a reasoning engine to overlay on statistical things and a couple different approaches that were being applied at the time.
12:49and obviously different, they've panned out to a different degree, but many things are coming together now to say, okay, you can actually, in a way, at a pace and a speed humans can never do, you could replicate human reasoning. And that's a huge deal. And if you could really break through that, you could start to apply it in so many ways in our industry, I believe, and obviously way beyond our industry.
13:19Thank you.
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15:04Crypto trading provided by XeroHash. Complete disclosures available at public.com slash disclosures. You talked about earlier generations trying to embed human knowledge. And I'm wondering if an analogy is like, I remember when Deep Blue came out and they had all the grandmasters sort of work with IBM to come up with this great computer program that was basically as good or eventually better than Garry Kasparov. But then the next generation of chess computers didn't even have the grandmasters playing. It just learned the game from ground up and crushed those previous generation. Is that sort of what we're talking about here with the transition from earlier engines to the new sort of LLM focus, which is like the sort of reasoning comes out of the computer rather than having to be taught directly by the experts?
15:55Yeah, I think something like that is happening, right? You got that in chess because once you had the ability, you had enough data and enough compute, you're able to do enough sampling that you got to the point where the pure data process with good human intuition on how to build that data process, but a data process was able to beat those rules-based things. Now, chess, unlike markets, is a little bit more static in the sense that while there are adversaries and the adversaries will try to learn your weaknesses, is it's more static and the rules of the game are steady and those types of things so that that sampling could work.
16:34Although it's interesting, I love the, like, because it is an analogy to some of the problems that pop up and will pop up. If you take AlphaGo, right, on the Go game, Go got also after chess, obviously, but Google was able to create this game that was beating the pros and radically beating the pros, killing everybody and getting better and better and better. Although, I don't know how up to date you are, but then there was this loophole in it that another person who was a mediocre Go player but a computer scientist who thought there might be a hole in this super AI used a little program to find the hole.
17:14And what it illustrated was the AI had no idea how to play the game because what a six-year-old wouldn't, the mistake the AI was prone to was a mistake a six-year-old playing Go would never make, where if you made a large enough encircling, if you now Go works, but if you encircle the other guy's pieces, you eliminate them all. And something that would never work in a human game is make a really big circle. And because it never came up in human games and because when they perturbed human games and started playing computer against computer, they basically started with a seed of human games. They never perturbed it enough to try this out, to try a massive circle.
17:56And a human would never let the massive circle happen. It's so easy to defend against. But actually, the best Go algorithm in the world allowed it to happen, right? And now a mediocre Go player with a little bit of AI found a way to beat this incredible Go game. Again, because the Go algorithm at that time had this tremendous amount of data, but the things that weren't in his data, it wasn't aware of. And it wasn't in any deep sense, understanding the principles of the game. So that's the type of, you know, data problem you can have even with a massive amount of data played, you know, millions and millions of games, but to play every possible go board, you'd have to, there's more possible go boards than there are atoms in the universe.
18:36So it was never going to calculate every possibility and it never got to reasoning. Right. And that therefore that was a weakness, right. And on the other hand, And had you mix that, blend that, even with a basic reasoner that a language model could come up with understanding the rules of Go and being able to talk about it, there's an element of knowing those things that humans already know that's possible with a blend of, let's say, a statistical technique like AlphaGo was using and a reasoner to prevent these types of mistakes. I like that story because it makes me think I have a chance against the super smart, super computer.
19:15Okay, that's kind of comforting. But I definitely want to ask you more about weaknesses in AI and large language models. But maybe before we do, you know, just sort of setting the groundwork once again, but when we see headlines like Bridgewater restructures will put more focus on AI, what does that mean exactly? What does it mean for a firm, an investment firm like Bridgewater to build up resources in AI? And then secondly, could you walk us through a concrete example of how AI would be deployed in a particular trading strategy? I feel like the more concrete we can get with this, the more helpful it'll be.
