The transcript from this week’s, MiB: Ben Hunt, co-founder Perscient, is below.

You can stream and download our full conversation, including any podcast extras, on Apple Podcasts, Spotify, YouTube, and Bloomberg. All of our earlier podcasts on your favorite pod hosts can be found here.

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Bloomberg Audio Studios, podcasts, radio News. This is Masters in business with Barry Riol on Bloomberg Radio

Barry Ritholtz: On the latest Masters in Business podcast. What a fascinating conversation. I sit down with Ben Hunt, he writes Epsilon Theory, but he is also the president and co-founder of Persuent. What a fascinating analytic story they’ve put together. They essentially take feeds of everything that’s published around the world, whether it’s in English or Chinese or Russian. They create these large language models and use artificial intelligence to identify rising narratives. In other words, they’re looking for the things that will become storylines, but haven’t quite hit that yet. I I found this conversation to be absolutely fascinating, and I think you will also, with no further ado, my conversation with Persuent Ben Hunt. Ben Hunt, welcome to Bloomberg.

Ben Hunt: Thanks for having me.

Barry Ritholtz: This is long. time coming

Ben Hunt: I love the intro. I gotta have you at all my events. It’s fantastic.

Barry Ritholtz: That’s right. I’m available for hire. I can introduce you at, at weddings bar Mitzvahs. Wherever you’re giving a toast, I’ll be happy to tee you up. This is long overdue. I’ve followed your work for so long. I’m fascinated by both what you put out in your blog, Epsilon Theory, thank you, and which is now a blog and a newsletter and, and the work you do at persuent. We’re going to get to that stuff, but before we do, I gotta ask PhD from Harvard. You were a tenured political science professor. Was academia the original career plan?

Ben Hunt: You know, it’s interesting, Barry. So I, academia was always a, I’ll call it a, a way station for me. It ended up being

Barry Ritholtz: A 10 year way station?

Ben Hunt: 10 years plus grad school.

Barry Ritholtz: That’s a little more than a way station. I

Ben Hunt: A little more than a waste 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, it is a bug. It’s not a feature.

Barry Ritholtz: Yes, for sure.

Ben Hunt: You can’t help yourself. And academia is not the place to be an aca,

Barry Ritholtz: So let me ask you a question about that.

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

Barry Ritholtz: 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, you are working on your own stuff. You follow your own intellectual bliss in a way that I’ve really never rediscovered. The the problem with academia, of course, is, you know, it’s very, very low stakes. That’s why the academic fights are so vicious, because

Ben Hunt: There’s nothing at stake. Right, right. And, and that that is actually true. That’s actually true. And, and so you learn survival techniques and that kind of jungle where nothing is really at stake, at least monetarily, because the, 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 your head. What’s the most devastating question I can ask?

00:04:07 [Speaker Changed] So you’re gonna rock this guy back on his heels with a devastating, a

00:04:11 [Speaker Changed] Devastating question. And boy, that gets old after a while, Barry, I gotta tell you. Yeah. It, it really does. I, I loved the teaching. I loved the, the research. ’cause like I say, nobody tells you what to work on, but the, the church of academia, the, the actual institution of academia, a it’s for the birds, even back when I was doing it. Right. And I think it’s gotten significantly worse.

00:04:40 [Speaker Changed] I can imagine. I can imagine what’s happening with that. So, but the question that this leads me to is, yeah, 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?

00:04:57 [Speaker Changed] 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? Same, same. You know, after I left academia finally to start a software company. And it, after we sold that software company Yeah. A, a a buddy of mine that I think this happens a lot. A buddy of, you know, you have a buddy who’s in the

00:05:29 [Speaker Changed] Business, Hey, you seem to be pretty smart. How would you like to apply this to

00:05:32 [Speaker Changed] This? We’re always, we’re always talking about company X or technology Y why don’t, 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

00:05:52 [Speaker Changed] For sure.

00:05:54 [Speaker Changed] And like I say, I’m a game player. I love games

00:05:57 [Speaker Changed] And a game theorist. Let’s, let’s, let’s work down that.

00:06:00 [Speaker Changed] Well, I, I don’t like to talk about that because, you know, because the,

00:06:03 [Speaker Changed] The real game theorists get angry.

00:06:05 [Speaker Changed] Yeah. Well, yes. And, and, and I, I understand I am a real one because that was, 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. Right? And you just want to just, you know, right. Shoot yourself when somebody does this.

00:06:25 [Speaker Changed] So, 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, and we will talk a little later about what you do today at Persuent with the narrative machine. But what, and

00:06:46 [Speaker Changed] Believe it or not, believe it or not, it all ties together. I,

00:06:48 [Speaker Changed] I doesn’t don’t doubt that for a second. And,

00:06:50 [Speaker Changed] And it’s

00:06:51 [Speaker Changed] What initially led you down that rabbit hole.

00:06:54 [Speaker Changed] When we think about kind of who’s been an influence on you in your, in your life, had 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 in, up at, up at Harvard. They call it government or something. Right? Something like that. But, but it’s political science. And, and the reason I say they were influential is that they really got me focused on the science side of political science

00:07:30 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 know, you’ve gotta learn how to deal with structured data. Right. And, and there was, there was a lot to learn. And I, it’s, 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, well, you know, apply math to data and, 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, the, the investment world. What,

00:08:20 [Speaker Changed] What we’ve certainly seen amongst the quants, a very successful application of math theory to data. The, in fact, some of the best performing hedge funds are quantitatively driven. That’s not where you’re going.

00:08:37 [Speaker Changed] 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, the social sciences where, where, yes, you start with numbers, structured numbers, right. Price over time, you know, 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, as a sideline. Right? It, it’s somehow lesser than the numbers. Right. So what, what I was kind of early on was applying the same techniques that we have for understanding, you know, 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, the AI that we have today.

