E (3:18)

You know, it's interesting. So academia was always a. I'll call it a way station. For me it ended up being a 10 year, 10 year weigh station plus grad school. But I.

D (3:31)

That's a little more than a way station.

E (3:32)

It's a little more than a way station, but I bet this will be familiar for a lot of your listeners. I always had an entrepreneurial bug. You know, I started my first company when I was in grad school, started another one when I was. When I was a professor. And as I know you know, a lot of your listeners, viewers know it is a bug, it's not a feature.

D (3:55)

Yes, for sure.

E (3:56)

You can't help yourself.

D (3:58)

So let me ask you a question about that.

E (3:59)

And academia is not the place to be, for sure.

D (4:02)

So I know why I've started a series of companies. I can't work for other people. Why did you have that bug? What motivated you to say, I gotta get this out into the world?

E (4:16)

I love playing games and solving problems. I have a similar issue about working for other people, which fortunately, academia solves that to a large degree. I mean, you are working on your own stuff. You follow your own intellectual bliss in a way that I've really never rediscovered. The problem with academia, of course, is it's very, very low stakes. It don't pay.

D (4:46)

That's why. That's why the academic fights are so.

E (4:49)

Vicious, because there's nothing at stake. And that is actually true. That's actually true. And so you learn survival techniques and that kind of jungle where nothing is really at stake, at least monetarily. Because the goal of any sort of academia conference or presentation like is to appear smart, right? It's not to actually be smart. You're not actually listening to a presentation to listen to it. What you learn to do is you're listening to the presentation the whole time. You're trying to calculate in your head what's the most devastating question I can ask.

D (5:31)

So you're gonna rock this guy back on his heels with a devastating.

E (5:35)

A devastating question. And boy, that gets old after a while. Barry, I gotta tell you, it really does. I loved the teaching, I loved the research. Cause like I say, nobody tells you what to work on. But the church of academia, the actual institution of academia A. It's for the birds, even Back when I was doing it. And I think it's gotten significantly worse.

D (6:04)

I can imagine. I can imagine what's happening with that. But the question that this leads me to is you've had all these jobs within the world of finance. How did your background in academia shape how you view investing? Risk management allocation?

E (6:21)

Barry, Starting from academia and then getting into our business of investing, I think it was the best thing that could have happened for me for when I got into it, which was kind of later in life. Right. Because, you know, after I left academia, finally start a software company. And it. After we sold that software company. Yeah. A buddy of mine, I think this happens a lot. A buddy of, you know, you have.

D (6:52)

A buddy who's, hey, you seem to be pretty smart. How would you like to apply.

E (6:55)

We're always, we're always talking about company X or technology Y. Why, you know, why don't you come in? Let's. Let's give this a try. So that was my path, if you want to call it a path. And what really sold me on it was that markets, it's the biggest game in the world, for sure. And like I say, I'm a game player.

D (7:20)

I love and a game theorist. Let's work down that.

E (7:24)

Well, I don't like to talk about that because, you know, because the.

D (7:27)

The real game theorists get angry.

E (7:29)

Well, yes, and I understand. I am a real one because that was my field for a while and it's a real field and it's a real thing. But it's been so trivialized when some talking head will come on, well, let's look at the game theory of this. And you just want to just shoot yourself when somebody does this.

D (7:50)

So the other part of your research, the other part of your academic focus was on narrative theory. And so let's talk about how did that focus develop? And we'll talk a little later about what you do today at Perseant with the narrative machine.

E (8:09)

But believe it or not, it all ties together.

D (8:12)

I don't doubt that for a second. What initially led you down that rabbit hole?

E (8:18)

When we think about kind of who's been an influence on you in your life. At a very influential undergrad professor in political science. And then I had a very influential graduate advisor. Again, they don't call it political science up at Harvard. They call it government or something like that, but it's political science. And the reason I say they were influential is that they really got me focused on the science side of political science. And that science side. Yes, it's kind of Some of the typical terrible stuff you see in all social sciences like economics, where you've got to learn how to deal with structured data. There was a lot to learn. It's worth talking about because I see the same mistakes being made over and over again by people in our business who want to try to, you know, apply math to data. And there are some real pitfalls and some, some real intellectual capital I think that you can achieve within academia that you can then bring in and apply to the investment world.

D (9:44)

Well, we've certainly seen amongst the quants a very successful application of math theory to data. In fact, some of the best performing hedge funds are quantitatively driven. That's not where you're going.

E (10:02)

Well, it's, it's part of where I'm going. Right, so, so there, there, there's a transition in all of, all of the sciences, honestly, but certainly the social sciences where, where, yes, you start with numbers, structured numbers, price over time, volatile things you can calculate and measure as those numbers. But what was clear immediately in politics, and I think has become increasingly clear in the world of investing, is that it's not just the numbers that you get on your Bloomberg terminal, it's also the words and the stories and the narratives that are told to us. Politicians have known this forever, right? So the story of politics is the story of people suggesting laws or policies and then having to present it in a way that gets them elected or keeps them in power or whatever that is. So there's always been a focus, I'll say more of a focus in political science than in economics. With words, economics is almost seen as a sideline, right? It's somehow lesser than the numbers. So what I was kind of early on was applying the same techniques that we have for understanding matrices and structured data, but applying it to unstructured data, which, you know, full circle. This is at the heart of all of the generative AI and the AI.

D (11:51)

Well, you're getting way ahead of me now with generative AI, we'll circle back.

E (11:55)

To that, but it's all the math has not changed in 35 years since I started working with network math around unstructured data. I mean, we didn't call it natural language processing back then and we didn't call it, you know, large language modeling, but that's exactly what we were doing.

D (12:17)

So what was, what was the moment or the catalyst for you to say, hey, I'm working in all these other areas? But the narratives continue to pop up over and over again on all sorts of different data sets. And I Think in the financial markets. I can use a novel approach to identifying narratives and anticipate where the market's going. What led to that sort of insight?

E (12:50)

Not the Great Recession. Right. But the AfterMath, meaning the 2010s following.

D (12:56)

The great financial crisis starting in 2009.

E (13:00)

And the recovery that we had out of 2009. And in particular when Ben Bernanke and the Federal Reserve moved towards a very explicit effort to use their words to impact markets.

D (13:18)

So let's talk a little bit about that. Cause I have some really specific memories of the low, of the run up afterwards, all the noise that was going on. Some of the phrases that have come out of that era like financial repression and other such things are just the tip of the narrative iceberg. So. So walk us through your insight. It's 2009, the market bottoms really kind of a V bottom and took off from that. What was that? March 9th, March 7th, something like that. Oh, nine. And there was no turning back. What were you seeing? How were you integrating that into a concept of let's identify narratives in order to anticipate market moves?

E (14:11)

Well, so I co founded a long short fund inside of a larger asset manager. Going back in 05, 05, 06, 07, we did well like everyone else did well. And then in 08 we did great.

D (14:32)

Really. You know, we did, I think want to say S and P down 37%.

E (14:36)

Yeah, we were up 20 something net.

D (14:38)

Anything in the green, not in the red is.

E (14:40)

Yeah, amazing. Now I'll tell you and we can come back to this. Like the real question you should ask is that given what we believed, why wasn't. Why weren't we up 40%? That's actually a question you can ask.

D (14:56)

But I'm going to say a 47% relative price swing. I'll take.

E (15:04)

Had a great year in 08.

D (15:07)

Did that continue in 09?

E (15:09)

Flatlined from 09. So from March of 09. So we did well in January first quarter, February in the first quarter, the rest from March of 09. Our returns flatlined. So we never lost money for our clients and our funds.

D (15:25)

But you didn't catch that recovery.

E (15:27)

That did not catch the recovery. Absolutely did not. And the recovery was interesting. Right? You're right. There was a V, but there were starts and stops to it. So the big move up from the bottom in late March, going into April, it's like, all right, that actually we caught a little bit of that and that made sense. Right. There was a second leg to the rally. Oh, for sure. And then in June In June, ferocious rally.

D (16:03)

Ferocious is the right word, but June.

E (16:05)

In particular was a classic crap rally. Right. It was a low quality, low quality stuff. Right. The end of March, going into April rally, it was. Right. This makes sense, right? We've bottomed Fed the June rally. No, no, it was. We didn't, we didn't touch any of that.

D (16:27)

Oh, that's fascinating.

E (16:29)

Go back, go back and look at it. Right. And let me tell you, I don't.