20:01Yeah, great. Right. So I think as we restructured, one of the things that as we made the transition at Bridgewater, you know, from Ray having the key ownership to ownership at a board level and that transition, we have done something we hadn't done in the past, which is essentially retain earnings in a very significant way, which allows us to invest in things that, you know, aren't going to be profitable right away, but are the big long term bets that we're making. and certainly recognizing that there's a way to reinvent a lot of what we do using AI machine learning techniques to improve what we're doing to understand the world, accelerate that.
20:43And specifically, what we've done on the AI ML side is we've set up this venture. Essentially, there's 17 of us with me leading it. I'm still very much involved in core Bridgewater, but the 16 others are 100 % dedicated to kind of reinventing Bridgewater in a way with machine learning. We're going to have a fund specifically run by machine learning techniques, which will kick me into, Tracy, what kind of strategies you could do. That's what we're working on right now in that lab and pressing the edges of what AI is capable of. Now, AI, like machine learning is capable of. Right now, there are big problems, right?
21:20A, you take large language models, and they have two types of problems. One thing is the basic problem is they are trained on structure of language. So they usually return something that looks like good structure of language. They don't always return accurate answers. So that's a problem. It hallucinates. It makes things up because it's more focused on the structure of what word or what concept would come next than whether it's accurate in what concept comes next. Can I just say, when I hear AI hallucinations, it becomes so science fiction for me. It's very like robots dream of electric sheep, kind of.
21:57It's just so surreal. Yeah, well, I mean, in this case, you can imagine what's happening, right? Because it's just what it's trained on, right? So if you're just, if basically the basic concept is give it any stream of words and it'll predict based on having read everything that's ever been read, what comes next, right? And that if it's a little bit wrong, in what comes next, it can misfire and give you something that sounds like something that could come next, but actually is wrong. And it's just what it's trained on. It's trained to predict the next word, slight errors in that, create those types of issues.
22:35Now, the algorithm is pretty remarkable, particularly like we, as I said, I've been tracking an AI as an investor for a long time and looking at their technology for a long time. and up until there's GPT-1, 2, 3, and many versions of between. And then at GPT-3, it started to have some use. GPT-1 and 2 were barely coherent. GPT-3 was somewhat usable for certain tasks. 3.5, which is what chat GPT is, got to a certain level. Like on Bridgewater's internal tests, you suddenly got to the point where it was able to answer our investment associate tests at the level of a first year IA right around with chat GPT 3.5 and Anthropix, most recent quad.
23:23And then GPT-4 was able to do significantly better. And these are at least what we thought were conceptual tests, significantly better than our average first year investment associate that went through training. And similarly, it's able to take the LSAT and do well, et cetera. So it can be basically pretty smart. It is pretty smart on a wide variety of things with errors, but pretty smart on a wide variety of whether it's the MCAT or the LSAT or Bridgewater's internal tests or whatever, a whole wide variety of things. This is a big deal that it can achieve all of those kind of academic things.
24:00And yet it's still 80th percentile kind of thing on a lot of those things, which is remarkable to be 80th percentile on many, many different things. But at the same time, it's 80th percentile for a reason. There are flaws, meaning it's not 100%. And so that leads to like, you need to find a way to work through those flaws, right? And that's really where, you know, so if somebody is going to use large language models to pick stocks, I think that's hopeless. That is a hopeless path. But if you use large language models to create some theories, which it can theorize about things, and you use other techniques to judge those theories, and you iterate between them to create a sort of an artificial reasoner where language models are good at certainly generating theories, any theories that already exist in human knowledge and putting those things connected, connect together.
24:55They're bad at determining whether they're true, but there are other ways to pair it with statistical models and other types of AI to combine those together. And that's really what we're focused on, which is combining large language models that are bad at precision with statistical models that are good at being precise about the past, but terrible about the future. And combining those together, you start to build an ecosystem that can achieve, I believe, can achieve the types of things that Bridgewater analysts combined with our stress testing process and compounding understanding process at Bridgewater can do, but it can do it at so much more scale Because all of a sudden, if you have an 80th percentile investment associate, technologically, you have millions of them at once.
