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Using AI to Find Investing Stories with Perscient Co-Founder Ben Hunt · Masters in Business

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  Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Masters in
• 0:11
  Speaker 1: Business with Barry Ritholts on Bloomberg Radio.
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  Speaker 2: On the latest Masters in Business podcast. What a fascinating
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  Speaker 2: conversation I sit down with Ben Hunt. He writes Epsilon Theory,
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  Speaker 2: but he is also the president and co founder of Percion.
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  Speaker 2: What a fascinating analytics story they've put together. They essentially
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  Speaker 2: take feeds of everything that's published around the world, whether
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  Speaker 2: it's in English or Chinese or Russian. They create these
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  Speaker 2: large language models and use artificial intelligence to identify rising narratives.
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  Speaker 2: In other words, they're looking for the things that will
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  Speaker 2: become storylines, but haven't quite that yet. I found this
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  Speaker 2: conversation to be absolutely fascinating, and I think you will also.
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  Speaker 2: With no further ado, my conversation with Persians Ben Hunt.
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  Speaker 2: Ben Hunt, welcome to Bloomberg.
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  Speaker 3: Thanks for having me. Well, this is a love intro.
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  Speaker 3: I gotta have you at all my.
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  Speaker 2: That's right, I'm available for hire. I can introduce you
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  Speaker 2: at weddings by mitzvahs. Wherever you're giving a toast, I'll
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  Speaker 2: be happy to tee you up. This is long overdue.
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  Speaker 2: I've followed your work for so long. I'm fascinated by
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  Speaker 2: both what you put out in your blog Epsilon Theory,
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  Speaker 2: thank you, and which is now a blog and a newsletter,
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  Speaker 2: and the work you do at Percion. We're gonna get
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  Speaker 2: to that stuff, but before we do, I gotta ask
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  Speaker 2: PhD from Harvard you were a tenured political science professor.
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  Speaker 2: Was academia the original career plan?
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  Speaker 3: You know? It's interesting, So I Academia was always a
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  Speaker 3: i'll call it away station for me. It ended up
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  Speaker 3: being a ten ten year way station plus grad school.
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  Speaker 2: But I that's a little more than a waste.
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  Speaker 3: It's a little more than a way station. But I
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  Speaker 3: bet this will be familiar for a lot of your listeners.
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  Speaker 3: I always had an entrepreneurial bug, you know. I started
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  Speaker 3: my first company when I was in grad school, started
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  Speaker 3: another one when I was when I was a professor.
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  Speaker 3: And as I know, you know a lot of your
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  Speaker 3: listeners viewers know it is. It is a bug. It's
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  Speaker 3: not a feature. Yes, for sure, you can't help yourself.
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  Speaker 2: So let me ask you a question about that.
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  Speaker 3: Academy is not the place to be an a for sure.
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  Speaker 2: So I know why I've started a series of companies
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  Speaker 2: I can't work for other people. Why did you have
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  Speaker 2: that bug? What motivated you to say I gotta get
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  Speaker 2: this out into the world.
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  Speaker 3: I love playing games and solving problems. I have a
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  Speaker 3: similar issue about working for other people, which fortunately the
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  Speaker 3: academia solves that to a large degree. I mean, you
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  Speaker 3: you're working on your own stuff. You follow your own
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  Speaker 3: intellectual bliss in a way that I've really never rediscovered that.
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  Speaker 3: The problem with academy, of course, is you know, it's
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  Speaker 3: very very low stakes. It don't pay.
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  Speaker 2: That's why, that's why the academic fights are so vicious.
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  Speaker 3: Because there's nothing at stake, and that that is actually true.
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  Speaker 3: That's actually true, and so you learn survival techniques and
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  Speaker 3: that kind of jungle where nothing is really at stake,
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  Speaker 3: at least monetarily. Because the goal of any sort of
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  Speaker 3: academia conference or presentation like is to appear smart, right,
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  Speaker 3: it's not to actually be smart. You're not actually listening
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  Speaker 3: to a presentation to listen to it. What you learned
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  Speaker 3: to do is you're listening to the presentation the whole
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  Speaker 3: time you're trying to calculate what's the most devastating question
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  Speaker 3: I can ask?
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  Speaker 2: So you're going to rock. This guy back on his
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  Speaker 2: heels with a devastating.
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  Speaker 3: Devastating question, and boy, that gets old after a while, Barry,
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  Speaker 3: I gotta tell you, yeah, it really does. I loved
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  Speaker 3: the teaching. I loved the research because, like I say,
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  Speaker 3: nobody tells you what to work on, but the church
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  Speaker 3: of academia, the actual institution of academia. Hey, it's for
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  Speaker 3: the birds, even back when I was doing right, and
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  Speaker 3: I think it's gotten significantly worse. I can imagine. I
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  Speaker 3: can imagine what's happening with that.
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  Speaker 2: So but the question that this leads me to is,
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  Speaker 2: you've had all these jobs when the world to finance,
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  Speaker 2: how did you background in academia shape how you view investing,
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  Speaker 2: risk management, allocation.
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  Speaker 3: Barry, Starting from academ and then getting into our business
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  Speaker 3: of investing, I think it was the best thing that
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  Speaker 3: could have happened for me for when I got into it,
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  Speaker 3: which is kind of later in life, right, because you know,
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  Speaker 3: after I left academic, if I start a software company
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  Speaker 3: and after we sold that software company, Yeah, a buddy
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  Speaker 3: of mine, I think this happens a lot a buddy
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  Speaker 3: of you know, you have a buddy who's you need
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  Speaker 3: to be pretty smart? How would you like to apply.
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  Speaker 3: We're always we're always talking about company X or technology.
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  Speaker 3: Why why you know, why don't come in. Let's let's
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  Speaker 3: give this a try. So that was my path, if
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  Speaker 3: you want to call it a path. And what really
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  Speaker 3: sold me on it was that markets. It's the biggest
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  Speaker 3: game in the world for sure. And like I say,
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  Speaker 3: I'm a game player. I love a game theorist.
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  Speaker 2: Let's work that.
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  Speaker 3: I don't like to talk about that because, you know,
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  Speaker 3: because the real game theorists get angry. Well, yes, and
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  Speaker 3: I am a real one because that was that was
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  Speaker 3: my field for a lot and it's a real field
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  Speaker 3: and it's a real thing, but it's been so trivialized
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  Speaker 3: when some talking head will come on, well, let's look
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  Speaker 3: at the game theory of this, and you just want
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  Speaker 3: to just, you know, shoot yourself when somebody does this.
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  Speaker 2: So the other part of your research, the other part
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  Speaker 2: of your academic focus, was on narrative theory. And so
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  Speaker 2: let's talk about how did that focus develop. And we'll
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  Speaker 2: talk a little later about what you do today at
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  Speaker 2: personant with the narrative machine.
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  Speaker 3: But not believe it or not, it all ties together.
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  Speaker 2: I doesn't don't doubt that for a sing, and it's
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  Speaker 2: what initially led you down that rabbit hole when.
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  Speaker 3: We think about kind of who's been an influence on
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  Speaker 3: you in your life. At a very influential undergrad professor
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  Speaker 3: in political science, and then I had a very influential
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  Speaker 3: graduate advisor. Again, they don't call it political science up
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  Speaker 3: at Harvard. They call it government or something like that,
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  Speaker 3: but it's political science. And the reason I say they
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  Speaker 3: were influential is that they really got me focused on
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  Speaker 3: the science side of political science, and that science side. Yes,
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  Speaker 3: it's kind of some of the typical terrible stuff you
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  Speaker 3: see in all social sciences like economics, where you've got
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  Speaker 3: to learn how to deal with structured data, right, and
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  Speaker 3: and there was a lot to learn. And it's worth
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  Speaker 3: talking about because I see the same mistakes being made
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  Speaker 3: over and over again by people in our business who
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  Speaker 3: want to try to, you know, apply math to data,
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  Speaker 3: and there are some real pitfalls and some some real
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  Speaker 3: intellectual capital. I think that you can achieve that within
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  Speaker 3: academia that you can then bring in and apply to
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  Speaker 3: the investment world.
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  Speaker 2: Well, we've certainly seen amongst the quants a very successful
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  Speaker 2: application of math theory to data. In fact, some of
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  Speaker 2: the best performing hedge funds are quantitatively driven.
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  Speaker 3: That's not where you're going, well, it's it's part of
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  Speaker 3: where I'm going, right, So so there there there's a
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  Speaker 3: transition in all of all of the sciences, honestly, but
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  Speaker 3: certainly the social sciences, where where yes, you start with numbers,
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  Speaker 3: structured numbers, right, uh, price over time, you know, follows
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  Speaker 3: the things you can calculate, late and measure as those numbers.
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  Speaker 3: But what was clear immediately in politics, and I think
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  Speaker 3: has become increasingly clear in the world of investing, is
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  Speaker 3: it's not just the numbers that you get on your
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  Speaker 3: Bloomberg terminal. It's also the words and the stories and
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  Speaker 3: the narratives that are told to us. Politicians have known
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  Speaker 3: this forever, right, So the story of politics is the
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  Speaker 3: story of people suggesting laws or policies and then having
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  Speaker 3: to present it in a way that gets them elected
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  Speaker 3: or keeps them in power or whatever that is. So
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  Speaker 3: there's always been a focus, I'll say, more of a
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  Speaker 3: focus in political science than in economics. With words, economics
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  Speaker 3: is almost seen as a sideline, right, it's somehow lesser
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  Speaker 3: than the numbers. So what I was kind of early
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  Speaker 3: on was applying the same techniques that we have for
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  Speaker 3: understanding matrices and structured data, but apply it to unstructured data,
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  Speaker 3: which you know, full circle, this is at the heart
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  Speaker 3: of all of the generative AI and the.
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  Speaker 2: AI you're getting way ahead of me now with generative
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  Speaker 2: I will circle back to that, but it's.
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  Speaker 3: All the math has not changed in thirty five years
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  Speaker 3: since I started working with network math around unstructured data.
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  Speaker 3: I mean, we didn't call it natural language processing back then,
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  Speaker 3: and we didn't call it, you know, large language modeling,
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  Speaker 3: but that's exactly what we were doing.
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  Speaker 2: So what was what was the moment or the catalyst
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  Speaker 2: for you to say, Hey, I'm working in all these
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  Speaker 2: other areas. But the narratives continue to pop up over
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  Speaker 2: and over again on all sorts of different data sets,
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  Speaker 2: and I think in the financial markets, I can use
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  Speaker 2: a novel approach to identifying narratives and anticipate where the
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  Speaker 2: market's going. What led to that sort of insight.
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  Speaker 3: Not the Great Recession, right, but the aftermath.
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  Speaker 2: Meaning the twenty tens following the Great Financial Crisis.
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  Speaker 3: Starting in two thousand and nine, in the recovery that
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  Speaker 3: we had out of two thousand and nine, and in
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  Speaker 3: particular when Ben Bernaki and the Federal Reserve moved towards
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  Speaker 3: a very explicit effort to use their words to impact markets.
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  Speaker 2: So let's talk a little bit about that, because I
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  Speaker 2: have some really specific memories of the low of the
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  Speaker 2: run up afterwards, all the noise that was going on.
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  Speaker 2: Some of the phrases that have come out of that era,
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  Speaker 2: like financial repression and other such things are just the
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  Speaker 2: tip of the narrative, Iceberg. So walk us through your insight.
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  Speaker 2: It's two thousand and nine. Yep, the market bottoms really
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  Speaker 2: kind of a v bottom and took off from that.
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  Speaker 2: What was that March ninth, March seventh, something like that,
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  Speaker 2: nine and there was no turning back. What were you seeing?
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  Speaker 2: How were you integrating that into a concept of let's
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  Speaker 2: identify narratives in order to anticipate market most well.
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  Speaker 3: So I was co founded a long short fund inside
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  Speaker 3: of a larger asset manager going back in five and five,
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  Speaker 3: six seven, and we did well, like everyone else did well.
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  Speaker 3: And then in O eight we did great.
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  Speaker 2: Really you know, would wait, I think want to say
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  Speaker 2: SMP down thirty seven percent?
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  Speaker 3: Yeah, we were up twenty something net.
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  Speaker 2: Anything in the green not in the red is amazing.
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  Speaker 3: Now, I'll tell you, and we can come back to this,
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  Speaker 3: like the real question you should ask is that, given
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  Speaker 3: what we believed, why wasn't why weren't we up forty percent?
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  Speaker 3: That that's actually that that's actually a question you can ask.
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  Speaker 2: But I'm going to say a forty seven percent relative
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  Speaker 2: price wing.
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  Speaker 3: I'll take had A had A had a great year
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  Speaker 3: in O eight.
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  Speaker 2: And and did that continue in nine?
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  Speaker 3: Flat lined right from O nine, So from March of
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  Speaker 3: O nine, so we did well in January February the
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  Speaker 3: first quarter right the rest from from March of nine,
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  Speaker 3: our returns flat lined. So we never we never lost
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  Speaker 3: money for our clients and our fund.
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  Speaker 2: But you didn't catch that recovery though.
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  Speaker 3: Did not catch the recovery, absolutely did not. And the
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  Speaker 3: the recovery was interesting, right, you're you're right, there was
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  Speaker 3: a v but there were there were starts and stops
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  Speaker 3: to it. So the the big move up from the
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  Speaker 3: bottom in late March going into April, it's like, all right,
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  Speaker 3: that actually we caught a little bit of that, and
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  Speaker 3: that that made sense. There was a second leg to
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  Speaker 3: the rally April May, and then in June. In June
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  Speaker 3: there was a ferocious rally, but June in particular it
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  Speaker 3: was a classic crap rally. Right. It was a low quality,
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  Speaker 3: low quality stuff right the end of March going into
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  Speaker 3: April rally. It was right, this makes sense, Right, we've
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  Speaker 3: bottomed the June rally. Wow, No, it was we didn't.
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  Speaker 3: We didn't.
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  Speaker 2: Really. Oh that's oh yes, fascinating.
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  Speaker 3: Go back, go back and look at it, right, And
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  Speaker 3: let me tell you I don't.
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  Speaker 2: Just so you know, I have a vivid recollection of
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  Speaker 2: chatting with Jim Bianco about this, and we were both bullish,
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  Speaker 2: but for completely different reasons. To me, anytime US equity
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  Speaker 2: markets are cut in half, I'm a buyer and people
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  Speaker 2: say nineteen twenty nine, I'm like, great, you got to
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  Speaker 2: go back a century to find the exception that proves
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  Speaker 2: the rule. But Jim was early on in the Tina trade. Hey,
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  Speaker 2: the fit has made everything cash trash bonds are They're
• 15:35
  Speaker 2: forcing you into equities, which is what led my post,
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  Speaker 2: which everybody stole the line, this is the most hated
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  Speaker 2: bull market in history.