D (16:32)

Just so you know, I have a vivid recollection of chatting with Jim Bianco about this. And we were both bullish, but for completely different reasons. To me, anytime US Equity markets are cut in half, I'm a buyer. And people say 1929, I'm like, great. You gotta go back a century to find the exception that proves the rule. But Jim was early on in the Tina trade. Hey, the Fed has made everything cash, trash bonds, they're forcing you into equities. Which is what led to my post, which everybody stole the line, this is the most hated bull market in history. And I wrote that up, I sent that out and I heard everybody borrow that. But I'm curious as to where the June rally took you.

E (17:19)

Well, and this is where I'm going about the role of forward guidance in Jim Bianco's point about. Because what the Fed did wasn't just its policies around interest rates. You know, they took them to zero, and that's where we stayed.

D (17:41)

Started buying mortgage backs and they did.

E (17:44)

Balance balance sheet operations.

D (17:46)

Right, Right. Quantitative easing.

E (17:47)

Actual. You know, actually, and I. Look, I, I think QE1, I think it saved the world. This is what.

D (17:53)

And what was that? A trillion dollars? Something.

E (17:55)

Something. Yeah, something.

D (17:56)

800 billion.

E (17:57)

Something like 800 billion.

D (17:58)

Unthinkable number.

E (17:59)

So I, I think that. So those were specific actions took. But even if you people often say things when they're leaving office. So Bernanke's last speech, his valedictory address.

D (18:12)

Right. More honest than intended.

E (18:15)

Much more so. And you see this all the time, right? George Washington leaving office. I was gonna say Eisenhower.

D (18:21)

Yeah, that's a big.

E (18:21)

I mean, when freaking Eisenhower warns you against the defense industrial complex.

D (18:30)

I'm just saying General like, to you, that's.

E (18:32)

General like to me, what Bernanke said when he's leaving his terms of office. He said, look, we had two toolkits. One was traditional stuff, interest rates down to zero. At the time, we didn't know we could have negative interest rates. So, you know, that's where we were. Second were the balance sheet operations. Large scale asset purchases. QE quantitative easing. They said, you know, QE1 was great, did what we hoped it would do. QE2, eh, Operation Twist, QE3. This is Bernanke saying, mind you, he says, I actually think that might have been a little counterproductive. He said, but we had another toolkit and that was our communication policy. That was forward guidance that we started using our words not to communicate to the market what we actually felt. We started using our words and coordinating our words to change the market, to change market behavior. This is what I mean about making a conscious effort to tell a story. It's not that it was necessarily lying, but they were using their words and choosing their words for effect, to shape.

D (19:53)

Perception of their underlying behavior, to shape market behavior.

E (19:58)

And he said that worked better than we had any hope that it could. And that's where we are now.

D (20:07)

So for the youngins listening, I have to point out, and you and I are old enough to remember back in the days where there were no minutes released, there wasn't an announcement, forget a press conference. You had to be watching the bond market to figure out what the Fed just did. Like today there's. We're holding the, having this conversation. There was a Fed, the October meeting, a quarter point rate cut. A conversation about all sorts of stuff. I really didn't pay a lot of attention to it. Lack of clarity, no data, blah, blah, blah.

E (20:44)

Worse than that, Greenspan would be intentionally vague and obtuse.

D (20:49)

If you understood what I said then. Then you misinterpret it, right?

E (20:54)

Exactly.

D (20:54)

If you think you understand what I'm saying.

E (20:57)

You know who led the committee to make all that change? Janet Yellen. When she was vice chair. Yep.

D (21:03)

Back in. During the financial crisis.

E (21:05)

Yep. So this was, it was a concerted effort. Bernanke, Yellen to. This is when they also started going putting all the Fed governors on a common calendar.

D (21:16)

Right.

E (21:17)

And when they started and assigning the fake. You're going to speak this day, you're going to going to speak that day. That's when all this started. It was an intentional effort. And again, this is something that politicians have known forever. Right. Politicians craft the message and use their words. So I knew the tools to try to understand this. But what I wasn't prepared for was how, and neither was Bernanke was how powerful this would become to the point where today it's not just central bankers using their words as their main policy toolkit, but it's every CEO. It's every CEO now you go on this network or one of the other networks. And what makes for a good CEO is can you tell the story, can you tell the narrative of your company to get a multiple? Because a multiple is a narrative. A multiple is a story. Well, stories.

D (22:17)

Look at some of the most successful CEOs throughout history. I would throw Jack Welsh into that pile because he was a fabulist. Fabulist. He was a fabulous fabulist because the stories he told were great right up until the point where we found out that he was running a hedge fund with G Capital. And they magically always beat by a penny.

E (22:41)

So I remember vividly when GE was coming to our shop, What they wanted was a financial multiple, right? So they were making. They wanted.

D (22:57)

Even though they're an old world industrial.

E (22:59)

Even though they're an industrial. This was. They wanted to tell a story that they should be seen as and get the multiple of a financial. That's what GE was all about in those years leading up to the gfc. So my poster child for this is Mark Benioff at Salesforce because he's. You often see this with people who come out of sales like Mark did.

D (23:32)

But it's all about storytelling.

E (23:34)

It's all about storytelling.

D (23:36)

And what isn't that true for go through the great look. Steve Jobs, Reed Hastings, Larry Ellison at Oracle to some degree, Steve Ballmer at Microsoft, who wasn't a great CEO, but he was a great cheerleader and a great storyteller.

E (23:56)

Great storyteller. What all of those companies have in common is that they're great storytellers. And those are trillion dollar companies today. What I would say to you is that you don't remember or hear about the companies that did not have CEOs who are great storytellers.

D (24:15)

Well, Ken Lay was a great storyteller until you found out that it was all nonsense. And you could say the same thing about folks like Bernie Madoff.

E (24:24)

There are a lot of stories that get told that are not true. Right. And I think even today, people think of this word narrative, they have a pejorative sense to it. It's like, oh, really?

D (24:40)

That's interesting.

E (24:40)

I didn't think of it that way. For sure. That's just your narrative, man, you know, the big Lebowski. And it's not that a story is a lie, it's that the story is constructed for effect and it's presented to you as if this is my true and inner thoughts. But the construction of the intentionality behind these stories. Phenomenal. Benioff, for example, created the metrics by which he wanted salesforce to be judged. Not metrics of profitability, but metrics of what he called pro forma net revenue growth, whatever the hell that means. Right. Because if you can construct the story, you can construct it in a way that, yes, I can beat and raise pretty much every quarter. So there were three, I think, big changes that happened to make the role of narrative overwhelming as it is today. Whereas before, it's always been there. To your point, it's always been there. Today, it's overwhelming. And I think it's not just the success that first central bankers and then CEOs. I mean, Wall Street's the greatest copying machine. Wall street copies what works.

D (26:06)

Sure.

E (26:06)

So when you see that something's working, they're getting a multiple by telling the story and going on Kramer four times a year.

D (26:14)

It's endless iteration. You're just constantly tweaking it. And if it works, do more of it. And if it doesn't, toss it out.

E (26:22)

So it was the, the fact that it works to tell a story and people got good at telling stories. It's the growth of 24 7. I'm going to use air quotes here. I'm glad we're taping this.

D (26:35)

Well, it's meeting social media news right now. News like News Light.

E (26:40)

That wasn't the case.

D (26:42)

So I want to, I want to annotate what you said slightly, because I think CEOs have always been storytellers, but they were storytellers to their boards, to their employees, to their shareholders.

E (26:56)

Correct.

D (26:56)

Always the. You're hitting now on the modern world of 24. 7 media. Telling a story in a boardroom is very different than sitting in a TV studio and talking about, hey, here's why our new chip is going to catch up to Nvidia and it's the greatest thing ever. Yeah, that's a different skill set.

E (27:16)

It changes the time horizon. It is a very different skill set because. Because you're not telling the story of, oh, I'm getting another turn of leverage in our operations or our capacity utilization in this factory went up by 5%. Which are the kind of stories you would tell even on earnings call or certainly to a board. Now, this is the story where you've.

D (27:40)

Got a segment a little bit.

E (27:42)

You've got a segment you're going on. Kramer, you gotta get him to say, bye, bye, bye. You got at most four minutes.

D (27:49)

Right, Right.

E (27:50)

How are you going to tell that story that sings to that audience? Enormous change. Change. Structural change in our media. Both quote, unquote, news media, but also financial news media. The Wall Street Journal Today is a 24, 7 news financial news organization.

D (28:12)

What is it? New York Times, Bloomberg, the Washington Post. They all have websites that get updated around the clock.