25:42And if you have the ability to control their hallucinations and their errors by having a rigorous statistical backdrop, you could do a tremendous amount at a rapid rate. And that's really what we're doing in our lab and proving out that that process can work. I see. So is the idea that AI could possibly generate theses or ideas that can then be rigorously, you know, statistically fact-checked by either the humans or, you know, existing algorithms and data sets? Is that the idea? Yeah. And then, yes, but the idea goes further. But yes, that's the start. Language models can do that. Statistical AI can then take theories and generate whether those have at least been true in the past and what the flaws with them are and refine them, offer suggestions on how to do them differently, which then you could dialogue with.
26:38So then the other strength the language model has that humans are weaker at is now take a complex statistical model and talk about what it's doing. And there's ways to train language models to do that that then allow sort of a judgment to say, okay, now let's think about what's happening here and reason over what's happening. So you use the way we've modeled this kind of out is language models can come up with potential theories. Now, there's a limit to that. It's not the most creative thing in the world, although it's theory at scale for sure. And then there's – and again, that's language models with good – you've got to tune your language models in a certain way so it's not straight out of the box.
27:23But then you can use statistical things to control that. Then you can use language models again to take what's coming out of that statistical engine and talk about it with a human or other machine learning agents and kind of report back on what you're finding and what that is and the types of theories that are out there that might run contrary to what you believe, which can lead to more tests and other things. So that's the loop that I'm very excited about. And as I said, up until the thing that statistical AI was limited because it was focused on the data of markets, where language models, the good thing is it has a much better sense of something that a statistical model wouldn't really have.
28:06A statistical model of markets doesn't get the concept of greed. Language models pretty much understand the concept of greed. They've read everything that's ever been written about greed and fear and whatever. So now they can start to think about statistical results in the context of the human condition that generates those results. Big deal and really a radical difference. Let me ask you one very simple question, and it might be one that speaks to an anxiety of listeners. if already GPT can perform at maybe the type of level that a high quality first year or second year associate or analyst at Bridgewater can do, does that mean fewer hires in the future, humans being hired at Bridgewater?
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28:48Or does it mean the same number or more humans doing even more? Like, is it a replacement? What does it mean for the type of person that would have been 10 years ago, first year employee at Bridgewater? What I think people should expect at Bridgewater and just generally at Bridgewater in a hurry is things are changing quick. That really requires people to be capable of playing whatever role is necessary in order to do that. If you go back at the clock at Bridgewater when I started or just before that, we had rules on how to trade, but we were using egg timers and humans to do these things. And over time, computers could do more and more of that.
29:33And we kind of got to this point where it was, I'd say, kind of humans settled into the role of intuition and idea generation. And we use computers for memory and for constantly running those rules accurately, et cetera. That was a transition that got to 50-50 technology and people. And now this is another leap, right? And it's definitely true that it's going to change the roles that investment associates play. Now, exactly how, and you still need the foreseeable future, you're going to want people around that working on those things. There's edges that these techniques I'm describing certainly won't do well for an extended period of time.
30:18And there's how to build the ecosystem of these machine learning agents, et cetera. And so what I've found, and certainly the people in the lab, you want people who are curious about these new technologies. You want to utilize them. And that's going to be really part of the future of work, I think. I think it's going to be very hard in any knowledge industry to not utilize these. And we're seeing this huge breakthrough in coding that is so democratizing in a sense that you really need to know what you want to code more than you need to know coding. And that's a big breakthrough. So a bunch of people that weren't as well-trained or as capable in C++ or in Python or whatever can suddenly get what they want so much faster.
31:03So all of a sudden, the skill sets are changing. And they're changing in ways that I think are a surprise to many because it's actually a lot of the knowledge work, A lot of the things where you have content creating and whatever that I think people thought would be later in computer replacement that are happening faster. So the main thing is I'd say right now there's so much in flux that having flexible, the more you need flexible generalists who can have an eye towards this, an eye towards the goal and be able to utilize whatever tools are necessary to get there. That's really where I think you're seeing a fair amount of change quickly.
31:43So you mentioned earlier that just the existence of machine learning can impact both the current environment and the future. So I think you said that the future data points aren't going to look like the past data points simply because machine learning exists. Does that sort of reflexivity between machine learning slash AI and markets become more of an issue as AI and machine learning becomes more and more popular and more entrenched? Yeah, I think it's a big deal, right? And I think it's both something that's going to cause accidents and something I'm super excited about. Obviously, I'm excited about the power of this that I think there's ways to utilize it really well.