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  Speaker 3: And I wrote that up, I.
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  Speaker 2: Send that out, and I heard everybody bar of that.
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  Speaker 2: But I'm curious as to where the June rally took you.
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  Speaker 3: Well this and this is where I'm going about the
• 15:57
  Speaker 3: role of forward guidance in Jim Beyonc's point about because
• 16:03
  Speaker 3: what the FED did wasn't just it's policies around interest rates.
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  Speaker 3: You know, they took them to zero and that's where
• 16:16
  Speaker 3: we stayed.
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  Speaker 2: Started buying mortgage backs, and.
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  Speaker 3: They balance balance sheet operations.
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  Speaker 2: Right.
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  Speaker 3: Actually, you know, actually, and I look, I think que won,
• 16:27
  Speaker 3: I think it saved the world.
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  Speaker 2: This is what and what was that a trillion dollars
• 16:31
  Speaker 2: something something? Yeah, something billions, something like eight hundred billion,
• 16:34
  Speaker 2: unthinkable number.
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  Speaker 3: So I think that So those were specific actions took.
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  Speaker 3: But but even if you people often say things when
• 16:43
  Speaker 3: they're leaving office. So so Bernanki's last speech is valedictory.
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  Speaker 2: Address, more honest than intended.
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  Speaker 3: Much more so. And you see this all the time, right,
• 16:53
  Speaker 3: George Washington leaves Eisenhower, I mean when when, when when
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  Speaker 3: freaking Eisenhower warns you against the defense industrial complex? Yeah,
• 17:05
  Speaker 3: you might, you might. I'm just saying, that's right. General,
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  Speaker 3: Like to me, what Bernanki said when he's leaving his
• 17:13
  Speaker 3: terms of office. He said, look, we had we had
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  Speaker 3: two toolkits. One was traditional stuff, interest rates, down to zero.
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  Speaker 3: At the time, we didn't know we could have nkeadventure straight.
• 17:25
  Speaker 3: So you know, that's where we were. Second were the
• 17:28
  Speaker 3: balance sheet operations, large scale asset purchases QE quantitative easy.
• 17:34
  Speaker 3: He said, you know Q one was great. Did what
• 17:37
  Speaker 3: we hoped he would do. Q E two Eh, operation
• 17:41
  Speaker 3: twist q E three. This is BRENANKI saying. He said,
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  Speaker 3: I actually think that might have been a little counterproductive. Huh,
• 17:49
  Speaker 3: he said, But we had another toolkit, and that was
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  Speaker 3: our communication policy. That was forward guidance. We started using
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  Speaker 3: our words not to communicate to the market what we
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  Speaker 3: actually felt. We started using our words and coordinating our
• 18:08
  Speaker 3: words to change the market, to change market behavior. This
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  Speaker 3: is what I mean about making a conscious effort to
• 18:17
  Speaker 3: tell a story. It's not that it was necessarily lying,
• 18:23
  Speaker 3: but they they were using their words and choosing their
• 18:26
  Speaker 3: words for effect.
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  Speaker 2: To shape perception of their shape.
• 18:31
  Speaker 3: Market Babe, to shape market behavior. And he said that
• 18:35
  Speaker 3: worked better than we had any hope that it could.
• 18:40
  Speaker 3: And that's where we are now.
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  Speaker 2: So for the young'ins listening, I have to point out,
• 18:46
  Speaker 2: and you and I are old enough to remember back
• 18:49
  Speaker 2: in the days where there were no minutes released. There
• 18:53
  Speaker 2: wasn't an announcement, forget a press conference. You had to
• 18:57
  Speaker 2: be watching the bond market to figure out what the
• 19:00
  Speaker 2: Fed just did. Like today, there's we're holding the having
• 19:05
  Speaker 2: this conversation. There was a FED the October meeting, a
• 19:09
  Speaker 2: quarter point rate cut, a conversation about all sorts of stuff.
• 19:12
  Speaker 2: I really didn't pay a lot of attention to it.
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  Speaker 2: Lack of clarity, no data, blah blah blah.
• 19:20
  Speaker 3: Worse than that, Greenspan would be intentionally vague and obtuse.
• 19:25
  Speaker 2: If you understood what I said, then then you miss unders.
• 19:29
  Speaker 2: You misinterpreted it exactly. You think you understand what I'm saying.
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  Speaker 3: You know who led the committee to make all that change,
• 19:36
  Speaker 3: Janet Yelling, which is a Spice chair yep.
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  Speaker 2: Back back in the financial crisis.
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  Speaker 3: Yep. So this was it was a concerted effort. Bernanki
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  Speaker 3: yelling to. This is when they also started going, putting
• 19:49
  Speaker 3: all the FED governors on a common calendar, right and when,
• 19:55
  Speaker 3: and assigning the fake you're going to speak this day,
• 19:57
  Speaker 3: You're going to speak that day. That's when all the started.
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  Speaker 3: It was an intentional effort. And again this is something
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  Speaker 3: that politicians have known forever, right, politicians craft the message
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  Speaker 3: and use their words. So I knew the tools to
• 20:11
  Speaker 3: try to understand this, but what I wasn't prepared for
• 20:16
  Speaker 3: was how it neither was Berneki was how powerful this
• 20:19
  Speaker 3: would become to the point where today it's not just
• 20:25
  Speaker 3: central bankers using their words as their main policy toolkit,
• 20:30
  Speaker 3: but is every ceo? Is every ceo now? I mean
• 20:33
  Speaker 3: you go on this network or one of the other networks,
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  Speaker 3: and what makes for a good CEO is can you
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  Speaker 3: tell the story? Can you tell the narrative of your
• 20:45
  Speaker 3: company to get a multiple? Because a multiple is a narrative,
• 20:50
  Speaker 3: a multiple is a story.
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  Speaker 2: Well, look at stories, look at some of the most
• 20:54
  Speaker 2: successful CEOs throughout history. I would throw Jack Welsh into
• 20:59
  Speaker 2: that a pile because he was a fabuloust fabulist. He
• 21:04
  Speaker 2: was a fabulous fabulouist because the stories he told were great,
• 21:09
  Speaker 2: right up until the point where we found out that
• 21:11
  Speaker 2: he was running a hedge fund with g Capital and
• 21:15
  Speaker 2: they magically always beat by a penny.
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  Speaker 3: So I remember vividly when ge was coming to our shop.
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  Speaker 3: What they wanted was a financial financial multiple, right, so
• 21:32
  Speaker 3: they were making they they wanted an old world even
• 21:35
  Speaker 3: though they're an industrial this that this was They wanted
• 21:39
  Speaker 3: to tell a story that they should be seen as
• 21:43
  Speaker 3: and get the multiple of a financial That's what GE
• 21:47
  Speaker 3: was all about in those years leading up to the GFC.
• 21:53
  Speaker 3: So hey, my my poster child for this is is
• 21:57
  Speaker 3: Mark Binnioff Salesforce because he's Prabay. You often see this
• 22:04
  Speaker 3: with people who come out of sales like like mar
• 22:07
  Speaker 3: I did.
• 22:08
  Speaker 2: But it's all about storytelling.
• 22:10
  Speaker 3: It's all about storytelling. And what isn't that true?
• 22:14
  Speaker 2: For go through the great look Steve Jobs, Reid Hastings,
• 22:22
  Speaker 2: Larry Ellison, an oracle to some degree, Steve Ballmer at
• 22:27
  Speaker 2: Microsoft who wasn't a great CEO, but he was a
• 22:30
  Speaker 2: great cheerleader and a great storyteller.
• 22:32
  Speaker 3: Great storyteller. What all of those companies have in common
• 22:37
  Speaker 3: is that they're great storytellers, and those are trillion dollar
• 22:40
  Speaker 3: companies today. What I would say to you is that
• 22:44
  Speaker 3: you don't remember or hear about the companies that did
• 22:48
  Speaker 3: not have CEOs who are great storytellers.
• 22:51
  Speaker 2: Well, Ken Lay was a great storyteller until you found
• 22:54
  Speaker 2: out that it was all nonsense. And you could say
• 22:57
  Speaker 2: the same thing about folks like Bernie Madoff.
• 23:00
  Speaker 3: There are a lot of stories that get told that
• 23:03
  Speaker 3: are not true, right, what And and I think even
• 23:08
  Speaker 3: today people think of this word narrative. They have a
• 23:12
  Speaker 3: pejorative sense to it. It's like, oh, for for sure,
• 23:18
  Speaker 3: that's just your narrative, man, you know, the big Lebowski.
• 23:21
  Speaker 3: You know, that's that's the And it's not that a
• 23:25
  Speaker 3: story is a lie. It's that the story is constructed
• 23:31
  Speaker 3: for effect. It's it's not and it's presented to you
• 23:36
  Speaker 3: as if this is my true and inner thoughts. But
• 23:42
  Speaker 3: the construction of the intentionality behind these stories phenomenal. Bniel,
• 23:47
  Speaker 3: for example, you know, created the metrics by which you
• 23:51
  Speaker 3: wanted salesforce to be judged, not metrics of profitability, but
• 23:56
  Speaker 3: metrics of what he called pro forma net revenue growth,
• 24:01
  Speaker 3: whatever the hell that means, right, Because if you can
• 24:04
  Speaker 3: construct the story, you can construct it in a way
• 24:08
  Speaker 3: that yes, I can beat and raise pretty much every quarter.
• 24:12
  Speaker 3: So there was there were three I think big changes
• 24:17
  Speaker 3: that happened to make the role of narrative overwhelming as
• 24:23
  Speaker 3: it is today. Whereas before it's always been there, to
• 24:26
  Speaker 3: your point, it's always been there. Today it's overwhelming. And
• 24:30
  Speaker 3: I think it's it's not just the success that first
• 24:34
  Speaker 3: central bankers and then CEOs. I mean, Wall Street's the
• 24:38
  Speaker 3: greatest copying machine. Wall Street copies what works. Sure, so
• 24:43
  Speaker 3: when you see the something's working, oh, they're getting a
• 24:46
  Speaker 3: multiple by telling the story and going on Kramer, you know,
• 24:49
  Speaker 3: four times a year.
• 24:50
  Speaker 2: It's endless iteration. You're just constantly tweaking it and if
• 24:55
  Speaker 2: it works, do more of it, and if it doesn't,
• 24:57
  Speaker 2: toiss it out.
• 24:58
  Speaker 3: So it was the fact that it works to tell
• 25:01
  Speaker 3: a story, and people got good at telling stories. It's
• 25:06
  Speaker 3: the growth of twenty four to seven. I'm want to
• 25:09
  Speaker 3: use air quotes here. I'm glad we're taking this.
• 25:11
  Speaker 2: Well, it's medius, social media news.
• 25:13
  Speaker 3: Right now, news like news liked. That wasn't the case.
• 25:18
  Speaker 2: So I want to annotate what you said slightly, okay,
• 25:22
  Speaker 2: because I think CEOs have always been storytellers. But they
• 25:26
  Speaker 2: were storytellers to their boards, to their employees, to their shareholders.
• 25:32
  Speaker 2: They're always you're hitting now on the modern world of
• 25:36
  Speaker 2: twenty four to seven media. Telling a story in a
• 25:40
  Speaker 2: boardroom is very different than sitting in a TV studio
• 25:44
  Speaker 2: and talking about, Hey, here's why our new chip is
• 25:48
  Speaker 2: going to catch up to video and it's the greatest thing. Ever, Yep,
• 25:51
  Speaker 2: that's a different skill set.
• 25:52
  Speaker 3: It changes the time horizon. It is a very different
• 25:55
  Speaker 3: skill set because you're not telling the story of oh
• 25:58
  Speaker 3: I'm getting another you know turn of leverage and our operations,
• 26:02
  Speaker 3: or you know, our capacity utilization and this factory went
• 26:07
  Speaker 3: up by five percent, which are the kind of stories
• 26:09
  Speaker 3: you would tell even on earnings call or certainly to
• 26:13
  Speaker 3: a board. Now, this is the story where you've got
• 26:17
  Speaker 3: a segment a little bit, Yes, you've got a segment.
• 26:19
  Speaker 3: You're going on Kramer, you say bye bye bye. You
• 26:23
  Speaker 3: got at most four minutes, right, How are you going
• 26:26
  Speaker 3: to tell that story that sings to that audience? Enormous
• 26:31
  Speaker 3: change change, structural change in our media, both quote unquote
• 26:38
  Speaker 3: news media, but also financial news media. The Wall Street
• 26:43
  Speaker 3: Journal today is a twenty four to seven news financial
• 26:46
  Speaker 3: news organization.
• 26:48
  Speaker 2: What is printing times in back to Washington Post? They
• 26:51
  Speaker 2: all have websites that can update it around the clock.
• 26:54
  Speaker 3: And here's the thing, there's not enough hard news to
• 26:58
  Speaker 3: fill the time or fill the space. So what takes
• 27:02
  Speaker 3: the space? Opinion story you channel glory takes the place
• 27:06
  Speaker 3: you were.
• 27:07
  Speaker 2: Channeling Michael Crichton from twenty five years ago. Most of
• 27:11
  Speaker 2: what you see in the media is speculation, opinion, and theory,
• 27:16
  Speaker 2: not news.
• 27:17
  Speaker 3: I've written so much about Criton in his study that.
• 27:20
  Speaker 2: I know that's why I threw that back.
• 27:22
  Speaker 3: He says he was he was so far ahead, a.
• 27:25
  Speaker 2: Quarter century ahead of what of what took place.
• 27:29
  Speaker 3: There's a third piece, though, give us.
• 27:31
  Speaker 2: The third piece before we go to our next.
• 27:34
  Speaker 3: Third piece has changed everything is our smartphones.
• 27:38
  Speaker 2: That you're you're walking around with not only a studio,
• 27:41
  Speaker 2: but a dopamine device that you're constantly it's.
• 27:47
  Speaker 3: Machine and I we do it to ourselves. It's not
• 27:51
  Speaker 3: that someone forces us to hear these stories over and
• 27:55
  Speaker 3: over again. We do it to ourselves. I mean, I
• 27:58
  Speaker 3: get a little nervous if I you know, pat, where's
• 28:00
  Speaker 3: my phone? Where's I get a little nervous. And it's
• 28:05
  Speaker 3: a it's It is absolutely a neurotransmitter addiction. I think
• 28:12
  Speaker 3: it's so important to keep that from our kids.