E (28:18)

And here's the thing. There's not enough hard news to fill the time or to fill the space. So what takes the space? Opinion.

D (28:28)

Story you are channeling.

E (28:29)

Story takes the place.

D (28:30)

You are channeling Michael Crichton from 25 years ago. Most of what you see in the media is speculation, opinion and theory, not news.

E (28:42)

I've written so much about Crichton and his.

D (28:44)

I know, that's why I threw that back to you.

E (28:46)

He says he was so far ahead.

D (28:49)

A quarter century ahead of what took place.

E (28:52)

And there's a third piece, though.

D (28:55)

Give us the third piece before we go to our next segment.

E (28:58)

Third piece that's changed everything is our.

D (29:00)

Smartphones that you're walking around with not only a studio, but a dopamine device that you're constantly.

E (29:11)

It's my dopamine machine. And we do it to ourselves. It's not that someone forces us to hear these stories over and over again. We do it to ourselves. I mean, I get a little nervous if I, you know, Pat, where's my phone? Where's my phone? I get a little nervous. And it is absolutely a neurotransmitter addiction. I think it's so important to keep that from our kids. That's a whole nother thing.

D (29:41)

There's a whole depression situation with teenagers today and it all traces back to the phone and social media.

E (29:47)

These are three, I think, real secular changes we've had. Markets become this political utility, the success of constructing a story, structural changes in social media and the devices that we insist on carrying with ourselves all the time.

D (30:11)

Absolutely fascinating. Coming up, we continue our conversation with Ben Hunt, president and co founder of Perseant, explaining how he's using AI to identify narratives in real time. I'm Barry Rithol. You're listening to Masters in Business on Bloomberg Radio.

A (30:39)

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D (32:21)

I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My special guest this week is Ben Hunt. He is a academic fund manager, risk manager, entrepreneur, tech startup person. He is currently co founder and president at Perseant, which applies AI tools to map and measure market narratives in real time. It's really more than market narratives. It's politics, it's economics, it's markets. You cover a whole lot of stuff 100%.

E (32:54)

So what we're able to do today, and this is the crazy change in the world back from when I was doing this on microfiche back in 1980s. Yeah, exactly. We get. We have access to everything that's published publicly in the world. And there are a couple of big data aggregators. Dow Jones One, LexisNexis another. Everything that gets published in the world. All these languages, it's available to you and it's not cheap, but it's not crazy expensive like it used to be. And it's always getting cheaper. So we're able to take everything in the world that gets published. All the newspapers, all the websites, all the transcripts. Everything that's published publicly we can pull in and then we can process it. Process it with really it's the same math that I was using 30 years ago. Nobody's invented cold fusion here, but the.

D (33:59)

Software tools are faster, stronger, better and infinite.

E (34:03)

So the calculations here are not particularly complicated, but you have to do them at enormous scale.

D (34:11)

It's a ton of volume.

E (34:12)

So I mean it's crazy.

D (34:17)

Just the scale of the petabytes, terabytes. Just crazy.

E (34:20)

I mean. Yeah. In the last couple of months We've processed over 200 billion tokens. Billion.

D (34:28)

And a token is how much A.

E (34:30)

Token is like a word or a phrase. And so that's the kind of the unit that you talk about when you're putting through. When you're putting something through. A linguistic calculator, which is what all of the. All the LLMs are. They're linguistic calculators. And so we've processed several hundred billion tokens. Again, it's not complex, but it is at scale. And what we're doing with that is we're reading the world's news to understand the world's stories and narratives. And you're right, it's much bigger than. Or it's much more focused. We have much higher resolution than just saying, I'm bullish on financials. I mean, that's a narrative, sure, but there are 20 different variations of that. You're bullish on financials, why.

D (35:30)

So wait, let me.

E (35:31)

Let me track all those. We can track all those stories and how they wax and wane over time.

D (35:35)

So let me roll back to. I want you to explain what Persian is. Who are the clients? I don't mean names, but what type of clients do you have and what do they do with Persean's output?

E (35:50)

So we started Perseant in 2018. My partner from. We were at an asset manager spun out there to take the technology that I've been working on for years and really been writing about with epsilon theory. So we started that in 2018 to do the basic research into processing enormous amounts of financial news data and to track the stories and how they rise and fall and wax and wane over time. That was the story.

D (36:29)

So I love that description because there are a lot of trades going on where the storyline changes on a regular basis. Probably the Mac daddy of that is crypto. First it's, hey, you know, there's fiat currency. This is outside of the system. It's defi. Then it's a hedge for deflation, Then it's a hedge for inflation. Now it's scarcity and digital gold.

E (36:55)

Right?

D (36:55)

Digital gold.

E (36:57)

There are no fundamentals, right, with crypto. And people will say there are, but there aren't. Right? It's driven by the waxing and waning of stories. And do they find purchase? Do they? Or do they kind of. People get tired of them?

D (37:14)

Well, they got tired of the Defi story. And then once JP Morgan and BlackRock started creating, like, the iBIT is the fastest ETF to $100 billion. And so the old story of Defi is gone and the new story is, oh no, this is an asset class that Wall Street's embracing. That's why you have to own it.

E (37:36)

That story, that story was, that was a very similar story, by the way, or transition in story from physical gold to GLD when that ETF came out, which was a very similar pattern because once it became a table at the Wall street casino, then it takes on a different meaning specifically around gold. Gold changed from being, okay, something that you bury in your backyard or you have in your vault along with ammo and seeds for when the hard times come.

D (38:14)

Bottled water, meals ready to eat. Gold and lead.

E (38:17)

It becomes a security and its meaning changes from that apocalyptic bottled water. And the meaning of gold today is as an insurance policy, a security against central bank error or government error. That's the meaning of gold today.

D (38:38)

Is that the dominant narrative that you're identifying as gold rallied over through 4,000?

E (38:45)

Absolutely. So we've really been able to track that one specifically.

D (38:49)

So here's the really big. Here's the million dollar or trillion dollar question and how do you identify a narrative and say, oh, gold is going to double from here based on this narrative? Or are we not there yet?

E (39:03)

No, you identify the narratives because the narrative, the stories don't ever change. So the story leave gold aside for a while. Think about, we're talking about. You're bullish on company xyz because there are about, I don't know, depending on how again, how finely you want to resolve that. There are only about a dozen stories for why you're bullish on something. They can be management change, top line growth, opportunity, consolidation in the industry, upcoming Catalyst Catalyst. So every catalyst story, new products, new.

D (39:42)

Products, FDA's going to prove the new drug, new molecule.

E (39:46)

So that story, you just change the name. That's the same story that's repeated over and over again about any pharma or biotech company. The stories, we think that they're amorphous and variable. The fact is that the core of the story, what we call the semantic signature, the meaning of a story, they're amazingly constant over time. So what we're looking for is for it can be dormant for a long time. But what you want to know is when that story starts picking up again, when someone starts playing that story, when it appears on Kramer and starts happening in the financial press, that's the stuff we can pick up with real precision. So it's both the stories that are starting to fade. But I think the really interesting stories are the stories that have been dormant for a long Time. And they start picking up again.

D (40:52)

You are reminding me of Campbell's hero's journey, that there's only so many.

E (40:57)

My hero. Right. There are only so many stories in the world. And that's a. Now I like to talk about. In Hollywood, famously, there are only like five scripts.

D (41:11)

That's right.

E (41:11)

Right. Tolstoy is supposedly Tolstoy. He said there are only two stories that a man goes on a journey or a stranger comes to town. Those are the only two stories in the world.

D (41:23)

Not quite. But he's not quite.

E (41:25)

But you're on the right track.

D (41:26)

Right? Right.

E (41:28)

The point is there's a finite number of stories you can drill down. So you can get a couple of dozen about any sector you want to talk about or like. But it's a finite number. And so what we do and what I think is really interesting is to track that finite number of stories. And you're right, it's not just around markets. We track several thousand of these stories today.

D (41:54)

So let's delve into that. So, so how do you. You have this massive database. You're sucking in every news feed, magazine, newspaper, everything that you could quantify and run into a linguistics model. What's the process for analyzing this? How to use artificial intelligence to go through this? And how do you make sense out of that heap of how do you find signal amidst all that noise?

E (42:26)

The crucial thing is you can't just ask AI an open ended question, say, what are the narratives for this company or for this sector? Don't do that. Right. And this is a mistake that people make all the time. They ask open ended questions of ChatGPT or whoever. The problem is. ChatGPT will give you an answer.

