Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal.
Speaker 1: Joe, I think it's fair to say there is a
Speaker 1: lot of excitement about investing in AI. There is also
Speaker 1: a lot of excitement about using AI to invest.
Speaker 2: Yes, I mean it's I think there's like a new
Speaker 2: like chat ETF I saw an ad for and there's like, oh,
Speaker 2: we're getting No. I think I saw another like project.
Speaker 2: It was like, we're gonna have chat GPT pick the
Speaker 2: stocks for us, And you know, I get it. It's
Speaker 2: kind of exciting and maybe there's some new way of
Speaker 2: like these super advanced digital brains that can beat the market,
Speaker 2: et cetera. But like, I don't totally get it.
Speaker 3: Well.
Speaker 1: I also feel like there's a tendency nowadays for people
Speaker 1: to talk about artificial intelligence in a sort of abstract manner.
Speaker 1: You hear people bring up AI almost as a synonym
Speaker 1: for just software at this point. I think you pointed
Speaker 1: out recently that the Kroger CEO mentioned AI like times
Speaker 1: on the earnings call. So a supermarket chain, right.
Speaker 2: Yeah, And you know it's like machine learning, tech, algebra, algorithms,
Speaker 2: it's all existed for a long time quantitative investing, but
Speaker 2: it feels like because of the excitement around a few
Speaker 2: specific consumer phasing products that have been unveiled over the
Speaker 2: last six months and the way they've captured people's attention,
Speaker 2: people like you know, suddenly there's a lot of interest
Speaker 2: in like, how are companies using this tech to do something?
Speaker 1: Yeah, well, I'm glad you mentioned that because today we
Speaker 1: really do have the perfect guest. This is someone we've
Speaker 1: actually spoken to about AI before last year, in fact,
Speaker 1: someone who is at a firm that has a lot
Speaker 1: of experience using machine learning and of different types, and
Speaker 1: we're going to get into the differences between all those technologies.
Speaker 1: I'm very pleased to say we're going to be speaking
Speaker 1: once again with Greg Jensen, the co chief investment officer
Speaker 1: at Bridgewater Associates. So, Greg, thank you so much for
Speaker 1: coming back on OD thoughts.
Speaker 3: Yeah, it's great to be here. Exciting topic.
Speaker 1: Yeah, So I actually revisited our conversation from last year,
Speaker 1: I think it was in May of twenty twenty two,
Speaker 1: and you said two things that stuck out in retrospect.
Speaker 1: So number one, you said that markets had further to fall,
Speaker 1: which turned out to be correct. And two you brought
Speaker 1: up artificial intelligence as a major point of interest for Bridgewater,
Speaker 1: And this was all before chat GPT really became a
Speaker 1: thing and everyone started talking about AI at every single
Speaker 1: conference and earnings call and so on. So I guess,
Speaker 1: just to begin with, maybe you could lay the scene
Speaker 1: and going back to Joe's point in the intro, we
Speaker 1: are used to hearing these terms. So Bridgewater does machine
Speaker 1: learning and systematic strategy strategies and quantitative trading strategies and
Speaker 1: AI and things like that. What's the difference between all
Speaker 1: of these things and how do they relate to each
Speaker 1: other at a firm like Bridgewater.
Speaker 3: Yeah, great question. So I think to answer that, let
Speaker 3: me take a step back for a second and give
Speaker 3: you a little bit of my background, because it all
Speaker 3: kind of comes together in a way. You can connect
Speaker 3: these different pieces. So you know, even as a kid
Speaker 3: or whatever, I was certainly interested in kind of translating
Speaker 3: and predicting things using some mix of my thinking and technology.
Speaker 3: So I can think back to in the late eighties
Speaker 3: using stratomatic baseball cards, know what they are, but programming
Speaker 3: them into computers to try to calculate the way to
Speaker 3: create the best baseball lineup and use that in fantasy
Speaker 3: baseball type situations and similar things wither and whatever, and
Speaker 3: try to learn how to kind of use technology to
Speaker 3: combine with human intuition to get at what was different
Speaker 3: ways to create edges. And then in college when I
Speaker 3: heard about Bridgewater Bridge Order. It was a tiny place
Speaker 3: at the time, but the basic idea that there was
Speaker 3: a place where we were trying to understand the world,
Speaker 3: trying to predict what was next, but doing that by
Speaker 3: taking human intuition and translating that into algorithms to predict
Speaker 3: what was next kind of mixed two things that I loved.
Speaker 3: I love to try to understand the world, and I
Speaker 3: love the idea of having the discipline to write down
Speaker 3: what you believed and stress test what you believed and
Speaker 3: utilize that. Right. So, if you go back, and this
Speaker 3: is now in the nineties kind of where artificial intelligence
Speaker 3: was at the time, most of the focus was still
Speaker 3: on expert systems, was still on the notion that you
Speaker 3: could take human intuition, you could translate that into algorithms,
Speaker 3: and if you did enough of that, if you kept
Speaker 3: kind of representing things in symbolic algorithm that you could
Speaker 3: build enough human knowledge to get kind of a superpowered human.
Speaker 3: And Bridgewater was a rare example of where that worked.
Speaker 3: Where given the focus of trying to predict what was
Speaker 3: next in markets, given the incredible investment that we made
Speaker 3: into creating the technology to take human intuition and translate
Speaker 3: that into algorithms and stress tests, that it's incredibly successful
Speaker 3: expert system essentially that was built over the years, I'd
Speaker 3: say probably the most profitable expert system out there. And
Speaker 3: that's really what Bridgewater has been about, which is building
Speaker 3: this great technology to help us take human intuition out
Speaker 3: of the brain, get it into technology where it's both
Speaker 3: then readable by let's say investment experts, but also runs
Speaker 3: on a technology basis. And that's kind of where algorithms,
Speaker 3: let's say, the mix of algorithms and human intuition it
Speaker 3: was really important. You know, if you go through the
Speaker 3: history of our competitors, they're littered by people that tried
Speaker 3: to do something more statistical, meaning that they would take
Speaker 3: the data, run regressions, and then after regressions, let's say
Speaker 3: basic machine learning techniques to predict the future. And the
Speaker 3: problem that always had is that there wasn't enough data like,
Speaker 3: the truth is that market data isn't like the data
Speaker 3: and the physical world in the sense that a you
Speaker 3: only have one run through human history, you don't have
Speaker 3: very many cycles, even cycles that debt cycles could take
Speaker 3: seventy years to play out. Economic cycles tend to plan
Speaker 3: around for seven years. There's just not enough data to
Speaker 3: represent the world. And secondly that the game changes as
Speaker 3: participants learned, So the existence of algorithms, as an example,
Speaker 3: change the nature of markets such that the history that
Speaker 3: preceded it was less and less relevant to the world
Speaker 3: you're living in. So those are big problems with let's
Speaker 3: say a more pure statistical technique to markets. So you
Speaker 3: had to get to a world where statistical techniques or
Speaker 3: machine learning could substitute for human intuition. And that's really
Speaker 3: where kind of the exciting leaps are. Now that you're
Speaker 3: getting closer. It's not totally there, but you're much closer
Speaker 3: than you've ever been, where large language models actually allow
Speaker 3: a path to something that at least mimics human intuition,
Speaker 3: if not is human intuition, and that you can then
Speaker 3: combine that with other techniques and suddenly you have a
Speaker 3: much more powerful set of tools that can deal at
Speaker 3: least in take a big leap forward on dealing with
Speaker 3: the problem of very small data sets and the fact
Speaker 3: that the world changes as people learn in a way
Speaker 3: that up until the big breakthroughs in large language models,
Speaker 3: I think we're much further away. So that's a huge
Speaker 3: change in the limits of ways that statistical machine learning
Speaker 3: could affect something with small amounts of data, something where
Speaker 3: the future varies from the past. All of those problems
Speaker 3: we're closer to having. At least way is to take
Speaker 3: on more and more of what humans have done at Bridgewater,
Speaker 3: what humans generally do in investment management firms, And that's
Speaker 3: that's a huge leap forward that's going on now.