• 28:16
  Speaker 2: That's a whole another there's a whole depression situation with
• 28:19
  Speaker 2: teenagers today, and it all traces back to the phone
• 28:22
  Speaker 2: and social media.
• 28:23
  Speaker 3: These are three I think real secular changes we've had.
• 28:27
  Speaker 3: Markets become this political utility, the success of constructing a
• 28:35
  Speaker 3: story structural changes in social media and the devices that
• 28:41
  Speaker 3: we insist on caring with ourselves all the time.
• 28:46
  Speaker 2: Huh, absolutely fascinating. Coming up, we continue our conversation with
• 28:50
  Speaker 2: Ben Hunt, president and co founder of Percion, explaining how
• 28:55
  Speaker 2: he's using AI to identify narratives in real time.
• 29:01
  Speaker 3: I'm Barry Redhults.
• 29:02
  Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm
• 29:18
  Speaker 2: Burry rid Halts. You're listening to Masters in Business on
• 29:21
  Speaker 2: Bloomberg Radio. My special guest this week is Ben Hunt.
• 29:25
  Speaker 2: He is a academic, fund manager, risk manager, entrepreneur, tech
• 29:32
  Speaker 2: startup person. He is currently co founder and president at percent,
• 29:38
  Speaker 2: which applies AI tools to map and measure market narratives
• 29:44
  Speaker 2: in real time. It's really more than market narratives. It's politics,
• 29:47
  Speaker 2: it's economics, it's markets. You cover up a whole lot
• 29:50
  Speaker 2: of stuff.
• 29:51
  Speaker 3: So what we're able to do today and this is
• 29:53
  Speaker 3: the crazy change in the world. Back from when I
• 29:57
  Speaker 3: was doing this on microfiche, back in yeah, exactly, we
• 30:06
  Speaker 3: get we have access to everything that's published publicly in
• 30:11
  Speaker 3: the world, and there are a couple of big data
• 30:15
  Speaker 3: aggregators dal Jones one, Lexis and Nexus another. Everything that
• 30:22
  Speaker 3: gets published in the world, all these languages. It's available
• 30:26
  Speaker 3: to you, and it's it's not cheap, but it's not
• 30:30
  Speaker 3: crazy expensive like it used to be. It's always getting cheaper.
• 30:34
  Speaker 3: So we're able to take everything in the world that
• 30:36
  Speaker 3: gets published, all the newspapers, all the websites, all the transcripts,
• 30:39
  Speaker 3: everything's published publicly. We can pull in and then we
• 30:45
  Speaker 3: can process it, process it with Really is the same
• 30:51
  Speaker 3: math that I was using thirty years ago. Nobody's invented
• 30:54
  Speaker 3: cold fusion here.
• 30:55
  Speaker 2: But the software tools are faster, stronger, better.
• 30:59
  Speaker 3: And infinite. So the calculations here are not particularly complicated,
• 31:05
  Speaker 3: but you have to do them at enormous scale.
• 31:08
  Speaker 2: It's a ton of volume.
• 31:09
  Speaker 3: So I mean, it's crazy, the just the scale of.
• 31:15
  Speaker 2: The abytes, terabytes, just crazy.
• 31:17
  Speaker 3: I mean, yeah, in the last couple of months we've
• 31:20
  Speaker 3: processed over two hundred billion tokens. Billion to is how
• 31:26
  Speaker 3: much a token is, like a word or a folk
• 31:29
  Speaker 3: And so that's the kind of the unit that you
• 31:31
  Speaker 3: talk about when you're putting through, when you're putting something
• 31:35
  Speaker 3: through a linguistic calculator, which is which is what all
• 31:39
  Speaker 3: of the all the llms are, they're linguistic calculators and
• 31:45
  Speaker 3: so you know, we've processed, you know, several hundred billion tokens. Again,
• 31:51
  Speaker 3: it's not complex, but it is at scale. And what
• 31:56
  Speaker 3: we're doing with that is we're reading the world's use
• 32:01
  Speaker 3: to understand the world's stories and narratives. And you're right,
• 32:06
  Speaker 3: it's much bigger than or it's much more focused that.
• 32:11
  Speaker 3: We have much higher resolution than just saying, oh, I'm
• 32:16
  Speaker 3: bullish on financials. I mean, that's a narrative, sure, but
• 32:21
  Speaker 3: there are twenty different variations of that. You're bullish on financials.
• 32:26
  Speaker 3: Why so wait, let me let me we attract all
• 32:29
  Speaker 3: those We attract all those stories and how they wax
• 32:31
  Speaker 3: and wane over time.
• 32:32
  Speaker 2: So let me roll back to I want you to
• 32:35
  Speaker 2: explain what Persient is. Who are the clients. I don't
• 32:39
  Speaker 2: mean names, but what type of clients do you have
• 32:42
  Speaker 2: and what do they do with Persian's output?
• 32:47
  Speaker 3: So Starry Percient in twenty eighteen, my partner from we
• 32:52
  Speaker 3: were at an asset manager, spun out there to take
• 32:56
  Speaker 3: the the technology that I've been working on for years
• 33:00
  Speaker 3: and really been writing about with epsilon theory. So we
• 33:04
  Speaker 3: started that in twenty eighteen to do the basic research
• 33:09
  Speaker 3: into processing enormous amounts of financial news data and to
• 33:16
  Speaker 3: track the stories and how they rise and fall and
• 33:21
  Speaker 3: wax and wane over time. So that was that was
• 33:24
  Speaker 3: the story.
• 33:25
  Speaker 2: That was I love that description because there are a
• 33:29
  Speaker 2: lot of trades going on where the storyline changes on
• 33:34
  Speaker 2: a regular basis. Probably the mac daddy of that is crypto. First,
• 33:39
  Speaker 2: it's hey, you know there's fiat currency, this is outside
• 33:43
  Speaker 2: of the system. It's DeFi. Then it's a hedge for deflation.
• 33:47
  Speaker 2: Then it's a hedge for inflation. Now it's scarcity.
• 33:51
  Speaker 3: And digital gold dig Gold's there are no fundamentals right
• 33:56
  Speaker 3: with with crypto and people will say there are, but
• 33:59
  Speaker 3: there aren't. It's it's driven by the waxing and waning
• 34:03
  Speaker 3: of stories. Uh huh and do they find purchase? Do they?
• 34:08
  Speaker 3: Or do they? Kind of people get tired of them.
• 34:11
  Speaker 2: Well, they got tired of the DeFi story and then Warnce,
• 34:15
  Speaker 2: JP Morgan and Black Rocks started creating it like the
• 34:19
  Speaker 2: by bit is the fastest etf to one hundred billion
• 34:23
  Speaker 2: dollars And so the old story of DeFi is gone
• 34:26
  Speaker 2: and a new story is, oh no, this is an
• 34:29
  Speaker 2: asset class at Wall Street's embracing that's why you have
• 34:31
  Speaker 2: to own it that story.
• 34:33
  Speaker 3: That story was and very that was a very similar story,
• 34:36
  Speaker 3: by the way, or a transition in story from physical
• 34:39
  Speaker 3: gold to GLD when that ETF came out, right, which
• 34:45
  Speaker 3: was a very similar pattern, because once it became a
• 34:50
  Speaker 3: Wall Street a table at the Wall Street casino, right,
• 34:54
  Speaker 3: then it takes on a different meaning, specially around go
• 35:00
  Speaker 3: gold change from being okay, something that you bury in
• 35:05
  Speaker 3: your back, your heart or you're having your vault, you know,
• 35:07
  Speaker 3: along with mo and seeds for when the hard times.
• 35:11
  Speaker 2: Coddled water right balls ready to eat gold and lead.
• 35:14
  Speaker 3: It becomes a security and it's meaning changes from that,
• 35:20
  Speaker 3: you know, apocalyptic modeled water. And the meaning of gold
• 35:24
  Speaker 3: today is as an insurance policy, a security against central
• 35:29
  Speaker 3: bank error or government error. That's the meaning of gold today.
• 35:35
  Speaker 2: And is that the dominant narrative that you're identifying as
• 35:38
  Speaker 2: gold rallied over through four thousand?
• 35:42
  Speaker 3: Absolutely so, And we've really been able to track that
• 35:45
  Speaker 3: one in.
• 35:46
  Speaker 2: So here's the really big here's the million dollar or
• 35:49
  Speaker 2: trillion dollar question. How do you identify a narrative and say, oh,
• 35:55
  Speaker 2: gold is going to double from here based on this
• 35:57
  Speaker 2: narrative or are we not there yet.
• 36:00
  Speaker 3: No, you you identify the narratives because the narrative the
• 36:03
  Speaker 3: stories don't ever change. So that the story that leave
• 36:09
  Speaker 3: Gold aside for a think about we're talking about your
• 36:12
  Speaker 3: bullish on company X y Z, because there are about
• 36:18
  Speaker 3: I don't know, depending on how again, how finally you
• 36:21
  Speaker 3: want to resolve that they're only about a dozen stories
• 36:25
  Speaker 3: for why you're bullish on something, right, They can be
• 36:29
  Speaker 3: management change, top line growth, opportunity, consolidation in the industry, catalyst.
• 36:36
  Speaker 3: So every catalyst story, new product is going to prove
• 36:40
  Speaker 3: the new drug, new molecule. So that story you just
• 36:45
  Speaker 3: change the name. That's the same story that's repeated over
• 36:49
  Speaker 3: and over again about any pharma or biotech company. The stories,
• 36:56
  Speaker 3: we think that they are amorphous and variable. The fact
• 37:03
  Speaker 3: is that the core of the story, what we call
• 37:05
  Speaker 3: the semantic signature, the meaning of a story, they're amazingly
• 37:12
  Speaker 3: constant over time. So what we're looking for is for
• 37:18
  Speaker 3: it could be dormant for a long time. But what
• 37:20
  Speaker 3: you want to know is when that story starts picking
• 37:24
  Speaker 3: up again, when someone starts playing that story, when it
• 37:31
  Speaker 3: appears on Kramer and starts happening in the financial press.
• 37:35
  Speaker 3: That's the stuff we can pick up with real precision.
• 37:38
  Speaker 3: So it's both the stories that are starting to fade,
• 37:41
  Speaker 3: but the I think the really interesting stories are the
• 37:45
  Speaker 3: stories that have been dormant for a long time and
• 37:47
  Speaker 3: they start picking up again.
• 37:49
  Speaker 2: You are reminding me of Campbell's hero's journey that there's
• 37:53
  Speaker 2: only so many.
• 37:54
  Speaker 3: My hero right, there are only so many stories right
• 37:58
  Speaker 3: in the world. And that's it now, I like to
• 38:02
  Speaker 3: talk about in Hollywood famously, there are only like five scripts, right, uh.
• 38:10
  Speaker 3: Tolstoy Is supposedly told Stoy he said, there are only
• 38:13
  Speaker 3: two stories that a man goes on a journey or
• 38:16
  Speaker 3: a stranger comes to town. Those are the only two
• 38:18
  Speaker 3: stories in the world.
• 38:20
  Speaker 2: And not quite but he's not quite.
• 38:22
  Speaker 3: But you're on the right track, right right. The point
• 38:25
  Speaker 3: is there's a finite number of stories you can drill down,
• 38:29
  Speaker 3: so you can get a couple of dozen about any
• 38:31
  Speaker 3: sector you want to talk about or like. But it's
• 38:34
  Speaker 3: it's a finite number. And so what we do, and
• 38:39
  Speaker 3: what I think is really interesting, is to track the
• 38:42
  Speaker 3: finite number of stories. And you're right, it's not just
• 38:45
  Speaker 3: around markets. We track several thousand of these stories today.
• 38:51
  Speaker 2: How So, let's let's delve into that. So, so, how
• 38:55
  Speaker 2: do you you have this massive database you're sucking in
• 38:58
  Speaker 2: every news feed, every magazine, newspaper, everything that you could
• 39:06
  Speaker 2: quantify and run into a linguistics model.
• 39:10
  Speaker 3: What's the process.
• 39:11
  Speaker 2: For analyzing this, how to use artificial intelligence to go
• 39:16
  Speaker 2: through this? And how do you make sense out of
• 39:18
  Speaker 2: that heap of how do you find signal amidst all
• 39:22
  Speaker 2: that noise?
• 39:23
  Speaker 3: The crucial thing is you can't just ask AI an
• 39:26
  Speaker 3: open ended questions, say what are the narratives in this
• 39:31
  Speaker 3: for this company or for this sector. Don't do that right,
• 39:36
  Speaker 3: And this is a mistake that people make all the time.
• 39:38
  Speaker 3: They ask open ended questions of chat GPT or whoever
• 39:43
  Speaker 3: the problem is. Chat gpt will give you an answer,
• 39:46
  Speaker 3: just not a good one, not a good one. It'll
• 39:48
  Speaker 3: hallucinate a lot. Right, It'll go out, it'll find its
• 39:52
  Speaker 3: own data. The secret to using AI successfully is to
• 39:58
  Speaker 3: take this magic genie. It's a magic genie, and you
• 40:02
  Speaker 3: stuff it into that bottle. You do not let it out.
• 40:05
  Speaker 3: You constrain it dramatically. You don't let it go out
• 40:08
  Speaker 3: and find data. You give it the data. Crucial thing
• 40:14
  Speaker 3: you don't allow it to think you tell it how
• 40:19
  Speaker 3: to think. So the most important step that we do
• 40:23
  Speaker 3: is we don't ask AI what are the narratives that Okay,
• 40:27
  Speaker 3: we tell it. This is human direction. You have to
• 40:30
  Speaker 3: have human control.
• 40:32
  Speaker 2: I'm hearing data set is controlled by you as well
• 40:36
  Speaker 2: as the thinking.
• 40:39
  Speaker 3: Yes, and it's more than prompt, right, So the it
• 40:43
  Speaker 3: includes prompt. But the phrase that's used in this world
• 40:48
  Speaker 3: is called not prompt engineering, but context engineering. So you
• 40:54
  Speaker 3: want to you want to control everything around the A
• 41:00
  Speaker 3: because you want to limit it to being that linguistic calculator.
• 41:05
  Speaker 3: You want it to be your operating system. That's really
• 41:08
  Speaker 3: the way the thing. And if you do that, then
• 41:12
  Speaker 3: it will give you the same answer twice for the
• 41:15
  Speaker 3: same inputs and the same question. That's the crucial thing.
• 41:18
  Speaker 3: So for this to be true, for it to be consistent,
• 41:20
  Speaker 3: for it to be real signal. So this is a
• 41:23
  Speaker 3: human directed process. You can't ask AI an open ended question.
