Markets Don’t Care About Data Anymore? feat. Ben Hunt & Cem Karsan | U Got Options | Ep.9
Summary
Consumer Narrative: The current story about the US consumer is deeply negative, yet forward-looking media narratives are loudly bullish on a spending rebound.
Positioning Signal: Dollar stores and subprime lenders have rallied on optimistic expectations, creating potential asymmetry if the narrative rolls over; options are highlighted as the best expression.
Sell America: A renewed "Sell America" narrative is intensifying, with foreign owners repatriating capital and early signs that US managers may begin allocating abroad.
De-dollarization Risk: Rising discussion about the erosion of US dollar dominance and home-bias suggests a slow-moving but important shift that could persist for years.
AI’s Market Impact: Generative AI and LLMs are reshaping how stories are created and measured; AI is already amplifying narratives even if full agent-based pod replacement remains far off.
Rates and FX Opportunity: A regime shift to more policy divergence (multipolarity) boosts opportunity in rates and FX, the core of global macro trading.
Fiscal Dominance: If fiscal dominance drives outcomes, traditional Fed-watching models lose efficacy, making narrative tracking and timing via options more valuable.
Transcript
You got you got [music] your trade. You bet. You bet. You bet. You bet. Your money just got made. [music] >> Welcome to You Got Options, an exciting series right here on Top Traders Unplugged, hosted by none other than Jim Carson, one of the sharpest minds when it comes to understanding what's really driving market moves beneath the surface. [music] In this series, Jim brings his deep expertise and unique perspective honed from years of experience on the trading floor to candid conversations with some of the [music] brightest minds in the industry. Together, they unpack the shifting tides and underlying forces that move markets [music] and the opportunities they create. A quick reminder before we dive in. You got options is forformational and educational purposes only. None of the discussions you're about to hear [music] should be considered investment advice. As always, please do your own research and consult with a professional adviser before making any investment [music] decisions. Now, what makes this series truly special is that it's recorded right from the heart of the action on the trading floor of the SIBO. That means you might catch a little background buzz, phones ringing, traders shouting as Jim and his guests unpack realorld insights in [music] real time. We wouldn't have it any other way because this is as authentic as it gets. And with that, it's time to hear from those who live and breathe this complex corner of the markets. Here is your host, Jim Carson. [music] Hello and welcome back to You Got Options from the SIBO floor presented to you by Kai Media and Top Traders Unplugged. Today we talk to Ben Hunt, one of my favorite of of all times. Uh Ben is an old friend going back 10 years. We talk about the power of narrative, how important it is to play the players in the game, and how AI and narrative are changing the game when it comes to markets. You're going to love this one. Check it out. >> Yo, live it up. Don't settle for less. You got options. Put your skills [music] to the test. It's your call. Time to strike. Make your move. Grab the mic. Mic. You got You got You got [music] execute your trade. You bet. You bet. You bet. You got your money just got made. >> [music] >> Hey, welcome back to a little You Got Options. I have one of my favorite people. Actually, we have an incredible uh story. We actually probably met about a decade ago. >> Yeah, it's been a long time. Yeah. >> Uh through a good friend of ours, Brian Portoi. uh he and I have a a a reading group, a book club uh that we do uh which is absolutely an incredible group of guys talking about the world and and how it's changing. Um and and we were fortunate enough to have Ben who's a good friend of Brian's as well. Popped through about a decade ago and that uh what an incredible time that was 10 years ago. Yeah. >> Um but uh so wonderful to get you here and kind of share those conversations here with the rest of the world. >> It's great to be here. I really appreciate it. Um, a lot of people are familiar with your kind of seminal work. Uh, and epsilon theory. I mean, if and if you're not, by the way, go read, subscribe. >> Oh, thank you. >> Um, I mean, some of the most insightful stuff. And and and he's been doing it for God, when did you start Epsilon Theory? >> About 13 years ago, crazy enough. Yeah. 14 years. Yeah. >> 13 14 years. And I think over 100,000 people subscribe and and and read your work. >> Yeah. Yeah. Yeah. Pretty interesting. Yeah. uh pretty incred incredible. Um and if you're not familiar, you know, a lot of talk about narratives, a lot of uh thinking about the messaging and and the players in the game >> and and how they react um kind of within within uh the broad story of of where this world is and and I love the the nudge, right? How how important the nudge is in in in our in our current world and political environment. Um, but a lot of people don't know your connection to financial markets as well. >> Yeah. And and origin story and and so I'd love for you to kind of just lead us off with a little >> um you know, how did you get involved in markets? Uh how did you start and how did you get involved in market? >> Yeah, sure. Well, I I mean ultimately I got involved in markets because it's the biggest game in the world and like you I know Jam I mean we're we're game players. We we love we love that intrinsically. And I'm sure like most of the people watching game players, poker players, you know, it's not just playing the cards. It's playing the player. >> Absolutely. >> And and this and this where we are, right? It's all about playing the player >> more than ever, too. >> More more than ever. And and what's always been my field and my focus. We'll talk about what that has been, right? and why it got focused on the markets is about well, you know, playing the players in an environment like a big distributed game like markets or politics. >> It's what you're really looking at is what are the stories? What are what's the the information flow out there? >> What's what's driving player behavior? what what you think you think that they think that you think you know and you would you would think that'd be an infinitely recursive loop but it's really not it's really something you can measure and study so I got started in markets late I was a political science professor of all oxymorons uh but the but the thing there it was it was at Harvard in the 80s and that was really where the scientific study of infer which is all the rage now. You talk about Jinsen Wong when he's talking about their revenues, it's about well there's the model training, but where the real money is from is from inference. It's from using generative AI to get to pull nuggets of information. >> That's what that Grock purchase was recently about, right? >> It's all about inference, right? And so I I was there at the beginning and inference is >> getting needles out of a hay stack. >> Where You know, in the past, most of those hay stacks have been structured data. What's possible now? This has always been my feel. How do you get those needles, those pieces of information, signal, out of unstructured data, out of the stuff we read, out of the things we hear, out of a transcript of this conversation, all of that. So, we're very used in markets to to dealing with structured data. flow data, tick data, you know, price, all of the structured data. What I'm telling you is that the real fertile ground, the un undiscovered country is in applying that same level of rigor to unstructured data. And that's what I've been doing for 35 years. >> Amazing. Absolutely amazing. So, how did you start that process of doing it? and and where where are you now? >> So the the the start of it was in like I say academia, right? So some of the very early work on inference. The math is the same math we were using back then. No one's invented cold fusion when it comes to understanding patterns in unstructured data. Those patterns, by the way, you can use fancy terms for it like semantic structures and semantic search. What we're really talking about is story. We're really talking about is narrative. There's a a meaning, the thesis, the idea. It's not sentiment. It's not are you using nice words or mean words. That's a mistake. >> There's very little signal in sentiment. Very little. There's also very little signal in what's called topic clustering. Like, oh, let's do a Google search for how many times they mentioned AI in this earnings report. There's actually very little signal in that >> where there's signal because this is what drives our own decision-m process is in how do what's the story that's being told? >> What are the logical frameworks on this or the >> you know I I'll call it logical maybe but but it doesn't matter if it's logical it matters if it rings true >> fair >> to you as a human. >> I meant the word logical. >> It's it's it's that old >> co careful with semantics. >> Yeah. Yeah. Yeah. You do. You do. So, it's that old Colbear saying of it it's of it's truthy. >> Yeah. >> Right. It's it's maybe not truth with a capital T. I have no idea what truth is with a capital T. >> Right. >> But I can tell you when a story is truthy >> when it sounds right. And and that's what clicks for humans. Always has and it always will now. Like I say, the math to understand that hasn't changed. It's very simple math. >> Three things have changed though. The first is our access to the data. Mhm. >> So right now, you know, our company, we get everything that's published in the world. Every language, every transcript, every broadcast, every blog, you name it. It's all available. You get it overnight. The other thing that's changed is just computing processing power. I mean, God, I was, you know, coding stuff on a deck mini frame. And that'll take some of your viewers back and others will have no idea what I'm talking about. But you, you know, you code it. It wasn't quite card punch, but it wasn't far from that. And you have to wait till the next day to get the computational power that's in my iPhone today. >> Yeah, I'm dating myself, but my dad, who was a PhD structure, used to bring me the punch cards. >> The punch cards. >> And I used to do like math and draw on the punch card. So it um >> it it's really insane because the the math you we do here it's not >> it's not fancy math right it's a little bit different from typical math that we deal in our normal world is kind of network math matrices you know that's not important what's important is it's not complicated you just have to do it at massive scale >> because what you're trying to find out are the connections between words and ideas on just a crazy scale and So, you know, the the computational power that's available today, I mean, we're [clears throat] a small company. There are 11 12 of us, but we've processed over 500 billion tokens just in the last couple of months. It's crazy. I mean, Open AI sends you a little medallion for every 100red billion tokens you process. And so, availability of data in a crazy degree, availability of processing power. And the third one is LLMs because we were doing this stuff by hand and now it's just all there you can just plug into the wall and use. So take a step back. Obviously the the computational uh ability, the amount of information, data available, it makes complete sense. You don't even have to have the exact right model, right? You can I mean obviously that matters. I'm going to get to why I'm saying that. But let's go back to the models, right? You said truthy, right? >> Uh there's a lot of gray in words. There's a lot of mapping, I would assume, is hard. And I don't want you to give away your secret sauce by any means. >> No. >> But at the same time, like conceptually, this is >> Yeah. >> How does one take and map language and and and and uh the the truthiness of it and the connections? You know, you said the math hasn't changed in a sense. like what are those um those core ideas? How do we conceptually think about them at least? >> So, the real secret to this >> Yeah. >> is you can't ask AI to do it. >> Yeah. >> Don't ask AI. You ask one of the guys around here who's who's been trading something for 10 years. Ask you about, you know, >> S&P 500. >> There there's something you have to have a sense of it, right? >> You have to have a sense of what are the stories in your experience. What are the stories? What are the ideas, the themes? They come and they go, >> but you know all the stories because you've lived this for a long time. >> So that's where you start. >> Mhm. And >> how's that? How do you do that mathematically? Again, I'm not asking you to give up your secret sauce on how you do this, but like I just I want to better conceptually understand where what are the starting points of of mapping language and and uh >> the first step is understanding that you're trying to get at meaning. You're trying to get at the idea. >> Yeah. >> I give you an example. Yeah. Right. So, >> a story that I'd like to track and we track