00:10:28 [Speaker Changed] Well, you’re, you’re getting way ahead of me now with generative ai. We’ll circle back to that. Yeah. But, but

00:10:32 [Speaker Changed] It’s all the, the math has not changed. Right. In 35 years since I started working with network math around unstructured data. I mean, we, 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.

00:10:53 [Speaker Changed] 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, a novel approach to identifying narratives and anticipate where the market’s going. What, what led to that sort of in insight,

00:11:25 [Speaker Changed] Not the, not the Great Recession, right? But the aftermath,

00:11:30 [Speaker Changed] Meaning the 2010s following the great financial crisis,

00:11:34 [Speaker Changed] Starting in, in, in 2009, and the recovery that we had out of 2009, and in particular when Win Ben Berke and the Federal Reserve move towards a very explicit effort to use their words to impact markets.

00:11:53 [Speaker Changed] So let’s talk a little bit about that. ’cause I have some really specific memories of the low, of the, the runup afterwards, all the noise Yeah. That was going on. Some of the phrases that have come out of that era, like financial repression and other such things are, are just the tip of the narrative iceberg. So, so walk us through your insight. It’s 2009. Yep. The market bottoms really kind of a v bottom and took off from that. What was that, March 9th, March 7th, something like that. Yep. 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? Well,

00:12:47 [Speaker Changed] So I was co-founded a, a long short fund inside of a, a larger asset manager going back in oh five and oh 5, 0 6, 0 7. We did well, like everyone else did. Well. And then in oh eight we did great.

00:13:08 [Speaker Changed] Really? In oh eight, we did great. Oh eight, I think I wanna say s and p down 37% something. Yeah. We

00:13:12 [Speaker Changed] Were up 20 something net.

00:13:14 [Speaker Changed] Anything in the green, not in the red is

00:13:16 [Speaker Changed] Smith. Yeah. Amazing. And, and 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, that’s actually, that, that’s actually a question you can ask. But

00:13:33 [Speaker Changed] I I’m, I’m gonna say a 47% relative price swing. I, I, I’ll take,

00:13:39 [Speaker Changed] Had a, had a, had a great year in oh eight and, and

00:13:42 [Speaker Changed] Did that continue in oh nine

00:13:45 [Speaker Changed] Flatlined.

00:13:46 [Speaker Changed] Alright.

00:13:47 [Speaker Changed] From oh nine, so from March of oh nine. So we did well in January 1st quarter, February and the first quarter, right. The rest from, from March of oh nine, our returns flatlined. So we never, we never lost money for our clients in our fund. But

00:14:01 [Speaker Changed] You didn’t catch that recovery. That

00:14:03 [Speaker Changed] V did not catch the recovery. Absolutely did not. And the, the recovery was interesting, right? You’re, you’re right. There was a v but there were, there were starts and stops to it. So the, 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 that made sense, right? There was a second leg to the rally. Oh, for sure. April, may. And then in June, in June, June to October was a ferocious rally.

00:14:39 [Speaker Changed] Ferocious is the right word, but,

00:14:40 [Speaker Changed] But June in particular was a classic crap rally.

00:14:45 [Speaker Changed] Right. Meaning it was a low

00:14:46 [Speaker Changed] Quality, right. Low quality stuff. Right. The, the end of March going into April rally, it was Right. This makes sense, right? We bottomed fed the June rally. No, no, it was, we didn’t, we didn’t touch any Oh,

00:15:03 [Speaker Changed] That’s a, that’s fascinating. Go back,

00:15:05 [Speaker Changed] Go back and look at it. Right? I, and, and let me tell

00:15:07 [Speaker Changed] You, oh, I don’t, just so you know. Yeah. I, 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 are, they’re forcing you into equities, which is what led my post, which everybody stole the line. This is the most hated bull market in history. Yeah, yeah. And I, I wrote that up. I send that out and I heard everybody borrow that. But I’m curious as to where the June rally took you. Well,

00:15:55 [Speaker Changed] This, 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 it’s policies around interest rates, you know, they took them to zero. And that’s where we stayed,

00:16:16 [Speaker Changed] Started buying mortgage backs and they did

00:16:19 [Speaker Changed] Qe, balance, balance sheet operations. Right, right.

00:16:22 [Speaker Changed] Quantitative easing,

00:16:23 [Speaker Changed] Actual, you know, actually, and I look, I, I think QE one, I think it saved the world. This

00:16:28 [Speaker Changed] Is what, right. And what was that? A trillion dollars something crazy.

00:16:31 [Speaker Changed] Something. Yeah, something something.

00:16:32 [Speaker Changed] 800 billion, something

00:16:33 [Speaker Changed] Something like 800 billion

00:16:34 [Speaker Changed] Unthinkable number.

00:16:35 [Speaker Changed] So I, I think that, so those were specific actions took, but, but even if you, people often say things when they’re leaving office. So, so Bernanke’s last speech is valedictory address.

00:16:48 [Speaker Changed] Right. More honest than intended.

00:16:51 [Speaker Changed] Much more so, and you see this all the time, right? George Washington leaving office.

00:16:55 [Speaker Changed] I was gonna say, I,

00:16:55 [Speaker Changed] I, I, Eisenhower Yeah.

00:16:57 [Speaker Changed] That’s a big,

00:16:57 [Speaker Changed] I mean, when, when, when, when freaking Eisenhower warns you against the defense industrial complex. You know, you might, you might. I’m just saying. No, I won’t listen.

00:17:06 [Speaker Changed] It’s general like to you. That’s

00:17:08 [Speaker Changed] Right. General, like to me, what Bernanke said when he is leaving his terms of office, he said, look, we had, 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. Right. Second were the balance sheet operations, large scale asset purchases, qe, quantitative easing. It said, you know, QE one was great. Did what we hoped it would do. QE two, eh, operation Twist, QE three, this is Bernanke saying, mind you. Yep. He says, I actually think that might have been a little counterproductive,

00:17:47 [Speaker Changed] Huh?