• 41:27
  Speaker 3: You have to control all the inputs. You have to
• 41:30
  Speaker 3: control the output, meaning you judge it, you run it
• 41:33
  Speaker 3: back through a different AI system to say had they
• 41:36
  Speaker 3: do did they go off the rails here? But the
• 41:39
  Speaker 3: most important thing is you have to give it the scaffolding.
• 41:43
  Speaker 3: You have to give it the the skeleton. You have
• 41:47
  Speaker 3: to tell it these are the thoughts you are allowed
• 41:50
  Speaker 3: to think about, and those are the signatures.
• 41:54
  Speaker 2: I've kind of learned. I have to avoid asking questions
• 41:59
  Speaker 2: that have a human emotional subtext, like tell me what
• 42:06
  Speaker 2: was most surprising about this? It doesn't know what a
• 42:09
  Speaker 2: surprise is. Tell me what was most interesting about this?
• 42:13
  Speaker 2: It's not able to do that. You really have to
• 42:16
  Speaker 2: treat it like it's a dumb machine.
• 42:18
  Speaker 3: Well, that's right this way I mean you treat it.
• 42:21
  Speaker 3: You need to treat it as an operating system. You
• 42:25
  Speaker 3: need to constrain every bit about it, particularly in how
• 42:28
  Speaker 3: you allow it to think, because it wants to please
• 42:30
  Speaker 3: you so badly it does. So if you ask it
• 42:33
  Speaker 3: what's interesting, it will look back at its history of
• 42:36
  Speaker 3: communication with you, and I'll think, what will Berry find interesting?
• 42:41
  Speaker 3: And it will give that answer to you. And if
• 42:43
  Speaker 3: you can't find it easily, it'll make it up. It'll
• 42:45
  Speaker 3: make up an answer that you will find interesting.
• 42:48
  Speaker 2: I find when I try and prompt, Hey, tell me
• 42:52
  Speaker 2: about Ben Hunt's background and give me the timeline of
• 42:57
  Speaker 2: his career that it's good at? Hey, what was Ben
• 43:00
  Speaker 2: Hunt really good at? It has got no idea.
• 43:04
  Speaker 3: So what you're able to do, if you're able to
• 43:08
  Speaker 3: again put the genie in the bottle, tell it. How
• 43:12
  Speaker 3: to think about a problem is you're able to identify.
• 43:17
  Speaker 3: And this does go back to the work from thirty
• 43:19
  Speaker 3: five years ago. The type of stories we tell that
• 43:23
  Speaker 3: we humans tell about stocks or politics. We tell two
• 43:28
  Speaker 3: types of stories. We tell descriptive stories. Oh, the you
• 43:35
  Speaker 3: know the Fed cut rates by twenty five basis points today,
• 43:40
  Speaker 3: a descriptive story. And we can also tell the description
• 43:44
  Speaker 3: of the you know.
• 43:45
  Speaker 2: It's the because clause because we're seeing slowing, increasing layoffs
• 43:51
  Speaker 2: and slowing consumers.
• 43:53
  Speaker 3: And that's high resolution and very descriptive. Right. The Powell
• 44:01
  Speaker 3: was surprisingly hawkish today, and he was, that's a description.
• 44:08
  Speaker 3: There's another type of story we tell, Barry, and that's prescriptive,
• 44:13
  Speaker 3: meaning meaning the FED should be hawkish, the Fed should
• 44:19
  Speaker 3: cut by twenty five basis points. Those are the stories
• 44:24
  Speaker 3: that are indicative of an effort being made to move
• 44:30
  Speaker 3: public opinion in a certain direction. That's like the forward
• 44:35
  Speaker 3: guidance that the FED still does right, using their words
• 44:40
  Speaker 3: for effect. They're using words to nudge you and how
• 44:44
  Speaker 3: you should think about the world. How to think about
• 44:47
  Speaker 3: the world. So the crucial thing when we're doing these,
• 44:50
  Speaker 3: when we're asking the AI to here's all the text
• 44:54
  Speaker 3: in the world. Here are the stories that we want
• 44:57
  Speaker 3: you to identify. We can all also boil that down
• 45:01
  Speaker 3: into identify the stories that are trying to tell the
• 45:07
  Speaker 3: reader how they should think or how policy should go.
• 45:12
  Speaker 3: That we find has a lot of predictive capability to it.
• 45:18
  Speaker 2: So you're in the business of analyzing the world's narratives
• 45:25
  Speaker 2: every day.
• 45:26
  Speaker 3: How is that even possible?
• 45:27
  Speaker 2: It seems like that is an impressing crazy crazy And.
• 45:32
  Speaker 3: It used to be it used to be crazy. I mean, Barry, I,
• 45:36
  Speaker 3: I really do remember in the academic days. I would
• 45:40
  Speaker 3: literally hire grad students and give them a cup of
• 45:43
  Speaker 3: dimes so they go down to the microfiche machine. Remember
• 45:46
  Speaker 3: those machines. Remember those machines, Yes, remember those. I would code,
• 45:51
  Speaker 3: hand code the or hand record the coded data. I
• 45:56
  Speaker 3: would type it into remote access for a digital equipment
• 46:02
  Speaker 3: mini frame and the next day, you know, something would
• 46:06
  Speaker 3: churn out for me. Today it's they say, we're processing
• 46:11
  Speaker 3: hundreds of billions of tokens. We get millions of documents
• 46:15
  Speaker 3: over an eight like that. We are infinite computing resources
• 46:19
  Speaker 3: available to us.
• 46:20
  Speaker 2: Is there anything you can't access that you wish you
• 46:22
  Speaker 2: had access to?
• 46:26
  Speaker 3: Any data source? So one of the things that's happened
• 46:31
  Speaker 3: on Wall Street is that the banks and the cell
• 46:34
  Speaker 3: side had become very jealous of their publications because they
• 46:39
  Speaker 3: tend to think, and they're wrong, that they're good at it, right,
• 46:44
  Speaker 3: that they're good at analysis. I personally don't think they are.
• 46:48
  Speaker 2: Well, let's just say some are better than others.
• 46:52
  Speaker 3: Some are better than others, but none of them are really.
• 46:57
  Speaker 3: If they were, if there was a call it kind
• 47:00
  Speaker 3: of significant alpha there, they wouldn't be a bank, that'd
• 47:05
  Speaker 3: be a hedge fund. Yeah, they wouldn't be a on
• 47:07
  Speaker 3: the cell side now. So I'm interested in reading the
• 47:11
  Speaker 3: cell side research not because I think there's some nugget
• 47:13
  Speaker 3: of truth in there, but because I want to see
• 47:16
  Speaker 3: what they're all talking about.
• 47:17
  Speaker 2: Right, it's reflective of, if not a consensus, certainly a
• 47:23
  Speaker 2: popular set of ideas.
• 47:24
  Speaker 3: And this is a crucial thing to talk about what
• 47:28
  Speaker 3: we do. I don't know what the truth is, right,
• 47:31
  Speaker 3: I have no idea.
• 47:31
  Speaker 2: Does it matter?
• 47:32
  Speaker 3: And I don't think it matters. I want to provide
• 47:36
  Speaker 3: this information to people who do have a view on
• 47:39
  Speaker 3: the truth. Let's say you're a you're a value investor, right,
• 47:42
  Speaker 3: you're running a fund. You've got your views, you've done
• 47:45
  Speaker 3: your homework, you've done your research.
• 47:47
  Speaker 2: You've got a good back test.
• 47:49
  Speaker 3: Yeah, yeah, yeah, you've got. So I say, I've got
• 47:52
  Speaker 3: Here are the companies where I think I've identified something special,
• 47:56
  Speaker 3: something that's valuable, that the market does not recognize, and
• 48:01
  Speaker 3: so I want to buy it. And then I'm just
• 48:03
  Speaker 3: gonna wait. I gotta wait until one day the market realizes,
• 48:09
  Speaker 3: the market comes to their senses and says, oh wow,
• 48:12
  Speaker 3: that should trade at a higher multiple or a higher
• 48:14
  Speaker 3: price because that special thing that you saw, that source
• 48:19
  Speaker 3: of value, the rest of the world comes to see that. Well,
• 48:25
  Speaker 3: what I can think I can show you is when
• 48:28
  Speaker 3: the rest of the world starts to wake up to
• 48:29
  Speaker 3: whatever it is you're looking for.
• 48:31
  Speaker 2: So you're catching the early lift off the bottom. I am.
• 48:35
  Speaker 3: So when a value investment only works when the market
• 48:40
  Speaker 3: recognizes it, and we are tracking when something like that
• 48:44
  Speaker 3: gets discovered by the market.
• 48:46
  Speaker 2: So before in video is five trillion, when it starts
• 48:50
  Speaker 2: ramping up to five hundred billion, Hey, something's going on here.
• 48:57
  Speaker 3: What I found in my experience as an investor, is
• 49:02
  Speaker 3: that you make the most money on a trade during
• 49:05
  Speaker 3: what I call is the discovery phase of a truck,
• 49:08
  Speaker 3: when the rest of the world wakes up to something
• 49:11
  Speaker 3: that you had noticed and identified before. That's when the
• 49:16
  Speaker 3: money is made. Once it gets.
• 49:17
  Speaker 2: Out there and it's reflected, then the market is eventually efficient.
• 49:23
  Speaker 3: It's a different it's a different risk and reward profile.
• 49:26
  Speaker 3: Let me let me put it that way. You you
• 49:28
  Speaker 3: get you get a lot more ups and downs post
• 49:34
  Speaker 3: that discovery phase than you do when you're enjoying the
• 49:38
  Speaker 3: discovery phase. It's a lot harder once you other now
• 49:42
  Speaker 3: we miss, oh, the other catalyst, and then say what.
• 49:45
  Speaker 2: But that discovery phase is I'm going to quote Doug
• 49:49
  Speaker 2: cast that's the there's a phrase he uses.
• 49:53
  Speaker 1: Uh.
• 49:54
  Speaker 2: It's like a contrariant perspective, a variant perspective perspect if
• 50:00
  Speaker 2: you have some insight that is not widely held right
• 50:03
  Speaker 2: and the market being mostly kind of sort of eventually efficient.
• 50:09
  Speaker 2: If there's a truth or at least a good story
• 50:13
  Speaker 2: in your variant perspectives.
• 50:15
  Speaker 3: All it takes is a story.
• 50:17
  Speaker 2: It's got to be a good story.
• 50:18
  Speaker 3: Though, Yeah, it's got to be a well i'll call
• 50:20
  Speaker 3: it it's got to be a compelling story, right right,
• 50:22
  Speaker 3: And that's that's why you're looking for a CEO who
• 50:27
  Speaker 3: can tell that compelling story. You're looking for the ability
• 50:30
  Speaker 3: to tell a compelling story, because that is what gives
• 50:34
  Speaker 3: you a multiple so is story.
• 50:37
  Speaker 2: So let me ask you a few more questions on
• 50:42
  Speaker 2: person and before we go into a few other areas. So, first,
• 50:47
  Speaker 2: you've been doing this for seven years. What are you
• 50:49
  Speaker 2: doing today that was unimaginable four, five, six, seven years ago.
• 50:55
  Speaker 3: I tell you something that was unimaginable really two years ago.
• 50:58
  Speaker 3: That's amazing, and that is the AI. So what we
• 51:02
  Speaker 3: were doing in our early days, we were basically doing
• 51:05
  Speaker 3: small language models. We were making the language models essentially
• 51:08
  Speaker 3: by hand. And you know, we did our first models
• 51:12
  Speaker 3: and our first versions of this, and we licensed it
• 51:15
  Speaker 3: some big banks. And here's the problem, Barry. We had
• 51:20
  Speaker 3: constructed a net and it was a pretty good net.
• 51:24
  Speaker 3: I mean, when we would dip it into a data stream,
• 51:27
  Speaker 3: we'd catch a fish, meaning a signal, and the signal
• 51:31
  Speaker 3: that signal works, you know, good, good hit on the
• 51:35
  Speaker 3: signal that we put up. The problem was we were
• 51:37
  Speaker 3: missing too many damn fish. Our net was too small
• 51:41
  Speaker 3: and we'd say, oh, we got this one, and then
• 51:44
  Speaker 3: we'd look back at whatever it was we were designing
• 51:47
  Speaker 3: the models, so, well, how did we miss all these
• 51:49
  Speaker 3: other fish? And the answer was the small language model
• 51:53
  Speaker 3: we were constructing. It's incredibly complex if you're building these
• 52:00
  Speaker 3: models without the probabilistic approach that modern lms allow you
• 52:07
  Speaker 3: to take. So this is the whole notion of embedding.
• 52:10
  Speaker 3: So that there are a million ways to say I'm
• 52:14
  Speaker 3: bullish about the management change at company XYZ. There are
• 52:19
  Speaker 3: million ways you can say that. And if you're in
• 52:21
  Speaker 3: the business as we were, of kind of handcrafting, let's
• 52:25
  Speaker 3: write down all the ways you can say that. Really
• 52:28
  Speaker 3: you miss a lot. You've made a small net and
• 52:31
  Speaker 3: what you're trying to a small data set, and it's
• 52:35
  Speaker 3: a pretty small data set. So we rebuilt all of
• 52:39
  Speaker 3: our software and using AI as an operating system, context
• 52:46
  Speaker 3: engineering control, how you dole out the text data, how
• 52:52
  Speaker 3: you test it, how you allow it to think. And
• 52:54
  Speaker 3: we thought, all right, you know what, we're building a
• 52:57
  Speaker 3: bigger net. I bet we see five I've maybe even
• 53:01
  Speaker 3: six x improvement in our signal to over one hundred x.
• 53:07
  Speaker 2: That's unbelievable.
• 53:08
  Speaker 3: Over one hundred x it's over and now it's we've
• 53:10
  Speaker 3: had a multiple of that again, it's it's It is
• 53:14
  Speaker 3: hard to describe in in the in the the expansion
• 53:17
  Speaker 3: of the net is in so many different directions. Everything
• 53:21
  Speaker 3: we were doing before was just English language, right.
• 53:24
  Speaker 2: So now you speak of global and how many languages
• 53:27
  Speaker 2: are you pulling into the.
• 53:28
  Speaker 3: Anything anything that an AI has been trained on, we
• 53:33
  Speaker 3: read it and we can say.
• 53:35
  Speaker 2: We can say, got forty languages? Is that like?
• 53:37
  Speaker 3: How many? Well there are only you know, there are
• 53:39
  Speaker 3: about a dozen languages that are useful in.