32:26And also, there will be a lot of mistakes. Like you're saying, there will be funds that will use GPDB to pick stocks and not really deeply understanding what's happening and what the weaknesses of that might be. There are already plenty of times where statistical, pure statistical, because there's not enough data, you're not building with those fundamental issues in mind. Not that it was directly markets, but in the housing market, what Zillow did is a great example. Zillow goes out and uses an AI technique that wasn't fit for purpose for what it's worth, but they use an AI technique to predict housing prices and then go into the market, start buying houses that they think are undervalued, right?
33:07And they have a couple problems. One is while they had a ton of housing data, it was over a relatively short period of time. So even though they had what looked like tons of data points because they have the price of every house and everywhere, whatever, there's still a macro cycle that affects everything that was underestimated in what they did. And secondly, they underestimated what it would be like in theory versus in practice when it's actually an adversarial market. Every time they won an auction, there was something about that particular lot that the other people bidding on that lot knew that they didn't.
33:39And so it ended up obviously being a huge problem for Zillow and they kind of had a big impact on the real estate market and then a big failure. And that's the kind of thing you're going to see over and over again. If because the basic problem that the data that you're looking at isn't necessarily the data you'll face in real world, you're not facing the adversarial problem when you're looking at that data the way they were. You're not a statistical technique that's very good at seasonality and trend following, might not be very good at understanding macro cycles and so on. So that was another case where, you know, Zillow is a case, and I think we'll see it over and over again, where the recognition that it's not as simple as taking machine learning out of the pack and applying it to this problem, even when there's a ton of data, right?
34:27Some of the places where there is a lot more machine learning going on. Very short-term trading arguably is better for machine learning because there's a lot of data and you can learn faster over that data. And there's some merit to that. And in terms of tangible places, this is now years ago, but where we started applying some of these techniques, we're in things like monitoring our transaction costs and looking for patterns in shorter term data because there's a lot more data. But on the other hand, the data often, it's like having the data of your heart rate for your whole life. You can feel like, wow, this is a, I've got every heartbeat for 49 years.
35:05That seems like a lot of data, but it's totally irrelevant when you have a heart attack. So that even when there's lots of data, it can be misleading. And that those are the types of issues that will lead to these techniques having huge problems, which means it's not out of the box, AI is going to solve all those problems. You really, and this comes back to, you have to understand the tools, what they're good at, what they're bad at, and put them together in a way that uses what they're good at and protects them from what they're bad at. Now, nothing, no process we're coming up with will do that perfectly.
35:37But the more and more you can do that, I think the more and more you can become, let's say, better than humans at that because humans have many of those fallibilities or versions of those fallibilities that these processes will have. And that's like that'll be the question of how far we can how far we could take that and how how much human judgment is better than those things, which is stuff we'll be experimenting with as we as we go along. Thank you.
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38:53to a large language model. It creates some output. You don't really know what it did to get there. And so that's sort of different than dealing with a human analyst. You can say, well, did you think about that? Did you think about that? Can you talk a little bit more about the sort of, I don't know if that's a weakness, or how do you sort of get around the fact that it's still difficult to query an AI model and say, how did you arrive at X or Y conclusion? Yeah, and I think that's really important, but also something that's more and more breakable. Because even with humans, one of the places where I think there are a lot of areas where Bridgewater has a strength, right?
39:32Bridgewater has a strength. And we never went from a statistical model, so we built data based on what we needed for reasoning. And as a result, we have a better, longer, cleaner database than I think anybody has. We've been thinking through this problem that you're referring, which is how do you actually get out what somebody means? you'd be surprised how hard it is to truly get from a human. Humans don't actually know why their synapses do what they do. They actually, like when you ask somebody to describe something, you get some partial version of what they're thinking. If you took like an intuitive trader and you start peeling back all the reasons, that's very hard.
40:07We've been doing that for a long time and have an expertise in doing it. And I would say that humans don't even know what they're doing often. But there are ways to, like you're saying, query and force questions. What about this? And what about that? That will help pull out human intuition. And what you find with machine learning algorithms, if you get good at this, and this is going back to 2016, 2017 has been critical to my work, is there's a way that you can query machine learning algorithms like you query. It's different, but the concept's the same as how you query humans to get at why they really believe what they believe.