00:17:49 [Speaker Changed] They 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. And it, it’s not that it was necessarily lying, but they, they were using their words and choosing their words for effect

00:18:28 [Speaker Changed] To shape perception of their underlying

00:18:31 [Speaker Changed] Behavior to market, to, to shape market behavior. And he said, that worked better than we had any hope that it could. And that’s where we are now.

00:18:42 [Speaker Changed] So, 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, 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. Yeah. Lack of clarity, no data, blah, blah, blah.

00:19:20 [Speaker Changed] Worse than that, Greenspan would be intentionally vague and obtuse.

00:19:25 [Speaker Changed] If you understood with you’re saying what I said then, then you misunders you misinterpreted it. Right? Exactly.

00:19:30 [Speaker Changed] Right.

00:19:30 [Speaker Changed] If you think you understand what I’m saying,

00:19:33 [Speaker Changed] You know, who led the committee to make all that change? Janet Yellen, she was vice chair. Yep.

00:19:39 [Speaker Changed] Back, back in, during the financial crisis. Yeah.

00:19:41 [Speaker Changed] 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.

00:19:52 [Speaker Changed] Right?

00:19:53 [Speaker Changed] Follow all the, and assigning the fact, okay, you’re gonna speak this day, you’re gonna 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, I knew the, 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, I mean, 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, right? Because, because a multiple is a narrative. A multiple is a story.

00:20:52 [Speaker Changed] They’re

00:20:52 [Speaker Changed] All stories. Well look at,

00:20:53 [Speaker Changed] Look at some of the most successful CEOs throughout history. I would throw Jack Welch into that pile. ’cause he was a fabulous, fabulous. Yeah, he was a fabulous, fabulous. ’cause the stories he told were great, right? Up until the point where we found out that he was running a, a, a hedge fund with GE Capital, and they magically always beat by a penny.

00:21:17 [Speaker Changed] So I remember vividly when GE was coming to our shop, what they wanted was a fin a financial multiple, right? So they were making, they, they wanted,

00:21:33 [Speaker Changed] Even though they’re an old world industrial,

00:21:35 [Speaker Changed] Even though they’re an industrial, this, that 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, the, the GFC. So a, my, my poster child for this is, is Mark Benioff Salesforce, because he’s pri you often see this with people who come out of sales like, like Mark did, right? But

00:22:08 [Speaker Changed] The way it’s all about storytelling.

00:22:10 [Speaker Changed] It’s all about storytelling. And

00:22:13 [Speaker Changed] What isn’t that true for go through the great, see, 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 story, and a great storyteller, great

00:22:32 [Speaker Changed] Storyteller. What all of those companies have in common is that they’re great storytellers, and those are trillion dollar companies today. Right. 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.

00:22:51 [Speaker Changed] 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.

00:23:00 [Speaker Changed] There are a lot of stories that get told that are not true. Right? What, and and I think even today, people think of this word narrative. They have a pejorative sense to it. It’s like,

00:23:15 [Speaker Changed] Oh, really? Really? That’s interesting. I didn’t think of it that

00:23:17 [Speaker Changed] Way. Oh, for, for, for, for for sure. That’s just your narrative, man. You know, the Big Lebowski, you know, that’s, that’s the, and it’s not that a story is a lie. It’s that the story is constructed for effect. It’s, it’s not, and it’s presented to you as if this is my true and inner thoughts, but the construction of the, the intentionality behind these stories, phenomenal. Benioff, for example, you know, created the metrics by which he wanted Salesforce to be judged. Not metrics of profitability, but metrics of what he called proforma, net revenue growth, whatever the hell that means. Right? Right. And 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 was, 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, and I think it’s, it’s not just the success that first central bankers and then CEOs. I mean, wall Street’s the greatest copying machine. We Wall Street copies what works. Sure. So when you see that something’s working, oh, they’re, they’re getting a multiple by telling the story and going on Kramer, you know, four times a

00:24:50 [Speaker Changed] Year. It’s endless. It’s endless iteration. You’re just constantly tweaking it, doing works. And if it works, do more of it. And if it doesn’t, toss it out. So

00:24:58 [Speaker Changed] 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 gonna use air quotes here and I’m glad we’re taping this news. Well,

00:25:11 [Speaker Changed] It’s media, social media news right

00:25:13 [Speaker Changed] Now.

00:25:14 [Speaker Changed] News, like news light.

00:25:16 [Speaker Changed] That wasn’t the case.

00:25:18 [Speaker Changed] So I wanna I wanna annotate what you said slightly. Okay. Because I think CEOs have always been storytellers, but they were storytellers to their boards, to their employees, to their shareholders. Correct. They 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 gonna catch up to Nvidia. Yep. And it’s the greatest thing ever. Yep. That’s a different skillset. It

00:25:52 [Speaker Changed] It changes the time horizon. It is a very different skillset because you’re not telling the story of, oh, I’m getting another, you know, turn of leverage in our operations, or, you know, 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 got a

00:26:16 [Speaker Changed] Gotta gotta

00:26:17 [Speaker Changed] A little bit. You, you’re, you’ve got a segment, you’re going on, Kramer, you got four you minutes and say bye bye bye. You got at most four minutes. Right? Right. How are you gonna 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. Right.

00:26:47 [Speaker Changed] What, what isn’t It’s printed New York Times, Bloomberg, the Washington Post. Exactly. They all have websites that get updated around the clock.

00:26:54 [Speaker Changed] 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

00:27:04 [Speaker Changed] Story you were channeling

00:27:05 [Speaker Changed] Story takes the place of

00:27:06 [Speaker Changed] The hard, you were channeling Michael Creon from 25 years ago. Most of what you see in the media is speculation, opinion, and theory, not news. I,

00:27:18 [Speaker Changed] I’ve written so much about Creighton and his

00:27:21 [Speaker Changed] I know. That’s why I threw that back to

00:27:22 [Speaker Changed] You. He says he, he was, he was so far ahead

00:27:25 [Speaker Changed] This quarter century ahead of what, of what took place.