D (42:49)

Just not a good one.

E (42:50)

Not a good one. It'll hallucinate a lot. Right. It'll go out, it'll find its own data. The secret to using AI successfully is to take this magic genie, because it's a magic genie, and you stuff it into that bottle. Right. You do not let it out, you constrain it dramatically. You don't let it go out and find data. You give it the data. Crucial thing, you don't allow it to think, you tell it how to think. So the most important step that we do is we don't ask AI what are the narratives that you look at. We tell it. This is human direction. You have to have human control.

D (43:35)

I'm hearing data set is controlled by you as well as the thinking.

E (43:40)

The prompt. Yes. And it's more than prompt.

D (43:44)

Right.

E (43:44)

So the. It includes prompt. But the, the phrase that's used in this world is called not prompt engineering, but context engineering.

D (43:56)

Okay, that makes sense.

E (43:57)

You want to, you want to control everything around the AI because you want to limit it to being that linguistic calculator. You want it to be your operating system. That's really the way. And if you do that, then it will give you the same answer twice for the same inputs and the same question. That's the crucial thing, for it to be consistent, for it to be real signal. So this is a human directed process. You can't ask AI an open ended question. You have to control all the input. You have to control the output, meaning you judge it, you run it back through a different AI system to say, how'd they do? Did they go off the rails here? But the most important thing is you have to give it the scaffolding. You have to give it the skeleton. You have to tell it. These are the thoughts you are allowed to think about, and those are the signatures.

D (44:57)

I've kind of learned I have to avoid asking questions that have a human emotional subtext, like, tell me what was most surprising about this. It doesn't know what a surprise is. Tell me what was most interesting about this? It's not able to do that. You really have to treat it like it's a dumb machine.

E (45:21)

Well, that's right. This is what I mean. You treat it. You need to treat it as an operating system. You need to constrain every bit about it, particularly in how you allow it to think because it wants to please you so badly. It does. So if you ask it what's interesting, it will look back at its history of communication with you and it'll think, what will Barry find interesting? And it will give that answer to you. And if it can't find it easily, it'll make it up. It'll make up an answer that you will find interesting.

D (45:51)

I find when I try and prompt, hey, tell me about Ben Hunt's background and give me the timeline of his career that it's good at. Hey, what was Ben Hunt really good at? It has got no idea.

E (46:08)

So what you're able to do, if you're able to again, put the genie in the bottle, tell it how to think about a problem is you're able to identify. And this does go back to the work from 35 years ago. The type of stories we tell that we humans tell about stocks or politics. We tell two types of stories. We tell descriptive stories. The Fed cut rates by 25 basis points today. A descriptive story, but and we can also tell the description.

D (46:48)

It's the because clause. Because we're seeing slowing, increasing layoffs and slowing consumer demand.

E (46:56)

And that's high resolution and very descriptive. Right. Powell was surprisingly hawkish today. And he was. That's a description. There's another type of story we tell Barry, and that's prescriptive.

D (47:16)

Meaning.

E (47:17)

Meaning the Fed should be hawkish. The Fed should cut by 25 basis points. Those are the stories that are indicative of an effort being made to move public opinion in a certain direction. That's like the forward guidance that the Fed still does. Right. Using their words for effect. They're using words to nudge you and how you should think about the world. How to think about the world. So the crucial thing when we're doing these, when we're asking the AI to here's all the text in the world. Here are the stories that we want you to identify. We can also boil that down into identify the stories that are trying to tell the reader how they should think or how policy should go. That we find has a lot of predictive capability to it.

D (48:22)

So you're in the business of analyzing the world's narratives every day. How is that even possible? It seems like things. That is an impossible.

E (48:32)

Crazy.

D (48:33)

Crazy.

E (48:34)

Right. And it used to be. It used to be crazy. I mean, Barry, I really do remember in the academic days, I would literally hire grad students and give them a cup of dimes. So they go down to the microfiche machine.

D (48:49)

I remember those machines.

E (48:50)

Remember those machines.

D (48:51)

Yes.

E (48:53)

I would code, hand code or hand record the coded data. I would type it into remote access for a digital equipment mini frame, and the next day something would churn out for me. Today they say we're processing hundreds of billions of tokens. We get millions of documents overnight like that, and we have infinite computing resources available to us.

D (49:23)

Is there anything you can't access that you wish you had access to?

E (49:26)

To.

D (49:29)

Any data source?

E (49:31)

So one of the things that's happened on Wall street is that the banks and the sell side have become very jealous of their publications because they tend to think, and they're wrong. That they're good at it. Right. That they're good at analysis. Personally, I don't think they are.

D (49:52)

Well, let's just say some are better.

E (49:54)

Than others, some are better than others, but none of them are really. If there was. I'll call it kind of significant alpha there, they wouldn't be a bank, they'd.

D (50:08)

Be a hedge fund.

E (50:09)

Yeah. They wouldn't be on the sell side now. So I'm interested in reading the sell side research not because I think there's some nugget of truth in there, but because I want to see what they're all talking about.

D (50:21)

Right. It's reflective of if not a consensus, certainly a popular set of ideas.

E (50:27)

And this is a crucial thing to talk about, what we do. I don't know what the truth is, right? I have no idea.

D (50:34)

Does it matter?

E (50:36)

And I don't think it matters. I want to provide this information to people who do have a view on the truth. Let's say you're a value investor, right? You're running a fund, you've got your views, you've done all your homework, you've done your research, you've got a good back test. Yeah, yeah. You've got. So I'd say I've got. Here are the companies where I think I've identified something special, something that's valuable that the market does not recognize. And so I want to buy it. And then I'm just going to wait. I got to wait until one day the market realizes, the market comes to their senses and says, oh wow, that should trade at a higher multiple or a higher price because that special thing that you saw, that source of value, the rest of the world comes to see that. Well, what I can, think I can show you is when the rest of the world starts to wake up to whatever it is you're looking for.

D (51:34)

So you're catching the early lift off the bottom.

E (51:37)

That's the goal when a value investment only works when the market recognizes it. And we are tracking when something like that gets discovered by the market.

D (51:50)

So before Nvidia is 5 trillion, when it starts ramping up to 500 billion. Hey, something's going on here.

E (52:01)

What I found in my experience as an investor is that you make the most money on a trade during what I call the discovery phase of a trend. When the rest of the world wakes up to something that you had noticed and identified before, that's when the money is made. Once it gets out there, then it's reflected.

D (52:23)

Then the market is eventually efficient.

E (52:27)

It's a different risk and reward profile. Let me put it that way. You get, you get a lot more ups and downs post that discovery phase than you do when you're enjoying the discovery phase. It's a lot harder once you have that. Now we may say, oh, but there's this other catalyst. And then say, well, but that discovery.

D (52:49)

Phase is, I'm going to quote Doug Kass, that's the, there's a phrase he uses. It's like a contrarian perspective, a variant perspective. You have some insight that is not widely held.

E (53:06)

Right.

D (53:07)

And the market being mostly kind of sort of eventually efficient. If there's truth, or at least a good story in your variant perspective.

E (53:19)

All it takes is a story.

D (53:20)

It's got to be a good story, though.

E (53:22)

Yeah, it's got to be a. Well, I'll call it. It's got to be a compelling story.

D (53:24)

Right, Right.

E (53:25)

And that's. That's why you're looking for a CEO who can tell that compelling story. You're looking for the ability to tell a compelling story because that is what gives you a multiple. Multiple is story.

D (53:40)

So let me ask you a few more questions on perseant before we go into a few other areas. So first, you've been doing this for seven years. What are you doing today that was unimaginable four, five, six, seven years ago?

E (53:58)

I tell you something, that was unimaginable really two years ago.

D (54:01)

That's amazing.

E (54:01)

And that is the AI. So what we were doing in our early days, we were basically doing small language models. We were making the language models essentially by hand, and we did our first models and our first versions of this, and we licensed it to some big banks. Here's the problem, Barry. We had constructed a net, and it was a pretty good net. I mean, when we would dip it into a data stream, we'd catch a fish, meaning a signal. And the signal, that signal works good hit on the signal that we put up. The problem was we were missing too many damn fish, our net was too small. And we'd say, oh, we got this one. And then we'd look back at whatever it was we were designing the models for and say, well, how did we miss all these other fish? And the answer was the small language model we were constructing. It's incredibly complex if you are building these models without the probabilistic approach that modern LLMs allow you to take. So this is the whole notion of embedded, so that there are a million ways to say, I'm bullish about the management change at company xyz. There are a million ways you can say that. And if you're in the business, as we were, of kind of handcrafting, let's write down all the ways you can say that.