40:43And as I was saying, I think there's actually elements of large language models interpreting what statistical AI is doing that allows that process to accelerate. And I think it's very critical. You really want to know because that's the way you find the flaws. If you go back to my Go example and you say, you can think about, if you can query a model and think about what it's done and what it hasn't done, then you can figure out what data is missing, And you need to set up adversarial techniques in order to keep querying an algorithm for what it's doing. And again, I think that's still an area of research, but a process that's moving along quickly to basically get to the point where the standard is, even though a machine learning technique might be doing something very different than a human is, that it can still explain itself.
41:31And it might not perfectly explain itself, just like humans don't perfectly explain themselves, but to a very high degree of confidence across a wide range of outcomes that you have a sense of what's going on is possible. And that's part of the design of what we're putting in, which is, well, how do you query it? How do you give it more information, remove information, et cetera, see how it changes its mind to determine roughly what's going on? You know, you mentioned the data sets there, and I guess it's a cliche nowadays to say, well, a model is only as good as the data that it's trained on.
42:04But it's a cliche because it's true. Do you use your own internal data for the large language models or where are you actually pulling in data from? And then secondly, like what type of data have you found so far is most useful for these types of projects? Well, I think the things that are most interesting to us, A, we're trying to learn things that we don't already know. So we're being careful about what kind of Bridgewater knowledge we put in here because it's not that helpful if we reinvent Bridgewater. Somewhat helpful, but it's not as helpful as, let's say, reinventing everything that we don't know about, that other people have thought about, et cetera.
42:43And so point one, in the lab right now, at least we're focused on not making this too Bridgewater-centric on purpose because it's in that way we'll learn things that we don't already know. And if you just fed a Bridgewater information, which we may well do, that could be a productivity-enhancing thing, but you'll quickly produce something very similar to Bridgewater where what's been amazing so far is we're producing good results by Bridgewater standards, but different, very, very different conclusions and different thoughts than what we have internally. So I think that's point one choice. Now on raw data and cleaning data and how you put together data.
43:19Now we are benefiting from Bridgewater scale on that. That's a big deal that over the years, again, precisely because we took human intuition and said, what data do we need to replicate that intuition? We have a unique database where if everybody else is pulling from Data Stream, Bloomberg, et cetera, we put together the data we needed to feed our intuitions. Oftentimes that data didn't exist. We had to figure out the way to create it. And also, we're big believers that you need to stress tests across a very long period of time. So we have much longer data histories. Now, those things are certainly valuable in a context of small data, any quantity of data, any understanding the data and being able to therefore, for a given theory, find appropriate unoptimized data.
44:06Those are big deals. And that we are using, and that does allow us to move forward more. And on the large language models, there's still a lot of work to be done, but you certainly can train through reinforcement learning to make sure that they're not making mistakes that you know about. And so there's ways to do that. Now, we've been trying to avoid that for the reasons I was describing before, avoid doing too much of that, of injecting our own knowledge and use external sources to do that. But that's still part of the tool set that will be available, that yes, you can train it more directly on things you already believe to be true if you want to do that.
44:49And that certainly will lead to answers that replicate your thinking more quickly. So just on this point, one thing I wanted to get your opinion on is how good is AI at predicting big turning points or structural breaks in market regimes? Because I don't know about you, Joe, but one of the first things I did with ChatGPT was I asked it to write a financial news article about inflation just to see whether our jobs were in danger. And you could tell that it was trained on not quite current data. It was talking about how inflation has been stubbornly low for many years and the Fed is trying to get it to the 2 % target.
45:32But how good is AI at predicting those regime changes? Because if you're running a macro fund, I imagine that's one of the important things that you need to do is try to figure out when something is fundamentally changing in the market. Yeah. And I'd say terrible if you use it in the sense that you're using it, right? It's a little bit like saying, well, how good are people at that. Well, people are pretty darn bad at that, right? That doesn't mean that there isn't a way or some people who could do such a thing, right? So AI, it's hard to just think about AI as a thing or think of like, okay, well, if I'm just going to use chat GPT for that, you're exactly right.