00:27:28 [Speaker Changed] And there’s a third piece though,

00:27:31 [Speaker Changed] Give us the third piece before we go to our next segment. Third

00:27:34 [Speaker Changed] Piece has changed. Everything is our smartphones

00:27:37 [Speaker Changed] That you walking around with. Right? Not only a studio, but a, a a a, a dopamine device that you’re constantly playing.

00:27:46 [Speaker Changed] It’s my dopamine machine, right. And I, 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? That’s right. That’s right. I, I get a little nervous. And it’s a, it’s, it is absolutely a neurotransmitter addiction. I think it’s so important to keep that from our kids. That’s a whole nother thing.

00:28:17 [Speaker Changed] There’s a whole depression situation with teenagers today, and it all traces back to the phone and social media.

00:28:22 [Speaker Changed] 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 caring with our ourselves all the time,

00:28:46 [Speaker Changed] Huh. Absolutely Fascinating. Coming up, we continue our conversation with Ben Hunt, president and co-founder of Perent, explaining how he’s using AI to identify narratives in real time. I’m Barry Ltz. You’re listening to Masters in Business on Bloomberg Radio. I am Barry Ritholtz. You are 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 persent, which applies AI tools to map and measure market narratives in real time. It’s really more than market mar narratives. It’s politics, it’s economics, it’s markets. You cover a whole lot of stuff. A

00:29:51 [Speaker Changed] Hundred percent. 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

00:30:02 [Speaker Changed] The

00:30:02 [Speaker Changed] 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 LexiNexis another, everything that gets published in the world, all these languages, it’s available to you. And it’s, 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,

00:30:55 [Speaker Changed] But the software tools are faster, stronger, better,

00:30:59 [Speaker Changed] And infinite. So the, the, the calculations here are not particularly complicated, but you have to do them at enormous scale.

00:31:08 [Speaker Changed] It’s, it’s a ton of volume. So,

00:31:10 [Speaker Changed] I mean, it’s crazy. The, the, just the, the scale of

00:31:15 [Speaker Changed] The numbers. Petabytes, terabytes just crazy.

00:31:17 [Speaker Changed] I mean, yeah. In the last couple of months we’ve processed over 200 billion tokens. Billion,

00:31:25 [Speaker Changed] Right. And a token is how much

00:31:26 [Speaker Changed] A, a token is like a word or a phrase. Okay. 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. So 200, which is, which is what all of the, the

00:31:41 [Speaker Changed] LLMs,

00:31:41 [Speaker Changed] All the LLMs are, they’re linguistic calculators. And so, you know, we’ve processed, you know, several hundred billion tokens. Again, it’s not complex, but it is at scale. And what, 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, it’s much bigger than, or it’s much more focused that we have much higher resolution than just saying, oh, I’m bullish on financials. I mean, that’s a, that’s a narrative. Sure. Right.

00:32:20 [Speaker Changed] But

00:32:21 [Speaker Changed] There are 20 different variations of that. You’re, you’re bullish on financials. Why?

00:32:27 [Speaker Changed] So wait, let me, let me, you,

00:32:28 [Speaker Changed] And we can track all those, we can track all those stories and how they wax and wane over time. That’s really cool. So

00:32:32 [Speaker Changed] Let, really cool me roll back to, I want you to explain what Perent is. Who are the clients? I don’t mean names, but No, sorry. What type of of clients do you have and, and what do they do with Persian’s output?

00:32:47 [Speaker Changed] So we started Persent in 2018. My partner from, we were at a, a asset manager spun out there to take the, the technology that I’ve been working on for years and, and really been writing about with Epsilon theory. So we started that in 2018 to do the, 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. Is that, that was, that was the story. That was the

00:33:25 [Speaker Changed] Goal. So I, 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,

00:33:51 [Speaker Changed] And digital gold. Right? Digital

00:33:53 [Speaker Changed] Gold.

00:33:53 [Speaker Changed] It’s, it’s, there are no fundamentals, right? With, with, with crypto. And I people will say there are, but there aren’t. Right? It’s, it’s driven by the waxing and waning of stories. And do, do they find purchase? Do they or do they kind of, people get tired of them?

00:34:11 [Speaker Changed] 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 a hundred billion dollars. And so the old story of Defi is gone. And the new story is, oh no, this is an asset class at Wall Street’s embracing. That’s why you have to own it.

00:34:32 [Speaker Changed] That story, that story was, and very, that was a very similar story by the way, or a transition story from physical gold to GLD when that ETF came out. Right. Which was a very similar pattern because once it became a Wall Street, a, a table at the Wall Street Casino, right? Then it takes on a different meaning specifically around gold. Gold changed from being, okay, something that you bury in your backyard or you’re having your vault, you know, along with ammo and seeds for when the, the, the hard times come

00:35:11 [Speaker Changed] Bottled water, right? Bottled meals ready to eat, right, gold and lead. It

00:35:15 [Speaker Changed] Becomes a security. And its meaning changes from that, you know, apocalyptic bottled water. Right? 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.

00:35:35 [Speaker Changed] And is that the dominant narrative that you’re identifying as gold rallied over through 4,000? Ab

00:35:41 [Speaker Changed] Ab Absolutely. So, I mean, we’ve really been able to track that one in

00:35:46 [Speaker Changed] Specifically. So, so here’s the really big, here’s the million dollar or trillion dollar question. How do you identify a narrative and say, oh, gold is gonna double from here based on this narrative? Or, or are we not there yet?

00:36:00 [Speaker Changed] No, you, you identify the narratives. ’cause the narrative, the stories don’t ever change, right? So the, the story that leave gold aside for a while, think about you we’re talking about your bullish on company X, Y, Z 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, right? They can be management change, top line growth, opportunity consolidation in the industry,

00:36:35 [Speaker Changed] Upcoming catalyst,

00:36:36 [Speaker Changed] Catalyst. So, so every catalyst story, new

00:36:38 [Speaker Changed] Products, new product, FDA’s gonna prove the new mo, new drug, new molecule.