D (55:30)

Oh, really?

E (55:31)

You miss a lot. You've made a small net and what you're trying to.

D (55:37)

And a small data set.

E (55:38)

And it's a pretty small data set. So we rebuilt all of our software again using AI as an operating System, context, engineering, control, how you dole out the text data, how you test it, how you allow it to think. And we thought all right, you know what, we're building a bigger net. I bet we see 5, maybe even 6x improvement in our signal test.

D (56:08)

And what did you end up with?

E (56:09)

Over 100x.

D (56:10)

That's unbelievable.

E (56:11)

Over 100x. And now we've had a multiple of that. Again it is hard to describe. The expansion of the net is in so many different directions. Everything we were doing before was just English language. Right.

D (56:27)

So now you're global. And how many languages are you pulling.

E (56:31)

Into the anything that an AI has been trained on?

D (56:35)

Uh huh.

E (56:36)

We read it and we can say.

D (56:38)

Is that 40 languages, is that like how many languages?

E (56:41)

Well they're only, you know, there are about a dozen languages that are useful in markets.

D (56:46)

So it'll be Japanese, Chinese, a variety of European languages.

E (56:50)

So we can tell Indian. Here's a story, here's the story about Chinese domestic markets. There's a Western story, Western narrative and.

D (57:01)

There'S a domestic Chinese story and the domestic Chinese story which is very different.

E (57:05)

Often it's very different. So we were able, for example picking up, we were able to pick up way before it got picked up in Western press. The demand for luxury goods in China went off a cliff, cliff in last November.

D (57:26)

Listen, you can't go from a double digit GDP to like low to mid single digits and not have it affect especially in a country like that.

E (57:34)

Well but it's interesting, right, because they've had ups and downs on business cycle before. But this is the first time where you saw consumer behavior that really was kind of similar to what you might find in a Western consumer behavior. Point being you didn't hear that from LVMH or the Macau gaming guys until February and we were picking that up in November from the domestic Chinese media.

D (58:01)

So that raises a really interesting question. You know, at the end of the day clients want to be able to make money on your research. How are people putting this to work? How does what you're building help your clients generate alpha?

E (58:18)

What we think we've discovered is an entirely new source of data. And what we're confident is that that we've built the systems that are very different from what you see with this sort of analytics that are out there from anyone else. Right. So this is not sentiment. Right. We're not tracking or using mean words.

D (58:39)

Or nice words by the way, that was a big thing. I don't know is that 10 years ago we're, we're scanning Twitter to identify investor sentiment. Oh, my God.

E (58:47)

I mean, but I was just looking today. And not to pick on Bloomberg, but, you know, it was their live coverage of the Fed statement. They were analyzing the sentences in the Fed statement for hawkish and dovish sentiment.

D (59:05)

How it changed from the last meeting. So you're saying there's not a whole lot of signal there.

E (59:12)

I want to be careful with what I say, which is that there are a number of. I'll call them high frequency stat arb guys, where if it's important for you to note the difference in word choice on a millisecond level, I think you can get something out of that. I do. And so there are firms that can do that very well. That's not our game. This is not the. What we're trying to identify is not just sentiment, not just word choice. This is not Google Trends. And how many times did they mention AI in the earnings report? We're able to track the actual stories that drive behavior.

D (59:58)

So that's the next question, and I'm going to give Dave Notting credit for asking this. Does every narrative turn into a decision? How do you know when something is merely noisy versus where there's a significant tradable signal?

E (60:17)

I don't. Right. So this is. My goal is not to do the trades. My goal is Mr. Hedge Fund Guy, Mr. Asset Allocator. You know China, right? You know your companies, you know your commodities. Here is data that I think you'll find useful. The efficacy of it, though, is up to you. I don't know what you want to do with it. I want to sell the picks and shovels, honestly, for this vast new data set that we all know is important, but we haven't been able to measure it in a very predictive way before.

D (61:02)

So let's talk about a few things related to.

E (61:04)

Can I say one more thing?

D (61:05)

Sure.

E (61:05)

It is not just about using this for investment. So we have a product for financial advisors, which is your client's coming in, you've got a portfolio. You need to be able to say, what is my client? What are they worried about? What are they nervous about? What are they hopeful for? What are the stories they're reading?

D (61:29)

And you're pulling this out of the flow of media.

E (61:32)

And we can tell you exactly for the portfolio you've got for that client. Here's what they're going to be asking about, worried about, and here are the answers you can give them to show. This is when it's happened before, when this story has come up Stay the course. It's going to be fine. This is the sort of stuff we can do for financial advisors.

D (61:49)

Huh? That's really interesting. I would love to see some of that.

E (61:52)

It's not just in markets, Barry, I.

D (61:54)

Got to tell you, the policymakers, corporate executives, thinkers.

E (62:00)

So we did before in the run up to the Russian invasion of Ukraine and we published this on Epsilon theory. And this is my old academic work, getting to war. Before a country starts a war, they mobilize public opinion. Right. And so we were looking at domestic Russian media, looking at saying, friends, this isn't going to be a limited thing.

D (62:25)

This is happening.

E (62:26)

This is happening. It's going to be a full scale invasion because that was the messaging in domestic Russian media to the Russian people. Wow. So brands, right? So you are, you know, we're working with some, it sounds great, but pro sports teams, you want to tell a story to your fan base, Build a stadium, sell some tickets to sell some tickets, build a stadium. Give me some examples of people who've told good stories and how did that work for them? How can we do the same thing? So let's just look at storytelling.

D (63:01)

If Jeff Bezos was a subscriber to this back when he was trying to build a tax funded HQ on the Hudson, had he had your data and you were crunching all the New York City news stories about this, might you have been able to give him advice that he suffered, he suffered such a backlash because you know what we have.

E (63:25)

Today, we have polls which are terrible, which are terrible, which are mostly terrible now, you know, and I love the poly market stuff and other things where you try to get as many people as possible to put money on something, right?

D (63:37)

So you know, when you ask a person a question, you're asking them, hey, what do you think you think and what do you think you're gonna do in the future when we know people are terrible at both of those things.

E (63:48)

Terrible at both of those things.

D (63:49)

But if you put a little money on it, all right, maybe they might be a little more circumspect that, that, that help.

E (63:54)

That helps a ton. So, so in places where you can have them make a bet, I think that improves the kind of information you can get. It still lends itself to a lot of manipulation.

D (64:04)

A lot of they're not issues. Listen, poly markets and calcium, all those things. It's not the bond market, it's not 100 something trillion dollars. It's a couple of bucks on each of these and sometimes a million dollars moves them or half a million Dollars move the market.

E (64:20)

But for a lot of things, let's say you're polling for a political candidate, right? You can't ask them to put money down on something. But you really want to know, these policies that my candidate is thinking about taking on, is that popular? Does it resonate? Is it a compelling story? We can absolutely see if that is true by looking at local media, local social media, all of that. So it's pretty wild, Barry. I mean, once you start looking at how important stories are and once you've got a tool where you can actually measure them and visualize them, it's like, I feel like it was like when they invented whoever it was, Leeuwenhoek or whoever invented the microscope, when you're actually to see something that we all know is there.

D (65:08)

Well, now we do back then, like, remember, germ theory took what, a century to catch on?

E (65:13)

It took a century. And it takes seeing it, right. It takes the instrument to actually measure it before you actually believe in it. And so I feel like that's kind of where we are right now. It's these early days. But to actually see and measure the storytelling at this level of resolution, this magnification is pretty freaking cool.

D (65:33)

So we're having this conversation with market at all time highs. And you've written about the ravine.

E (65:39)

Yep.

D (65:40)

Tell us a little bit about what is the ravine. How does does your data identify that? Tell us what this means?

E (65:49)

Well, there's, There's. There's clearly been a change in policy regime out of, of Washington and new.

D (65:57)

Administration and a radically different, radically different approach, even from the first Trump president.

E (66:03)

Even from the, the first presidency. And so what able to measure, really measure is how does that, I'll say, play. But also what comes. Narratives never happen in a vacuum. There's always a counter story for every bull story. There's a bear story. And you can almost kind of see the battlefield of ideas, the battlefield of stories and how it emerges. So my strong sense, Barry, is that we are going towards politically in this country, towards trench warfare greater and greater. I'll call it narrative violence. And if you look historically, how that plays out in countries, it doesn't play out well.