46:11Chat GPT as it comes out of the box is only trained over to a certain history and it doesn't care unless you know how to make it care. It doesn't care that it's just answering a question about inflation based on everything it's ever read about inflation. Time isn't even that important unless you make time be very important to it. And predicting. And so you have to know how to use the tools to generate the type of outcome that you're describing. So do I think like AI out of the box will do that? No, absolutely not. It'll be awful at that. Are there ways to take what's embedded in AI to come up with a way to do that?
46:48I would, embedded in language models. And if you combine that with statistical tools, yeah, there's a path there, but it's not going to be as simple as open up ChatTPD and ask it that question. There's more involved. But if you basically, it is helpful to have an analyst that's read everything that was ever produced, even if they stopped reading in 2022, in 2021, I should say. There's a way to use that, but you have to use it correctly and not misuse it in order to try to generate that answer. All right. So I can't just ask a large language model when will inflation get back to the Fed's target.
47:26But I'm speaking – I'm not speaking to a large language model. I'm speaking to CIO at Bridgewater. And I do – I am curious. I do want to talk a little – we do want to talk a little macro. And before we sort of like – I'm not going to directly ask you when inflation will be back at the Fed's target. But what strikes me about the last year and since the last time we talked that's really blowing my mind is that rate hikes have been a lot faster than people expected. Inflation is hotter than people expected. The unemployment rate is lower than people expected. What is it that people misunderstood a year ago about the economic machine such that the Fed has hiked rates much faster than people expected?
48:09And yet it's been surprisingly ineffective at cooling things down. And to this day, there seems to be a surprising amount of economic momentum with Fed funds at like five and a half percent. Yeah, it's a great question. I have a bunch of thoughts on it. Certainly I can't speak for all people, but I can speak for myself. I've been wrong about a bunch of those things. So just to talk about what I certainly, and let's say we at Bridgewater, didn't nail. Like you're saying, I thought the degree of, and certainly are everything that we had understood in our statistical models or whatever, we knew that we could easily be wrong.
48:43But that the degree of tightening was fast and high relative to history and that any tightening like this in the past had led to significant downturns. Although the lead lag is somewhat variable and still possible, that's right. But I think a lot of things that happened different than I expected was, A, usually when, let's say, as they were last year, stocks were falling and short rates were rising, that formula in history always led to the personal savings rate rising. People seeing higher interest rates available to them, asset prices falling, housing slowing down, etc. Usually people save more money, which meant there was less revenue for companies, which meant there were layoffs, which meant savings rates rose more when the employment market weakened.
49:30And a recession was caused through that mechanism. And what's happened in this period is that I think, and now I could be wrong, that normal, let's say, impact of the higher interest rate and wealth effect impact was offset by the fact that wealth had been changed so radically in the 2020-21 period by fiscal policy and that we have fiscal policy as extreme as the war. and the ripple of the length to which that disrupted, let's say, those other relationships was interesting. The degree of it was interesting. I think there's ways we should have, you know, looking back now, I think there are reasons that I should have known that.
50:11And some people were pointing to that, but that created much less of a reaction in household savings rates as you normally did. You came out of the recession with better balance sheets than ever. People were willing to disave. So even as rates climbed and actually debt growth collapsed as it normally would, but what simultaneously collapsed outside of debt is, or let's say increased, was the willingness to spend down the cash that households had built up. And that cash doesn't just disappear when one person spends it, it goes on to others' balance sheets, whether it's corporate balance sheets, other household balance sheets.
50:48And so what's been happening, it appears, is that money's been spinning around in a way that made the rate hike have much less impact than I believe it would have had pre-COVID if you had anything like that rate hike. On top of that, within the US economy in particular, corporates had extended their duration. So the impact is taking longer, the effect on corporates, although I think it's happening, but it is taking longer. And so there are a few other things. And then obviously, the benefit of when nominal, what did happen is rate rises created a decline in nominal demand, but that's mostly shown up in inflation.