00:36:43 [Speaker Changed] So that story, you just change the name. That’s the same story that’s repeated over and over again about any pharma or, or, or biotech company. The stories, we think that they’re amorphous and variable. The fact is that the, 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, and it’s, it could 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,

00:37:41 [Speaker Changed] But,

00:37:42 [Speaker Changed] But the, I think the really interesting stories are the stories that have been dormant for a long time and they start picking up again,

00:37:48 [Speaker Changed] You, you are reminding me of Campbell’s hero’s journey, that there’s only so many

00:37:54 [Speaker Changed] My hero, right? There are only so many stories Right. In the, in, in the world. And that, that’s a now I like to talk about in Hollywood, famously there are only like five scripts.

00:38:08 [Speaker Changed] That’s right. Right?

00:38:10 [Speaker Changed] Tolstoy is supposedly Tolstoy. He said there were 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. And

00:38:20 [Speaker Changed] Not quite, but he’s, he’s

00:38:21 [Speaker Changed] Not quite but you on, he’s pretty close. You’re on the right track.

00:38:23 [Speaker Changed] Right? Right. The

00:38:24 [Speaker Changed] The point is, there’s a finite number of stories, right? 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, 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.

00:38:50 [Speaker Changed] How so? Let’s, let’s delve into that. Yeah. So, so how do you, you have this massive database you’re sucking in every news feed. Yep. Everything magazine, newspaper, everything. Anything that you could quantify and, and run into a linguistics model. What’s the process for analyzing this? How to use artificial intelligence to, to go through this, and how do you make sense out of that heap of how do you find signal amidst all that noise?

00:39:23 [Speaker Changed] The crucial thing is you can’t just ask AI an open-ended question. Say, what are the narratives in Right? This comp 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 chat, GPT or or or whoever. The problem is chat, GPT will give you an answer.

00:39:45 [Speaker Changed] Just not a good one.

00:39:47 [Speaker Changed] Not a good one. It’ll hallucinate a lot. Yeah. Right. It’ll go out, it’ll find its own data. The, the secret to 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, 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.

00:40:31 [Speaker Changed] So I’m hearing dataset is controlled by you as well as the thinking prompts. The seman.

00:40:39 [Speaker Changed] Yes. And it’s more than prompt, right? So the, it it, it includes prompt. But the, the phrase that’s used in this world is called not prompt engineering, but context engineering.

00:40:53 [Speaker Changed] Okay. That makes sense.

00:40:54 [Speaker Changed] So 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, the thing. And, 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. So it’s

00:41:18 [Speaker Changed] Consistent

00:41:18 [Speaker Changed] For this to be true for, 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 inputs. 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, the skeleton. You have to tell it. These are the thoughts you are allowed to think about. You know what, and those are the, those are the, the signatures.

00:41:53 [Speaker Changed] I, I’ve kind of learned, I have to avoid asking questions that have a, a, a human emotional subtext. Like, tell me what was most surprising about this? Doesn’t know what a surprise is. Tell me what was most interesting about this. It does, it’s not able to do that. You really have to treat it like it’s a dumb machine.

00:42:18 [Speaker Changed] Well, that’s right. This is why, I mean, you treat it, you, 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, right? 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.

00:42:48 [Speaker Changed] I, I find when I, I, I try and prompt, Hey, tell me about Ben Hunt’s background and gimme the timeline of his career that it’s good at. Hey, what was Ben Hunt really good at? It has, has got no idea.

00:43:04 [Speaker Changed] So what, 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 the, the about stocks or politics. We tell two types of stories. We tell descriptive stories. Oh, the, you know, the fed cut rates by 25 basis points today. A descriptive story. But, and we can also tell the description of the, you know,

00:43:45 [Speaker Changed] It’s the because clause, because we’re seeing slowing increasing layoffs and slowing consumer terms.

00:43:53 [Speaker Changed] And, and that’s high resolution and dec and, and very descriptive, right? The, the 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,

00:44:13 [Speaker Changed] Meaning,

00:44:13 [Speaker Changed] 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.

00:44:33 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, 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.

00:45:18 [Speaker Changed] So you are in the business of analyzing the world’s narratives every day. How is that even possible? It seems like that is an impossible

00:45:29 [Speaker Changed] Test. That’s seem crazy.

00:45:30 [Speaker Changed] Crazy, right?

00:45:31 [Speaker Changed] And, and, and it used to be, it used to be crazy. I, I mean, Barry, I I really do remember my, 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.

00:45:46 [Speaker Changed] I remember those machines in the library. You

00:45:47 [Speaker Changed] Remember those

00:45:48 [Speaker Changed] Machines? Yes.

00:45:48 [Speaker Changed] Yes. You remember those machines? I would code, hand code the, or hand record the coded data. I would type it into remote access for a digital equipment mini frame. And the next day, you know, something would churn out for me. Right? Today it’s, I say we are, we’re processing hundreds of billions of tokens. We get millions of documents overnight like that.

00:46:16 [Speaker Changed] Is there there

00:46:17 [Speaker Changed] Any, is there infinite computing resources available to us? I,

00:46:20 [Speaker Changed] Is there anything you can’t access that you wish you had access to any data source?

00:46:28 [Speaker Changed] 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 in their wrong that they’re good at it. Right? They’re that they’re good at analysis. I personally don’t think they are. Well,

00:46:49 [Speaker Changed] Let, let’s just say some are better than others. Some

00:46:52 [Speaker Changed] Are better than others, but, but none of ’em are really, if if they were, if there was, I’ll call it kind of significant alpha there, they wouldn’t be a bank.

00:47:04 [Speaker Changed] They’d be a hedge fund.