thousands of these is I'm bullish on financial services. Okay. >> Right. There's that story. >> Now, that's a story. There are a thousand different ways to say I'm bullish on financial services. And there are a couple of dozen of reasons why you might be bullish on and and those reasons they'll be duplicated. It's the same story. There are a finite number of scripts >> for saying I'm bullish on financial services. I'm bullish on this company. I'm bullish on consumer discretionary. I'm bullish on you know boons. What whatever the thing is. >> Yeah. >> There are finite number of ways to say that. >> Got it. >> To communicate that meaning of bullishness. >> Yeah. Now what we used to do is we would try to deterministically construct all those different ways you could say bullishness. Yeah. And it >> it ends up being this vast model, language model. >> And it's actually pretty good at then going through everything that's published in the world and saying, "Oh, here's a here's a hit for that." and you track how these stories are waxing and waning over time. >> The the problem with that though is that there are more ways to say toote commote that meaning than I could figure out or you could figure out by writing something down. And that's why these language models are so powerful because they have a probabilistic embeddings. the probability of any word or group of words of being associated with what we're talking about, >> right? >> So, >> you get all the grays as well. Basically, >> you get all the grays. So, you cast a much wider net, >> right? >> And actually, the net is actually a much better net than something you construct yourself. >> Just like options are, right? >> 100%. >> Right. So, what it allows you to do is it lets you use LLMs as an operating system. >> And I and I can't tell the viewers how important it is not to ask open-ended questions of of AI because they'll give you an answer, but the answer is going to be they're going to tell you what basically what you think you want to hear, >> right? What what you must do is you must it's called context engineering and this was true 30 years ago and it's true today. You have to think of AI LLMs as an operating system >> and you have to not just dole out little bits at a time what it's reading and looking at. >> You have to do all that indexing and stuff yourself, >> right? The most important thing is you have to tell it what it's allowed to think about. >> Yeah. >> So you only you only let it think about those stories that you want it to to to look at. >> Yeah. >> And if you do that and then there are other steps too. >> Sure. It's it's an amazing tool for not just getting at word choice, not just getting at like Google search, how many times do they talk about this, but really getting at the heart of this bullishness story. When did it start? How's it peaked? How's it changing? And what's and where is it going? So, we had breakfast before we sat down today and one of the things you mentioned when we were talking before was that you know you used to manage money in several different hedge funds and use some of these tools. You've returned money and the reason largely is because these models are so powerful and useful in so many places across Yeah. >> not just markets, right? >> Yeah. >> Yeah. really is this concept that you started with. It's just playing the players. The game theory piece and every decision we're making. So much of our decision is not just what are the uh just mathematical odds of things happening is what is the other person thinking and how does that change the probabilities? >> Yeah. >> Or what is the other party doing? So speak a little bit about the other types of things that you're doing and how this fits into broad decision-m and then we can come back to >> well and and this is if if I've got one piece of advice for all the people you know it's kind of starting out with >> managing money right is >> if what you're doing and you'll know if if if if how you're managing money isn't working you should give the money back >> before you start losing money. >> Yeah. Because I mean our business is like I'm a big Godfather fan, right? So So in in Godfather 2, you know that is this great line, you know, you know, he always made money for his partners. >> You got to say it in the voice. >> Yeah. You got to say it in the voice. You got to say [laughter] it in the voice. >> But that's when you're managing other people's money, A, that has to be your only focus. Y >> and B, you just >> you always have to make money for your partners. So the the the hedge fund we had um the first time I gave money back, yeah, it was a lot of money. It was a little over a billion dollars, right? Long short equity. We'd been running it for about 8 years. Did great in '05, 06, 07, and we had a great year in '08. Great year in '08. That's when money came flowing in. But in March of '09 when the Fed moved us to forward guidance and intervention, it's like you you flipped the switch in our returns. >> Yeah. >> Right. Um and we were, you know, long short, equity, value with a catalyst, kind of all these kind of typical things, right? But even then, I was really focused on stories. And what changed was that the stories we had about fundamentals didn't matter any it didn't matter anymore. It was the story that the central banks were telling. It was the influx of CEOs who tell a story. >> Yeah. >> Tell the right kind of narrative about their company. >> Didn't matter what reality was. So our returns flatlined. We never lost money for our clients. But I could tell, you know, what we were doing. It just wasn't working. So we gave all the money back in 2012. And that's when I started writing epsilon theory about theor well how how do you manage other people's money when the fundamentals don't matter when it's not reality and your knowledge of reality but it's about how is reality being presented >> yeah the perception of reality so that took me back to my my all the academic days the software company I'd started and that's why I started writing epsilon theory was about this how do we understand the structure of unstructured data. How do we understand stories and the stories of markets? So, that's been this effort, you know, since then to to figure that [ __ ] out. And we made some real advances in 2020. We made some more advances in 2022 and thought, "Oh man, you know, we should, you know, get the gang back together again and put together a fund to trade on this." >> And so we did. I mean, it we started off with a couple hundred million and it's a good thing. Performance was was good. But then we had another big advance in the technology and at that point it was the technology here is so >> touches everything. >> It touches everything and is so powerful. So it was like you invented the microscope to really see at a high degree of magnification these invisible things, these stories that we know are really important but but are invisible. Yeah. So, we gave all that money back again so we could really focus on the technology and that's what we're doing now. >> Yeah. >> So, cool. >> Couple different stories. What other types of things if you're at liberty to talk about it? Sure. You know, what other >> what other types of things are you using this in just to kind of paint a rounded picture of a real life kind of application other than markets? >> Oh, well, I mean like we just we just signed a deal with a uh Major League Baseball team. Uh, so the MLB draft is coming up. >> And again, there's this old notion of, you know, you don't just play the cards, you play the players. >> And so what we can pick up is what are the draft tendencies of this team. What are the draft tendencies of every other team? So that when this MLB team is doing the draft and they know the >> you know different rounds of the draft, you got three teams ahead of you, >> you know what their tendencies are, >> right? >> And then it anyway, we're really excited about it. I think it can totally change the game on this. >> Yeah. Especially when you have that many participants with each having probably distinct >> Yep. >> personalities, that's like a perfect application. So working with a a a defense department group for looking at and this is my political science pat from way back when, right? Which is looking at domestic media in other countries as they're trying to tell the story to get their >> to drum up popular support for either police action at home or for aggressive action otherwise. So like we ran this on Russian domestic media before the Ukraine invasion, wrote about it on epsilon theory because when you look at the again the stories they were telling in Russian domestic media to their own people. Forget about what they're saying internationally that's not it. You need to look at what they're telling their own people. And the stories they're telling was about the the threat of NATO, how Ukraine was this front, how and our conclusion was this isn't a faint, this isn't a limited anything. They're going in full force and we're able to write about that before they actually did. It's those kind of things we're able to do for Marcus. >> I remember reading about that at the time. Amazing. Yeah. [music] So on that note, now let's take you have this [music] political science background. Yeah. >> You have this incredible tool that you've developed and this way of thinking about decision-m and how you can use that to your benefit, >> right? >> Um what are the big narratives that are going on now and and all facets of kind of >> I'll give you two. Yeah, >> I'll give I'll give you two. And one important thing to know is that the stories we tell and here I'm going to talk about markets there are three kind of big categories of stories that we tell. The first story that we tell is what is what's happening now, >> right? The Fed is hawkish. Let's say that could that could be a story. Another type of story is well what happened in the past which is actually kind of interesting because oftenimes we kind of retcon the past totally >> to tell you something about the current future. Interesting. And so there's a set of stories that'll be about well the Fed was dovish in the past because XYZ and those can be interesting right because they're when we're kind of changing the way like say retconing the past that can be interesting. >> So I think the most interesting ones and you see these a lot in markets because markets are forwardlooking >> absolutely >> creatures. Yeah. What do we expect the Fed or whoever to be, right? What are the expectations? There are stories about what's what's coming down the pike. And it's those I'll call them forwardlooking stories. It's it's like it's like I don't know uh the PMI survey, right? Where they they ask the purchasing managers, what are the current conditions and what are your expectations of future conditions, >> right? It's those expectations that get the most play and have the most impact. Sure. >> Because that's how markets work. Right. >> Right. >> So what I will tell you is that right now >> in the description of what is >> with the American consumer, it's as bad I'll say negative sentiment to use that word as we've ever measured. And for all of this, we've got 10, 12, 15 years of data going back here. >> Off the charts for the consumer is afraid to spend. Household credit over overextended. These are stories that you would get in not necessarily just in financial media, but just in general media >> about what is the current experience of the US consumer. >> Okay? >> Right? Now, contrast that with the stories you get in financial media about expectations of consumer spending, >> the future expectations, >> right? >> We're crazy positive. I I I mean the the the stories the the volume and that's the way to think of it. It's >> it's not like I say it's not word search, right? It's the this the story that the US consumer is going to rebound, spend more. there's a bottom formed. All these different ways of saying it. >> The density of these stories is off the chart. You haven't seen this density of stories saying loud story, the loudness, the volume of stories saying consumer spending is going to surprise to the upside. you haven't seen this volume of stories since um since a we were coming out of >> coh >> and so it's it's like 2021 since you've seen this level of >> volume for these stories. >> Yeah. At the same time, even though the stories about what is the consumer being really weak are very loud, stories of we expect the consumer to be weak are incredibly low and soft. That's why, right? So, so look at a chart of I don't know, I'll pull up. So, you know, >> the dollar, you know, Dollar Tree, Dollar General, y >> those stocks have killed it this year, >> right? >> Uh subprime finance companies this year. They've, you know, year to date these guys are up, you know, 10 15%. >> Right. Easy. Yeah. >> Easy. Easy. Because expectations are it's going to be great. Now, I'm a long volatility guy at heart, you know. And so when I look at this, my take is the market has gotten ahead of itself in its expectations >> and that the the asymmetric riskreward is actually that there can be a news event either a scheduled macro event, earnings event where we go, oh crap, you know, the consumer is not rebounding the way we've gotten