• 53:43
  Speaker 2: Markets totally Japanese, Chinese, a variety of European languages.
• 53:47
  Speaker 3: So we can we can tell Indian, here's the here's
• 53:50
  Speaker 3: a story, here's the story about Chinese domestic markets. There's
• 53:55
  Speaker 3: a Western story, Western narratives, there's a domestic Chinese story,
• 53:59
  Speaker 3: and the domestic Chines story is very different. Oftun it's
• 54:02
  Speaker 3: very different. So we were able, for example, picking up
• 54:05
  Speaker 3: we were able to pick up way before it got
• 54:10
  Speaker 3: picked up in Western press. The demand for luxury goods
• 54:17
  Speaker 3: in China went off a clifliff in last November.
• 54:23
  Speaker 2: Listen, you can't go from a double digit GDP to
• 54:26
  Speaker 2: like load to mid single digits and not have an effect,
• 54:30
  Speaker 2: especially in a country like that.
• 54:31
  Speaker 3: Well, but it's interesting, right because because they've had you know,
• 54:36
  Speaker 3: ups and downs on business cycle before, but this is
• 54:38
  Speaker 3: the first time where you saw consumer behavior that really
• 54:43
  Speaker 3: was kind of similar to what you might find in
• 54:45
  Speaker 3: a Western consumer behavior. Point being, you didn't hear that
• 54:48
  Speaker 3: from LVMH or the Macau gaming guys until February, and
• 54:54
  Speaker 3: we were picking that up in November from the domestic
• 54:57
  Speaker 3: Chinese meatus.
• 54:58
  Speaker 2: So that raises a really interesting question. You know, at
• 55:02
  Speaker 2: the end of the day, clients want to be able
• 55:04
  Speaker 2: to make money on your research. How are people putting
• 55:09
  Speaker 2: this to work? How does what you're building help your
• 55:13
  Speaker 2: clients generate alpha.
• 55:15
  Speaker 3: What we think we've discovered is an entirely new source
• 55:21
  Speaker 3: of data, and what we're confident is that we've built
• 55:25
  Speaker 3: the systems that are very different from what you see
• 55:28
  Speaker 3: with this sort of analytics that are out there from
• 55:32
  Speaker 3: anyone else. Right, So this is not sentiment, right, We're
• 55:35
  Speaker 3: not tracking or using mean words or nice.
• 55:37
  Speaker 2: By the way, that was a big thing I don't
• 55:38
  Speaker 2: know is that ten years ago we're scanning Twitter to
• 55:42
  Speaker 2: identify investor sentiment.
• 55:44
  Speaker 3: Oh my god, I mean, but I was just looking
• 55:46
  Speaker 3: today and not to pick on Bloomberg, but you know
• 55:50
  Speaker 3: it was their live coverage of the FED statement, right,
• 55:56
  Speaker 3: and they were analyzing the sentences and the thing statement
• 56:00
  Speaker 3: for hawkish and dubvish sentiment.
• 56:02
  Speaker 2: How would change from the last meeting, And so so
• 56:06
  Speaker 2: you're saying there's not a whole lot of signal there.
• 56:09
  Speaker 3: I want to be careful with what I say, right,
• 56:11
  Speaker 3: which is that there are a number of i'll call
• 56:15
  Speaker 3: them high frequency stat ARP guys where if it's important
• 56:20
  Speaker 3: for you to note the difference in word choice on
• 56:25
  Speaker 3: a millisecond level, I think there. I think you can
• 56:29
  Speaker 3: get something out of that I do, and so there
• 56:33
  Speaker 3: are firms that can do that very well. That's not
• 56:35
  Speaker 3: our game, right, This is not the What we're trying
• 56:39
  Speaker 3: to identify is not just sentiment, not just word choice.
• 56:43
  Speaker 3: This is not Google trends and how many times did
• 56:46
  Speaker 3: they mention AI and the earnings report? Right, there's we're
• 56:51
  Speaker 3: able to track the actual stories that drive behavior.
• 56:55
  Speaker 2: So that that's the next question. And I'm gonna give
• 56:59
  Speaker 2: Dave not a credit for asking this. Does every narrative
• 57:04
  Speaker 2: turn into a decision? How do you know when something
• 57:08
  Speaker 2: is merely noisy versus where there's a significant tradable signal.
• 57:14
  Speaker 3: I don't, right, So this is my goal is not to, oh,
• 57:21
  Speaker 3: you know, do the trades. My goal is mister hedge
• 57:25
  Speaker 3: fun guy, mister asset allocator. You've got to you know China, right,
• 57:31
  Speaker 3: you know your companies, you know your commodities. Here is
• 57:37
  Speaker 3: data that I think you'll find useful. The efficacy of it, though,
• 57:44
  Speaker 3: is up to you. I don't know what you would
• 57:46
  Speaker 3: what you want to do with it. I want to
• 57:47
  Speaker 3: sell the picks and shovels, honestly for this vast new
• 57:52
  Speaker 3: data set that we all know is important, but we
• 57:55
  Speaker 3: haven't been able to measure it in a very predictive
• 57:58
  Speaker 3: way before.
• 57:59
  Speaker 2: So let's talk about a few things related to and
• 58:02
  Speaker 2: it's not just.
• 58:03
  Speaker 3: About using this for investment. So we have a product
• 58:07
  Speaker 3: for financial advisors. Uh huh, right, which is your client's
• 58:13
  Speaker 3: coming in? You've got a portfolio. You need to be
• 58:17
  Speaker 3: able to say, what what is what is my client? What?
• 58:21
  Speaker 3: What are they worried about, what are they nervous about?
• 58:23
  Speaker 3: What are they hopeful for? What are the stories they're reading?
• 58:26
  Speaker 2: And you're pulling this out of the flow of media, and.
• 58:29
  Speaker 3: We can tell you exactly for the portfolio you've got
• 58:32
  Speaker 3: for that client. Here's what they're going to be asking about,
• 58:35
  Speaker 3: worried about, and here are the answers you can give
• 58:37
  Speaker 3: them to show. This is when has happened before, when
• 58:40
  Speaker 3: this story has come up, Stay, the course is going
• 58:42
  Speaker 3: to be fine. This is the sort of stuff we
• 58:45
  Speaker 3: can do for financial advisors.
• 58:46
  Speaker 2: Huh, that's really interesting. Would I would love to see
• 58:48
  Speaker 2: some of that.
• 58:49
  Speaker 3: It's not just in markets, Berry, I got to tell you.
• 58:52
  Speaker 2: The policymakers, corporate executivesinkers.
• 58:57
  Speaker 3: So we did you know, we did before in the
• 59:01
  Speaker 3: run up to the Russian invasion of Ukraine, and we
• 59:04
  Speaker 3: published this on epsolon theory and this is my old
• 59:07
  Speaker 3: academic work book, getting to War. Before a country starts
• 59:11
  Speaker 3: a war, they mobilize public opinion. And so we were
• 59:15
  Speaker 3: looking at domestic Russian media looking as a friends, this
• 59:20
  Speaker 3: isn't going to be.
• 59:21
  Speaker 2: A limited thing.
• 59:23
  Speaker 3: This is happening. It's going to be a full scale invasion.
• 59:26
  Speaker 3: Because that was the messaging in domestic Russian media to
• 59:30
  Speaker 3: the Russian people. Wow, So brands, right, so you are
• 59:36
  Speaker 3: you know we're working with some some it sounds great,
• 59:40
  Speaker 3: but pro sports teams you want to tell a story
• 59:44
  Speaker 3: to build a stadium, some tickets build a stadium. Give
• 59:49
  Speaker 3: me some examples of people have told good stories and
• 59:52
  Speaker 3: how did that work for them? How can we do
• 59:54
  Speaker 3: the same thing? So looking at storytelling.
• 59:58
  Speaker 2: If Jeff Bezos were as a subscriber to this back
• 1:00:02
  Speaker 2: when he was trying to build a tax funded HQ
• 1:00:06
  Speaker 2: on the Hudson, had he had your data and you
• 1:00:10
  Speaker 2: were crunching all the New York City news stories about this,
• 1:00:14
  Speaker 2: might you have been able to give him advice that
• 1:00:18
  Speaker 2: he suffered. He suffered such a backlash.
• 1:00:21
  Speaker 3: Because you know we have today, we have polls right because.
• 1:00:24
  Speaker 2: We're terrible, which are terrible, which.
• 1:00:27
  Speaker 3: Are mostly terrible now, you know, and I love the
• 1:00:30
  Speaker 3: polymarket stuff and other things where you try to get
• 1:00:32
  Speaker 3: as many people as possible to put money on something right.
• 1:00:34
  Speaker 2: So you know, when you ask a personal question, you're
• 1:00:37
  Speaker 2: asking them, hey, what do you think you think and
• 1:00:40
  Speaker 2: what do you think you're going to do in the future.
• 1:00:42
  Speaker 2: When we know people are terrible at both of them terrible,
• 1:00:46
  Speaker 2: But if you put a little money on it, all right,
• 1:00:47
  Speaker 2: maybe they might be a little more circumspect.
• 1:00:50
  Speaker 3: That that that that helps the ton So in places
• 1:00:53
  Speaker 3: where you can kin make a bet. I think that
• 1:00:55
  Speaker 3: improves the kind of information you can get. It still
• 1:00:59
  Speaker 3: lends itself to a lot of manipulation and a lot of.
• 1:01:04
  Speaker 2: Poly markets and calshi and all those things. It's not
• 1:01:08
  Speaker 2: the bond market. It's not one hundred and something trillion dollars.
• 1:01:12
  Speaker 2: It's a couple of bucks on each of these, and
• 1:01:13
  Speaker 2: sometimes special million dollars move them. Half a million dollars
• 1:01:17
  Speaker 2: can move the market.
• 1:01:17
  Speaker 3: But for a lot of things. Let's say you're polling
• 1:01:19
  Speaker 3: for a political candidate, right, you can't ask them to
• 1:01:22
  Speaker 3: put money down on something, but you really want to
• 1:01:25
  Speaker 3: know these these policies that my candidate is thinking about
• 1:01:30
  Speaker 3: taking on. Is that popular, does it resonate? Is it
• 1:01:33
  Speaker 3: a compelling story? We can absolutely see if that is
• 1:01:38
  Speaker 3: true by looking at local media, local social media, all
• 1:01:42
  Speaker 3: of that. So it's it's pretty wild bare. I mean,
• 1:01:45
  Speaker 3: once you start looking at how important stories are, and
• 1:01:50
  Speaker 3: once you've got a tool where you can actually measure
• 1:01:53
  Speaker 3: them and visualize them, it's like it's like, I feel
• 1:01:57
  Speaker 3: like it was like when they invented whoever was oven hook,
• 1:02:00
  Speaker 3: whoever vinded the microscope, When you're actually to see something
• 1:02:04
  Speaker 3: that we all know is there, Well, now we do.
• 1:02:06
  Speaker 2: Back then, remember jump theory took what a century to
• 1:02:09
  Speaker 2: catch up?
• 1:02:10
  Speaker 3: It took a century, And it takes it takes seeing
• 1:02:12
  Speaker 3: it right, It takes the instrument to actually measure it
• 1:02:15
  Speaker 3: before you actually believe in it. And so I feel
• 1:02:19
  Speaker 3: like that's kind of where we are right now. It's
• 1:02:21
  Speaker 3: these early days. But to actually see and measure the
• 1:02:24
  Speaker 3: storytelling at this level of resolution, this magnification, it's pretty
• 1:02:28
  Speaker 3: freaking cool.
• 1:02:29
  Speaker 2: So so we're having this conversation with market at all
• 1:02:32
  Speaker 2: time highs and you've written about the ravine. Yes, tell
• 1:02:37
  Speaker 2: us a little bit about what is the ravine? How
• 1:02:40
  Speaker 2: does does your data identify that? Tell us what this means?
• 1:02:46
  Speaker 3: Well, there's there's there's clearly been a change in policy
• 1:02:50
  Speaker 3: regime out of Washington and.
• 1:02:54
  Speaker 2: New administration and are radically different.
• 1:02:56
  Speaker 3: Radically different.
• 1:02:57
  Speaker 2: Even from the first Trump president.
• 1:03:00
  Speaker 3: From the first presidency. And so what we're able to measure,
• 1:03:05
  Speaker 3: really measure is how does that i'll say play, But
• 1:03:09
  Speaker 3: also what comes narratives never happened in a vacuum. There's
• 1:03:17
  Speaker 3: always a counter story. For every bull story, there's a
• 1:03:21
  Speaker 3: bear story, and they they they you can almost kind
• 1:03:25
  Speaker 3: of see the battlefield of ideas, the battlefield of stories
• 1:03:29
  Speaker 3: and how it emerges. So my strong sense, Parry, is
• 1:03:35
  Speaker 3: that we are going towards politically in this country, towards
• 1:03:43
  Speaker 3: trench warfare greater and greater. I'll call it narrative violence.
• 1:03:50
  Speaker 3: And it if you look historically how that plays out
• 1:03:53
  Speaker 3: in countries, it doesn't It doesn't play out well, the.
• 1:03:56
  Speaker 2: Civil war, domestic political violence, things like that.
• 1:04:01
  Speaker 3: Sadly, yes, exactly like that, exactly like that. So there's
• 1:04:06
  Speaker 3: that element, and so that's a that's a sad one
• 1:04:10
  Speaker 3: or a very troubling one and trying to well, how
• 1:04:12
  Speaker 3: do we navigate that? But even when in markets, and
• 1:04:16
  Speaker 3: I alluded to this earlier, how capital markets have become
• 1:04:20
  Speaker 3: a political utility, uh, and the role of markets in
• 1:04:27
  Speaker 3: our society, you can really see how that changes in
• 1:04:30
  Speaker 3: the stories we tell ourselves about the role of markets,
• 1:04:34
  Speaker 3: what it's there for.
• 1:04:37
  Speaker 2: And I end of day options in speculation, what else
• 1:04:40
  Speaker 2: is it supposed to be there for?
• 1:04:44
  Speaker 3: But that but that's kind of what I'm getting at, right.
• 1:04:47
  Speaker 3: You can see an enormous change in the meaning of markets,
• 1:04:53
  Speaker 3: and it connects with a yes, they'll call it the
• 1:04:57
  Speaker 3: speculation layer. But it also connects with financial needs. Yolo
• 1:05:04
  Speaker 3: it it leads to a very I think, less attractive
• 1:05:12
  Speaker 3: future for how we think about money and the role
• 1:05:17
  Speaker 3: of markets and the role of capitalism.