51:24So nominal demand's fallen pretty much as much as I've expected. It's been more inflation falling than real growth falling, which again, I think there's reasons that that's the case. But before there was this massive demand shock from what the central banks and the treasury had done to get everybody's balance sheets up. And supply was struggling to keep up with this massive demand shock. And now demand's falling, but supply is still catching up to that old level. So on net, real growth has come out stronger. Now, I could see all that in the rear view mirror. I didn't by any means predict that that would be the way it would play out.
52:05But I think that's why you've had this stubborn strength in the economy and that's created a certain amount of stability. Now, equities have rallied significantly since then. Some of the negative wealth effects have eased. At the same time, though, a lot of that excess cash that was on balance sheets have been distributed. So there's a mix of pressures here that looking forward, we do think inflation is still coming down a bit, although on net, We've entered what we think is a more inflationary environment such that 2 % inflation probably more likely to be more of a bottom than a cap. And we do think fiscal policy as the way to deal with the recessions is probably politically the more likely outcome than, let's say, moving back in the next recession to more QE.
52:51And fiscal policy is a lot more inflationary and effective in a sense of stimulating growth quickly as we've seen. And so I think you're going to see a world where we are still adjusting to a higher inflation world that's deglobalizing. Although everything we're talking about on the productivity front, maybe machine learning changes that we'll see. But largely X, a major productivity miracle, I think deglobalization, the move towards fiscal policy has changed the long term inflation path in a way that markets haven't fully adjusted to. because markets right now believe the Fed is totally credible, that inflation is going to return to target, basically with very little problems.
53:35When we measure the pressures, we don't think so. We think it's going to be much more challenging to get inflation where markets expect it. The impact on earnings is going to be a lot more negative than the markets are currently expecting, and it's going to take longer and be harder. So big differences between what we're seeing and expecting and what the markets are currently pricing. So I think last year you were talking about the possibility of a recession in 2023. Is that off the table now? So you're still positioned, it sounds like, for a level of higher inflation, but it sounds like maybe you're a bit more optimistic on the growth front.
54:16Yeah, we've been wrong on growth. So I'd say, look, we think it's going to be a struggle. We're in a state of disequilibrium in the sense that relative to a given level of growth, we think the level of inflation to the Fed target, that they're going to have a difficulty achieving growth and inflation at the levels they want and are going to have to give on something. In the short run, I think that's leading to higher rates. The expectation that the massive easing is coming is unlikely. The Fed's going to continue to have to be tighter longer than the markets expect. And so that's bad for, let's say, bonds and long-dated short rates.
54:50It's also probably bad for equities. And at the same time, we think growth will be struggling. It's nominal growth slowing. I think nominal growth is going to continue to slow. And as nominal growth slows, while you're more in stickier inflation, things like wage growth and some of the service areas, more sticky inflation, you get more of a challenge as nominal growth falls for it to just flow through to inflation. So my view is you end up with growth disappointing a bit and inflation disappointing on the high side a bit, ending up probably bad for bonds and a little bit bad for equities and generally weak growth.
55:31And if that weak growth starts to translate into rising savings rate, you could easily end up into a recession and one that's going to be difficult to deal with. But yeah, I'd say we've tamed, I've tamed and we've tamed at Bridgewater to some degree our view on growth while still negative, not as extreme as it appeared. And it's a more gradual process that's unfolding. And then on the inflation front, while we've had, we expected a quick decline in inflation as now the GDP fell, we do think we're in the range where you're in the much more stubborn part of inflation. It's going to be harder to continue to get those inflation falls going forward.
56:08So just to be clear, though, you do think there is a gap between either what the market sees in terms of how much more work the Fed is going to have to do or what the Fed thinks, how much more work the Fed is going to have to do and what basically you think the Fed is going to have to do if it actually is serious about getting inflation back to something resembling its target? Yeah, I think so. I mean, I'd say the Fed seems a little bit more realistic than the markets do on what it's going to take. But right, that we think that's right. That when you look at what the markets are saying, that it's super optimistic.
56:39It could come true. You do need essentially to get an equity rally from here, you have to have lower rates fairly quickly into a world where earnings are pretty good. That's kind of the discounted line. To get above that, you need even more than that. And I think that line is super optimistic relative to what we measure. And again, I'm using the words, but I'm describing a process that's based on studying hundreds of years of economic history and how these linkages work and building all of that into a systematic process, but just spitting out kind of the output of that is that it doesn't appear that the Fed will be able to achieve that and that we're in this disequilibrium where you still have more inflation relative to growth and you don't have an easy way to close that gap.