so bowled up about, >> right? The market has gotten incredibly bull on consumer spending going into Q1. My personal sense, and this isn't for my personal sense is the consumer is actually really weak. Certainly the bottom slice of the K-shaped economy is >> y >> maybe that doesn't matter. Maybe I'm wrong. But we're all familiar with what these stories are about to bull us up on consumer spending, refunds, uh the tariffs are going to be reversed, you know. just and tune in. You'll hear those stories. >> And so just as a signal now, does the does the market let's say the news conflicts with that current story and now moves that story, right? >> You'll see it roll over. >> You see it roll over. >> We see so we see the expectation story roll over and it has not. >> Right? So this expectation story has been going straight up. bets before it rolls over or do you wait for the signal that it's rolling over? >> That's the thing, right? I'm not going to put this on cuz I'm not going to get in in front of this, >> right? >> Because >> you don't have, >> you know, cuz I don't have to. This can run, right? >> So, there's got to be an This is why I've always wanted to to think and talk about pairing this with >> options. >> Options where you've got an opinion on timing. You got an opinion on timing >> and you've got these scheduled events where >> maybe the news happens, maybe it doesn't. >> You know how the news is being priced. >> What I'm telling you is the story is being overpriced. >> Yeah. You know where the con likely convex outcomes >> could be. You can highlight that. >> That's it, right? >> That's it. >> And then you can uh you can kind of take a >> Yeah. a a small a high impact kind of uh lowrisisk that >> but but I'm sure as hell I'm not going to set it up until I see see this the story start to roll. >> Interesting. Yeah, that's what I would think. That makes sense. Very interesting. So that's on a maybe more micro level. Actually, let me add one more question here on the micro level before we get to a bigger macro conversation as well tied to that. >> I got one for you on that too. >> Oh, I'd love I that's that's the fun I just talked to Neil Howard. So macro is like going great. Yeah, we're we're all friends. Um so um but on any any signal on things like inflation and other broader macro I mean you're talking about growth and demand which is important obviously but obviously in this world you know interest rates and inflation bigger kind of macro signal other things like that that you're seeing or thinking about. >> Oh yeah so so I think one of the real values of what we do is that we show the volume of the story >> on both sides of a coin. usually and we're all we're all human like this like you maybe you've got a view on inflation I've got a view and so when I'm listening to the news I'm keen to hear when people are agreeing with me >> right and and I'll say oh everybody's talking you know everybody's agreeing with me I get excited I get I get bullled up because I get the confirmation right and I don't pay attention to the volume of the story on the other Right. So for a lot of issues AI capex built there's enormous volume on both sides of that coin >> that oh my god this is a bubble it's going to burst or nothing stops this train. Right. Right. There's enormous volume on both sides of that. >> Interesting. >> Right. Which honestly when there's a lot of volume story volume on both sides >> I don't think there's a lot to be done with it. >> Sure. Yeah. Makes sense. >> But it does tell you that's what the market cares about. Mhm. >> So if you have your own information, your own view on it, the market cares. >> Yeah. >> Bring you back to inflation, bring you back to recession, market doesn't care about either of those topics right now. >> The the level of volume on both sides of that coin, both sides of the inflation coin, both sides of the recession coin is way below average. Right. Because it's going to be one or the other and people are going to be surprised. >> It's going to like being >> or or right by all when the story starts to turn >> starts to turn. That's right. That's the signal >> because because stories can be dormant and muted >> right >> for a long time >> because nobody cares large. >> Nobody cares. What you're looking for are the inflection points on the story. >> That's super interesting. Yeah. So because you can use it for direction, but you can also use it for distribution. Uh right. Uh >> yeah, exactly. So So the the the the >> easiest use of what we do is >> look for a story that's dormant. >> Yeah. >> That you think >> and be prepared and be watching for it. And when the dormant story starts to get undorrant, >> then get involved. >> Then you get involved because it hasn't been discovered yet. >> Right. >> And that that's where you make your easy returns. is when the market discovers a story. >> Yeah. >> We say, "Oh yeah, that's a problem." That's an opportunity little tick and whether it's in dormcancy or whether it's conflict to signal and that's really the key that you really >> and it's going to be cheap. >> Yeah. >> On terms because it's been dormant for so long. >> Incredible. I love it. I love it. So, let's shift to kind of a bigger now. Let's use the same tools and the same lens and stock talk a little bit bigger picture. So, politics and things that aren't just markets that will affect markets, but uh global conflicts. Uh what is the administration doing? What is it? What does the people think it's doing versus maybe what it is? Do you have any thoughts or any signal on >> Yeah, I'm going to I'm going to give you one specifically. And again, not sure when this will air, right? >> Which I love. Yeah. >> But but but right now as we're talking, you're in the middle of the Greenland thing, right? And um you know there was an article yesterday about a small Danish fund getting rid of its treasuries. Today there's an article about a somewhat larger Swedish pension fund saying we're out. >> Yep. >> Yeah. So it's this I'll call it. It's the sell America trade. Y >> right which was a prominent narrative after liberation day after April of last year. Mhm. >> What we're seeing, so we've we've got a whole set, we call them, we call them semantic signatures, which is sounds complicated, but what we mean is it's a story. It's an idea. It's a theme. There are lots of ways to think about sell America. >> There's is it foreign central banks looking for alternatives to uh US treasuries? No. Right. That's one. Is it um foreign uh asset owners pulling money out of US equity markets? Right? Is it US asset owners moving assets abroad? Right? So, what we've seen just in the last week is an enormous spike in all of these repatriation merits. It's not where it was for April, but it is growing with a bullet. With an absolute bullet. And the really interesting part of this is that look, it's the thing I'd argue that has driven US home bias and US outperformance for the last whatever 15 years has been inflow of capital to US markets and in every way you want to express that >> that tide is changing. Mhm. >> That now this is a melting iceberg. >> Yep. >> Right. This is not an oh my god overnight thing. >> But like any melting iceberg, this is important. This can go on for a long time. That is absolutely happening. >> Yeah. >> Foreign asset owners moving money back home. >> Yep. >> That's happening again. >> Where becomes a crisis is if US money managers start moving money >> out out of the US. >> You never saw that narrative start >> in April and May and June of last year. That narrative stayed dormant even though you had these big spikes in foreign central banks doing this, foreign asset owners doing that, US home bias, you know, is being questioned, US losing dollar dominance as a, you know, the reserve currency. All those stories spiked, but the US asset owner leaving never really even budged. that story is starting to move now. It's it's it's not at a anywhere near kind of alarm bells yet, but you know, I was mentioning about a long dormant story that's starting to pick up. >> Yeah. >> This is a story I don't know that we've seen in 30 years and it's starting to pick up. >> Well, the incredibly important part about and how you can fit this in when we're talking macro or something big like this too is is the the reflexivity of it, right? Because if people lose confidence >> and that creates a belief broadly like for example in a big picture uh that maybe the currency is overvalued or that you know the exorbitant privilege of the dollar the feds losing control that can lead reflexively to those outcomes right >> so so I'm often asked right do narratives drive price or does price drive narrative both >> and the answer is yes >> the answer is yes >> the answer is Yeah. >> So what you're describing is exactly what gives story narrative a momentum quality to it. >> Y >> however because there is all this orthogonal stuff happening and it has a dynamic of its own. The momentum quality of story is, you know, $10 word alert, orthogonal to the momentum aspect of Christ, >> right? >> And that's pretty freaking cool. >> That is cool. And and uh it's super interesting how how it goes on for a long time when that narrative is right, but when it starts to turn, it can be a dramatic kind of move. And again, great great place to use options, right? great time and way to >> phenomenally it is I mean I like I say this has always been my dream is to try to you know share this with people >> who know who have forgotten more about options than I will ever know >> right uh but but that is exactly the opportunity and there are the dynamic of when what I'll call it so there's a name for it it's called common knowledge >> and kes wrote about this back in the 30s is at the heart of his newspaper beauty contest And you know it's not what you know >> it's not what you think. It's not even what you think that other people think. It's what we all think that we all think. >> Yeah. >> That's what that's what common knowledge is and that's what drives the outcome of newspaper beauty contest which are markets. >> Right. >> So the dynamic of that is you're looking for these common knowledge moments which is a variation of the the emperor has >> no clothes. So easy example was uh Biden's debate performance. That was a common knowledge moment where we all saw what we all saw, >> right? And there's no amount of, you know, the party functionary saying, "Oh, no, he's actually quite sharp when you get it back." No, we all saw it, >> right? >> We all saw with our own eyes. We just didn't know if everybody else saw it or if that was a common >> but because it was we know that everybody was watching that's what becomes a common knowledge human. So you can measure that you can measure the halflife of stories and whether they begin to be amplified through a common knowledge effect. That's exactly what we do. and no bigger common knowledge problem or thing than than US Federal Reserve is dominant and there's exorbitant privilege of the US dollar. So that of all things to be watching a shift in that obviously >> the idea of fiscal dominance again these are other narratives that we track >> right >> it's it's hugely important is mostly it's hugely important because all of your models that you've got and a lot of people have got I'll call them linguistic models they're basically looking at a Fed statement to see if this word changed or that word changed or what happened to the dot plots right if if fiscal dominance is what matters, then the efficacy of those models you've built on dot plots and Fed statements, they're much less impactful. They won't work the way you thought they would work. >> 100%. Just read Arthur Burns. Yeah, >> exactly right. >> I couldn't agree more. Um, and that is, by the way, a story we've been talking about for a while, but that hasn't been central narrative. And I am, even though I don't use your models, there is a lot more tuning into that. And you're seeing it work. Uh again this is what I meant about how traders right immediately get what I'm I'm at because you are immersed in news flow all the time >> right >> and you may have your own view of things I'm sure you do but what you know is that your view doesn't really matter >> doesn't matter till >> right >> until the rest of the world comes around to it >> I guess to kind of put a put a bow on this obviously you know game theory Uh and playing the players is critical. Um uh abilities are not just what you think uh in a uh kind of blank slate world based on the numbers you see is happening. It's also a matter of what others think and and how they might move and their sentiment like you talked about. Um if we start deploying that using options as we talked about is an incredible way >> to take something that is not conventional wisdom that is start may start to turn and may see a incredible shift like you're getting signal that >> that that um that's a vulnerable sentiment maybe that's turning so incredibly powerful in that sense. >> Um but but how do you locate what those items are? Is that all qualitative? Is that all like just a sense of what's important? Um, you know, how do you how do you know what to look at and what opinions matter the most and where are the things that might be driving the