00:47:05 [Speaker Changed] Yeah. They wouldn’t be a ba on the, 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 wanna see what they’re all talking about,

00:47:17 [Speaker Changed] Right? It’s reflective of, if not a consensus. Yeah. Certainly a a popular set of ideas.

00:47:24 [Speaker Changed] And, and this is a crucial thing to talk about what we do. I I don’t know what the truth is, right? I have

00:47:31 [Speaker Changed] No idea. Does it matter?

00:47:32 [Speaker Changed] And I don’t think it matters. I I want to provide this information to people who do have a view on the truth. Let’s say you’re a, you’re a value investor, right? You’re running a fund, you’ve got your views, you’ve done a, your homework, you’ve done your research,

00:47:47 [Speaker Changed] You’ve got a good back test. Yeah.

00:47:49 [Speaker Changed] You, 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 wanna buy it, and then I’m just gonna wait. I gotta 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.

00:48:31 [Speaker Changed] So you’re catching the early lift off the bottom. That’s the

00:48:35 [Speaker Changed] Goal. I see. When the, when, when a value investment only works, when the market recognizes it. And we are tracking when something like that gets discovered by the market.

00:48:46 [Speaker Changed] So before Nvidia is 5 trillion, when it, when it starts ramping up to 500 billion, hey, something’s going on here.

00:48:57 [Speaker Changed] What I have 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 trade. 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,

00:49:19 [Speaker Changed] Then it’s reflect then the, the market be ev is eventually efficient.

00:49:23 [Speaker Changed] It’s a different, it is a different risk and reward profile. Let me, let me put it that way. Fair. You, you, 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 the, now you may say, oh, but there’s gonna be this other catalyst. And then say, well,

00:49:45 [Speaker Changed] But that, that discovery phase is, I’m gonna quote Doug, Cass, that’s the, there’s a phrase he uses it, it’s like a, a contrarian perspective. A variant perspective where a perspective, you have some insight that is not widely held. Right. And the market being mostly kind of sorta of eventually efficient if there’s a truth or at least a good story in your variant perspective, all

00:50:15 [Speaker Changed] It is a story, right? All it takes is a story. It’s

00:50:17 [Speaker Changed] Gotta be a good story though. Yeah.

00:50:19 [Speaker Changed] It’s gotta be a, well, I’ll call it, it’s gotta be a compelling story, right? Right. And that’s, that’s why you are 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. So let me multiple is story.

00:50:37 [Speaker Changed] So let me ask you a few more questions on, on Perent before we go into a few other areas. Sure. So first, you, you’ve been doing this for seven years. What are you doing today? That was unimaginable 4, 5, 6, 7 years ago.

00:50:54 [Speaker Changed] I, I tell you something that was unimaginable really two years ago. That’s amazing. 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, you know, we did our first models and our first versions of this, and we licensed it to some big banks. And here’s the problem, Barry. We, 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, oh, that, that signal works. You know, good, good. Hit on the, the signal that we put up. The problem was we were missing too many damn fish, right? Our, our net was too small. And we’d say, oh, we got this one. And then we’d look back at whatever it was.

00:51:46 We were designing the model. So we 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, right? So this is the whole notion of embedding. So that there are a million ways to say I’m bullish about the management change at company X, Y, Z. 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. Oh, really? You miss a lot. Yeah. You’ve made a small net and what you’re trying to, to, to,

00:52:33 [Speaker Changed] And a and a small data set, and it’s

00:52:35 [Speaker Changed] A pretty small data set. So we, 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 five, maybe even six x improvement in our signal

00:53:05 [Speaker Changed] Catching. And what did you end up with over

00:53:06 [Speaker Changed] A hundred x?

00:53:07 [Speaker Changed] That’s unbelievable.

00:53:08 [Speaker Changed] Over a hundred x, it’s over. And now it’s, we’ve had a multiple of that. Again, it’s, it’s, it is hard to describe in, in the, in the, the expansion of the net is in so many different directions. Everything we were doing before was just English language,

00:53:23 [Speaker Changed] Right? So now you’re global and how many languages are you pulling into the,

00:53:29 [Speaker Changed] Anything, anything that an AI has been trained on, we read it and we can say we can,

00:53:35 [Speaker Changed] Is that 40 languages? Is that like how many languages? Well, there

00:53:38 [Speaker Changed] Are only a, you know, there are about a dozen languages that are useful in markets. So

00:53:43 [Speaker Changed] It’ll be Japanese, Chinese, a variety of European languages. So

00:53:47 [Speaker Changed] We can, we can

00:53:48 [Speaker Changed] Tell Indian,

00:53:49 [Speaker Changed] Here’s the, here’s a story here. The story about Chinese domestic markets. There’s a western story, western narrative,

00:53:57 [Speaker Changed] And there’s a domestic Chinese story. And the domestic

00:53:59 [Speaker Changed] Chinese story,

00:54:00 [Speaker Changed] Which is very different.

00:54:01 [Speaker Changed] Often it’s very different. So we were able, for example, picking up, we were able to pick up way before it, it, it got picked up in Western press. The demand for luxury goods in China went off a cliff.

00:54:19 [Speaker Changed] Cliff, yeah.

00:54:21 [Speaker Changed] In last November.

00:54:23 [Speaker Changed] 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. Well,

00:54:31 [Speaker Changed] And, and, but, but it’s interesting, right? Because, because they’ve had, you know, 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, in, in November from the domestic Chinese media.

00:54:58 [Speaker Changed] So, so that raises a, 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, how does what you are building help your clients generate alpha?

00:55:15 [Speaker Changed] What we think we’ve discovered is an entirely new source of data. And what we’re confident is 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 or nice words. By

00:55:37 [Speaker Changed] The way, that was a big thing, I dunno, was that 10 years ago we’re s we’re scanning Twitter to identify investor sentiment. Oh

00:55:44 [Speaker Changed] My god. I mean I, but I was just looking today and not to pick on Bloomberg, but you know, it was their live coverage of the Fed statement, right? Were at those meeting, they were analyzing the sentences and the fed statement for hawkish and dovish sentiment,

00:56:02 [Speaker Changed] How it changed from the last meeting. And,

00:56:05 [Speaker Changed] And

00:56:05 [Speaker Changed] So, so so you’re saying there’s not a whole lot of signal there.