Speaker 2: I have one very short quick question. I realized just
Speaker 2: know that not long after we talked to last year,
Speaker 2: last spring, like a month later, you won your first
Speaker 2: World Series of poker bracelets. So congratulations on that. I
Speaker 2: at least say that because you mentioned poker, did you
Speaker 2: play the World Series this year?
Speaker 3: I'm heading out actually after this.
Speaker 2: Because I know there are okay, congrats and good luck.
Speaker 3: Yeah, And it kind of connects to this because I
Speaker 3: never get to I don't get to play very much poker,
Speaker 3: but I really studied what machines were learning about poker.
Speaker 3: So much has been learned in the last five years,
Speaker 3: ten years, and and one of the you know, basically
Speaker 3: trying to translate that into intuitions that I could use,
Speaker 3: you know that basically can't actually replicate Peter Place. Poker
Speaker 3: are very complex way, but you can pull the concepts
Speaker 3: out right. And this actually mirrors to what part of
Speaker 3: what we're doing at Bridge Order, which is that as
Speaker 3: you get to computer generated theories that if you can
Speaker 3: pull the concepts out of these complex algorithms, you know,
Speaker 3: you can make more of an assessment human assessment of
Speaker 3: whether they make sense and what the problems might be.
Speaker 3: And that's really a big deal. So there's actually a
Speaker 3: link between what I'm doing in poker, imperfectly for sure,
Speaker 3: and many of the concepts that we're trying to apply
Speaker 3: at Bridge Order. And like you said, just we had
Speaker 3: talked kind of before the lms had really hit the
Speaker 3: public scene. But yeah, I mean, just to give you
Speaker 3: a little bit of background for me the you know,
Speaker 3: if you go back to twenty twelve, First off, we
Speaker 3: brought Dave Ferriucci, who had run the Watson project at
Speaker 3: IBM that had beat Jeopardy into Bridgewater, and that was
Speaker 3: that was a time when I was trying to experiment with, okay,
Speaker 3: what can we do with more machine learning techniques? And
Speaker 3: Dave was trying to take what he had done to
Speaker 3: win a Jeopardy but actually put in more of a
Speaker 3: reasoning engine, because while what happened to on Jeopardy was impressive,
Speaker 3: it was pure data. It had no idea why it
Speaker 3: was doing what it was doing, and therefore really a
Speaker 3: lot of the path with Watson or whatever was going
Speaker 3: to be very hard to move forward with because because
Speaker 3: at its end, it was just statistical and it didn't
Speaker 3: really have any reasoning capability. So Dave came to Bridge
Speaker 3: Order and later partnered with Bridge Order roll out of
Speaker 3: company Elemental Cognition that's focused on using large language models,
Speaker 3: et cetera, but overlaying a reasoning engine that essentially helps
Speaker 3: with things like hallucinations that out that large language models
Speaker 3: have and focus on how what is human reasoning and
Speaker 3: how does it work and how does that limit views
Speaker 3: that are unlikely to be true? So that's one thing,
Speaker 3: And then in twenty sixteen or seventeen, I was introduced
Speaker 3: to open ai and actually as they transition from a
Speaker 3: charity to a company. I was one of the in
Speaker 3: that first round, and it was like met a lot
Speaker 3: of the people and looked hard at their vision to
Speaker 3: using scale and technical scale to build general intelligence and
Speaker 3: build reasoning. So I both was working with Dave Rucci
Speaker 3: and sort of understood many of the people at open
Speaker 3: ai at the time and moving forward with those things.
Speaker 3: And then I was literally the first check for anthropic
Speaker 3: and other large language model kind of people that had
Speaker 3: been at open AI. And so I've been passionate about this,
Speaker 3: realized trying to take different paths to how will we
Speaker 3: build a reasoning engine to overlay on statistical things, and
Speaker 3: a couple of different approaches that were being applied at
Speaker 3: the time, and obviously different they panned out to a
Speaker 3: different degree, but many things are coming together now to say, Okay,
Speaker 3: you can actually in a way at a pace and
Speaker 3: a speed humans can never do, you could replicate human reasoning.
Speaker 3: And that's a huge deal. And if you could really
Speaker 3: break through that, you could start to apply it in
Speaker 3: so many ways in our industry, I believe, and obviously
Speaker 3: way beyond our industry.
Speaker 2: You talked about earlier generations trying to embed human knowledge.
Speaker 2: And I'm wondering, you know if an analogy is like
Speaker 2: I remember when Deep Blue came out and they had
Speaker 2: all the grand masters sort of work with IBM to
Speaker 2: like come up with this a great computer program that
Speaker 2: was basically as good or eventually better than Gary Kasparov.
Speaker 2: But then the next generation of a chess computers didn't
Speaker 2: even have the grand masters playing it. It just learned the
Speaker 2: game from ground up and crushed those crush those previous generation.
Speaker 2: Is that sort of the what we're talking about here
Speaker 2: with with the transition from earlier engines to the new
Speaker 2: sort of LLM folks, which is like the sort of
Speaker 2: reasoning come becomes comes out of the computer rather than
Speaker 2: having to be taught directly by the experts.
Speaker 3: Yeah, I think something like that is happening. Right. You
Speaker 3: got that in chess because once you had the ability
Speaker 3: you had enough data and enough compute, you were able
Speaker 3: to do enough sampling that the pure that you got
Speaker 3: to the point where the pure data process, with good
Speaker 3: human intuition on how to build that data process, but
Speaker 3: a data process, was able to beat that those rules
Speaker 3: based things. Now, chess, unlike markets, is you know a
Speaker 3: little bit more static in the sense that while while
Speaker 3: there are adversaries, and the adversaries they'll try to learn
Speaker 3: your weaknesses, it's more static in the rules of the
Speaker 3: game are steady and those types of things, so that
Speaker 3: that sampling could work right. Although it was interesting, I
Speaker 3: love the like because it is an analogy to some
Speaker 3: of the problems that pop up and will pop up
Speaker 3: if you take Alpha go right on. The Go game
Speaker 3: Go got you also after Chess, obviously, but the Google
Speaker 3: was able to create this game that was beating the
Speaker 3: pros and radically beating the pros, killing everybody and getting
Speaker 3: better and better and better, although you know, I don't
Speaker 3: know how up to the day you are. But then
Speaker 3: there was this loophole in it where that's that another
Speaker 3: person who was a mediocre Go player, but a computer
Speaker 3: scientiists who thought there might be a hole in this
Speaker 3: super AI used a little program to find the hole.