next sentiment? >> Um, so the way to use this the way to use this and this is why we said, you know, we're not going to do a little hedge fund on this. We want to make the data available right >> to everyone involved in the ESCO because >> I think I know I think I I think I've got a good view of what's important with I'll call it AI technology like I've been doing this for 35 years. I think I actually have deep substantive knowledge of this area. I also think I've got deep substantive knowledge of the companies and the the trading dynamics at least in equity world. That's been my my field. >> I I don't know a damn thing about FX, right? I don't know a damn thing about consumer discretionary, right? I don't know I don't know a damn thing about so much. Right? The way to use this is what do you really know? What do you really know? And that that could be flow data. That could be I know I know structured data. I know market dynamics of this particular trade. It could be oh I know the fundamentals of this space really well. And way to use this is to figure out when does my substantive knowledge, my deep knowledge, when does it freaking matter? And and you just set off like a big light bulb in my head as you're talking. And is it going to find this interesting? And I I think the listener will as well. I've talked about before have you look at historic data when we see period broadly like we're seeing now >> right um uh like the 60s and 70s or maybe like the 30s 40s in a sense where there's less um control from Federal Reserve and uh dominance in a sense of of one entity who has clear power. We're more kind of placid time with less kind of conflict. I get to why we're kind of similar there. But when you enter these periods, you have a dramatic departure in how things move. >> Mhm. >> Regime change truly is a a change dramatic change in the distribution of not just one thing but of everything. In these periods, I'll give the 60s and 70s as the the the most near >> yeah kind of example, >> we see incredible FX movement. You mentioned FX. Obviously, interest rate volatility and distribution go through the roof. Precious metals, things like that, commodities, while they start to move in ways that are completely different, >> right? But equities themselves, you would think, oh, those would be more volatile in some strange ways become less volatile. uh nominal assets you know in that sense that are have a push pull become less volatile on there's certain assets that dramatically change their distributional it's not just everything that becomes more volatile >> and can I tell you why that is >> sure >> the the reason why that is and you put your finger eye say there's a regime change right >> right so when you move towards you want to call it multipolar when you call it more independent policy >> there is after the great financial crisis There was no difference between central bank policy and the in the developed world. They were all singing from the same >> himnel, the same choir book, right? There was no difference, >> right? >> To make money in an asacle, whether it's FX, whether it's >> don't fight the >> rates. Rates and FX are the two best example of this. >> You need difference. >> Course, >> you need difference between national policies. Today, we have that difference because, as you put it, the regime has changed. >> Put another way, I completely agree. We're in a room and there's one giant uh strong person with a all the weapons piece to control. >> Exactly. >> But the second there starts to be for any reason a lock lack of control. I'm not saying that person is not powerful anymore but there are others uh it becomes a multi-polar uh much more. You don't go from just uh you know slightly volatile to more volatile or whatever. You have a complete uh the the dimensions of volatility go from two to infinite. >> The global global macro is trading rates and FX. >> That's what it is. That's global macro, right? And to make money with that, >> it only requires difference. >> Yeah. >> And that's what we've got today. >> Yeah. And so critically now tying this into what you were talking about, not to go off on a tangent, >> the more untethered markets may be to a certain kind of fixed system with certain fixed rules, um the more narrative is going to matter. The more uh because we don't know a change, it's easy to see hurting and um because there's less constraint, the possibilities are greater, I would think. I don't know. Would you agree with that or or do you >> Oh, no. So, so there have been three structural changes that have that cement the role of narrative telling. >> Yeah. >> And the efficacy of narrative telling in our lives. >> Right. So, the first one is there's been a structural change in the way news is delivered. >> Absolutely. >> To 247, you see it here, the financial, and I'm using air quotes here, the financial news channels. I'm using air quotes. because there's there's not enough hard news to fill the time. So on all of the financial news stations, the time is filled by people presenting story, by people giving their opinion, right? That's what fills the time. So that's that's structural change number one. Structural change number two is these little things we carry around with ourselves all the time. A little dopamine machine. We immerse ourselves in constant messaging, right? We do it to ourselves. We we give ourselves over to the constant messaging. Mhm. >> Third structural change, and it really was the the GFC that did this, where central bankers said, "We're going to start using our words for effect, not for what we really think, not that we're lying, but we're using them intentionally to try to change investor behavior." And the enormous success of that was picked up by every CEO. Today's CEO, you're not being evaluated on can I get an additional turn of operating leverage or you know what's my capacity utilization in the De Moines factory. It's can I tell a story that gets a multiple. >> That's right. >> Right. Cuz multiple is Elon Musk is the wealthiest man in the world. >> Absolutely. It doesn't matter what Tesla's deliveries were. It doesn't matter. it matters is can you tell a story about robots and space 20 years from now? >> I couldn't agree more. I think that's so true. Um, >> so all of this stuff cementss the role in of narrative in our lives. We've done it to ourselves is embedded in the structure of our technology and our media systems [music] and it works. All right, the last piece here which I think is we talk about playing the players but now [music] what about playing the system that plays the players as we grow right cuz we talk about reflexivity you know AI is going to become omnipresent in some way you are on the cutting edge on the front edge of something that I think will transform critical it's a huge part of and the power of AI is bringing it uh I often talk about this concept of knowledge is all dampening, right? The more we learn and the better that we get at doing these things, the more reflexively it uh it reverses the outcomes obviously uh that that that >> is it is it knowledge is all dampening or the spread of knowledge is is all dampened out. >> Well, yeah. I mean universal not not a single person's knowledge, >> right? Right. Because private information is what drives alpha, right? Yeah. Yeah. >> Semantics matter. >> Yeah. Yeah. Yeah. Yeah. Yeah. >> But 100%. So um and so my my next question this is kind of a two-part question would be one how do you think AI and narrative are going to begin to I mean I know how you can use it now but how is it more universally going to be shape how we are how markets trade and and and and not you could talk about it in time frames like in your mind like sure >> the next one two five years the next 10 years next 20 years what is your thought I mean who knows right Well, >> if anybody would have a sense in front of you, >> I mean, one enormous dynamic is what all the pod shops are doing, right? Is there's trying to del to, you know, it's this idea of agentics, right? You're trying to to create an AI agent that can replace your consumer discretionary pod. >> Yep. >> You know, so you know, Izzy and Bla, that whole crew, right? I mean, what they would love to do is replace their very expensive people >> with AI agents that would do the same thing. >> Yes, sir. >> Everyone involved in this space of AI and markets has been gunning for that. >> Yeah. >> And we're still a million miles from >> Yeah. >> We're still a million freaking miles from you. and my >> and your narrative is is much closer than people think which probably should be a >> well and that's the other side. So, so the way that AI is being used very effectively today is for AI slop, right? It's for the generation of content and stories to magnify the nudge that we would get from a political party, that we would get from a corporation, that we'd get from a central bank. That's that's totally happening now. Now, personally for what we do, I don't care, right? Because because I often get that question, well, is is the is the story that you're picking up on is it right? And I said, I don't know. And I don't care, right? Cuz I that's your job, right? Honestly, to have that view of what reality actually is. All I can tell you is how reality is being presented. And the two trends we see are a there's been a fragmentation of storytelling meaning there are a lot of discrete stories that are being told in markets all the time but the firepower behind those stories has grown significantly. So I don't see any spread of knowledge. What I see is the spread of truthiness of stories that sound were accurate, >> right? >> So, um, it's like people out there who realize the power of this that are then trying to like move the the the sent not sentiment but the the story. Yeah. >> And look, and we track this stuff, too. We published a couple pieces on >> differentiates. So, I mean, you can basically you're tracking like p patient zero and the the the easiest place to track this is on some of the Reddit in the subreddit boards >> where you can actually trace >> where where >> where it started and and and the way it works is you'll get somebody with a position >> and you can I call it snowballs, right? You start like a dozen different snowballs at the top of a hill in hopes that one of them picks up traction and starts being organically amplified so it becomes an avalanche because you're positioned >> to profit from the avalanche. That's when you that's when you that's the wrong point. >> It's like the new form of activist investing in a sense like Yeah. In the same way that the job of any S&P 500 CEO is to tell a story that generates a multiple >> y >> activists and you know it's the names we know um around the meme stocks we know >> the whole game is to start that snowball rolling down the hill. That's how you make your money with activist and you can track that. Do you have clients that and don't need names but that that uh that don't just use it in the original way we're talking about but really to find out where there's vulnerability and that where they can actually themselves drive stories to uh to create better outcomes for themselves because I would think that's also a place where this ultimately goes right >> not in markets right not in markets but um >> politically >> it also applies to politics right >> it applies a million% politics where where we've had a some conversations, let's say, has been within consumer brands cuz >> brand awareness and brand creation, >> the original nudge, right? >> It's all story, it's all nudge. and and to date it hasn't been although you could do it say where is fertile ground for creating a brand that applies to this rising self-perception of identity of a consumer graphic as a consumer demographic where we see it now mostly is with some how's my brand working because that's a story about your marketing dollars you know you know half of it is wasted. You just don't know which half, >> right? >> And so you're actually we're actually able to track >> perception >> what did these ideas that you're trying to drive into markets are actually getting traction. >> Well, one thing's very clear, incredibly powerful stuff, whether in markets or otherwise. And and and also, I mean, >> it's just fun. >> Oh, it's definitely fun. I I we could talk about this for hours. I'm sure we will uh again, but um but wow, what what incredible powerful stuff and and and a brave new world. I will say I think you know for we talked 10 years ago at at at the TR I can only imagine what 10 years from now given the advancements you know what those conversations would be like. >> Look my hope my strongest hope is that we get decentralized distributed AI. We need open source AI in the worst I mean open source AI just means that the weights are then you can run it on a local machine. Um >> right >> that's that's going to tell the tale. >> Yeah. The risk is that we get >> centralized control AI >> especially with these types of tools and this ability to nudge analytical way. >> Exactly right. >> Ben, thank you so much for sitting down with me. What an incredible convers. Thanks for having me. >> And hopefully we'll do it again in a year or two. >> I'd love that. All right, we'll take care. [music] >> Thanks [music] for listening to Top Traders Unplugged. If you feel you learned something of value from today's episode, the best way to stay updated is to go on over to your favorite podcast platform [music] and follow the show so that you'll be sure to get all the new episodes as they're released. We have some amazing guests lined up for [music] you. And to ensure our show continues to grow, please leave us an honest rating and review. It only takes a minute and it's the best way to show us you love the podcast. We'll see you next time on Top Traders Unplugged. 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Markets Don’t Care About Data Anymore? feat. Ben Hunt & Cem Karsan | U Got Options | Ep.9