D (67:00)

Civil war, domestic political violence, things like that.

E (67:04)

Sadly, yes, exactly like that. Exactly like that. So there's that element. And so that's a sad one or a very troubling one in trying to say, well, how do we navigate that? But even when in markets, and I alluded to this earlier, how capital markets have become a political utility and the role of markets in our society. You can really see how that changes in the stories we tell ourselves about the role of markets, what it's there for.

D (67:41)

End of day options and speculation. What else is it supposed to be there for?

E (67:47)

But that's kind of what I'm getting at, Barry. You can see an enormous change in the meaning of markets and it connects with, yes, they'll call it the speculation layer, but it also connects with financial nihilism. Yolo. It leads to a very, I think, less attractive future for how we think about money and the role of markets and the role of capitalism.

D (68:23)

Coming up, we continue our conversation with Ben Hunt, president and co founder of Perseant, discussing how money managers use their output to generate alpha. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio.

B (68:55)

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D (70:08)

I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest today is Ben Hunt. He is president and co founder of Perseant ALU Data analytics narrative storytelling large language model using artificial intelligence to find some signal amongst the noise firm. Their clients range everything from large money managers to hedge funds to academics and corporate America. So you write Epsilon theory and some of it is for subscribers. Some of it is public.

E (70:47)

A lot of it's public. Yeah.

D (70:48)

One of the things you wrote is absolutely my favorite item from the first few months of the Trump presidency because I like to talk in probabilities because I don't know what's gonna happen. But here's the best case scenario, here's the worst case, here's all the middle scenarios. You wrote a piece, the end of Pax Americana that I thought was the most cogent, imaginative, well thought out. Hey, here's the worst case scenario. And we are becoming dangerously flirting with this possible outcome. And I use that as all right, so here's what I think is high probability. Here's the best case. But if you really want to think about how this can go off the rails, check out what Ben wrote. And so tell us a little bit about how did Persian inform the end of Pax Americana? Because I get the sense that domestically that wasn't really how a lot of people were thinking, but I got the sense from overseas that was a much more common thought. Tell us a little bit about both the piece and how the data informed it.

E (72:07)

I'll start with. I'll call it the international dimension. I want to come back to the domestic dimension because I think we've got some really interesting data recently to share. It goes back to this. Again, I cringe when I talk about it, but it is game theory. All game theory is strategic interaction. It's that the United States, yes, is the most powerful player on the world stage, but every country has some degrees of freedom and some autonomy in the policies that they that they implement. There is not a dominant strategy, meaning an outcome, an equilibrium outcome. Again, I hate using these words, but I'll use them anyway. All an equilibrium means is it's a balancing point where both parties, let's call them the United States and a European country. Where do they end up? Where they all say, okay, I can live with this. Where I can live with this. And the America first set of policies. And there's all these set of policies. What you end up with is less potential economic growth, trade, all of these.

D (73:24)

Things, all the things that we enjoyed post World War II, that realignment that imbued to our benefit so greatly, enormously to our advantage.

E (73:34)

Enormous, enormously to our advantage. It goes by the name of soft power and the dollar system, the Bretton.

D (73:41)

Woods system, all of this reserve currency.

E (73:44)

On and off reserve currency to finance our deficit. All of this is enormously to our advantage. And yes, there are free riders on that system. Yes, there are costs to that, particularly on defense and some other areas. Tariffs are another area where there's absolutely free riders on the. So there's improvement that can be made in that system for sure. But this is not, this is a different system. This is A different set of rules of the road, and it leads to a different strategic interaction between countries. So that's what the note was about. And I'm not trying to predict. I'm just trying to observe, which is a great line, actually, by George Soros, which is a. I'm not predicting, I'm observing. Which I love that as a line. I'm not. And our technology is not there to try to predict the future. It's trying to tell you what is true in the present today.

D (74:43)

I'm amazed how many people think they can forecast the future when they have no idea what's happening right now.

E (74:48)

I want to nowcast is what I want to do, not forecast. I want to nowcast. And that gets to what I wanted to talk about. Kind of domestic. So we started tracking the different narratives around immigration policy early last year. And what you saw in some of the kind of simple.

D (75:11)

This was during the run up to the election.

E (75:13)

Yes, exactly. And what you saw really going back, because we take this stuff back for a decade or more. And what you absolutely saw up to last October, there was an event from last October, not the election, but an event from last October. You see a steady increase in. Regardless of your political affiliation. You know what? Immigration isn't working for us. As Americans saw a steady increase, starting with the. They're eating the cats.

D (75:49)

They're eating the cats. They're eating their dogs.

E (75:51)

They're eating the dogs. When you saw the Columbus, that moment.

D (75:57)

One of the most surreal moments in debate history.

E (76:02)

And we see very clearly in our media data, and this is not mainstream media. This is everything that we're pulling up. We've seen a really significant decline in the volume and density of. Oh, my God. Immigration is a problem. We need mass deportations. On the contrary, we've seen an enormous increase, again, regardless of political affiliation, including Republicans. No, immigration is a good thing for this country. The stories of America that are pro immigrant and immigration now, you would not believe that if you were looking at the policies that the White House has implemented during these first, you know, 10 months of the administration.

D (76:50)

So wait, when did you first notice that regardless of policy, we think immigration is a net benefit to America? When did that first start showing up?

E (77:01)

It started changing right in October because.

D (77:04)

It following those too far.

E (77:06)

It was like, this is just stupid. This is silly and stupid. And it's been a steady increase, you know, and you wouldn't, you wouldn't believe that if you were immersed in Twitter. But, but so this country is actually quite pro immigrant. I know that Sounds.

D (77:25)

But we are a nation of immigrants here. Here's another data point that's kind of mind blowing. We were talking about a different data point in the entirety of the US history. 2025 looks like the first year where the US population will decrease. So it's a decrease in legal immigrants. Not only illegal immigrants, but legal immigrants. Add that with the deportations and just people staying away.

E (77:53)

Well, this gets back to the economic picture and from other. So what you've had is a clear. And again, we see this in our data from other countries. There's a clear effort to repatriate assets and funds away from the U.S. there's been a clear effort outside the U.S. you see this with central banks, but also with.

D (78:16)

Hence gold, the big gold.

E (78:18)

Correct. Treasuries used to be a safe haven asset.

D (78:22)

Right.

E (78:23)

No more.

D (78:25)

Do you understand how significant that charge is that you're making? You're basically accusing the President. Well, of submarining one of the single greatest assets America holds.

E (78:38)

Well, that it's a, it is built up to such an extent. Right. That the way I think of this is an iceberg that is melting, but it is melting. What we do not see, we do not see capital flight. Right.

D (78:52)

We, we saw it for like a week in April and then it quickly reversed.

E (78:56)

That was it. So there's, there's no capital fight. There are no. But there was US Investors leaving the country. There were fears and we can track it. If that starts to happen, we'll see it immediately. That's not happening. Repatriation continues to happen.

D (79:12)

Slowly measured, balanced melting iceberg.

E (79:15)

Right. Because there are limits to. If you're a Bermuda reinsurer, I mean, you're stuck with Treasuries.

D (79:23)

I mean, that you're, you could buy some gold to offset it, but you can't sell $100 billion worth of treasuries.

E (79:28)

You can't. You can't. So it's a melting iceberg. But we can clearly see that the dog that's not barking yet and maybe never will is capital flight. What we see politically, domestically, is that actually, and this was validated by a Gallup poll, they've been doing 20 years, which also showed what we had started seeing a lot earlier, where immigration as a thing is actually pretty darn popular in the United States. Yeah.

D (79:59)

But so is restricting assault rifles, and we can't get any change that. That's 75, 80% or the extra large.

E (80:10)

I got to tell you, that depends very much on how that question is asked.

D (80:14)

Well, that's true. For all polling.

E (80:15)

Well, this is the benefit of what I'm trying. So when we're doing these, we call the semantic signatures. It has nothing to do with how you're phrasing the question. We're not doing polling. We're seeing what people are actually the meaning of what they're talking about in media.

D (80:32)

So how does that play out if people are legitimately saying, no, immigration is a good thing for America? Is there an impact on population and the economy? Is there an impact on markets? Is there an impact on policy and politics?

E (80:47)

I think there's an impact on the election, the midterms next year.

D (80:52)

So we're talking. We're talking literally 12 months from now.