57:27So we'll see. We've been wrong about that in terms of at least what the market outcomes have been for the last six months or so after having been incredibly right for an extended period of time. And that's part of it. We get a lot of things wrong and that's normal. But I think when you break down why we got it wrong and the ways in which that, you know, we've learned from that and the ways in which our processes have taken in new information still leads to this view that the markets are overly optimistic about how easy that's going to be. All right. Well, Greg, we appreciate you coming on and outlining your thought process, both around the markets and AI and how you're actually deploying this new technology.
58:11So really appreciate it. Thanks for coming back on the show. My pleasure. Good to talk to you. Good luck in Vegas. Yeah. Bring home another bracelet. I'll try. Thanks, Greg. That was great.
58:35so joe i feel like i have a slightly better conception of exactly how this kind of technology can be used for investing so the idea of maybe you have the ai models come up with theses or ideas that could then be rigorously fact-checked because all the AIs are hallucinating and things like that. That makes some sense. Yes, absolutely. And I think you asked the question that's like, can AI do our jobs? And I don't think the answer is yes. And I think it's like, can the AI replace the stock picker? It doesn't sound like the AI is yes, but can the AI augment the way someone's thinking test come up with theories that then can be rapidly tested have that sort of go back and forth and sort of do some of the work that currently sort of like junior analysts do in terms of like theory testing ideas and stuff like that like you could see how it could be a a force multiplier at a uh at a large fund yeah but i mean to that sort of turning point question that also seems to be maybe the big weakness here is that if you have an algorithm or a model that's been trained on years and years of prior data so rates going lower and lower and inflation staying below two percent seems very difficult to project what might change which to greg's point humans aren't very good at that either but you would hope like right like that's We want to just be able to ask Chad GPT or whatever, you know.
1:00:12I'm using that as like a stand-in for this technology. Yeah, or maybe you ask AI like what would you need to see in order to start taking the prospect of regime change seriously. Yeah, I like – I mean he talked about this idea of like this sort of like adversarial way of thinking about it, which I think is like really important. And he pointed out the sort of like disaster of the home eye buyers. Yes, the Zillow analogy was really interesting. And then they got adversely selected because it's like, well, if Zillow is in the market, we know they're going to overpay. And so everyone suddenly dumps all the homes on Zillow.
1:00:46And it was not anticipating its own role in the market in response to your question, which I think is like a really interesting dimension to all of this. Yeah, that sort of reflexivity between the models and the markets, I think we're probably going to be hearing a lot more about in the future. On that note, shall we leave it there? Let's leave it there. All right. This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me on Twitter at Tracy Alloway. And I'm Joe Weisenthal. You can follow me on Twitter at The Stalwart. Follow our producers on Twitter, Carmen Rodriguez at Carmen Armin and Dashiell Bennett at Dashbot.
1:01:21Follow all of the Bloomberg podcasts under the handle at podcasts. And for more Odd Thoughts content, go to Bloomberg.com slash OddLots, where we have transcripts, a blog and a newsletter. and for even more, if you want to chat with fellow listeners about all these topics, there's even an AI channel in there. Check out our Discord. 24-7, people talk about all these things. Discord.gg slash oddlots. And if you enjoy Oddlots, if you like these conversations, please leave us a review, a positive review, please, on your favorite podcast platform. We'd really appreciate it. Thanks for listening.
1:02:04Thank you.
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From the publisher
Every industry is trying to figure out just how AI or Large Language Models can be used to do business. But Bridgewater Associates, the world's largest hedge fund, has already been at it for a long time. For years, it has explored AI and adjacent technologies in order to analyze data, test theories, develop novel investment strategies and help its employees make better decisions. But how does it actually use the tech in practice? And what's next going forward? On this episode, we speak with co-CIO Greg Jensen about both the possibilities and limitations of these advances. We also discuss markets and macro, and why he believes that investors are still too optimistic about the Federal Reserve's ability to get inflation back to target.
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