Speaker 3: And what it illustrated was the a I had no
Speaker 3: idea how to play the game, because what a six
Speaker 3: year old wouldn't The mistake the AI was prone to
Speaker 3: was a mistake of six year old playing Go would
Speaker 3: never make where if you made a large enough in circling,
Speaker 3: if you now go works, but if you encircle the
Speaker 3: other guy's pieces, right, you eliminate them all. And something
Speaker 3: that would never work in a human game is you
Speaker 3: make a really big circle. And because it never came
Speaker 3: up in human games, and because when they perturbed human
Speaker 3: games and started playing computer against computer, they basically started
Speaker 3: with a seed of human games, they never perturbed it
Speaker 3: enough to try this out, to try a massive circle,
Speaker 3: and a human would never let the massive circle have
Speaker 3: it. It's so easy to defend against. But actually the best
Speaker 3: Go algorithm in the world allowed it to happen, right,
Speaker 3: And now a mediocre Go player with a little bit
Speaker 3: of AI found a way to beat this incredible Go
Speaker 3: game again because the Go algorithm at that time had
Speaker 3: this tremendous amount of data, but the things that weren't
Speaker 3: in this data wasn't aware of and it wasn't in
Speaker 3: any deep sense understanding the principles of the game. So
Speaker 3: that's the type of you know, data problem you can
Speaker 3: have even with a massive amount of data played, you know,
Speaker 3: millions and millions of games, but to play every possible
Speaker 3: Go board, you'd have to there's more possible Go boards
Speaker 3: than there are atoms in the universe. So it was
Speaker 3: never going to calculate every possibility and it never got
Speaker 3: to reasoning, right, and that therefore that was a weakness, right.
Speaker 3: And on the other hand, you mix that blend that
Speaker 3: even with a basic reason error, that a language model
Speaker 3: could come up with understanding the rules of GO and
Speaker 3: being able to talk about it. There's an element of
Speaker 3: knowing those things that humans already know that's possible with
Speaker 3: a blend of let's say a statistical technique like alpha
Speaker 3: GO was using and a reasoner to prevent these types
Speaker 3: of mistakes.
Speaker 1: I like that story because it makes me think I
Speaker 1: have a chance against the super smart supercomputer. Okay, that's
Speaker 1: kind of comforting, But I definitely want to ask you
Speaker 1: more about weaknesses in AI and large language models, but
Speaker 1: maybe before we do, you know, just sort of setting
Speaker 1: the groundwork once again. But when we see headlines like
Speaker 1: Bridgewater restructures will put more focus on AI, what does
Speaker 1: that mean exactly? What does it mean for a firm,
Speaker 1: an investment firm like Bridgewater to build up resources in AI?
Speaker 1: And then secondly, could you walk us through a concrete
Speaker 1: example of how AI would be deployed in a particular
Speaker 1: trading strategy. I feel like the more concrete we can
Speaker 1: get with this, the more helpful it'll be.
Speaker 3: Yeah. Great. So I think as we restructured, one of
Speaker 3: the things that as we've made the transition at Bridgewater,
Speaker 3: you know, from Ray having the key ownership to ownership
Speaker 3: at a board level and that transition, we have done
Speaker 3: something we hadn't done in the past, which is essentially
Speaker 3: retain earnings in a very significant way, which allows us
Speaker 3: to invest in things that you know, are aren't going
Speaker 3: to be part profitable right right away, but are the
Speaker 3: big long term bats that we're making, and certainly recognizing
Speaker 3: that there's a way to reinvent a lot of what
Speaker 3: we do using AI machine learning techniques to improve what
Speaker 3: we're doing to understand the world, accelerate that, and specifically
Speaker 3: what we've done on the aimlside is we've set up
Speaker 3: this venture. Essentially they're seventeen of us with me leading it.
Speaker 3: You know, I'm still very much involved in core bridge Order,
Speaker 3: but the sixteen others are one hundred percent dedicated to
Speaker 3: kind of reinventing Bridge Order in a way with machine learning.
Speaker 3: We're going to have a fund specifically run by machine
Speaker 3: learning techniques which will take me into tracy what kind
Speaker 3: of strategies you can do, you know, that's what we're
Speaker 3: working on right now in that lab and pressing the
Speaker 3: edges of what AI is capable of now a like
Speaker 3: machine learning is capable of now right now. There are
Speaker 3: big problems right A. You take large language models and
Speaker 3: they have two types of problems. One thing is the
Speaker 3: basic problem is there they are trained on the structure
Speaker 3: of language, so they usually return something that looks like
Speaker 3: good structure of language. They don't always return accurate answers,
Speaker 3: so that's a problem. It hallucinates, It makes things up
Speaker 3: because it's more focused on the structure of what word
Speaker 3: or what concept would come next, then whether it's accurate
Speaker 3: in what comes.
Speaker 1: Can I just say when I hear AI hallucinations, it
Speaker 1: becomes so science fiction for me. It's very like robot
Speaker 1: stream of electric cheap kind of it's just so surreal.
Speaker 3: Yeah, well, I mean in this case, you can imagine
Speaker 3: what's happening, right because it's just what it's what it's
Speaker 3: trained on. Right. So if you're just if basically the
Speaker 3: basic concept is give it any stream of words and
Speaker 3: it'll predict based on having read everything that's ever been read.
Speaker 3: What comes next, right, and that if it's a little
Speaker 3: bit wrong in what comes next, it can misfire and
Speaker 3: give you something that sounds like something that could come next,
Speaker 3: but actually wrong, you know. And it's just what it's
Speaker 3: trained on, right, It's trained to predict the next word.
Speaker 3: Slight errors in that create those types of issues. Now,
Speaker 3: the algorithm is pretty remarkable, particularly like we as I said,
Speaker 3: I've been tracking in AI as an investor for a
Speaker 3: long time and looking at their technology for a long
Speaker 3: time time. And you know, up until there's GPT one, two, three,
Speaker 3: and many versions of between, and then at GPT three
Speaker 3: it started to have some use. GPT one and two
Speaker 3: were you know, barely coherent, GPT three was you know,
Speaker 3: somewhat usable for certain tasks. Three and a half, which
Speaker 3: is what CHAT GPT is, you know, got to a
Speaker 3: certain level, like on Bridgewater's internal tests, you suddenly got
Speaker 3: to the point where it was able to answer our
Speaker 3: investment associate tests at the level of a first year
Speaker 3: IA right around with chat GPT three point five and
Speaker 3: anthropics most recent quad and then GPT four was able
Speaker 3: to do significantly better. And these are you know, at
Speaker 3: least what we thought were conceptual tests significantly better than
Speaker 3: our average you know, first year investment associate that went
Speaker 3: through training. And similarly, it's able to take the LSAD
Speaker 3: and do well, et cetera. So it can be basically
Speaker 3: pretty smart. It is pretty smart in a wide variety
Speaker 3: of things with errors, but pretty smart on a wide
Speaker 3: variety of whether it's BMCAT or the LSAD or Bridgewaters
Speaker 3: internal tests or whatever, a whole wide variety of things.