E (80:58)

Yeah, because that's how this stuff gets on the political front. Narratives and opinions, they get cashed out in elections. Right. Markets are different. You cash stuff out every day.

D (81:11)

Right. The markets open. Every loop is so rapid with markets.

E (81:14)

Exactly. Politics is a different story. So it gets cashed out in the elections.

D (81:20)

If you were to ask me before this conversation, what's the most significant impact on the midterm elections? There was just a Gallup poll yesterday, GOP questions on the economy. They were plus 14% two years ago. They're minus 4% this year.

E (81:38)

I saw that.

D (81:39)

And that's an amazing swing. And I'm saying to myself, you know, listen, the out of power party usually picks up, you know, 10, 15 seats. You have the redistricting question, which may blunt that. But if. If the most important question during the election was on the economy, and that's an 18% swing, this is looking like a pretty substantial shift. What I'm hearing from you is, hey, this isn't just about the economy.

E (82:07)

It's not. It's not.

D (82:09)

So it's the economy, it's immigration. What else. What else are you seeing? That's interesting.

E (82:14)

That's pretty. Now, there are other aspects, though, where the stories. These are people talking about immigration as a thing. Now, whether that gets translated into a political party position, because I gotta tell you, the Democratic Party is enormous enough, but there are no narratives that are being put forward by the Democrats that are powerful or popular.

D (82:42)

So other than the mayoral contest in New York.

E (82:46)

Yeah, yeah, right.

D (82:48)

Says one.

E (82:49)

What I'm saying is that many of the policies that are being presented by this administration are unpopular, not just with the Democrats, but with Republicans and increasingly. So immigration being one of them, I think, economy being another one.

D (83:10)

Tariffs aren't really popular amongst. But people perceive that as a tax increase or a pressure on small business.

E (83:19)

Government shutdown Right. So both. And also in this it. Because the question, oh well, the Democrats will get blamed. Well, that's not really true. That's not what's happening now. How that all paid.

D (83:32)

And by the way, listeners should know you're not a Democrat.

E (83:37)

Oh my God, no.

D (83:38)

Like I know you and your politics and the stuff you're saying. I could hear people shrugging and saying, well, that Ben Hunt is just a liberal Democrat. I'm like, no, no, no, that's not who Ben is.

E (83:49)

No, no, it's not.

D (83:51)

You're just talking about, here's what I see in the data.

E (83:54)

I'm saying I'm observing. I'm observing. And I think that a lot of times if we are like me very online and on Twitter too much, you don't see the broader picture of what is happening on blog posts and local newspapers and everything else. So I think having the ability to read everything and track these stories that, you know, they don't like I say they wax and wane, but they don't ever go away. It's something we're very excited about. To track the stories of America. I'll give you another one and this one actually, I don't know what to do with this. One of the stories of America is that America has raised the world's wealth, right?

D (85:00)

Global standard of living.

E (85:02)

Global standard of living. That America has been this powerful force to and capitalism and America have been this powerful force to raise people out of poverty. That's a story that in other periods of time has been out, has been.

D (85:18)

Prominently talked about, very resonant.

E (85:21)

That story is non existent today.

D (85:23)

Well, you cut things like hiv, a few million dollars to inoculate all of Africa from hiv. That's going to have repercussions.

E (85:33)

Well, it's, it's not just that that has the concrete those repercussions that you're describing. What I'm saying is that the story that would support that, it's gone.

D (85:50)

Is it gone forever? No, no.

E (85:52)

That's the point. These things are never gone. These stories. Stories have, they never die. Right. They're waiting for a new force to.

D (86:01)

Give them the next version of it. And if US is not seen as a force for raising people out of poverty around the world, do people see China as that force?

E (86:12)

Haven't looked at that. We've been looking at the US story, but my personal sense is that we've essentially ceded the field CED with a C, not an E, with a C, not an S, particularly in South Asia and Africa, to China. I mean, that seems pretty clear.

D (86:33)

I'm glad I asked the question about the end of Pax Americana, because I just found that piece so insightful and so useful. And I could keep you here talking about this stuff for hours. But out of deference and respect for our listeners time, I'm gonna jump to our favorite questions that I ask. Oh, sure, all of our. All of our guests. But before I do, there's a question that I have to pose to you, which is so you get to see things before they sort of bubble up into the mainstream, before they become a well understood narrative. What's a narrative that you're just starting to sniff out that most of the world or most of the country or most of the Wall street and finance hasn't seen yet?

E (87:28)

It's a story that people are talking about since Tricolor and First Brands went out. So it's a story on credit, story of credit.

D (87:41)

And I mean, First Brands is legitimate. It sounds like felonies were happening there, so we'll have to see how that plays out.

E (87:48)

They probably Tricolor as well. But my point is that every day you have a new bank CEO come out and say, no, no, everything's fine. So yesterday it was David Solomon at Goldman Sachs saying, no, no, it's fine. Today I was hearing the Brookfield guy saying, no, no, it's fine. For every story, every interview, the guy says, it's fine. You're getting two articles in the FT or the Journal saying ain't fine. So the.

D (88:21)

Do you think thou doth protesteth too much? Is it that.

E (88:25)

All I'm saying is I am observing. I'm not predicting, I am observing the level of volume for alternative asset managers, their exposure to private credit. This is problematic. I'm wondering what's going to happen when the music stops. Oh, I think that this blows back into the commercial banking system. Those stories, those narratives are higher by an order of magnitude than they've been at any point in the last 10 years. At any point, including during COVID when you had similar concerns over, oh my God, private credit is going to be.

D (89:03)

Covid was easy to rationalize. Everything. Hey, listen, we're all frozen. Just ignore it.

E (89:08)

But this is a story that has legs. It is growing in a way that I rarely see. And once a story like this grows, it doesn't just go away. And it's not fixed by Bernanke saying, oh, subprime is contained.

D (89:25)

Right. It's immediately what I thought of as soon as you.

E (89:28)

Absolutely. It does not get fixed by people saying, don't worry, there's no problem.

D (89:33)

Jim Grant said he was right. It was contained to Earth. The rest of the solar system was fine. Which turned out to be a very witty and clever observation.

E (89:44)

This story, this worry, this concern absolutely has legs. And we're seeing no signs of it easing off in financial media and press.

D (89:55)

So the big question is, is it systemic or. Or is it the specific?

E (90:02)

I don't know the truth. I don't know reality. All I can tell you is we're.

D (90:07)

Seeing more of this.

E (90:08)

Well, we're seeing this is the story.

D (90:12)

Really, really interesting. Let's jump to our favorite questions, starting with who are your mentors? Who helped shape your career?

E (90:22)

That's good. Well, I mentioned that the people both in undergrad and graduate school who turned me on to the science part of political science and in particular in graduate school. Gary King, who runs the whole social science research center up there at Harvard and has for a long time now, he wrote the original. Really? The book on inference. You hear about inference all the time.

D (90:46)

Cialdini, of course.

E (90:48)

So the notion of inference. Inference. Taking large data sets and pulling out.

D (90:53)

Oh, inference. I thought you said influence.

E (90:55)

No, no, no, no.

D (90:56)

Inference.

E (90:57)

Inference. So the science of inference. I learned that from Gary 30 years ago. And it's so interesting to see that come full circle because that's at the core of Jensen Huang's always talking about it. And the whole notion of AI to that inference spend they talk about. I was there at the beginning for how you. What inference is and why it is so powerful. That was a huge influence on me.

D (91:27)

Let's talk about books. What are you reading now? What are some of your favorites?

E (91:31)

I'm a science fiction guy.

D (91:33)

Okay.

E (91:33)

I really am.

D (91:34)

You're talking to another sci fi guy. So let's give us something new and something classic.

E (91:39)

Well, something classic would be Liu Shishin and the Three Body Problem.

D (91:44)

Right. By the way, the Netflix show on that was surprisingly watchable.

E (91:48)

I thought it was excellent before that and I named a company after him. Was the Foundation Trilogy.

D (91:56)

Asimov.

E (91:56)

With Asimov, those books hold up less well, honestly. And I thought the series was. It had its moments.

D (92:02)

I didn't love the series.

E (92:03)

It was so pre. So. So to me, current science fiction. Rebecca, she goes by RF Kwong and she wrote a book called Babble and she's got a new one out. I like Tower of Babel. B A B E L. And she's got a new one out. Katabasis, I think it is. But science fiction dealing with language and linguistics, I love that stuff.

D (92:32)

That sounds like that's right in your sweet spot. Let's talk about streaming. What are you watching or listening today? Podcasts or Netflix or whatever.