00:56:08 [Speaker Changed] Hmm. I wanna be careful with what I say, right? Which is that there are a number of, I’ll call ’em 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 there, I think you can get something outta that. I do. And so there are firms that can do that very well. That’s not our game, right? This is not the, what we’re trying to, 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, right? There’s, we’re able to track the actual stories that drive behavior.

00:56:54 [Speaker Changed] So that, that’s the next question. And, and I’m gonna give Dave not a 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, a significant tradable signal?

00:57:14 [Speaker Changed] I don’t. Right. So this is, my goal is not to, oh, you know, do the trades. My goal is Mr. Hedge fund guy, Mr. Asset, allocator, you’ve got a, 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 would, what, what you wanna 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.

00:57:59 [Speaker Changed] So let’s talk about a few things related to that. Can I say one more

00:58:02 [Speaker Changed] Thing? Sure. And it’s not just about using this for investment. So we have a product for financial advisors, right? Which is your clients coming in, you’ve got a portfolio. You need to be able to say, what, what is, what is my client? What, what are they worried about? What are they nervous about? What are they hopeful for? What are the stories they’re reading?

00:58:26 [Speaker Changed] And you are pulling this out of the flow of media and

00:58:29 [Speaker Changed] We can tell you exactly for of, for the portfolio you’ve got for that client, here’s what they’re gonna be asking about, worried about. And here are the answers you can give them to show this is when has happened before, when this story has come up. Stay the course, it’s gonna be fine. This is the sort of stuff we can do for financial advisors. Huh.

00:58:46 [Speaker Changed] That’s really interesting. I would, I would love to see some of that. It’s not just

00:58:49 [Speaker Changed] In markets. Barry, I gotta tell you, the

00:58:53 [Speaker Changed] Policy makers, corporate executives, bankers,

00:58:56 [Speaker Changed] Policy. So we did a, you know, 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, right? The book 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 and saying, friends, this isn’t gonna li be a limited thing.

00:59:22 [Speaker Changed] This is happening.

00:59:23 [Speaker Changed] This is happening. It’s gonna be a full scale invasion. Because that was the messaging in domestic Russian media to the Russian people.

00:59:31 [Speaker Changed] Wow.

00:59:32 [Speaker Changed] So brands, right? So you are, you know, we’re working with some, some it sounds, but pro sports teams, you want to tell a story to

00:59:44 [Speaker Changed] Your fan

00:59:45 [Speaker Changed] Base, build a stadium,

00:59:46 [Speaker Changed] Sell some tickets to sell some

00:59:47 [Speaker Changed] Stadium. Yeah. Sell tickets, build a stadium, gimme some examples of people who have told good stories and how did that work for them? How can we do the same thing? So let’s,

00:59:56 [Speaker Changed] Let’s just start looking

00:59:57 [Speaker Changed] At storytelling.

00:59:58 [Speaker Changed] 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

01:00:19 [Speaker Changed] Suffered? A percent

01:00:20 [Speaker Changed] Suffered such a backlash. Because you

01:00:22 [Speaker Changed] Know what we have today? We have polls. Right? Right. Because,

01:00:24 [Speaker Changed] Which are terrible,

01:00:26 [Speaker Changed] Terrible, terrible. Right. Which are mostly terrible now, you know, and, 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?

01:00:34 [Speaker Changed] 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?

01:00:45 [Speaker Changed] Terrible at both of those things.

01:00:45 [Speaker Changed] But if you put a little money on it, alright, maybe they might be a little more circumspect that,

01:00:50 [Speaker Changed] That, that helped. That helps a ton. So, so in places where you can have a make a bet, I think that improves the kind of information you can get. It still lends itself to a lot of manipulation. Sure. And a lot of,

01:01:02 [Speaker Changed] They’re not issues around it, poly markets and cashie and all those things. It’s not the bond market. It’s not a hundred and something trillion dollars. It’s a couple of bucks on each of these. And sometimes that’s right, a million dollars moves a half a million dollars can move a market.

01:01:17 [Speaker Changed] 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. Right? Right. You, but, but you really wanna know the, these, 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, it’s pretty wild there. 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, it’s like, I feel like it was like when they invented whoever was Lovin Hook or whoever invented the microscope when you’re actually to see something that we all know is there,

01:02:05 [Speaker Changed] Well now we do back then. Right? Like remember germ theory took what a century to catch on it.

01:02:10 [Speaker Changed] It took a century. Yeah. And it takes, it takes seeing it, right? It takes the instrument to actually measure it before you actually believe in it. And so I, I, 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.

01:02:29 [Speaker Changed] So, so we’re having this conversation with market at all time highs, and you’ve written about the ravine. Yep. Tell us a little bit about what is the ravine, how does, does your data identify that? Tell us what this means.

01:02:46 [Speaker Changed] Well there’s, there’s, there’s clearly been a change in policy regime out of, of Washington and

01:02:53 [Speaker Changed] New administration, and a radically different,

01:02:56 [Speaker Changed] Radically different

01:02:57 [Speaker Changed] Approach, even from the first Trump presidency,

01:02:59 [Speaker Changed] Even from the, the, the first presidency. And so what we’re able to, 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 they, they, they, 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 it, if you look historically how that plays out in countries, it doesn’t, it doesn’t play out well.

01:03:56 [Speaker Changed] Civil war, domestic political violence, things like that.

01:04:01 [Speaker Changed] Sadly. Yes. Exactly like that. Exactly like that. So there’s that element. And so that’s a, that’s a sad one, you know, or a, a very troubling one and trying to, well, how do we navigate that? But even when in, in markets, and I alluded to this earlier, how capital markets have become a political utility and the, 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. And I

01:04:38 [Speaker Changed] End of day options and speculation, what else is it supposed to be there for?