Speaker 3: This is a big deal that it can achieve all
Speaker 3: of those kind of academic things, and yet it's still
Speaker 3: eightieth percentile kind of thing on a lot of those things,
Speaker 3: which is remarkable to be eightieth percentile on many many
Speaker 3: different things. But at the same time, it's eightieth percentile
Speaker 3: for a reason. There are flaws, meaning it's not one
Speaker 3: hundred percentile, and so that leads to like you need
Speaker 3: to find a way to work through those flaws, right,
Speaker 3: and that's really where you know. So if somebody's going
Speaker 3: to use large language models to pick stocks, I think
Speaker 3: that's hopeless. That is a hopeless path. But if you
Speaker 3: use large language models to create some theories which it
Speaker 3: can theorize about things, and you use other techniques to
Speaker 3: judge those theories and you iterate between them to create
Speaker 3: a sort of an artificial reasoner. Where language models are
Speaker 3: good at certainly generating theories any theories that already exist
Speaker 3: in human knowledge, and putting those things connect together, they're
Speaker 3: bad at determining whether they're true. But there are other
Speaker 3: ways to pair it with statistical models and other types
Speaker 3: of AI to combine those together. And that's really what
Speaker 3: we're focused on, which is combining large language models that
Speaker 3: are bad at precision with statistical models that are good
Speaker 3: at being precise about the past but terrible about the future,
Speaker 3: and combining those together you start to build an ecosystem
Speaker 3: that can achieve I believe can achieve the types of
Speaker 3: things that bridge order analysts combined with our stress testing
Speaker 3: process and compounding understanding process at Bridgeworker can do, but
Speaker 3: it can do it at so much more scale, because
Speaker 3: all of a sudden, if you have an eightieth percentile
Speaker 3: investment associate, technologically you have millions of them at once,
Speaker 3: and if you have the ability to control their hallucinations
Speaker 3: in their errors by having a rigorous statistical backdrop, you
Speaker 3: could do a tremendous amount at a rapid rate. And
Speaker 3: that's that's really what we're doing in our lab and
Speaker 3: proving out that that process can work. I see.
Speaker 1: So, so is the idea that AI could possibly generate
Speaker 1: theses or ideas that can then be rigorously, you know,
Speaker 1: statistically fact checked by either the humans or you know,
Speaker 1: existing algorithms and data sets. Is that the idea?
Speaker 3: Yeah? And then yes, and but the idea goes further,
Speaker 3: But yes, that's the start. Language models could do that.
Speaker 3: Statistical AI can then take theories and generate whether like
Speaker 3: those have at least been true in the past, and
Speaker 3: what the flaws with them are and refine them, offer
Speaker 3: suggestions on how to do them differently, which then you
Speaker 3: could dialogue with. So then the other strength of language
Speaker 3: model has that that humans are weaker at is now
Speaker 3: take a complex statistical model and talk about what it's doing,
Speaker 3: and there's ways to train language models to do that.
Speaker 3: That then allow sort of a judgment to say, okay,
Speaker 3: now let's think about what's happening here and reason over
Speaker 3: what's happening. So you use the way we've modeled this
Speaker 3: kind of out as language models can come up with
Speaker 3: potential theories. Now there's a limit to that. It's not
Speaker 3: the most creative thing in the world, although it's met
Speaker 3: theory at scale for sure. And then there's and again
Speaker 3: that's language models with good you know, you got to
Speaker 3: tune your language models in a certain way so it's
Speaker 3: not straight out of the box. But then you can
Speaker 3: use statistical things to control that. Then you can use
Speaker 3: language models again to take what's coming out of that
Speaker 3: statistical engine and talk about it with a human or
Speaker 3: other machine learning agents, and we kind of report back
Speaker 3: on what you're finding and what that is and the
Speaker 3: types of theories that are out there that might run
Speaker 3: contrary to what you believe, which can lead to more
Speaker 3: tests and and other thing. So that's the loop that
Speaker 3: you know that I'm very excited about. And as I said,
Speaker 3: up until the thing that statistical AI was limited because
Speaker 3: it was focused on the data of markets, where language
Speaker 3: models the good thing is it has a much better
Speaker 3: sense of something that a statistco model wouldn't really have.
Speaker 3: Statistical model markets doesn't get the concept of greed. Language
Speaker 3: models pretty much understand the concept of greed. They've read
Speaker 3: everything that's ever been written about greed and fear and whatever.
Speaker 3: So now it can start to think about statistical results
Speaker 3: in the context of the human condition that generates those results.
Speaker 3: Big deal and really a radical difference.
Speaker 2: Let me ask you one very simple question, and it
Speaker 2: might be one that speaks to an anxiety of listeners.
Speaker 2: If already GPT can perform at maybe the type of
Speaker 2: level that high quality first year or second year associator
Speaker 2: Analystic Bridgewater can do, does it mean fewer highers in
Speaker 2: the future humans being hired at Bridgewater or does it
Speaker 2: mean the same number or more humans doing even more?
Speaker 2: Like do use is it a replacement? Like what does
Speaker 2: it mean for like the type of person that would
Speaker 2: have been the ten years ago? First your employee at Bridgewater.
Speaker 3: What I think people should expect at bridgeworder but and
Speaker 3: just generally at bridgeworker in a hurry is things are
Speaker 3: changing quick that it really requires people to be capable
Speaker 3: of playing whatever role is necessary in order to do that. Right,
Speaker 3: Like if you go back at the clock at Bridgewater
Speaker 3: when I started or just before that, right, we were
Speaker 3: you know, we were using egg time, Like we had
Speaker 3: rules on how to trade, but we were using egg
Speaker 3: timers and humans to like do these things. And over
Speaker 3: time computers could do more and more of that. We
Speaker 3: kind of got to this point where it was i'd say,
Speaker 3: kind of humans settled into the role of intuition and
Speaker 3: idea generation, and we use computers for memory and for
Speaker 3: constantly running those rules accurate, et cetera. That was a
Speaker 3: transition half like something it got to fifty to fifty
Speaker 3: technology and people. And now this is another leap, right,
Speaker 3: And it's definitely true that it's going to change the
Speaker 3: roles that investment associates play now exactly how and you
Speaker 3: still need the for as far foreseeable future. You're going
Speaker 3: to want people around that out that working on those things.
Speaker 3: There's edges that these techniques I'm describing certainly won't do
Speaker 3: well for an extented period of time, and there's how
Speaker 3: to build the ecosystem of these machine learning agents, et cetera.
Speaker 3: And so what I've found is certainly the people in
Speaker 3: the lab. You want people who are curious about these
Speaker 3: new technologies, you want with to utilize them, and that's
Speaker 3: that's going to be really part of the future of work.
Speaker 3: I think. I think it's going to be very hard
Speaker 3: in any knowledge industry to not utilize these And we're
Speaker 3: seeing this huge breakthrough encoding, right that is so democratizing
Speaker 3: in a sense that you don't you really need to
Speaker 3: know what you want to code more than you need
Speaker 3: to know coding, you know, And that's a big breakthrough.
Speaker 3: So a bunch of people that weren't as well trained
Speaker 3: or as capable in C plus plus or in Python
Speaker 3: or whatever can suddenly get what they want so much faster.
Speaker 3: So all of a sudden, the skill sets are changing,
Speaker 3: and they're changing in ways that I think are as
Speaker 3: surprise to many because it's actually a lot of the
Speaker 3: knowledge work, a lot of the things where you content
Speaker 3: creating and whatever that that I think people thought would
Speaker 3: be later in computer replacement that are happening faster. So
Speaker 3: the main thing is, i'd say, right now, there's so
Speaker 3: much in flux that having flexible the more you need
Speaker 3: flexible generalists who can have an eye towards this and
Speaker 3: eye towards the goal and be able to utilize whatever
Speaker 3: tools are necessary to get there. That's really where I think,
Speaker 3: you know, you're seeing a fair amount of change quickly.
Speaker 1: So you mentioned earlier that just the existence of machine
Speaker 1: learning can impact both the current environment and the future.
Speaker 1: So I think you said the future data points aren't
Speaker 1: going to look like the past data points simply because
Speaker 1: machine learning exists. Does that sort of reflexivity between machine
Speaker 1: learning slash AI and markets become more of an issue
Speaker 1: as AI and machine learning becomes more and more popular
Speaker 1: and more entrenched.