Summary
Transcript
You got you got [music] your trade. You bet. You bet. You bet. You bet. Your money just got made. [music] >> Welcome to You Got Options, an exciting series right here on Top Traders Unplugged, hosted by none other than Jim Carson, one of the sharpest minds when it comes to understanding what's really driving market moves beneath the surface. [music] In this series, Jim brings his deep expertise and unique perspective honed from years of experience on the trading floor to candid conversations with some of the [music] brightest minds in the industry. Together, they unpack the shifting tides and underlying forces that move markets [music] and the opportunities they create. A quick reminder before we dive in. You got options is forformational and educational purposes only. None of the discussions you're about to hear [music] should be considered investment advice. As always, please do your own research and consult with a professional adviser before making any investment [music] decisions. Now, what makes this series truly special is that it's recorded right from the heart of the action on the trading floor of the SIBO. That means you might catch a little background buzz, phones ringing, traders shouting as Jim and his guests unpack realorld insights in [music] real time. We wouldn't have it any other way because this is as authentic as it gets. And with that, it's time to hear from those who live and breathe this complex corner of the markets. Here is your host, Jim Carson. [music] Hello and welcome back to You Got Options from the SIBO floor presented to you by Kai Media and Top Traders Unplugged. Today we talk to Ben Hunt, one of my favorite of of all times. Uh Ben is an old friend going back 10 years. We talk about the power of narrative, how important it is to play the players in the game, and how AI and narrative are changing the game when it comes to markets. You're going to love this one. Check it out. >> Yo, live it up. Don't settle for less. You got options. Put your skills [music] to the test. It's your call. Time to strike. Make your move. Grab the mic. Mic. You got You got You got [music] execute your trade. You bet. You bet. You bet. You got your money just got made. >> [music] >> Hey, welcome back to a little You Got Options. I have one of my favorite people. Actually, we have an incredible uh story. We actually probably met about a decade ago. >> Yeah, it's been a long time. Yeah. >> Uh through a good friend of ours, Brian Portoi. uh he and I have a a a reading group, a book club uh that we do uh which is absolutely an incredible group of guys talking about the world and and how it's changing. Um and and we were fortunate enough to have Ben who's a good friend of Brian's as well. Popped through about a decade ago and that uh what an incredible time that was 10 years ago. Yeah. >> Um but uh so wonderful to get you here and kind of share those conversations here with the rest of the world. >> It's great to be here. I really appreciate it. Um, a lot of people are familiar with your kind of seminal work. Uh, and epsilon theory. I mean, if and if you're not, by the way, go read, subscribe. >> Oh, thank you. >> Um, I mean, some of the most insightful stuff. And and and he's been doing it for God, when did you start Epsilon Theory? >> About 13 years ago, crazy enough. Yeah. 14 years. Yeah. >> 13 14 years. And I think over 100,000 people subscribe and and and read your work. >> Yeah. Yeah. Yeah. Pretty interesting. Yeah. uh pretty incred incredible. Um and if you're not familiar, you know, a lot of talk about narratives, a lot of uh thinking about the messaging and and the players in the game >> and and how they react um kind of within within uh the broad story of of where this world is and and I love the the nudge, right? How how important the nudge is in in in our in our current world and political environment. Um, but a lot of people don't know your connection to financial markets as well. >> Yeah. And and origin story and and so I'd love for you to kind of just lead us off with a little >> um you know, how did you get involved in markets? Uh how did you start and how did you get involved in market? >> Yeah, sure. Well, I I mean ultimately I got involved in markets because it's the biggest game in the world and like you I know Jam I mean we're we're game players. We we love we love that intrinsically. And I'm sure like most of the people watching game players, poker players, you know, it's not just playing the cards. It's playing the player. >> Absolutely. >> And and this and this where we are, right? It's all about playing the player >> more than ever, too. >> More more than ever. And and what's always been my field and my focus. We'll talk about what that has been, right? and why it got focused on the markets is about well, you know, playing the players in an environment like a big distributed game like markets or politics. >> It's what you're really looking at is what are the stories? What are what's the the information flow out there? >> What's what's driving player behavior? what what you think you think that they think that you think you know and you would you would think that'd be an infinitely recursive loop but it's really not it's really something you can measure and study so I got started in markets late I was a political science professor of all oxymorons uh but the but the thing there it was it was at Harvard in the 80s and that was really where the scientific study of infer which is all the rage now. You talk about Jinsen Wong when he's talking about their revenues, it's about well there's the model training, but where the real money is from is from inference. It's from using generative AI to get to pull nuggets of information. >> That's what that Grock purchase was recently about, right? >> It's all about inference, right? And so I I was there at the beginning and inference is >> getting needles out of a hay stack. >> Where You know, in the past, most of those hay stacks have been structured data. What's possible now? This has always been my feel. How do you get those needles, those pieces of information, signal, out of unstructured data, out of the stuff we read, out of the things we hear, out of a transcript of this conversation, all of that. So, we're very used in markets to to dealing with structured data. flow data, tick data, you know, price, all of the structured data. What I'm telling you is that the real fertile ground, the un undiscovered country is in applying that same level of rigor to unstructured data. And that's what I've been doing for 35 years. >> Amazing. Absolutely amazing. So, how did you start that process of doing it? and and where where are you now? >> So the the the start of it was in like I say academia, right? So some of the very early work on inference. The math is the same math we were using back then. No one's invented cold fusion when it comes to understanding patterns in unstructured data. Those patterns, by the way, you can use fancy terms for it like semantic structures and semantic search. What we're really talking about is story. We're really talking about is narrative. There's a a meaning, the thesis, the idea. It's not sentiment. It's not are you using nice words or mean words. That's a mistake. >> There's very little signal in sentiment. Very little. There's also very little signal in what's called topic clustering. Like, oh, let's do a Google search for how many times they mentioned AI in this earnings report. There's actually very little signal in that >> where there's signal because this is what drives our own decision-m process is in how do what's the story that's being told? >> What are the logical frameworks on this or the >> you know I I'll call it logical maybe but but it doesn't matter if it's logical it matters if it rings true >> fair >> to you as a human. >> I meant the word logical. >> It's it's it's that old >> co careful with semantics. >> Yeah. Yeah. Yeah. You do. You do. So, it's that old Colbear saying of it it's of it's truthy. >> Yeah. >> Right. It's it's maybe not truth with a capital T. I have no idea what truth is with a capital T. >> Right. >> But I can tell you when a story is truthy >> when it sounds right. And and that's what clicks for humans. Always has and it always will now. Like I say, the math to understand that hasn't changed. It's very simple math. >> Three things have changed though. The first is our access to the data. Mhm. >> So right now, you know, our company, we get everything that's published in the world. Every language, every transcript, every broadcast, every blog, you name it. It's all available. You get it overnight. The other thing that's changed is just computing processing power. I mean, God, I was, you know, coding stuff on a deck mini frame. And that'll take some of your viewers back and others will have no idea what I'm talking about. But you, you know, you code it. It wasn't quite card punch, but it wasn't far from that. And you have to wait till the next day to get the computational power that's in my iPhone today. >> Yeah, I'm dating myself, but my dad, who was a PhD structure, used to bring me the punch cards. >> The punch cards. >> And I used to do like math and draw on the punch card. So it um >> it it's really insane because the the math you we do here it's not >> it's not fancy math right it's a little bit different from typical math that we deal in our normal world is kind of network math matrices you know that's not important what's important is it's not complicated you just have to do it at massive scale >> because what you're trying to find out are the connections between words and ideas on just a crazy scale and So, you know, the the computational power that's available today, I mean, we're [clears throat] a small company. There are 11 12 of us, but we've processed over 500 billion tokens just in the last couple of months. It's crazy. I mean, Open AI sends you a little medallion for every 100red billion tokens you process. And so, availability of data in a crazy degree, availability of processing power. And the third one is LLMs because we were doing this stuff by hand and now it's just all there you can just plug into the wall and use. So take a step back. Obviously the the computational uh ability, the amount of information, data available, it makes complete sense. You don't even have to have the exact right model, right? You can I mean obviously that matters. I'm going to get to why I'm saying that. But let's go back to the models, right? You said truthy, right? >> Uh there's a lot of gray in words. There's a lot of mapping, I would assume, is hard. And I don't want you to give away your secret sauce by any means. >> No. >> But at the same time, like conceptually, this is >> Yeah. >> How does one take and map language and and and and uh the the truthiness of it and the connections? You know, you said the math hasn't changed in a sense. like what are those um those core ideas? How do we conceptually think about them at least? >> So, the real secret to this >> Yeah. >> is you can't ask AI to do it. >> Yeah. >> Don't ask AI. You ask one of the guys around here who's who's been trading something for 10 years. Ask you about, you know, >> S&P 500. >> There there's something you have to have a sense of it, right? >> You have to have a sense of what are the stories in your experience. What are the stories? What are the ideas, the themes? They come and they go, >> but you know all the stories because you've lived this for a long time. >> So that's where you start. >> Mhm. And >> how's that? How do you do that mathematically? Again, I'm not asking you to give up your secret sauce on how you do this, but like I just I want to better conceptually understand where what are the starting points of of mapping language and and uh >> the first step is understanding that you're trying to get at meaning. You're trying to get at the idea. >> Yeah. >> I give you an example. Yeah. Right. So, >> a story that I'd like to track and we track thousands of these is I'm bullish on financial services. Okay. >> Right. There's that story. >> Now, that's a story. There are a thousand different ways to say I'm bullish on financial services. And there are a couple of dozen of reasons why you might be bullish on and and those reasons they'll be duplicated. It's the same story. There are a finite number of scripts >> for saying I'm bullish on financial services. I'm bullish on this company. I'm bullish on consumer discretionary. I'm bullish on you know boons. What whatever the thing is. >> Yeah. >> There are finite number of ways to say that. >> Got