• 1:05:20
  Speaker 2: Coming up, we continue our conversation with Ben Hunt, president
• 1:05:25
  Speaker 2: and co founder of person and discussing how money managers
• 1:05:30
  Speaker 2: use their output to generate alpha. I'm Barry Ridults. You're
• 1:05:35
  Speaker 2: listening to Masters in Business on Bloomberg Radio. I'm Barry Ridults.
• 1:05:57
  Speaker 2: You're listening to Masters in Business on Bloomberg Radio. My
• 1:06:00
  Speaker 2: extra special guest today is Ben Hunt. He is president
• 1:06:04
  Speaker 2: and co founder of Percient, a data analytics, narrative storytelling
• 1:06:12
  Speaker 2: large language model using artificial intelligence to find some signal
• 1:06:17
  Speaker 2: amongst the noise. Firm their clients range everything from large
• 1:06:23
  Speaker 2: money managers to hedge funds, to academics and corporate America.
• 1:06:29
  Speaker 2: So you write epsilon theory, and some of it is
• 1:06:33
  Speaker 2: for subscribers, some of it is public. One of the
• 1:06:37
  Speaker 2: things you wrote is absolutely my favorite item from the
• 1:06:43
  Speaker 2: first few months of the Trump presidency because I like
• 1:06:48
  Speaker 2: to talk in probabilities because I don't know what's going
• 1:06:50
  Speaker 2: to happen. But here's the best case scenario, here's the
• 1:06:53
  Speaker 2: worst case here's all the middle scenarios. You wrote a
• 1:06:57
  Speaker 2: piece the end of Paksamariana that I thought was the
• 1:07:02
  Speaker 2: most cogent, imaginative, well thought out. Hey, here's the worst
• 1:07:08
  Speaker 2: case scenario, and we are becoming dangerously flirting with this
• 1:07:15
  Speaker 2: possible outcome, and I use that as all right, So
• 1:07:18
  Speaker 2: here's what I think is high probability. Here's the best case.
• 1:07:22
  Speaker 2: But if you really want to think about how this
• 1:07:24
  Speaker 2: can go off the rails, check out what Ben wrote.
• 1:07:27
  Speaker 2: And so tell us a little bit about how did
• 1:07:30
  Speaker 2: Persian inform the end of Pax Americana, Because I get
• 1:07:37
  Speaker 2: the sense that domestically that wasn't really where a how
• 1:07:42
  Speaker 2: a lot of people were thinking, but I got the
• 1:07:44
  Speaker 2: sense from overseas that was a much more common thought.
• 1:07:48
  Speaker 2: Tell us a little bit about both the peace and
• 1:07:51
  Speaker 2: how the data informed it.
• 1:07:55
  Speaker 3: I'll start with I'll call it the international dimension. I
• 1:07:58
  Speaker 3: want to come back to the domestics, and I think
• 1:08:00
  Speaker 3: we've got some really interesting data recently to share. It
• 1:08:06
  Speaker 3: goes back to this again. I cringe whiz I talk about,
• 1:08:09
  Speaker 3: but but it is. It is game theory, right, and
• 1:08:12
  Speaker 3: all game theory is is strategic interaction. It's that the
• 1:08:18
  Speaker 3: United States, Yes, is the most powerful player on the
• 1:08:22
  Speaker 3: world stage. But every country has some degrees of freedom
• 1:08:28
  Speaker 3: and some autonomy in there in the policies that they
• 1:08:31
  Speaker 3: that they implement, there is not a dominant strategy, meaning
• 1:08:35
  Speaker 3: an outcome, an equilibrium outcome. Again, I hate using these words,
• 1:08:39
  Speaker 3: but I'll use them anyway. All an equilibrium means is
• 1:08:43
  Speaker 3: it's a balancing point where both parties, let's call them
• 1:08:48
  Speaker 3: the United States and a European country, where do they
• 1:08:53
  Speaker 3: end up where they all say, okay, I can live
• 1:08:54
  Speaker 3: with this, Where I can live with this, and the
• 1:08:57
  Speaker 3: America first set of policies them is all these set
• 1:09:00
  Speaker 3: of policies. What you end up with is less potential,
• 1:09:07
  Speaker 3: economic growth, trade, all all of these things.
• 1:09:12
  Speaker 2: All the things that we enjoyed post World War two.
• 1:09:15
  Speaker 2: That realignment that im due to our benefits so.
• 1:09:19
  Speaker 3: Great enormously advantage, enormously to our advantage. It goes by
• 1:09:25
  Speaker 3: the name of soft power. And you know the dollar system,
• 1:09:29
  Speaker 3: the breton Wood system.
• 1:09:30
  Speaker 2: All of this serve currency on and on the.
• 1:09:32
  Speaker 3: Reserve currency to finance our definity. All of this is
• 1:09:35
  Speaker 3: enormously to our advantage. And yes, there are free riders
• 1:09:39
  Speaker 3: on that system. Yes there are costs to that, particularly
• 1:09:42
  Speaker 3: on defense and some other some other areas. Tariffs are
• 1:09:46
  Speaker 3: another area where there's absolutely free riders on that. So
• 1:09:49
  Speaker 3: there's improvement that can be made in that system, for sure,
• 1:09:53
  Speaker 3: But this is not This is a different system. This
• 1:09:57
  Speaker 3: is a different set of rules of the road, and
• 1:09:59
  Speaker 3: it leads to a different strategic interaction between between countries.
• 1:10:04
  Speaker 3: So that's what the note was about, and it's it's
• 1:10:09
  Speaker 3: I'm not trying to predict. I'm just I'm just trying
• 1:10:11
  Speaker 3: to observe, which is a great line actually by George Sorows,
• 1:10:16
  Speaker 3: which is say, I'm not predicting, I'm observing, which I
• 1:10:19
  Speaker 3: love that as a line. I'm not and our technology
• 1:10:24
  Speaker 3: is not there to try to predict the future, is
• 1:10:26
  Speaker 3: trying to tell you what is true in the present today.
• 1:10:31
  Speaker 2: I'm amazed how many people think they can forecast the
• 1:10:33
  Speaker 2: future when they have no idea what's happening.
• 1:10:35
  Speaker 3: I want to I want to now cast is what
• 1:10:38
  Speaker 3: I want to do, not not forecast. I want to
• 1:10:40
  Speaker 3: now cast. And I guess what I wanted to talk
• 1:10:44
  Speaker 3: about domestic. So we started tracking the different narratives around
• 1:10:51
  Speaker 3: immigration policy early last year, and what you saw in
• 1:10:57
  Speaker 3: some of the kind of simple.
• 1:10:59
  Speaker 2: This is during the run up to the election.
• 1:11:01
  Speaker 3: Yes, exactly, and what you saw in really going back
• 1:11:04
  Speaker 3: because we can take we take this stuff back for
• 1:11:06
  Speaker 3: a decade or more. And once you absolutely saw up
• 1:11:12
  Speaker 3: to last October. There's a there was an event from
• 1:11:15
  Speaker 3: last October. Not the election, but an event from last October.
• 1:11:19
  Speaker 3: You see a steady increase in regardless of your political affiliation.
• 1:11:25
  Speaker 3: You know what immigration isn't working for us, isn't as Americans,
• 1:11:29
  Speaker 3: So a steady increase starting with the they're eating the cats.
• 1:11:37
  Speaker 2: They're eating the cats, They're eating their dogs, they're eating.
• 1:11:40
  Speaker 3: The dogs, the Columbus mode when you that that moment.
• 1:11:45
  Speaker 2: One of the most that's the real moments in debate history.
• 1:11:49
  Speaker 3: And we see very clearly in our media data, and
• 1:11:55
  Speaker 3: this is this is not mainstream media. This is everything
• 1:11:58
  Speaker 3: that we're pulling up. We've seen a really significant decline
• 1:12:05
  Speaker 3: in the volume and density of oh my god, immigration
• 1:12:09
  Speaker 3: is a problem, we need mass deportations. On the contrary,
• 1:12:13
  Speaker 3: we've seen an enormous increase again regardless of political affiliation,
• 1:12:17
  Speaker 3: including Republicans. No immigration is a good thing for this country.
• 1:12:23
  Speaker 3: The stories of America that are pro immigrant in immigration
• 1:12:28
  Speaker 3: now you would not believe that if you were looking
• 1:12:31
  Speaker 3: at the policies that the White House has implemented during
• 1:12:35
  Speaker 3: these first you know, ten months of the administration.
• 1:12:38
  Speaker 2: So wait, when did you first notice that, regardless of policy,
• 1:12:45
  Speaker 2: we think immigration is a net benefit to America.
• 1:12:48
  Speaker 3: When did that first start showing up? It started changing
• 1:12:51
  Speaker 3: right in October because it was like a breach too far.
• 1:12:54
  Speaker 3: It was like, this is just stupid. This is stilly
• 1:12:57
  Speaker 3: and stupid, and it's been a steady reece, you know,
• 1:13:01
  Speaker 3: and you wouldn't you wouldn't believe that if you were
• 1:13:03
  Speaker 3: immersed in Twitter. But so this country is actually quite
• 1:13:10
  Speaker 3: pro immigrant.
• 1:13:12
  Speaker 2: I know that sounds but we are a nation of immigrants.
• 1:13:16
  Speaker 2: Here here's another data point that's kind of mind blowing.
• 1:13:19
  Speaker 2: We were talking about a different data point in the
• 1:13:23
  Speaker 2: entirety of the US history. Twenty twenty five looks like
• 1:13:27
  Speaker 2: the first year where the US population will decrease.
• 1:13:32
  Speaker 3: So it's a decrease.
• 1:13:33
  Speaker 2: In legal immigrants, not not only illegal immigrants, but legal immigrants.
• 1:13:38
  Speaker 2: Add that with the deportations and just people staying away.
• 1:13:41
  Speaker 3: Well, this gets back to the economic picture and from others.
• 1:13:45
  Speaker 3: So what you've had is a clear and again we
• 1:13:49
  Speaker 3: see this in our data from other countries, there's a
• 1:13:52
  Speaker 3: clear effort to repatriate assets and funds away from the US.
• 1:13:58
  Speaker 3: There's been a clear effort outside the US. You see
• 1:14:01
  Speaker 3: this was central banks, but also with course hence gold,
• 1:14:04
  Speaker 3: the gold gold treasuries used to be a safe haven asset.
• 1:14:11
  Speaker 3: No more. That's that's it's if.
• 1:14:13
  Speaker 2: You understand how significant that charge is that you're making.
• 1:14:18
  Speaker 2: You're basically accusing the president well of submarining one of
• 1:14:23
  Speaker 2: the single greatest assets America.
• 1:14:25
  Speaker 3: Halts well that it's a it is built up to
• 1:14:29
  Speaker 3: such an extent, right that the way I think of
• 1:14:31
  Speaker 3: this is an iceberg that is melting. But it is melting.
• 1:14:36
  Speaker 3: What we do not see we do not see capital flight. Right.
• 1:14:40
  Speaker 2: We saw it for like a week in April, and
• 1:14:43
  Speaker 2: then that we reversed.
• 1:14:44
  Speaker 3: That was it. So there's there. There is there's no
• 1:14:46
  Speaker 3: capital flight there, there are no leaving the country. There
• 1:14:50
  Speaker 3: were fierce and we can track it. If that starts
• 1:14:52
  Speaker 3: to happen, we'll see it immediately. That's not happening. Repatriation
• 1:14:57
  Speaker 3: continues to happen mm hmm, slowly measured balanced melting iceberg. Right,
• 1:15:03
  Speaker 3: Because there are limits to if you're a Bermuda reinsure.
• 1:15:08
  Speaker 3: I mean you're kind of you you're stuck with treasury, right.
• 1:15:10
  Speaker 2: I mean that you could buy some gold to offset it,
• 1:15:13
  Speaker 2: but you can't sell one hundred billion dollars worth of treasuries.
• 1:15:16
  Speaker 3: You can't. You can't. So it's a melting iceberg. But
• 1:15:19
  Speaker 3: we can clearly see that the dog is not barking
• 1:15:22
  Speaker 3: yet and maybe never will. Is capital flight. What we
• 1:15:27
  Speaker 3: see in politically domestically is that actually, and this was
• 1:15:30
  Speaker 3: validated by a gallop pool they've been doing twenty years,
• 1:15:33
  Speaker 3: which also showed what we had saw started seeing a
• 1:15:37
  Speaker 3: lot earlier, where immigration as a thing is actually pretty
• 1:15:44
  Speaker 3: darn popular in the United States.
• 1:15:47
  Speaker 2: Yeah, but so so is restricting assault rifles. And we
• 1:15:51
  Speaker 2: can't get any change on that. I think sevent.
• 1:15:56
  Speaker 3: Or the Act. I gotta tell you, I gotta tell
• 1:15:59
  Speaker 3: you that depends very much and how that question is asked. Well,
• 1:16:02
  Speaker 3: that's true for all polling. Well, this is the benefit
• 1:16:05
  Speaker 3: of what I'm tied. So so when we're doing these
• 1:16:07
  Speaker 3: semantic we call the semantic signatures, it has nothing to
• 1:16:10
  Speaker 3: do with how you're phrasing the question. We're not doing polling.
• 1:16:14
  Speaker 3: We're seeing what people are actually the meaning of what
• 1:16:18
  Speaker 3: they're talking about in media.
• 1:16:20
  Speaker 2: So how does that play out if people are legitimately
• 1:16:25
  Speaker 2: saying no, immigration is a good thing for America. Is
• 1:16:29
  Speaker 2: there an impact on population and the economy. Is there
• 1:16:32
  Speaker 2: an impact on markets? Is there an impact on policy
• 1:16:35
  Speaker 2: and politics?
• 1:16:35
  Speaker 3: I think there's an impact on the election the midterms
• 1:16:38
  Speaker 3: next year.
• 1:16:40
  Speaker 2: So we're we're talking literally twelve months from now.
• 1:16:46
  Speaker 3: Yeah, because that's how this stuff gets on the political front.
• 1:16:52
  Speaker 3: Narratives and opinions, they get cashed out in elections, right,
• 1:16:56
  Speaker 3: Mark is a different You cash stuff out every day,
• 1:16:59
  Speaker 3: right the market. That loop is so rapid with markets exactly.
• 1:17:04
  Speaker 3: Politics is a different story. So hedy gets cashed out
• 1:17:07
  Speaker 3: in the election.
• 1:17:08
  Speaker 2: If you were to ask me before this conversation, what's
• 1:17:11
  Speaker 2: the most significant impact on the midterm elections? There was
• 1:17:15
  Speaker 2: just a Gallop poll yesterday, Gop questions on the economy.