Speaker 3: Yeah, I think it's a big deal, right, And I
Speaker 3: think it's both something that's going to cause act and
Speaker 3: something I'm super excited about. Obviously, I'm excited about the
Speaker 3: power of this that I think there's ways to utilize
Speaker 3: it really well. And it'll also there will be a
Speaker 3: lot of mistakes. Like you're saying, there will be funds
Speaker 3: that will use GPD to pick stocks and not really
Speaker 3: deeply understanding what's happening and why or why what the
Speaker 3: weaknesses that might be there are already plenty of times
Speaker 3: where statistical pure statistical because there's not enough data. You're
Speaker 3: not building with those fundamental issues in mind. You know,
Speaker 3: not that it was directly markets, but in the housing market,
Speaker 3: what Zilo did is a great example. Right. Zillo goes
Speaker 3: out and uses an AI technique that wasn't fit for
Speaker 3: purpose for when it's worth but they use an AI
Speaker 3: technique to predict housing prices and then go into the
Speaker 3: market to start buying houses that they think are undervalued, right,
Speaker 3: And they have a couple problems. One is, while they
Speaker 3: had a ton of housing data, it was over a
Speaker 3: relatively short period of time. So even though they had
Speaker 3: tons what looked like tons of data points because they
Speaker 3: have the price of every house and everywhere or whatever,
Speaker 3: there's still a macro cycle that affects everything that was
Speaker 3: underestimated in what they did. And secondly, they underestimated what
Speaker 3: it would be like in theory versus in practice, whether
Speaker 3: it's actually an adversarial market. Every time they won an auction,
Speaker 3: there was something about that particular lot that the other
Speaker 3: people bidding on that lot knew that they didn't, and
Speaker 3: so it ended up obviously being a huge problem for Zillo,
Speaker 3: and they kind of had a big impact on the
Speaker 3: real estate market and then a big failure. And that's
Speaker 3: the kind of thing you're going to see over and
Speaker 3: over again. If because the basic problem that the data
Speaker 3: that you're looking at isn't necessarily the data you'll face
Speaker 3: in real world. You're not facing the adversarial problem when
Speaker 3: you're looking at that data the way they were. You're
Speaker 3: not a statistical technique that's very good at seasonality and
Speaker 3: trend following might not be very good at understanding macro
Speaker 3: cycles and so on. So that was another case where
Speaker 3: Zillow is a case and I think we'll see it
Speaker 3: over and over again where the recognition that it's not
Speaker 3: as simple as taking machine learning out of the pack
Speaker 3: and applying it to this problem. Even when there's a
Speaker 3: ton of data, right some of the places where there
Speaker 3: is a lot more machine learning going on, very short
Speaker 3: term trading arguably is better for machine learning because there's
Speaker 3: a lot of data and you can learn faster over
Speaker 3: that data, and there's some merit to that. And in
Speaker 3: terms of tangible places this is now years ago, But
Speaker 3: where we started applying some of these techniques were in
Speaker 3: things like monitoring our transaction costs and looking for patterns
Speaker 3: and shorter term data because there's a lot more data.
Speaker 3: But on the other hand, the data often it's like
Speaker 3: having the data of your heart rate for your whole life.
Speaker 3: You could feel like, wow, this is a yeah, I've
Speaker 3: got every heartbeat for you know, you know, forty nine years.
Speaker 3: That seems like a lot of data, but its not.
Speaker 3: It's totally irrelevant when you've artitacked. So that even when
Speaker 3: there's lots of data can be misleading. And that those
Speaker 3: are those are the types of issues that will lead
Speaker 3: to these techniques having huge problems, which means it's not
Speaker 3: out of the box AI is going to solve all
Speaker 3: these problems. You really, and this comes back to you
Speaker 3: have to understand the tools, what they're good at, what
Speaker 3: they're bad at, and put them together in a way
Speaker 3: that use what they're good at and protects them from
Speaker 3: what they're bad at. Now, nothing, no process work coming
Speaker 3: up with will do that perfectly. But the more and
Speaker 3: more you could do that, I think, the more and
Speaker 3: more you could become, let's say, better than humans at that,
Speaker 3: because humans have many of those fallibilities or versions of
Speaker 3: those abilities that these processes will have. And that's like
Speaker 3: that'll be the question of how far we can how
Speaker 3: far we could take that and how how much human
Speaker 3: judgment is better than those things, which is stuff you
Speaker 3: know we'll be experimenting with as we as we go along.
Speaker 2: So you know, one thing that you know, your founder
Speaker 2: Ray Dalio years ago, like sort of he wrote down
Speaker 2: a set of rules. You've talked about this before. He
Speaker 2: wrote down a set of rules about how he understood
Speaker 2: the sort of the machine of the markets to work,
Speaker 2: and one of the issues with AI, and I think
Speaker 2: you're sort of been hit getting at this is that
Speaker 2: like AI legibility and the understanding of like, okay, you
Speaker 2: put in that you pose a query to a large
Speaker 2: language model, it creates some output you don't really know
Speaker 2: what it did to get there, and so that's you know,
Speaker 2: that's sort of different than dealing with a human analyst.
Speaker 2: Do you get say, well, what did you think about that?
Speaker 2: Did you think about that? Can you talk a little
Speaker 2: bit more about like the sort of I don't know
Speaker 2: if that's a weakness or how do you sort of
Speaker 2: get around the fact that, like it's still difficult to
Speaker 2: query an AI model and say like how did you
Speaker 2: arrive at X or Y conclusion?
Speaker 3: Yeah, and I think that's really important and that we
Speaker 3: but also something that's more and more breakable because even
Speaker 3: with humans. One of the things like one of the
Speaker 3: places where I think there are a lot of areas
Speaker 3: where Bridgewater has a strength, right, Bridgewater has a strength
Speaker 3: And we never went from a statistical model, So we
Speaker 3: built data based on what we needed for reasoning, and
Speaker 3: as a result, we have a better, longer, cleaner database
Speaker 3: than I think anybody has. We've been thinking through this
Speaker 3: problem that you're referring, which is how do you actually
Speaker 3: get out what somebody means? You'd be surprised how hard
Speaker 3: it is to truly get from a human. Humans don't
Speaker 3: actually know why their synapses do what they do. They
Speaker 3: actually like when you ask somebody to describe something, you
Speaker 3: get some partial version of what they're thinking. If you
Speaker 3: took like an intuitive trader and you start peeling back
Speaker 3: all the reasons, that's very hard. We've been doing that
Speaker 3: for a long time and have an expertise in doing it,
Speaker 3: and I would say that humans don't even know what
Speaker 3: they're doing often, but there are ways to you know,
Speaker 3: like you're saying, query and force questions and what about
Speaker 3: this and what about that? That will help pull out
Speaker 3: human intuition. And what you find with machine learning algorithms
Speaker 3: if you get good at this and this is you know,
Speaker 3: going back to two thousand and sixty. Two thousand and
Speaker 3: seventy has been critical to my work is there's a
Speaker 3: way that you can query machine learning algorithms like query
Speaker 3: like it's different, but the concepts the same as how
Speaker 3: you query humans to get at why they really believe
Speaker 3: what they believe. And as I was saying I think
Speaker 3: there's actually elements of large language models interpreting what statistical
Speaker 3: AI is doing that allows that process to accelerate. And
Speaker 3: I think it's very critical you really want to know
Speaker 3: because that's the way you find the flaws. If you
Speaker 3: go back to my go example and you say you
Speaker 3: can think about if you can querry a model and
Speaker 3: think about what it's done and what it hasn't done,
Speaker 3: then you can figure out what data is missing, right,
Speaker 3: and you need to set up adversarial techniques in order
Speaker 3: to keep querying an algorithm for what it's doing. And again,
Speaker 3: I think that's still an area of research, but a
Speaker 3: process that's moving along quickly to basically get to the
Speaker 3: point where the standard is even though a machine learning
Speaker 3: technique might be doing something very different than a human
Speaker 3: is that it can still explain itself, and it might
Speaker 3: not perfectly explain itself, just like humans don't perfectly explain themselves,
Speaker 3: but to a very high degree of confidence across a
Speaker 3: wide range of outcomes that you have a sense of
Speaker 3: what's going on is possible. And that's the you know,
Speaker 3: that's part of the design of what we're putting in,
Speaker 3: which is, well, how do you query it, how do
Speaker 3: you give it more information, remove information, etc. See how
Speaker 3: it changes its mind to determine roughly what's going on.