E (92:41)

So honestly, I don't listen to any podcasts. Isn't that terrible to admit?

D (92:44)

Well, if you host. I will say this. If you host a podcast, you're either preparing for a podcast, doing a podcast, or auditing a podcast. So it's like, all right, that's three or four.

E (92:55)

That's all it is. Yeah.

D (92:56)

Right. Every now and then, I'll catch something because I want to either listen to this. A specific guest. Like, I will not listen to any podcast that you're on before we do our podcast because I don't want to steal anybody else's.

E (93:09)

Yeah.

D (93:10)

Or narrative or questions or line of thinking. So that's purposeful. But every now and then. So I listen to. The things I listen to are like John Pizzarelli's Radio Deluxe. Yeah, yeah. Which is sort of like a podcast, slash, music series.

E (93:29)

Something a little different. Yeah, exactly. Right.

D (93:31)

What. What about streaming? What do you.

E (93:33)

Well, you and I are both big Godfather fans, so we love all the mobster. Have you seen Mobland yet? No. All right. Worth your time. Pierce Brosnan eats up every scene. Tom Hardy is awfully good. I just love him in anything. I don't know if you ever saw Peaky Blinders. Right.

D (93:52)

I started that, and I kind of false.

E (93:55)

Couldn't get into it.

D (93:56)

So my issue is I have to watch something that my wife tolerates. I have friends.

E (94:01)

That's a struggle.

D (94:02)

Who. Like, he goes into this room, she goes into that room. They don't watch anything together. Like, I don't know how that works.

E (94:09)

Like, it's.

D (94:10)

She will watch some stuff without me. I will watch some stuff with her.

E (94:14)

You gotta have your own space.

D (94:15)

80% of what we watch. So I have fallen down. The British Upstairs, downstairs, Gilded Age. So we watched the Crown, we watched the Gilded Age. We finally.

E (94:27)

Let me give you one.

D (94:28)

We went back to Downton Abbey, which I missed when I first rolled out.

E (94:32)

Check out. So my guilty pleasure. And I do watch this with my wife is a Diplomat.

D (94:37)

The new series just. The new season just dropped.

E (94:39)

Yeah.

D (94:40)

And that's teed up for this weekend. I'm looking forward to that.

E (94:43)

Excellent.

D (94:43)

That the first was the season two or season three. The first season was great.

E (94:48)

Yeah. Yeah.

D (94:49)

And I'm going to give you. If you like that. So my wife finds he's really interested. She got me into Killing Eve, which was a little more spy fair than diplomacy. And if you get a chance to watch Slow Horses.

E (95:04)

Oh, that's the one I've got to see.

D (95:05)

Yeah, it's really so the first season is great. And people told me the most recent season, it, it. Some people will say I don't like. I know people who loved it all the way through, but it's, it's really, it's really an interesting, well told, beautiful cast.

E (95:25)

Fantastic.

D (95:26)

Yeah, you'll love that. Our final two questions.

E (95:30)

Yeah.

D (95:31)

What sort of advice would you give a recent college grad interested in either investing or working with large language models, working with narrative analysis and artificial intelligence?

E (95:47)

Don't. And I say that tongue in cheek.

D (95:53)

You had a lot of fits and starts going back seven, eight years for sure.

E (95:58)

And I tell you what I think is important. You have to. What I think you can accomplish in academia, either in grad school or whatever it is, is you build your intellectual capital. And so I think a lot of times when you get out of college, you say, okay, I'm just ready to kind of live my life and start something. And you haven't built that intellectual capital yet. The issue, though, is that once you enter, particularly the investment world, where if you're responsible for managing other people's money, brother, that's it, right? I mean, there's not. That's got to be your total focus. You are spending your intellectual capital. You're not gaining intellectual capital once you take on a role like that. So my first advice is find a path where. And that's often in academia, where you're building intellectual capital. Because once you leave that environment, then.

D (97:15)

You'Re spending it down.

E (97:16)

You're spending it down.

D (97:17)

Makes sense. Our final question, what do you know about the world of fill in the blank? Investing, data analytics, narrative storytelling, today would have been helpful 25, 30 years ago.

E (97:34)

In investing. I wish I had understood the role of at least step back a second. I think that whether you're talking about data or whether you're talking about investing, I'll speak for myself. But I think there are a lot of people like me, we think there's an answer with a capital A right there in the numbers. And if you just look hard enough and if you work hard enough, you'll find that answer, right? You'll find the secret, the secret formula.

D (98:13)

And you have learned since then, ain't no such thing.

E (98:16)

Right now there is magic and there are patterns, but it's not in the structured data. It's not in the numbers. It's actually in the error. It's in the probabilities. It's in. We're calling the stochastic element, right? The role of chance and understanding that there are patterns and there's real magic in understanding that. I didn't get that when I was either starting with data analysis or with investing. I was looking for the answer with a capital A as opposed to a.

D (98:54)

Process with a capital P. I love that answer because. So my exercise in confirmation bias is I love the fact that you. Looking back, we all have the benefit of hindsight, and unfortunately, that can be a bias to certain people. But when you're looking back at it, you know how it happens at the moment when you don't know what the outcome is. Thinking about it probabilistically is a much healthier approach than saying, here's a binary up or down, yes or no. And I'm either right or wrong. And I see people struggle with that constantly.

E (99:33)

Constantly. When I first started in the investing world again late, I got two pieces of good advice, right? One was never go all in, right? Which is really interesting because it's this business of investing. It's a wonderful life. We're solving puzzles, we meet interesting people, we get to have these sort of conversations. It's a career for a lifetime. And the two pieces of advice are never go all in. And also, reputation is absolutely the most important thing. Again, especially when you're young, you think, oh, well, that's not the most important thing. Being right's the most important thing. It ain't. It's playing the long game here, which you want to. It's never go all in and the right decisions.

D (100:29)

By never go all in, you mean never put everything at risk so that if it doesn't work out, you're out of the game.

E (100:37)

Stay in the game.

D (100:38)

Gerald Loeb's book, the Battle for Investment Survival.

E (100:41)

Stay in the game. Avoid that risk of ruin. You just. Or don't ever risk ruin.

D (100:48)

That makes sense because people don't think in those terms. But that's really all in means risk of ruin.

E (100:54)

It's the risk of ruin. And it also connects with reputation because once you.

D (101:03)

Once you blow up, it's tough to go back.

E (101:05)

And you start saying, oh, I can fix it by taking this shortcut or doing this other thing. And once you do that, it never ends well. And you tell yourself, oh, just this one time. It's never this one time.

D (101:19)

Ben, this has been absolutely delightful. I'm so glad we finally got around to doing this. I'm just entranced by your thought process. Some of the things like, I love reading stuff of yours that I totally disagree with. And then because it forces me to say, well, he's not just making this up. I know how your brain works. And it's like, all right, if Ben is saying this, then I'm going to make this my worst case. Because it's easy to dismiss it. Everybody goes out seeking confirming information. And rather than being dismissive of it, all right, you claim to think probabilistically. Where does this fit into the range of probabilities? And once you start thinking in those terms, it's like, oh, so the worst case scenario is worse than my worst case scenario. I gotta move the bottom of my range down further. Cause if this goes off the rails, this is really bad. That's what your Pax Americana piece did with me.

E (102:20)

Well, thank you.

D (102:21)

And it really helped me figure out how to think about, especially in that week between April 2nd and 9th where everyone was losing their mind. It's like, oh no. So they're losing their mind because, hey, this could happen. But maybe something good comes out of it.

E (102:38)

Trying to avoid tunnel vision, I think is so important in our business, or any business, but especially our business, in.

D (102:43)

Our business in particular. Well, thank you for being so generous with your time. We have been speaking with Ben Hunt. He is the co founder and president of Prescience and you can find his writing at Epsilon Theory. If you enjoy this conversation, well, check out any of the 593 we've done over the past 11 and a half years. You can find those at Bloomberg, iTunes, Spotify, YouTube. Wherever you find your favorite podcasts, be sure and check out my new book, how not to Invest the Ideas, Numbers and be Behaviors that Destroy wealth and how to Avoid Them at your favorite bookseller. I would be remiss if I did not thank the crack staff that helps put these conversations together each week. Alexis Noriega is my video producer. Sean Russo is my researcher. Anna Luke is my podcast producer. Sage Bauman is the head of podcasts here at Bloomberg. I'm Barry Ritholt. You've been listening to Masters in Business on Bloomberg Radio.