it. >> To communicate that meaning of bullishness. >> Yeah. Now what we used to do is we would try to deterministically construct all those different ways you could say bullishness. Yeah. And it >> it ends up being this vast model, language model. >> And it's actually pretty good at then going through everything that's published in the world and saying, "Oh, here's a here's a hit for that." and you track how these stories are waxing and waning over time. >> The the problem with that though is that there are more ways to say toote commote that meaning than I could figure out or you could figure out by writing something down. And that's why these language models are so powerful because they have a probabilistic embeddings. the probability of any word or group of words of being associated with what we're talking about, >> right? >> So, >> you get all the grays as well. Basically, >> you get all the grays. So, you cast a much wider net, >> right? >> And actually, the net is actually a much better net than something you construct yourself. >> Just like options are, right? >> 100%. >> Right. So, what it allows you to do is it lets you use LLMs as an operating system. >> And I and I can't tell the viewers how important it is not to ask open-ended questions of of AI because they'll give you an answer, but the answer is going to be they're going to tell you what basically what you think you want to hear, >> right? What what you must do is you must it's called context engineering and this was true 30 years ago and it's true today. You have to think of AI LLMs as an operating system >> and you have to not just dole out little bits at a time what it's reading and looking at. >> You have to do all that indexing and stuff yourself, >> right? The most important thing is you have to tell it what it's allowed to think about. >> Yeah. >> So you only you only let it think about those stories that you want it to to to look at. >> Yeah. >> And if you do that and then there are other steps too. >> Sure. It's it's an amazing tool for not just getting at word choice, not just getting at like Google search, how many times do they talk about this, but really getting at the heart of this bullishness story. When did it start? How's it peaked? How's it changing? And what's and where is it going? So, we had breakfast before we sat down today and one of the things you mentioned when we were talking before was that you know you used to manage money in several different hedge funds and use some of these tools. You've returned money and the reason largely is because these models are so powerful and useful in so many places across Yeah. >> not just markets, right? >> Yeah. >> Yeah. really is this concept that you started with. It's just playing the players. The game theory piece and every decision we're making. So much of our decision is not just what are the uh just mathematical odds of things happening is what is the other person thinking and how does that change the probabilities? >> Yeah. >> Or what is the other party doing? So speak a little bit about the other types of things that you're doing and how this fits into broad decision-m and then we can come back to >> well and and this is if if I've got one piece of advice for all the people you know it's kind of starting out with >> managing money right is >> if what you're doing and you'll know if if if if how you're managing money isn't working you should give the money back >> before you start losing money. >> Yeah. Because I mean our business is like I'm a big Godfather fan, right? So So in in Godfather 2, you know that is this great line, you know, you know, he always made money for his partners. >> You got to say it in the voice. >> Yeah. You got to say it in the voice. You got to say [laughter] it in the voice. >> But that's when you're managing other people's money, A, that has to be your only focus. Y >> and B, you just >> you always have to make money for your partners. So the the the hedge fund we had um the first time I gave money back, yeah, it was a lot of money. It was a little over a billion dollars, right? Long short equity. We'd been running it for about 8 years. Did great in '05, 06, 07, and we had a great year in '08. Great year in '08. That's when money came flowing in. But in March of '09 when the Fed moved us to forward guidance and intervention, it's like you you flipped the switch in our returns. >> Yeah. >> Right. Um and we were, you know, long short, equity, value with a catalyst, kind of all these kind of typical things, right? But even then, I was really focused on stories. And what changed was that the stories we had about fundamentals didn't matter any it didn't matter anymore. It was the story that the central banks were telling. It was the influx of CEOs who tell a story. >> Yeah. >> Tell the right kind of narrative about their company. >> Didn't matter what reality was. So our returns flatlined. We never lost money for our clients. But I could tell, you know, what we were doing. It just wasn't working. So we gave all the money back in 2012. And that's when I started writing epsilon theory about theor well how how do you manage other people's money when the fundamentals don't matter when it's not reality and your knowledge of reality but it's about how is reality being presented >> yeah the perception of reality so that took me back to my my all the academic days the software company I'd started and that's why I started writing epsilon theory was about this how do we understand the structure of unstructured data. How do we understand stories and the stories of markets? So, that's been this effort, you know, since then to to figure that [ __ ] out. And we made some real advances in 2020. We made some more advances in 2022 and thought, "Oh man, you know, we should, you know, get the gang back together again and put together a fund to trade on this." >> And so we did. I mean, it we started off with a couple hundred million and it's a good thing. Performance was was good. But then we had another big advance in the technology and at that point it was the technology here is so >> touches everything. >> It touches everything and is so powerful. So it was like you invented the microscope to really see at a high degree of magnification these invisible things, these stories that we know are really important but but are invisible. Yeah. So, we gave all that money back again so we could really focus on the technology and that's what we're doing now. >> Yeah. >> So, cool. >> Couple different stories. What other types of things if you're at liberty to talk about it? Sure. You know, what other >> what other types of things are you using this in just to kind of paint a rounded picture of a real life kind of application other than markets? >> Oh, well, I mean like we just we just signed a deal with a uh Major League Baseball team. Uh, so the MLB draft is coming up. >> And again, there's this old notion of, you know, you don't just play the cards, you play the players. >> And so what we can pick up is what are the draft tendencies of this team. What are the draft tendencies of every other team? So that when this MLB team is doing the draft and they know the >> you know different rounds of the draft, you got three teams ahead of you, >> you know what their tendencies are, >> right? >> And then it anyway, we're really excited about it. I think it can totally change the game on this. >> Yeah. Especially when you have that many participants with each having probably distinct >> Yep. >> personalities, that's like a perfect application. So working with a a a defense department group for looking at and this is my political science pat from way back when, right? Which is looking at domestic media in other countries as they're trying to tell the story to get their >> to drum up popular support for either police action at home or for aggressive action otherwise. So like we ran this on Russian domestic media before the Ukraine invasion, wrote about it on epsilon theory because when you look at the again the stories they were telling in Russian domestic media to their own people. Forget about what they're saying internationally that's not it. You need to look at what they're telling their own people. And the stories they're telling was about the the threat of NATO, how Ukraine was this front, how and our conclusion was this isn't a faint, this isn't a limited anything. They're going in full force and we're able to write about that before they actually did. It's those kind of things we're able to do for Marcus. >> I remember reading about that at the time. Amazing. Yeah. [music] So on that note, now let's take you have this [music] political science background. Yeah. >> You have this incredible tool that you've developed and this way of thinking about decision-m and how you can use that to your benefit, >> right? >> Um what are the big narratives that are going on now and and all facets of kind of >> I'll give you two. Yeah, >> I'll give I'll give you two. And one important thing to know is that the stories we tell and here I'm going to talk about markets there are three kind of big categories of stories that we tell. The first story that we tell is what is what's happening now, >> right? The Fed is hawkish. Let's say that could that could be a story. Another type of story is well what happened in the past which is actually kind of interesting because oftenimes we kind of retcon the past totally >> to tell you something about the current future. Interesting. And so there's a set of stories that'll be about well the Fed was dovish in the past because XYZ and those can be interesting right because they're when we're kind of changing the way like say retconing the past that can be interesting. >> So I think the most interesting ones and you see these a lot in markets because markets are forwardlooking >> absolutely >> creatures. Yeah. What do we expect the Fed or whoever to be, right? What are the expectations? There are stories about what's what's coming down the pike. And it's those I'll call them forwardlooking stories. It's it's like it's like I don't know uh the PMI survey, right? Where they they ask the purchasing managers, what are the current conditions and what are your expectations of future conditions, >> right? It's those expectations that get the most play and have the most impact. Sure. >> Because that's how markets work. Right. >> Right. >> So what I will tell you is that right now >> in the description of what is >> with the American consumer, it's as bad I'll say negative sentiment to use that word as we've ever measured. And for all of this, we've got 10, 12, 15 years of data going back here. >> Off the charts for the consumer is afraid to spend. Household credit over overextended. These are stories that you would get in not necessarily just in financial media, but just in general media >> about what is the current experience of the US consumer. >> Okay? >> Right? Now, contrast that with the stories you get in financial media about expectations of consumer spending, >> the future expectations, >> right? >> We're crazy positive. I I I mean the the the stories the the volume and that's the way to think of it. It's >> it's not like I say it's not word search, right? It's the this the story that the US consumer is going to rebound, spend more. there's a bottom formed. All these different ways of saying it. >> The density of these stories is off the chart. You haven't seen this density of stories saying loud story, the loudness, the volume of stories saying consumer spending is going to surprise to the upside. you haven't seen this volume of stories since um since a we were coming out of >> coh >> and so it's it's like 2021 since you've seen this level of >> volume for these stories. >> Yeah. At the same time, even though the stories about what is the consumer being really weak are very loud, stories of we expect the consumer to be weak are incredibly low and soft. That's why, right? So, so look at a chart of I don't know, I'll pull up. So, you know, >> the dollar, you know, Dollar Tree, Dollar General, y >> those stocks have killed it this year, >> right? >> Uh subprime finance companies this year. They've, you know, year to date these guys are up, you know, 10 15%. >> Right. Easy. Yeah. >> Easy. Easy. Because expectations are it's going to be great. Now, I'm a long volatility guy at heart, you know. And so when I look at this, my take is the market has gotten ahead of itself in its expectations >> and that the the asymmetric riskreward is actually that there can be a news event either a scheduled macro event, earnings event where we go, oh crap, you know, the consumer is not rebounding the way we've gotten so bowled up about, >> right? The market has gotten incredibly bull on consumer spending going into Q1. My personal sense, and this isn't for my personal sense is the consumer is actually really weak. Certainly the bottom slice of the K-shaped economy is >> y >> maybe that doesn't matter. Maybe I'm wrong. But we're all familiar with what these stories are about to bull us up on consumer spending, refunds, uh the tariffs are going to be reversed, you know. just and tune in. You'll hear those stories. >> And so just as a signal now, does the does the market let's say the news conflicts with that