• 1:17:21
  Speaker 2: They were plus fourteen percent two years ago. They're minus
• 1:17:25
  Speaker 2: four percent this year. I saw that, and that's an
• 1:17:28
  Speaker 2: amazing swing. And I'm I'm saying to myself, you know, listen,
• 1:17:33
  Speaker 2: the out of power party usually picks up, you know,
• 1:17:36
  Speaker 2: ten to fifteen seats. You have the redistricting question, which
• 1:17:40
  Speaker 2: may blunt that. But if the most important question during
• 1:17:44
  Speaker 2: the election was on the economy and that's an eighteen
• 1:17:47
  Speaker 2: percent swing, this is looking like a pretty substantial shift.
• 1:17:51
  Speaker 2: What I'm hearing from you is, hey, this isn't just
• 1:17:54
  Speaker 2: about the economy. It's not It's not so it's the economy,
• 1:17:58
  Speaker 2: it's immigration.
• 1:17:59
  Speaker 3: What else?
• 1:18:00
  Speaker 2: What else are you seeing? That's interest?
• 1:18:01
  Speaker 3: That's that's that's pretty now. There are other aspects though,
• 1:18:04
  Speaker 3: where the stories it This is, this is people talking
• 1:18:11
  Speaker 3: about immigration is the thing. Now, Whether now whether that
• 1:18:14
  Speaker 3: gets translated into a political party position, because I got
• 1:18:17
  Speaker 3: to tell you, the Democratic Party is enormously there. There
• 1:18:22
  Speaker 3: are no there are no narratives that are being put
• 1:18:25
  Speaker 3: forward by the Democrats that are powerful or or or
• 1:18:30
  Speaker 3: or popular, so.
• 1:18:30
  Speaker 2: I think, other than the mayoral contest in New York.
• 1:18:34
  Speaker 3: Yeah, yeah, right, what is one. What I'm saying is
• 1:18:37
  Speaker 3: that the is that the many of the policies that
• 1:18:41
  Speaker 3: are being presented by this administration are unpopular, not just
• 1:18:47
  Speaker 3: with Democrats but with Republicans and increasingly so, immigration being
• 1:18:53
  Speaker 3: one of them, I think economy being another one.
• 1:18:58
  Speaker 2: Tariffs aren't really popular amongst the but people perceive that
• 1:19:03
  Speaker 2: as a tax increase or or a pressure on small business.
• 1:19:07
  Speaker 3: Government shut down, right, So both calls and also in
• 1:19:10
  Speaker 3: this it's it's it's that because the question, oh, well,
• 1:19:13
  Speaker 3: the Democrats will get blamed, Well, that's not really true.
• 1:19:16
  Speaker 3: That's not what's happening. Now, how that all pays the way.
• 1:19:22
  Speaker 2: You're not a heart You're not a Democrat. God, like,
• 1:19:26
  Speaker 2: I know you and your politics and the stuff you're saying.
• 1:19:30
  Speaker 2: I could hear people shrugging and saying, well that Ben
• 1:19:33
  Speaker 2: Hunt is just a liberal Democrat. I'm like, no, no, no,
• 1:19:36
  Speaker 2: that's not who Ben is. No, no, it's not You're
• 1:19:39
  Speaker 2: just talking about here's what I see in the data.
• 1:19:42
  Speaker 3: I'm saying. I'm observing. I'm observing, And I think that
• 1:19:45
  Speaker 3: a lot of times, if we are, like me, very
• 1:19:49
  Speaker 3: online and on Twitter too much, you don't see the
• 1:19:54
  Speaker 3: broader picture of what is happening on blog posts and
• 1:20:01
  Speaker 3: local newspapers and and and everything else. So I think
• 1:20:06
  Speaker 3: having the ability to read everything and track these stories
• 1:20:15
  Speaker 3: that you know, they don't they they like say they
• 1:20:19
  Speaker 3: wax and wane, but they don't. They don't ever go away.
• 1:20:23
  Speaker 3: It's it's it's something we're very excited about to track
• 1:20:27
  Speaker 3: the stories of America I'll give you another one, and
• 1:20:31
  Speaker 3: this one, actually I don't know what to do with this.
• 1:20:37
  Speaker 3: One of the stories of America is that America has
• 1:20:43
  Speaker 3: raised the world's wealth, right, that that global standard of living,
• 1:20:49
  Speaker 3: global center of living, That that America has been this
• 1:20:52
  Speaker 3: powerful force to and capitalism in America have been this
• 1:20:57
  Speaker 3: powerful force to raise people out of poverty. That's a
• 1:21:00
  Speaker 3: story that in other periods of time has been out,
• 1:21:05
  Speaker 3: that has been prominently talked about, very resonant. That story
• 1:21:10
  Speaker 3: is non existent today.
• 1:21:11
  Speaker 2: Well, you cut things like HIV a few million dollars
• 1:21:15
  Speaker 2: to inoculate all of Africa from HIV, that's going to
• 1:21:20
  Speaker 2: have repercussions.
• 1:21:21
  Speaker 3: Well, it's it's not just that that that has the
• 1:21:26
  Speaker 3: conquer those repercussions that you're describing. What I'm saying is
• 1:21:30
  Speaker 3: that the story that would support that it's gone.
• 1:21:37
  Speaker 2: Is it gone forever? No?
• 1:21:39
  Speaker 3: No, doesn't. These things are never gone. These stories stories
• 1:21:42
  Speaker 3: have they never die, right, They're they're they're waiting for
• 1:21:47
  Speaker 3: a new force to give them this version of it.
• 1:21:50
  Speaker 2: If US is not seen as a force for raising
• 1:21:53
  Speaker 2: people out of poverty around the world, do people see
• 1:21:58
  Speaker 2: China as that force?
• 1:22:00
  Speaker 3: I haven't looked at that we've been looking at the
• 1:22:02
  Speaker 3: US story. But I but I my my personal seness
• 1:22:05
  Speaker 3: is that we've essentially seeded the field and a particularly
• 1:22:13
  Speaker 3: in South Asia and Africa. Uh huh to to to China.
• 1:22:18
  Speaker 3: I mean that that seems that seems pretty you know,
• 1:22:20
  Speaker 3: pretty clear.
• 1:22:21
  Speaker 2: This is I'm glad I asked the question about the
• 1:22:23
  Speaker 2: end of Past Americana because I've just found that piece
• 1:22:27
  Speaker 2: so insightful and so useful, and I could keep you
• 1:22:31
  Speaker 2: here talking about this stuff for hours, but out of
• 1:22:35
  Speaker 2: deference and respect for our listener's time, I'm going to
• 1:22:39
  Speaker 2: jump to our favorite questions that I ask, oh all
• 1:22:43
  Speaker 2: of our all of our guests. But before I do,
• 1:22:47
  Speaker 2: there's a question that I have to pose to you,
• 1:22:51
  Speaker 2: which is, so you get to see things before they
• 1:22:56
  Speaker 2: sort of bubble up into the mainstream, before they be
• 1:23:00
  Speaker 2: I'm a well understood narrative. What's a narrative that you're
• 1:23:04
  Speaker 2: just starting to sniff out that most of the world,
• 1:23:08
  Speaker 2: or most of the country, or most of Wall Street
• 1:23:11
  Speaker 2: and Finance hasn't seen yet.
• 1:23:15
  Speaker 3: It's a story that people are talking about since Tricolor
• 1:23:21
  Speaker 3: and first Brands went out. So it's the story on credit,
• 1:23:27
  Speaker 3: story of credit, and.
• 1:23:29
  Speaker 2: I mean first Brands is legitimate. It sounds like felonies
• 1:23:33
  Speaker 2: were happening there, so we'll have to see.
• 1:23:35
  Speaker 3: What tricolor as well. But my point is that every
• 1:23:41
  Speaker 3: day you have a new bank CEO come out and
• 1:23:44
  Speaker 3: say no, no, everything's fine. So yesterday it was David
• 1:23:48
  Speaker 3: Solomon at Golden Sacks saying no, no, it's fine. Today
• 1:23:52
  Speaker 3: I was hearing the Brookfield guys saying no, no, it's fine.
• 1:23:57
  Speaker 3: For every story, every interview, the guy says it's fine.
• 1:24:03
  Speaker 3: You're getting two articles in the ft or the journal
• 1:24:06
  Speaker 3: saying ain't fine.
• 1:24:09
  Speaker 2: So they think thou dost protest this much.
• 1:24:12
  Speaker 3: All of that. All I'm saying is I am observing.
• 1:24:15
  Speaker 3: I'm not predicting. I am observing the level of volume
• 1:24:20
  Speaker 3: for alternative asset managers, their exposure to private credit. This
• 1:24:24
  Speaker 3: is problematic. Oh, I'm wondering what's going to happen when
• 1:24:28
  Speaker 3: the music stops.
• 1:24:29
  Speaker 1: Oh.
• 1:24:29
  Speaker 3: I think that these this blows back into the commercial
• 1:24:33
  Speaker 3: banking system. Those stories, those narratives are higher by an
• 1:24:39
  Speaker 3: order of magnitude than they've been at any point in
• 1:24:42
  Speaker 3: the last ten years, at any point including during COVID,
• 1:24:46
  Speaker 3: during when you had similar concerns over Oh, my god,
• 1:24:50
  Speaker 3: private credits COVID.
• 1:24:51
  Speaker 2: It was easy to rationalize everything. Hey listen, we're all frozen.
• 1:24:54
  Speaker 3: Just but I this is a story that has legs.
• 1:25:00
  Speaker 3: It is growing in a way that I rarely see.
• 1:25:04
  Speaker 3: And what's a story like this grows? It doesn't just
• 1:25:07
  Speaker 3: go away, And it's not fixed by bernanke saying oh,
• 1:25:11
  Speaker 3: subprime is contained immediately what I thought of as soon
• 1:25:15
  Speaker 3: as you absolutely it does not get fixed by people
• 1:25:18
  Speaker 3: saying don't worry, there's no problem.
• 1:25:21
  Speaker 2: Jim Grant said he was right. It was contained to Earth.
• 1:25:26
  Speaker 2: The rest of the solar system was fine, which turned
• 1:25:29
  Speaker 2: out to be a very witty and clever observation.
• 1:25:31
  Speaker 3: This story, this worry, this concern absolutely has legs. Huh.
• 1:25:36
  Speaker 3: And we're seeing no signs of it easing off in
• 1:25:42
  Speaker 3: financial media and press.
• 1:25:43
  Speaker 2: So the big question is is it systemic or is
• 1:25:47
  Speaker 2: it the specifics.
• 1:25:49
  Speaker 3: I don't know the truth. I don't know reality. All
• 1:25:52
  Speaker 3: I can tell you is we're seeing more of this. Well,
• 1:25:57
  Speaker 3: we're seeing this is the story really really interesting.
• 1:26:01
  Speaker 2: Let let's jump to our favorite questions, starting with who
• 1:26:06
  Speaker 2: are your mentors who helped shape your career?
• 1:26:10
  Speaker 3: It's good, well, I mentioned that the people both in
• 1:26:14
  Speaker 3: undergrad and graduate school who turned me on to the
• 1:26:18
  Speaker 3: science part of political science, and in particular in graduate school,
• 1:26:22
  Speaker 3: Gary King, who runs the whole social science research center
• 1:26:26
  Speaker 3: up there at Harvard, and as for a long time now,
• 1:26:30
  Speaker 3: he wrote the original, really the book on inference. You
• 1:26:33
  Speaker 3: hear about inference all.
• 1:26:34
  Speaker 2: The time Aldini, of course.
• 1:26:36
  Speaker 3: So the notion of inference, taking large data sets and
• 1:26:40
  Speaker 3: pulling out.
• 1:26:41
  Speaker 2: Oh, inference. I think you said influence, No, no, no, no,
• 1:26:44
  Speaker 2: inference inference.
• 1:26:45
  Speaker 3: Huh So the science of inference. I learned that from Gary,
• 1:26:51
  Speaker 3: you know, thirty years ago. And it's so interesting to
• 1:26:54
  Speaker 3: see that come full circle because that's at the core
• 1:26:57
  Speaker 3: of you know, Jensen Wong is always talking about it
• 1:27:00
  Speaker 3: and the whole notion of AI to that inference spind
• 1:27:03
  Speaker 3: do they talk about. I was there at the beginning
• 1:27:06
  Speaker 3: for how you what inference is and why it is
• 1:27:10
  Speaker 3: so powerful. That was a huge influence on me.
• 1:27:15
  Speaker 2: Let's talk about books.
• 1:27:16
  Speaker 3: What are you reading now?
• 1:27:17
  Speaker 2: What are some of your favorites.
• 1:27:19
  Speaker 3: I'm a science fiction guy.
• 1:27:21
  Speaker 2: Okay, I really talking to another sci fi guy. So
• 1:27:24
  Speaker 2: let's so I give us something new and something classic.
• 1:27:27
  Speaker 3: Well, something classic would be Luci Shin and the three
• 1:27:31
  Speaker 3: Body Problem.
• 1:27:33
  Speaker 2: The Netflix show on that was surprisingly watchable.
• 1:27:36
  Speaker 3: I that was excellent before that, And you know, I
• 1:27:41
  Speaker 3: named a company after him. Was the Foundation Trilogy as
• 1:27:45
  Speaker 3: those books hold up less well, honestly, And I thought
• 1:27:48
  Speaker 3: the series was. It had his moments. It was so
• 1:27:52
  Speaker 3: pretty so so to me.
• 1:27:53
  Speaker 2: Uh.
• 1:27:54
  Speaker 3: Current science fiction, Rebecca Goes, she goes by R. F.
• 1:28:01
  Speaker 3: Quog and she wrote a book called Babbel and she's
• 1:28:06
  Speaker 3: got a new one out. I like Tower of Babel
• 1:28:10
  Speaker 3: b A B E L. And she's got a new
• 1:28:12
  Speaker 3: one out, Catabasis. I think it is. But science fiction
• 1:28:16
  Speaker 3: dealing with language and linguistics. I love that stuff.
• 1:28:19
  Speaker 2: That sounds like that's right in your your sweet spot.
• 1:28:23
  Speaker 2: Let's talk about streaming. What are you watching or listening today?
• 1:28:26
  Speaker 2: Podcasts or Netflix or whatever.
• 1:28:28
  Speaker 3: So honestly, I don't listen to any podcasts. Isn't that
• 1:28:31
  Speaker 3: terrible to admit?