Speaker 1: You know, you mentioned the data sets there, and I
Speaker 1: guess it's a cliche nowadays to say, well, a model
Speaker 1: is only as good as the data that it's trained on.
Speaker 1: But it's a cliche because it's true. Do you use
Speaker 1: your own internal data for the large language models or
Speaker 1: where are you actually pulling a data from? And then secondly, like,
Speaker 1: what type of data have you found so far? Is
Speaker 1: most useful for these types of projects?
Speaker 3: Well, I think the things that are most interesting to
Speaker 3: us A we're trying to learn things that we don't
Speaker 3: already know. So we're being careful about what kind of
Speaker 3: Bridgewader knowledge we put in here, because it's not that
Speaker 3: helpful if we reinvent Bridgewater somewhat helpful, but it's about
Speaker 3: it as helpful as let's say, reinventing everything that we
Speaker 3: don't know about that other people have thought about, etc.
Speaker 3: And so point one in the lab right now, at least,
Speaker 3: we're focused on not making this through Bridgewader centric on purpose,
Speaker 3: because it's in that way learn things that we don't
Speaker 3: already know and if you just fed a bridgeworder information,
Speaker 3: which we may well do, that could be a productivity
Speaker 3: enhancing thing, but you'll quickly produce something very similar to Bridgewater.
Speaker 3: Where what's been amazing so far as we're producing good
Speaker 3: results by Bridgewater standards, but different, very very different conclusions
Speaker 3: and different thoughts than what we have internally. So I
Speaker 3: think that's zero point one choice now on raw data
Speaker 3: and cleaning data and how you put together data. Now
Speaker 3: we are benefiting from Bridgewater scale on that. That's been
Speaker 3: a big that's a big deal that over the years,
Speaker 3: again precisely because we took human intuition and said, what
Speaker 3: data do we need to replicate that intuition. We have
Speaker 3: a unique database where if everybody else is pulling from
Speaker 3: data stream Bloomberg, et cetera, we put together the data
Speaker 3: we needed to feed our intuitions. Oftentimes that data didn't exist.
Speaker 3: We had to figure out the way to create it.
Speaker 3: And also we're big believers that you need to stress
Speaker 3: us across a very long period of time, so we
Speaker 3: have much longer data histories now. Those things are certainly
Speaker 3: valuable in a context of small data, any quantity of data,
Speaker 3: any like the understanding the data being able to therefore
Speaker 3: for a given theory find appropriate unoptimized data. Those are
Speaker 3: big deals and that that we are using and and
Speaker 3: you know that does allow us to move forward more
Speaker 3: and on the land large language models. You know, there's
Speaker 3: still a lot of work to be done, but you
Speaker 3: certainly can train through reinforcement learning to you know, to
Speaker 3: make sure that they're not making mistakes that you know about.
Speaker 3: And so there's ways to to do that. Now we've
Speaker 3: been trying to avoid that for the reasons I was
Speaker 3: describing before, avoid doing too much of that of ejecting
Speaker 3: our own knowledge and use external sources to do that.
Speaker 3: But that's still part of uh, you know, part of
Speaker 3: the tool set that will be available that yes, you
Speaker 3: could train it more directly on things you already believe
Speaker 3: to be true if you want to do that, and
Speaker 3: that certainly will lead to answers that replicate your thinking
Speaker 3: more quickly.
Speaker 1: So just on this point, one thing I wanted to
Speaker 1: get your opinion on is how good is AI at
Speaker 1: predicting big turning points or structural breaks in market regimes?
Speaker 1: Because I don't know about you, Joe, but one of
Speaker 1: the first things I did with chat GPT was I
Speaker 1: asked it to write, you know, a financial news article
Speaker 1: about inflation, just to see whether whether our jobs were
Speaker 1: in danger, and you could tell that it was trained
Speaker 1: on not quite current data. It was talking about how
Speaker 1: inflation has been stubbornly low for many years and the
Speaker 1: FED is trying to get it to the two percent target.
Speaker 1: But how good is AI at predicting those regime changes?
Speaker 1: Because if you're running, you know, a macro fund, I
Speaker 1: imagine that's one of the important things that you need
Speaker 1: to do, is try to figure out when something is
Speaker 1: fundamentally changing in the market.
Speaker 3: Yeah, and I'd say terrible if you use it in
Speaker 3: the sense that you're using it right like that. It's
Speaker 3: a little bit like saying, well, how good are people
Speaker 3: at that? Well, people are pretty darn bad at that, right.
Speaker 3: That doesn't mean that there isn't a way where some
Speaker 3: people who could do such a thing right, So AI
Speaker 3: like it. It's hard to just think about AI as
Speaker 3: a thing or think of like, Okay, well, if I'm
Speaker 3: just gonna use chat Gypt for that, You're exactly right.
Speaker 3: Chatgypt as it comes out of the box is only
Speaker 3: trained over to a certain history and it doesn't care
Speaker 3: like unless you know how to make it care. It
Speaker 3: doesn't care that it's you know, it's just to answer
Speaker 3: your question about inflation. Based on everything it's ever read
Speaker 3: about inflation, time isn't even that important unless you make
Speaker 3: time be very important to it and predicting, and so
Speaker 3: you have to know how to use the tools to
Speaker 3: generate the type of outcome that you're describing. So do
Speaker 3: I think like AI out of the box will do that. No,
Speaker 3: absolutely not, It'll be awful at that. Are there ways
Speaker 3: to take what's embedded in AI to come up with
Speaker 3: a way to do that? I was embedded in language models,
Speaker 3: and if you combine that with statistical tools, yeah, there's
Speaker 3: a path there. But it's not going to be as
Speaker 3: simple as open up JATGPD and ask it that question.
Speaker 3: It's it's a there's more involved. But if you basically
Speaker 3: it is helpful to have an analyst that's read everything
Speaker 3: that was ever produced, even if they stopped reading in
Speaker 3: twenty twenty two in twenty twenty one, I should say
Speaker 3: it's there's a way to use that, but you have
Speaker 3: to use it correctly and not misuse it in order
Speaker 3: to try to generate that answer.
Speaker 2: All right, So I can't just ask a large language
Speaker 2: model when will inflation get back to the Fed's target.
Speaker 2: But I'm speaking I'm not speaking to a large language model.