current story and now moves that story, right? >> You'll see it roll over. >> You see it roll over. >> We see so we see the expectation story roll over and it has not. >> Right? So this expectation story has been going straight up. bets before it rolls over or do you wait for the signal that it's rolling over? >> That's the thing, right? I'm not going to put this on cuz I'm not going to get in in front of this, >> right? >> Because >> you don't have, >> you know, cuz I don't have to. This can run, right? >> So, there's got to be an This is why I've always wanted to to think and talk about pairing this with >> options. >> Options where you've got an opinion on timing. You got an opinion on timing >> and you've got these scheduled events where >> maybe the news happens, maybe it doesn't. >> You know how the news is being priced. >> What I'm telling you is the story is being overpriced. >> Yeah. You know where the con likely convex outcomes >> could be. You can highlight that. >> That's it, right? >> That's it. >> And then you can uh you can kind of take a >> Yeah. a a small a high impact kind of uh lowrisisk that >> but but I'm sure as hell I'm not going to set it up until I see see this the story start to roll. >> Interesting. Yeah, that's what I would think. That makes sense. Very interesting. So that's on a maybe more micro level. Actually, let me add one more question here on the micro level before we get to a bigger macro conversation as well tied to that. >> I got one for you on that too. >> Oh, I'd love I that's that's the fun I just talked to Neil Howard. So macro is like going great. Yeah, we're we're all friends. Um so um but on any any signal on things like inflation and other broader macro I mean you're talking about growth and demand which is important obviously but obviously in this world you know interest rates and inflation bigger kind of macro signal other things like that that you're seeing or thinking about. >> Oh yeah so so I think one of the real values of what we do is that we show the volume of the story >> on both sides of a coin. usually and we're all we're all human like this like you maybe you've got a view on inflation I've got a view and so when I'm listening to the news I'm keen to hear when people are agreeing with me >> right and and I'll say oh everybody's talking you know everybody's agreeing with me I get excited I get I get bullled up because I get the confirmation right and I don't pay attention to the volume of the story on the other Right. So for a lot of issues AI capex built there's enormous volume on both sides of that coin >> that oh my god this is a bubble it's going to burst or nothing stops this train. Right. Right. There's enormous volume on both sides of that. >> Interesting. >> Right. Which honestly when there's a lot of volume story volume on both sides >> I don't think there's a lot to be done with it. >> Sure. Yeah. Makes sense. >> But it does tell you that's what the market cares about. Mhm. >> So if you have your own information, your own view on it, the market cares. >> Yeah. >> Bring you back to inflation, bring you back to recession, market doesn't care about either of those topics right now. >> The the level of volume on both sides of that coin, both sides of the inflation coin, both sides of the recession coin is way below average. Right. Because it's going to be one or the other and people are going to be surprised. >> It's going to like being >> or or right by all when the story starts to turn >> starts to turn. That's right. That's the signal >> because because stories can be dormant and muted >> right >> for a long time >> because nobody cares large. >> Nobody cares. What you're looking for are the inflection points on the story. >> That's super interesting. Yeah. So because you can use it for direction, but you can also use it for distribution. Uh right. Uh >> yeah, exactly. So So the the the the >> easiest use of what we do is >> look for a story that's dormant. >> Yeah. >> That you think >> and be prepared and be watching for it. And when the dormant story starts to get undorrant, >> then get involved. >> Then you get involved because it hasn't been discovered yet. >> Right. >> And that that's where you make your easy returns. is when the market discovers a story. >> Yeah. >> We say, "Oh yeah, that's a problem." That's an opportunity little tick and whether it's in dormcancy or whether it's conflict to signal and that's really the key that you really >> and it's going to be cheap. >> Yeah. >> On terms because it's been dormant for so long. >> Incredible. I love it. I love it. So, let's shift to kind of a bigger now. Let's use the same tools and the same lens and stock talk a little bit bigger picture. So, politics and things that aren't just markets that will affect markets, but uh global conflicts. Uh what is the administration doing? What is it? What does the people think it's doing versus maybe what it is? Do you have any thoughts or any signal on >> Yeah, I'm going to I'm going to give you one specifically. And again, not sure when this will air, right? >> Which I love. Yeah. >> But but but right now as we're talking, you're in the middle of the Greenland thing, right? And um you know there was an article yesterday about a small Danish fund getting rid of its treasuries. Today there's an article about a somewhat larger Swedish pension fund saying we're out. >> Yep. >> Yeah. So it's this I'll call it. It's the sell America trade. Y >> right which was a prominent narrative after liberation day after April of last year. Mhm. >> What we're seeing, so we've we've got a whole set, we call them, we call them semantic signatures, which is sounds complicated, but what we mean is it's a story. It's an idea. It's a theme. There are lots of ways to think about sell America. >> There's is it foreign central banks looking for alternatives to uh US treasuries? No. Right. That's one. Is it um foreign uh asset owners pulling money out of US equity markets? Right? Is it US asset owners moving assets abroad? Right? So, what we've seen just in the last week is an enormous spike in all of these repatriation merits. It's not where it was for April, but it is growing with a bullet. With an absolute bullet. And the really interesting part of this is that look, it's the thing I'd argue that has driven US home bias and US outperformance for the last whatever 15 years has been inflow of capital to US markets and in every way you want to express that >> that tide is changing. Mhm. >> That now this is a melting iceberg. >> Yep. >> Right. This is not an oh my god overnight thing. >> But like any melting iceberg, this is important. This can go on for a long time. That is absolutely happening. >> Yeah. >> Foreign asset owners moving money back home. >> Yep. >> That's happening again. >> Where becomes a crisis is if US money managers start moving money >> out out of the US. >> You never saw that narrative start >> in April and May and June of last year. That narrative stayed dormant even though you had these big spikes in foreign central banks doing this, foreign asset owners doing that, US home bias, you know, is being questioned, US losing dollar dominance as a, you know, the reserve currency. All those stories spiked, but the US asset owner leaving never really even budged. that story is starting to move now. It's it's it's not at a anywhere near kind of alarm bells yet, but you know, I was mentioning about a long dormant story that's starting to pick up. >> Yeah. >> This is a story I don't know that we've seen in 30 years and it's starting to pick up. >> Well, the incredibly important part about and how you can fit this in when we're talking macro or something big like this too is is the the reflexivity of it, right? Because if people lose confidence >> and that creates a belief broadly like for example in a big picture uh that maybe the currency is overvalued or that you know the exorbitant privilege of the dollar the feds losing control that can lead reflexively to those outcomes right >> so so I'm often asked right do narratives drive price or does price drive narrative both >> and the answer is yes >> the answer is yes >> the answer is Yeah. >> So what you're describing is exactly what gives story narrative a momentum quality to it. >> Y >> however because there is all this orthogonal stuff happening and it has a dynamic of its own. The momentum quality of story is, you know, $10 word alert, orthogonal to the momentum aspect of Christ, >> right? >> And that's pretty freaking cool. >> That is cool. And and uh it's super interesting how how it goes on for a long time when that narrative is right, but when it starts to turn, it can be a dramatic kind of move. And again, great great place to use options, right? great time and way to >> phenomenally it is I mean I like I say this has always been my dream is to try to you know share this with people >> who know who have forgotten more about options than I will ever know >> right uh but but that is exactly the opportunity and there are the dynamic of when what I'll call it so there's a name for it it's called common knowledge >> and kes wrote about this back in the 30s is at the heart of his newspaper beauty contest And you know it's not what you know >> it's not what you think. It's not even what you think that other people think. It's what we all think that we all think. >> Yeah. >> That's what that's what common knowledge is and that's what drives the outcome of newspaper beauty contest which are markets. >> Right. >> So the dynamic of that is you're looking for these common knowledge moments which is a variation of the the emperor has >> no clothes. So easy example was uh Biden's debate performance. That was a common knowledge moment where we all saw what we all saw, >> right? And there's no amount of, you know, the party functionary saying, "Oh, no, he's actually quite sharp when you get it back." No, we all saw it, >> right? >> We all saw with our own eyes. We just didn't know if everybody else saw it or if that was a common >> but because it was we know that everybody was watching that's what becomes a common knowledge human. So you can measure that you can measure the halflife of stories and whether they begin to be amplified through a common knowledge effect. That's exactly what we do. and no bigger common knowledge problem or thing than than US Federal Reserve is dominant and there's exorbitant privilege of the US dollar. So that of all things to be watching a shift in that obviously >> the idea of fiscal dominance again these are other narratives that we track >> right >> it's it's hugely important is mostly it's hugely important because all of your models that you've got and a lot of people have got I'll call them linguistic models they're basically looking at a Fed statement to see if this word changed or that word changed or what happened to the dot plots right if if fiscal dominance is what matters, then the efficacy of those models you've built on dot plots and Fed statements, they're much less impactful. They won't work the way you thought they would work. >> 100%. Just read Arthur Burns. Yeah, >> exactly right. >> I couldn't agree more. Um, and that is, by the way, a story we've been talking about for a while, but that hasn't been central narrative. And I am, even though I don't use your models, there is a lot more tuning into that. And you're seeing it work. Uh again this is what I meant about how traders right immediately get what I'm I'm at because you are immersed in news flow all the time >> right >> and you may have your own view of things I'm sure you do but what you know is that your view doesn't really matter >> doesn't matter till >> right >> until the rest of the world comes around to it >> I guess to kind of put a put a bow on this obviously you know game theory Uh and playing the players is critical. Um uh abilities are not just what you think uh in a uh kind of blank slate world based on the numbers you see is happening. It's also a matter of what others think and and how they might move and their sentiment like you talked about. Um if we start deploying that using options as we talked about is an incredible way >> to take something that is not conventional wisdom that is start may start to turn and may see a incredible shift like you're getting signal that >> that that um that's a vulnerable sentiment maybe that's turning so incredibly powerful in that sense. >> Um but but how do you locate what those items are? Is that all qualitative? Is that all like just a sense of what's important? Um, you know, how do you how do you know what to look at and what opinions matter the most and where are the things that might be driving the next sentiment? >> Um, so the way to use this the way to use this and this is why we said, you know, we're not going to do a little hedge fund on this. We want to make the data available right >> to everyone involved in the ESCO because >> I think I know I think I I think I've got a good view of what's important with I'll call it AI technology like I've been doing this for 35 years. I think I actually have deep substantive knowledge of this area. I also think I've got deep substantive knowledge of the companies and the the trading dynamics at least in equity world. That's been my my field. >> I I