• 1:28:31
  Speaker 2: Well, if you host, I will say this, if you
• 1:28:34
  Speaker 2: host a podcast, you're either preparing for a podcast, doing
• 1:28:38
  Speaker 2: a podcast, or ordering a podcast, right, So it's like,
• 1:28:42
  Speaker 2: all right, that's three of that.
• 1:28:43
  Speaker 3: It is right.
• 1:28:44
  Speaker 2: Every now and then I'll catch something because I want
• 1:28:47
  Speaker 2: to either listen to this a specific guest like I,
• 1:28:51
  Speaker 2: I will not listen to any podcasts that you are
• 1:28:53
  Speaker 2: on before we do our podcast because I don't want
• 1:28:56
  Speaker 2: to steal anybody else's or narrative or questions you'll line
• 1:29:00
  Speaker 2: of thinking. So that's purposeful. But every now and then,
• 1:29:04
  Speaker 2: so I listened to the things I listened to are
• 1:29:08
  Speaker 2: like John Pizzarelli's Radio Deluxe, which is sort of like
• 1:29:14
  Speaker 2: a podcast slash music series.
• 1:29:17
  Speaker 3: Something a little different, exactly right.
• 1:29:19
  Speaker 2: What about streaming?
• 1:29:21
  Speaker 3: What do you well? You and I are both big
• 1:29:23
  Speaker 3: Godfather fans, so we love all the Mobster Have you
• 1:29:26
  Speaker 3: seen Mobland yet?
• 1:29:27
  Speaker 2: With no? All?
• 1:29:28
  Speaker 3: Right? Worth your time? Okay, Pierce Brosnan eats up every scene.
• 1:29:33
  Speaker 3: Tom Hardy is awfully good. I just thank him in
• 1:29:35
  Speaker 3: anything if you I don't know if you ever saw
• 1:29:38
  Speaker 3: Peaky Blinders.
• 1:29:39
  Speaker 2: Right, I kind of full. So my issue is I
• 1:29:45
  Speaker 2: have to watch something that my wife tolerates.
• 1:29:47
  Speaker 3: Like I have friends that's a struggle.
• 1:29:50
  Speaker 2: Who like he goes into this room, she goes into
• 1:29:53
  Speaker 2: that room. They don't watch anything together, Like, I don't
• 1:29:56
  Speaker 2: know how that works. Like she will watch some stuff
• 1:29:59
  Speaker 2: without me. I will watch some stuff with right, eighty
• 1:30:03
  Speaker 2: percent of what we watch. So I have fallen down
• 1:30:07
  Speaker 2: the British Upstairs, downstairs, Gilded Age six, So we watched
• 1:30:12
  Speaker 2: the Crown, we watched the Gilded Age. We finally let
• 1:30:15
  Speaker 2: me give you one you want back to Dalton Abbey,
• 1:30:18
  Speaker 2: which I missed when I first rolled out.
• 1:30:20
  Speaker 3: Check out, so my guilty pleasure and I did watch
• 1:30:23
  Speaker 3: this with my wife. Is a Diplomat.
• 1:30:25
  Speaker 2: The new series, just the new season, just and that's
• 1:30:28
  Speaker 2: teed up for this weekend.
• 1:30:29
  Speaker 3: I'm looking forward to that.
• 1:30:31
  Speaker 2: Excellent that the first was the season two or season three?
• 1:30:34
  Speaker 3: The first season was great.
• 1:30:36
  Speaker 2: Yeah, and I'm going to give you if you like that.
• 1:30:40
  Speaker 2: So my wife finds these really interesting. She got me
• 1:30:43
  Speaker 2: into Killing Eve, which was a little more spyfair than Diplomacy.
• 1:30:50
  Speaker 2: And if you get a chance to watch Slow Horses.
• 1:30:52
  Speaker 3: Oh that's the one I've got to see.
• 1:30:53
  Speaker 2: Yeah, it's really so the first season is great and
• 1:30:57
  Speaker 2: people will told me the most recent season it's some
• 1:31:01
  Speaker 2: people will say I don't like I know people who
• 1:31:04
  Speaker 2: loved it all the way through, but it's it's really
• 1:31:09
  Speaker 2: it's really an interesting, well told.
• 1:31:12
  Speaker 3: Beautiful cast, fantastic.
• 1:31:13
  Speaker 2: Yeah, you'll you'll love that. Our final two questions, Yeah,
• 1:31:19
  Speaker 2: what sort of advice would you give a recent college
• 1:31:21
  Speaker 2: grad interested in either investing or working with large language models,
• 1:31:29
  Speaker 2: working with narrative analysis and artificial intelligence.
• 1:31:35
  Speaker 3: Don't I say that tongue in cheek.
• 1:31:40
  Speaker 2: I you had a lot of fits and starts going
• 1:31:42
  Speaker 2: back seven eight years for sure. And I.
• 1:31:50
  Speaker 3: I tell you what, I I think some important you
• 1:31:58
  Speaker 3: have to would you would? I think think you can
• 1:32:00
  Speaker 3: accomplish in academia, either grad school or whatever it is,
• 1:32:04
  Speaker 3: is you build your intellectual capital. And so I think
• 1:32:08
  Speaker 3: a lot of times when you get out of college
• 1:32:13
  Speaker 3: you say, okay, I'm just ready to kind of live
• 1:32:15
  Speaker 3: my life and start something, and you haven't built that
• 1:32:18
  Speaker 3: intellectual capital yet. The issue, though, is that once you enter,
• 1:32:24
  Speaker 3: particularly the investment world, where if you're responsible for managing
• 1:32:28
  Speaker 3: other people's money. Brother, that's it, right, I mean, you're
• 1:32:34
  Speaker 3: you're there's there's not you. That's got to be your
• 1:32:39
  Speaker 3: your total focus. You are spending your intellectual capital. You're
• 1:32:45
  Speaker 3: not gaining intellectual capital once you take on a role
• 1:32:49
  Speaker 3: like that. So I my first advice is find a
• 1:32:54
  Speaker 3: path where and that's often in in in academia where
• 1:32:58
  Speaker 3: you're building intellectual capital, because once you leave that environment,
• 1:33:02
  Speaker 3: then you're spending it down you're spending it down. Make
• 1:33:05
  Speaker 3: sen spending it down.
• 1:33:06
  Speaker 2: Our final question, what do you know about the world
• 1:33:09
  Speaker 2: of filling the blank? Investing? Data, analytics, narrative storytelling today
• 1:33:16
  Speaker 2: would have been helpful twenty five to thirty years ago.
• 1:33:22
  Speaker 3: And investing. I wish I had understood the role of
• 1:33:34
  Speaker 3: at least step back a second. I think that whether
• 1:33:36
  Speaker 3: you're talking about data or whether you're talking about investing.
• 1:33:42
  Speaker 3: And I'll speak for myself, but I think there are
• 1:33:44
  Speaker 3: a lot of people like me. We think there's an
• 1:33:46
  Speaker 3: answer with a capital a huh right there in the
• 1:33:50
  Speaker 3: in the numbers, and if you just look hard enough,
• 1:33:53
  Speaker 3: and if you work hard enough, you'll find that answer. Right,
• 1:33:58
  Speaker 3: you'll find the secret the secret formula.
• 1:34:00
  Speaker 2: And you have learned since then.
• 1:34:03
  Speaker 3: Ain't no such thing? Right now, There is magic and
• 1:34:07
  Speaker 3: there are patterns, but it's it's it's not in the
• 1:34:10
  Speaker 3: structured data. It's not in the numbers. It's actually in
• 1:34:15
  Speaker 3: the error. It's in the probabilitabilities. It's in we're calling,
• 1:34:19
  Speaker 3: you know, the stochastic element, right, the role of chance,
• 1:34:25
  Speaker 3: and understanding that there are patterns and there's real magic,
• 1:34:29
  Speaker 3: and understanding that I didn't get that when I was
• 1:34:34
  Speaker 3: either starting with data analysis or with investing. I was
• 1:34:38
  Speaker 3: looking for the answer with the capital A as opposed
• 1:34:41
  Speaker 3: to a process with a capital P.
• 1:34:43
  Speaker 2: I love that answer because so my exercise in confirmation
• 1:34:49
  Speaker 2: bias is I love the fact that you looking back,
• 1:34:54
  Speaker 2: we all have the benefit of hindsight. Yeah, and unfortunately
• 1:34:58
  Speaker 2: that can be a bias to certain p people. But
• 1:35:00
  Speaker 2: when you're looking back at it, you know how it happens.
• 1:35:04
  Speaker 2: At the moment when you don't know what the outcome is,
• 1:35:08
  Speaker 2: thinking about it probabilistically is a much healthier approach than saying,
• 1:35:14
  Speaker 2: here's a binary up or down, yes or no, and
• 1:35:16
  Speaker 2: I'm either right or wrong. And I see people struggle
• 1:35:20
  Speaker 2: with that constantly, constantly.
• 1:35:22
  Speaker 3: When first started investing, we're again late. I got two
• 1:35:25
  Speaker 3: pieces of good advice right. One was never go all in, right,
• 1:35:31
  Speaker 3: which is which is really interesting because it's this, this
• 1:35:35
  Speaker 3: business of investing. It's a wonderful life. You were solving puzzles,
• 1:35:39
  Speaker 3: we meet interesting people, we get to have these sort
• 1:35:41
  Speaker 3: of conversations. It's a it's a career for a lifetime.
• 1:35:46
  Speaker 3: And the two pieces of advice early it never go
• 1:35:51
  Speaker 3: all in. And also, reputation is absolutely the most important thing.
• 1:35:59
  Speaker 3: And again, especially when you're young, you think, oh, well,
• 1:36:02
  Speaker 3: that's not the most important thing. You know, being right's
• 1:36:05
  Speaker 3: the most important thing. It ain't. It's playing the long
• 1:36:09
  Speaker 3: game here would you want to? It's never go all
• 1:36:13
  Speaker 3: in and the right never.
• 1:36:17
  Speaker 2: By never go in, you mean never put everything at
• 1:36:21
  Speaker 2: risks so that if it doesn't work out.
• 1:36:23
  Speaker 3: You're you're out of the game. You're stay in the game.
• 1:36:26
  Speaker 2: Gerald Loeb's book The Battle for Investment Survival.
• 1:36:29
  Speaker 3: Stay in the game, avoid that risk of ruin. You
• 1:36:33
  Speaker 3: just or don't ever risk ruin. That makes sense because
• 1:36:37
  Speaker 3: once you don't think in those terms.
• 1:36:39
  Speaker 2: But that's really it.
• 1:36:40
  Speaker 3: All in means risk of ruin. It's the risk of ruin.
• 1:36:46
  Speaker 3: And it also connects with the reputation because once.
• 1:36:49
  Speaker 2: You once you blow up, it's tough to go back
• 1:36:53
  Speaker 2: and you start.
• 1:36:54
  Speaker 3: Saying, oh, I can fix it by taking this shortcut
• 1:36:57
  Speaker 3: or doing this other thing, and once you do that,
• 1:37:01
  Speaker 3: it never ends well all right, and you tell yourself, oh,
• 1:37:05
  Speaker 3: just this one time. It's never this one time.
• 1:37:07
  Speaker 2: Ben, This has been absolutely delightful. I'm so glad we've
• 1:37:11
  Speaker 2: finally too gone around to doing this. I'm just entranced
• 1:37:17
  Speaker 2: by your thought process. Some of the things like I
• 1:37:21
  Speaker 2: love reading stuff of yours that I totally disagree with,
• 1:37:25
  Speaker 2: and then because it forces me to say, well, he's
• 1:37:28
  Speaker 2: not just making this up. I know how your brain works,
• 1:37:32
  Speaker 2: and it's like, all right, if Ben is saying this,
• 1:37:35
  Speaker 2: then I'm going to make this my worst case because
• 1:37:38
  Speaker 2: it's easy to dismiss it. Everybody is out seeking confirming
• 1:37:42
  Speaker 2: information and rather than being dismissive of it, all right,
• 1:37:47
  Speaker 2: you claim to think probabilistically, where does this fit into
• 1:37:50
  Speaker 2: the range of probabilities? And once you start thinking in
• 1:37:53
  Speaker 2: those terms, it's like, oh, so the worst case scenario
• 1:37:56
  Speaker 2: is worse than my worst case scenario. I got to
• 1:38:00
  Speaker 2: move the bottom of my range down further because if
• 1:38:03
  Speaker 2: this goes off the rails, this is really bad. That's
• 1:38:06
  Speaker 2: what your past americanopies did with me, and really it
• 1:38:10
  Speaker 2: really helped me figure out how to think about especially
• 1:38:14
  Speaker 2: in that week between April twond and ninth where everyone
• 1:38:18
  Speaker 2: was losing their mind. It's like, oh, no, so they're
• 1:38:21
  Speaker 2: losing the mind because hey, this could happen, but maybe
• 1:38:24
  Speaker 2: something good comes out of it.
• 1:38:26
  Speaker 3: Trying to avoid tunnel vision, I think is so important
• 1:38:29
  Speaker 3: in our business or any business, but especially.
• 1:38:31
  Speaker 2: In our business in particular. Well, thank you for being
• 1:38:35
  Speaker 2: so generous with your time. We have been speaking with
• 1:38:38
  Speaker 2: Ben Hunts. He is the co founder and president of Prescients,
• 1:38:42
  Speaker 2: and you can find his writing at Epsilon Theory. If
• 1:38:46
  Speaker 2: you enjoy this conversation, well check out any of the
• 1:38:49
  Speaker 2: five hundred and ninety three we've done over the past
• 1:38:53
  Speaker 2: eleven and a half years. You can find those at Bloomberg, iTunes, Spotify,
• 1:38:59
  Speaker 2: you two Tube, wherever you find your favorite podcasts. Be
• 1:39:03
  Speaker 2: sure and check out my new book, How Not to
• 1:39:06
  Speaker 2: Invest The Ideas, numbers and behaviors that destroy Wealth and
• 1:39:10
  Speaker 2: How to Avoid Them at your favorite bookseller. I would
• 1:39:13
  Speaker 2: be remiss if I not thank the crack staff that
• 1:39:16
  Speaker 2: helps put these conversations together each week. Alexis Noriega is
• 1:39:21
  Speaker 2: my video producer. Sean Russo is my researcher. Anna Luke
• 1:39:26
  Speaker 2: is my podcast producer. Sage Bauman is the head of
• 1:39:30
  Speaker 2: podcasts here at Bloomberg. I'm Barry Retults. You've been listening
• 1:39:35
  Speaker 2: to Masters in Business on Bloomberg Radio