Speaker 2: I'm speaking to Cio Bridgewater. And you know, I do
Speaker 2: I am curious, you know, I do want to talk
Speaker 2: a little. We do want to talk a little macro
Speaker 2: and I, you know, before we sort of like, I'm
Speaker 2: not going to directly ask you when inflation will be
Speaker 2: back to the Fed's target. But what strikes me about
Speaker 2: the last year and since the last time we talked,
Speaker 2: that's really blowing my mind is that rate hikes have
Speaker 2: been a lot faster than people expected. Inflation is hotter
Speaker 2: than people expect, did the unemployment rate is lower than
Speaker 2: people expected. What is it that people misunderstood a year
Speaker 2: ago about the economic machine? Such that the FED has
Speaker 2: hyped rates much faster than people expected, and yet it's
Speaker 2: been surprisingly ineffective at cooling things down. And to this
Speaker 2: day there seems to be a surprising amount of economic
Speaker 2: momentum with FED funds at like five and a half percent.
Speaker 3: Yeah, it's a great question. I have a bunch of
Speaker 3: thoughts on it. You know, certainly I can't speak for
Speaker 3: all people, but I can speak for myself. I've been
Speaker 3: wrong about a bunch of a bunch of those things.
Speaker 3: So just to talk about what I certainly and let's
Speaker 3: say we at Bridgewater didn't now like you're saying. I
Speaker 3: thought the degree of and certainly are everything that we
Speaker 3: had understood in our statistic models or whatever that we
Speaker 3: knew that we could easily be wrong, but that the
Speaker 3: degree of tightening was fast and high relative to history,
Speaker 3: and that any tightening like this in the past had
Speaker 3: led to significant downturns. Although the lead life is somewhat
Speaker 3: variable and its still possible that's right. But I think
Speaker 3: a lot of things that happened different than I expected.
Speaker 3: Was a Usually, when let's say, as they were last year,
Speaker 3: stocks were falling and short rates were rising, that formula
Speaker 3: in history always led to the personal savings rate rising,
Speaker 3: people seeing higher interest rates available to them, asset prices falling,
Speaker 3: housing slowing down, etc. Usually people save more money, which
Speaker 3: meant there was less revenue for companies, which meant there
Speaker 3: were layoffs, which meant savings rates rose more when the
Speaker 3: employment market weakened, and you know, our recession was caused
Speaker 3: through that mechanism. And what's happened in this period is
Speaker 3: that I think now I could be wrong, that normal
Speaker 3: let's say impact of the higher interest rate and wealth
Speaker 3: effect impact was offset by the fact that wealth had
Speaker 3: been changed so radically in the twenty twenty twenty one
Speaker 3: period by fiscal policy, and that we have fiscal policy
Speaker 3: as extreme as the war, and the ripple of the
Speaker 3: length to which that disrupted let's say, those other relationships
Speaker 3: was interesting. The degree of it was interesting. I think
Speaker 3: there's ways we should have, you know, looking back now,
Speaker 3: I think there are reasons that we should have I
Speaker 3: should have known that, and some people were pointing to that,
Speaker 3: but that created much less of a reaction in household balance,
Speaker 3: in household savings rates as you normally did. You came
Speaker 3: out of the recession with better balance sheets than ever.
Speaker 3: People were willing to dissave. So even as rates climbed
Speaker 3: and actually debt growth collapsed as it normally would. But
Speaker 3: what simultaneously collapsed outside of debt is let's say, increase,
Speaker 3: was the willingness to spend down the cash that that
Speaker 3: households had built up. And that cash doesn't just disappear
Speaker 3: when one person spends it, it goes on to others.
Speaker 3: Balance sheets whether it's corporate balance sheets, other household balance sheets,
Speaker 3: and so that what's been happening, it appears, is that
Speaker 3: money's been spinning around in a way that made the
Speaker 3: rate hike have much less impact than I believe would
Speaker 3: have had pre COVID, if you had anything like that,
Speaker 3: that rate hike. On top of that, within the US economy,
Speaker 3: in particular, corporates had extended their duration, So the impact
Speaker 3: is taking longer on the effect on corporates, although I
Speaker 3: think it's happening, but it is taking longer, and so
Speaker 3: there are a few other things. And then obviously the
Speaker 3: benefit of when nominal what did happen is rate rise
Speaker 3: is created a decline in nominal demand, but that's mostly
Speaker 3: shown up in inflation. So nominal demands fallen pretty much
Speaker 3: as much as I've expected. It's been more inflation falling
Speaker 3: than real growth falling, which again I think there's reasons
Speaker 3: that that that's the case. But before there was this
Speaker 3: massive demand shock from the what the Fed, what the
Speaker 3: central banks and the Treasury had done to get everybody's
Speaker 3: balance sheets up, and supply was struggling to keep up
Speaker 3: with this massive demand shock, and now demand's falling. But
Speaker 3: supply is still catching up to that old level, so
Speaker 3: in on net, real growth has come out stronger. Now
Speaker 3: I could see all that in the rear view mirror
Speaker 3: by anything predict that that would be the way it
Speaker 3: would play out. But I think that's why you've had
Speaker 3: this stubborn strengthen the economy and that, you know, and
Speaker 3: that's created a certain amount of stability. Now equities have
Speaker 3: rallied significantly since then, there's like some of the negative
Speaker 3: wealth effects have eased. At the same time, though a
Speaker 3: lot of that excess cash that was on balance sheets
Speaker 3: have been distributed, so there's a mix of pressures here
Speaker 3: that looking forward, you know, we do think inflation is
Speaker 3: still coming down a bit, although on net we've entered
Speaker 3: what we think is a more inflationary environment, such that
Speaker 3: two percent inflation probably more likely to be more of
Speaker 3: a bottom than a cap. And we do think fiscal
Speaker 3: policy as the way to deal with the recessions is
Speaker 3: probably the politically the more likely outcome. Then let's say
Speaker 3: moving back in the next recession to more que and
Speaker 3: fiscal policy is a lot more inflationary and effective in
Speaker 3: a sense of stimulating growth quickly as we've seen. So
Speaker 3: I think you're going to see a world where we
Speaker 3: are still adjusting to a higher inflation, world that's de globalizing.
Speaker 3: Although everything we're talking about on the productivity front, maybe
Speaker 3: machine learning changes that we'll see, but largely X a
Speaker 3: major productivity miracle. I think deglobalization, the move towards fiscal
Speaker 3: policy has changed the long term inflation path in a
Speaker 3: way that markets haven't fully adjusted to. Because markets right
Speaker 3: now believe the fat is totally credible that inflation is
Speaker 3: going to return to target basically with very little problems.
Speaker 3: When we measure the pressures, we don't think, so we
Speaker 3: think it's going to be much more challenging to get
Speaker 3: inflation where markets expected. The impact on earnings is going
Speaker 3: to be a lot more negative than the markets are
Speaker 3: currently expecting, and it's going to take longer and be harder.
Speaker 3: So big differences between what we're seeing and expecting and
Speaker 3: what the markets are currently priced.
Speaker 1: So I think last year you were talking about the
Speaker 1: possibility of a recession in twenty twenty three. Is that
Speaker 1: off the table now? So you're still positioned. It sounds
Speaker 1: like for a level of higher inflation, but it sounds
Speaker 1: like maybe you're a bit more optimistic on the growth front.
Speaker 3: Yeah, we've been wrong on growth, So I'd say, look,
Speaker 3: we think it's going to be a struggle. We're in
Speaker 3: a state of disequilibrium in the sense that relative to
Speaker 3: a given level of growth, we think the level of
Speaker 3: inflation to the bad target that they're going to have
Speaker 3: a difficulty achieving growth and inflation at the levels they
Speaker 3: want and are going to have to give on something
Speaker 3: in the short run. I think that's leading to you know,
Speaker 3: higher rates. The expectation that the massive easing's coming is unlikely.