don't know a damn thing about FX, right? I don't know a damn thing about consumer discretionary, right? I don't know I don't know a damn thing about so much. Right? The way to use this is what do you really know? What do you really know? And that that could be flow data. That could be I know I know structured data. I know market dynamics of this particular trade. It could be oh I know the fundamentals of this space really well. And way to use this is to figure out when does my substantive knowledge, my deep knowledge, when does it freaking matter? And and you just set off like a big light bulb in my head as you're talking. And is it going to find this interesting? And I I think the listener will as well. I've talked about before have you look at historic data when we see period broadly like we're seeing now >> right um uh like the 60s and 70s or maybe like the 30s 40s in a sense where there's less um control from Federal Reserve and uh dominance in a sense of of one entity who has clear power. We're more kind of placid time with less kind of conflict. I get to why we're kind of similar there. But when you enter these periods, you have a dramatic departure in how things move. >> Mhm. >> Regime change truly is a a change dramatic change in the distribution of not just one thing but of everything. In these periods, I'll give the 60s and 70s as the the the most near >> yeah kind of example, >> we see incredible FX movement. You mentioned FX. Obviously, interest rate volatility and distribution go through the roof. Precious metals, things like that, commodities, while they start to move in ways that are completely different, >> right? But equities themselves, you would think, oh, those would be more volatile in some strange ways become less volatile. uh nominal assets you know in that sense that are have a push pull become less volatile on there's certain assets that dramatically change their distributional it's not just everything that becomes more volatile >> and can I tell you why that is >> sure >> the the reason why that is and you put your finger eye say there's a regime change right >> right so when you move towards you want to call it multipolar when you call it more independent policy >> there is after the great financial crisis There was no difference between central bank policy and the in the developed world. They were all singing from the same >> himnel, the same choir book, right? There was no difference, >> right? >> To make money in an asacle, whether it's FX, whether it's >> don't fight the >> rates. Rates and FX are the two best example of this. >> You need difference. >> Course, >> you need difference between national policies. Today, we have that difference because, as you put it, the regime has changed. >> Put another way, I completely agree. We're in a room and there's one giant uh strong person with a all the weapons piece to control. >> Exactly. >> But the second there starts to be for any reason a lock lack of control. I'm not saying that person is not powerful anymore but there are others uh it becomes a multi-polar uh much more. You don't go from just uh you know slightly volatile to more volatile or whatever. You have a complete uh the the dimensions of volatility go from two to infinite. >> The global global macro is trading rates and FX. >> That's what it is. That's global macro, right? And to make money with that, >> it only requires difference. >> Yeah. >> And that's what we've got today. >> Yeah. And so critically now tying this into what you were talking about, not to go off on a tangent, >> the more untethered markets may be to a certain kind of fixed system with certain fixed rules, um the more narrative is going to matter. The more uh because we don't know a change, it's easy to see hurting and um because there's less constraint, the possibilities are greater, I would think. I don't know. Would you agree with that or or do you >> Oh, no. So, so there have been three structural changes that have that cement the role of narrative telling. >> Yeah. >> And the efficacy of narrative telling in our lives. >> Right. So, the first one is there's been a structural change in the way news is delivered. >> Absolutely. >> To 247, you see it here, the financial, and I'm using air quotes here, the financial news channels. I'm using air quotes. because there's there's not enough hard news to fill the time. So on all of the financial news stations, the time is filled by people presenting story, by people giving their opinion, right? That's what fills the time. So that's that's structural change number one. Structural change number two is these little things we carry around with ourselves all the time. A little dopamine machine. We immerse ourselves in constant messaging, right? We do it to ourselves. We we give ourselves over to the constant messaging. Mhm. >> Third structural change, and it really was the the GFC that did this, where central bankers said, "We're going to start using our words for effect, not for what we really think, not that we're lying, but we're using them intentionally to try to change investor behavior." And the enormous success of that was picked up by every CEO. Today's CEO, you're not being evaluated on can I get an additional turn of operating leverage or you know what's my capacity utilization in the De Moines factory. It's can I tell a story that gets a multiple. >> That's right. >> Right. Cuz multiple is Elon Musk is the wealthiest man in the world. >> Absolutely. It doesn't matter what Tesla's deliveries were. It doesn't matter. it matters is can you tell a story about robots and space 20 years from now? >> I couldn't agree more. I think that's so true. Um, >> so all of this stuff cementss the role in of narrative in our lives. We've done it to ourselves is embedded in the structure of our technology and our media systems [music] and it works. All right, the last piece here which I think is we talk about playing the players but now [music] what about playing the system that plays the players as we grow right cuz we talk about reflexivity you know AI is going to become omnipresent in some way you are on the cutting edge on the front edge of something that I think will transform critical it's a huge part of and the power of AI is bringing it uh I often talk about this concept of knowledge is all dampening, right? The more we learn and the better that we get at doing these things, the more reflexively it uh it reverses the outcomes obviously uh that that that >> is it is it knowledge is all dampening or the spread of knowledge is is all dampened out. >> Well, yeah. I mean universal not not a single person's knowledge, >> right? Right. Because private information is what drives alpha, right? Yeah. Yeah. >> Semantics matter. >> Yeah. Yeah. Yeah. Yeah. Yeah. >> But 100%. So um and so my my next question this is kind of a two-part question would be one how do you think AI and narrative are going to begin to I mean I know how you can use it now but how is it more universally going to be shape how we are how markets trade and and and and not you could talk about it in time frames like in your mind like sure >> the next one two five years the next 10 years next 20 years what is your thought I mean who knows right Well, >> if anybody would have a sense in front of you, >> I mean, one enormous dynamic is what all the pod shops are doing, right? Is there's trying to del to, you know, it's this idea of agentics, right? You're trying to to create an AI agent that can replace your consumer discretionary pod. >> Yep. >> You know, so you know, Izzy and Bla, that whole crew, right? I mean, what they would love to do is replace their very expensive people >> with AI agents that would do the same thing. >> Yes, sir. >> Everyone involved in this space of AI and markets has been gunning for that. >> Yeah. >> And we're still a million miles from >> Yeah. >> We're still a million freaking miles from you. and my >> and your narrative is is much closer than people think which probably should be a >> well and that's the other side. So, so the way that AI is being used very effectively today is for AI slop, right? It's for the generation of content and stories to magnify the nudge that we would get from a political party, that we would get from a corporation, that we'd get from a central bank. That's that's totally happening now. Now, personally for what we do, I don't care, right? Because because I often get that question, well, is is the is the story that you're picking up on is it right? And I said, I don't know. And I don't care, right? Cuz I that's your job, right? Honestly, to have that view of what reality actually is. All I can tell you is how reality is being presented. And the two trends we see are a there's been a fragmentation of storytelling meaning there are a lot of discrete stories that are being told in markets all the time but the firepower behind those stories has grown significantly. So I don't see any spread of knowledge. What I see is the spread of truthiness of stories that sound were accurate, >> right? >> So, um, it's like people out there who realize the power of this that are then trying to like move the the the sent not sentiment but the the story. Yeah. >> And look, and we track this stuff, too. We published a couple pieces on >> differentiates. So, I mean, you can basically you're tracking like p patient zero and the the the easiest place to track this is on some of the Reddit in the subreddit boards >> where you can actually trace >> where where >> where it started and and and the way it works is you'll get somebody with a position >> and you can I call it snowballs, right? You start like a dozen different snowballs at the top of a hill in hopes that one of them picks up traction and starts being organically amplified so it becomes an avalanche because you're positioned >> to profit from the avalanche. That's when you that's when you that's the wrong point. >> It's like the new form of activist investing in a sense like Yeah. In the same way that the job of any S&P 500 CEO is to tell a story that generates a multiple >> y >> activists and you know it's the names we know um around the meme stocks we know >> the whole game is to start that snowball rolling down the hill. That's how you make your money with activist and you can track that. Do you have clients that and don't need names but that that uh that don't just use it in the original way we're talking about but really to find out where there's vulnerability and that where they can actually themselves drive stories to uh to create better outcomes for themselves because I would think that's also a place where this ultimately goes right >> not in markets right not in markets but um >> politically >> it also applies to politics right >> it applies a million% politics where where we've had a some conversations, let's say, has been within consumer brands cuz >> brand awareness and brand creation, >> the original nudge, right? >> It's all story, it's all nudge. and and to date it hasn't been although you could do it say where is fertile ground for creating a brand that applies to this rising self-perception of identity of a consumer graphic as a consumer demographic where we see it now mostly is with some how's my brand working because that's a story about your marketing dollars you know you know half of it is wasted. You just don't know which half, >> right? >> And so you're actually we're actually able to track >> perception >> what did these ideas that you're trying to drive into markets are actually getting traction. >> Well, one thing's very clear, incredibly powerful stuff, whether in markets or otherwise. And and and also, I mean, >> it's just fun. >> Oh, it's definitely fun. I I we could talk about this for hours. I'm sure we will uh again, but um but wow, what what incredible powerful stuff and and and a brave new world. I will say I think you know for we talked 10 years ago at at at the TR I can only imagine what 10 years from now given the advancements you know what those conversations would be like. >> Look my hope my strongest hope is that we get decentralized distributed AI. We need open source AI in the worst I mean open source AI just means that the weights are then you can run it on a local machine. Um >> right >> that's that's going to tell the tale. >> Yeah. The risk is that we get >> centralized control AI >> especially with these types of tools and this ability to nudge analytical way. >> Exactly right. >> Ben, thank you so much for sitting down with me. What an incredible convers. Thanks for having me. >> And hopefully we'll do it again in a year or two. >> I'd love that. All right, we'll take care. [music] >> Thanks [music] for listening to Top Traders Unplugged. If you feel you learned something of value from today's episode, the best way to stay updated is to go on over to your favorite podcast platform [music] and follow the show so that you'll be sure to get all the new episodes as they're released. We have some amazing guests lined up for [music] you. And to ensure our show continues to grow, please leave us an honest rating and review. It only takes a minute and it's the best way to show us you love the podcast. We'll see you next time on Top Traders Unplugged. 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