Speaker 3: The Fed's going to continue to have to be tighter
Speaker 3: longer than the markets expected. So that's bad for you know,
Speaker 3: let's say bonds and long dated short rates. It's also
Speaker 3: probably bad for equities. And at the same time, we
Speaker 3: think growth will be struggling. It's nominal growth slowing. Penomenal
Speaker 3: growth is going to continue to slow, and as nominal
Speaker 3: growth slows, while you're more in stick your inflation, things
Speaker 3: like wage growth and some of the service areas more
Speaker 3: sticky inflation, you get more of a challenge. It's nominal
Speaker 3: growth falls for it to just flow through to inflation.
Speaker 3: So my views, you end up with growth disappointing a bit,
Speaker 3: and inflation disappointing on the high side a bit ending
Speaker 3: up you know, probably bad for bonds and probably you
Speaker 3: know a little bit bad for equities, and generally weak,
Speaker 3: weak growth, and if that weak growth starts to translate
Speaker 3: into rising savings rate, you could easily end up into
Speaker 3: it into a recession, and one that's going to be
Speaker 3: difficult to deal with, you know. But yeah, I'd say
Speaker 3: we've teamed. I've tamed, and we've tamed a bridgewater some degree.
Speaker 3: Our view on growth, while still negative, not as extreme
Speaker 3: as it appeared, and and it's a more gradual process
Speaker 3: that's unfolding. And then on the inflation front, while we've
Speaker 3: had a week I did a quick decline inflation as
Speaker 3: novel GDP foul. We do think we're in the range
Speaker 3: where you're in the much more stubborn part of inflation.
Speaker 3: It's be harder to continue to get those inflation falls
Speaker 3: going forward.
Speaker 2: So just to be clear, though, you do think there
Speaker 2: is a gap between either what the market sees in
Speaker 2: terms of how much more work the FED is going
Speaker 2: to have to do or what the FED thinks how
Speaker 2: much more work the Fed is going to have to do,
Speaker 2: and what basically you think the FED is going to
Speaker 2: have to do if it actually is serious about getting
Speaker 2: inflation back to something resembling its target.
Speaker 3: Yeah, I think so. I mean, I'd say the FED
Speaker 3: seems a little bit more realistic than the markets do
Speaker 3: on what it's going to take. But right that, we
Speaker 3: think that's right that when you look at what the
Speaker 3: markets are saying, that it's super optimistic, it could come true.
Speaker 3: You do need essentially to get an equity rally from here,
Speaker 3: you have to have lower rates fairly quickly into a
Speaker 3: world where earnings are pretty good. That's kind of the
Speaker 3: discounted line. To get above that, you need even more
Speaker 3: than that. And I think that line is super optimistic
Speaker 3: relative to what we're you know, what we measure and
Speaker 3: again are I'm using the words, but I'm describing the
Speaker 3: process that's based on studying, you know, hundreds of years
Speaker 3: of economic history and how these linkages work and building
Speaker 3: all of that into a systematic process. But just spitting
Speaker 3: out kind of the output of that is that it
Speaker 3: doesn't appear that you'll that the FED will be able
Speaker 3: to achieve that, and that we're in this disequilibrium where
Speaker 3: you still have more inflation relative to growth, and you
Speaker 3: don't have an easy way to close that gap. So
Speaker 3: we'll see we've been wrong about that in terms of
Speaker 3: at least what the market outcomes have been for the
Speaker 3: last six months or so, after having been incredibly right
Speaker 3: for an extended period of time. And that's part of it.
Speaker 3: We get a lot of things wrong, and that's normal.
Speaker 3: But I think when you break down why we got
Speaker 3: it wrong and the ways in which that you know,
Speaker 3: we've learned from that, and the ways in which our
Speaker 3: processes have taken in new information, still leads to this
Speaker 3: this view that that the markets are overly optimistic about
Speaker 3: how easy that's going to be.
Speaker 1: All right, Well, Greg, we appreciate you coming on and
Speaker 1: outlining your thought process both around the markets and AI
Speaker 1: and how you're actually deploying this new technology. So really
Speaker 1: appreciate it. Thanks for coming back on the show.
Speaker 3: My pleasure, good to talk to you.
Speaker 2: Good luck in Vegas. Yeah, bring home another bracelet.
Speaker 3: We'll try.
Speaker 2: Thanks Greg. That was great.
Speaker 1: So Joe, I feel like I have a slightly better
Speaker 1: conception of exactly how this kind of technology can be
Speaker 1: used for investing. So the idea of maybe you have
Speaker 1: the AI models come up with the cs or ideas
Speaker 1: that could then be rigorously fact checked because all they
Speaker 1: the eyes are hallucinating and things like that. That makes
Speaker 1: some sense.
Speaker 2: Yes, absolutely, And I think you know you asked the
Speaker 2: question it's like, can AI do our jobs? And I
Speaker 2: don't think the answer is yes. And I think it's
Speaker 2: like can AI replace the stock picker? It doesn't sound
Speaker 2: like the AI is yes, But like, can the AI
Speaker 2: augment augment the way someone's thinking, test come up with
Speaker 2: theories that then can be rapidly tested. Have that sort
Speaker 2: of go back and forth and sort of do some
Speaker 2: of the work that you currently sort of like junior
Speaker 2: analysts do in terms of like theory testing ideas and
Speaker 2: stuff like that. You could see how it could be
Speaker 2: a force multiplier at at a large fund.
Speaker 1: Yeah, but I mean to that sort of turning point
Speaker 1: question that also seems to be maybe the big weakness
Speaker 1: here is that if you have an algorithm or a
Speaker 1: model that's been trained on years and years of prior data,
Speaker 1: so rates going lower and lower, in inflation staying below
Speaker 1: two percent seems very difficult to project what might change.
Speaker 2: Which, to Greg's point, humans aren't very good at that either.
Speaker 2: But you would hope, like, right, like, that's what we
Speaker 2: won't want to just be able to ask ch GPT
Speaker 2: or whatever. You know, I'm using that as like a
Speaker 2: stand in for.
Speaker 1: This, Yeah, or maybe maybe you ask Ai, like what
Speaker 1: would you need to see in order to start taking
Speaker 1: the prospect of regime change seriously?
Speaker 2: Yeah, I like, I mean you talk about this idea
Speaker 2: of like the sort of like adversarial way of thinking
Speaker 2: about it, which I think is really important. And you
Speaker 2: pointed out the sort of like disaster of the how
Speaker 2: the home eye buyers and then they got adversely selected
Speaker 2: because it's like, well, if Zillo is in the market,
Speaker 2: we know they're going to overpay, and so everyone suddenly
Speaker 2: dumps all the homes on Zillo and it was not
Speaker 2: anticipating its own role in the market. In response to
Speaker 2: your question, which I think is like a really interesting
Speaker 2: dimension to all.
Speaker 1: Of this, Yeah, that's sort of reflexivity between the models
Speaker 1: and the markets. I think we're probably going to be
Speaker 1: hearing a lot more about in the future. On that note,
Speaker 1: shall we leave it there?
Speaker 2: Let's leave it there, all right?
Speaker 1: This has been another episode of the Odd Thoughts podcast.
Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at
Speaker 1: Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me on Twitter
Speaker 2: at the Stalwart. Follow our producers on Twitter Carmen Rodriguez
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