01:04:43 [Speaker Changed] But that, but that’s kind of what I’m getting at, Gary, right? You, you can see an enormous change in the meaning of markets. And it connects with a, yes, they’ll call it the speculation layer, but it also connects with financial nihilism, yolo, it, it, 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

01:05:20 [Speaker Changed] Coming up. We continue our conversation with Ben Hunts, president and co-founder of persuent, discussing how money managers use their output to generate alpha. I’m Barry Ritholtz, you, you’re listening to Masters in Business on Bloomberg Radio. I am Barry Ritholtz. You are listening to Masters in Business on Bloomberg Radio. My extra special guest today is Ben Hunt. He is president and co-founder of Persuent, a 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. A lot of

01:06:35 [Speaker Changed] It’s public. Yeah.

01:06:36 [Speaker Changed] 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. ’cause I don’t know what’s gonna happen. Yep. 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. Yep. 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 Perent inform the end of Pax Americana? Because I, I get the sense that domestically that wasn’t really where a lot, what, how a lot of people were thinking. But I got the sense from overseas that was a much more common thought. Tell, tell us a little bit about both the piece and, and how the data informed it.

01:07:55 [Speaker Changed] I’ll start with the, I’ll call it the, the international dimension. I wanna come back to the domestic dimension because I, I think we’ve got some really interesting data recently to, to, to share. It goes back to this, yeah. Again, I, I cringe what I was talking about, but, but, but it is, it is game theory, right? And all game theory is, is strategic interaction. It’s that the, 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 their, in the policies that they, that, that they implement this. 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 ’em 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 it’s all these set of policies. What you end up with is less potential economic growth, trade, all all of these things,

01:09:12 [Speaker Changed] All the things that we enjoyed post World War ii, that realignment, that imbued to our benefits so greatly,

01:09:20 [Speaker Changed] Enormously to our, to our, to our, to our advantage. Our advantage, yeah. Enormously to our advantage. It, it goes by the name of a soft power and you know, the dollar system, the bread, Bretton wood system, all of this

01:09:30 [Speaker Changed] Reserve currency on and off

01:09:32 [Speaker Changed] The 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 cost to that, particularly on defense and some other, some other areas. Tariffs are in other area where there’s absolutely free riders on that. So there’s improvement that can be made in that system, for sure. Right. 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, between countries. So that’s what the note was about. And it’s, it’s, I’m not trying to predict, I’m just, I’m just trying to observe that. And which is a great line actually by George Soros, which is, you know, I’m, 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 is trying to tell you what is true in the present today.

01:10:30 [Speaker Changed] I, I’m amazed how many people think they can forecast the future when they have no idea what’s happening. Right. I

01:10:36 [Speaker Changed] Wanna, I want to now cast what I want do. That’s exactly, yeah. Not, not forecast. I want to now cast. And that gets the, 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 simp

01:10:59 [Speaker Changed] This is during the run up to the election.

01:11:01 [Speaker Changed] Yes, exactly. And what you saw, and really going back, ’cause we can take, we take this stuff back for a decade or more. And what you absolutely saw up to last October, there’s a, 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 isn’t as, as Americans saw a steady increase starting with the, they’re eating the, the cats.

01:11:37 [Speaker Changed] They’re eating the cats, they’re eating their dogs. They’re

01:11:40 [Speaker Changed] Eating the dogs. When you saw one, the, the Columbus mo when you, that, that moment,

01:11:45 [Speaker Changed] One of the most, that’s where things, the real moments in debate history.

01:11:49 [Speaker Changed] And we see very clearly in our media data, and this is, this is not mainstream media. This is everything that we’re pulling up. We’ve seen an, 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. Huh? This is 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.

01:12:38 [Speaker Changed] 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?

01:12:49 [Speaker Changed] It started changing right? In October because it, following

01:12:52 [Speaker Changed] The dogs,

01:12:53 [Speaker Changed] Cats, it was too far. It was, it was like, this is just silly 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 I immersed in Twitter stuff. But, but,

01:13:06 [Speaker Changed] So this

01:13:07 [Speaker Changed] Is, our country is actually quite pro-immigrant. I know that sounds cr

01:13:13 [Speaker Changed] 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, in the entirety of the US history, 2025 looks like the first year where the US population will decrease. Yeah, yeah, yeah. So it’s a decrease in legal immigrants. Not, not only illegal immigrants, but legal immigrants. Add that with the deportations and just people staying away.

01:13:41 [Speaker Changed] Well, this, this gets back to the, the, the economic picture and from other, so what, what you’ve had is a clear, and again, we, we, we see this in our data from other countries. There’s a clear effort to repatriate assets and funds away from the us. There’s been a clear effort outside the us You see this with central banks, but also with

01:14:03 [Speaker Changed] Cor hence gold. The big and gold. Gold. Correct.

01:14:07 [Speaker Changed] Treasuries used to be a safe haven asset. Right?

01:14:11 [Speaker Changed] No

01:14:11 [Speaker Changed] More. That’s, that’s, it’s, it’s,

01:14:13 [Speaker Changed] Do you understand how significant that charge is? That you’re making, you’re basic, enormous, basically accusing the president, well, of submarining one of the single greatest assets America halts. Well,

01:14:26 [Speaker Changed] That it’s a, it is built up to such a, 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.

01:14:40 [Speaker Changed] Right? We, we saw it for like a week in April, and then that was quickly

01:14:44 [Speaker Changed] Reversed. That was it. So there’s, there, there is, there’s no capital flight there, there are no,

01:14:47 [Speaker Changed] But there was some fear US

01:14:48 [Speaker Changed] Investors that leaving the country that 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.

01:15:00 [Speaker Changed] Slowly measured, balanced,

01:15:01 [Speaker Changed] Melting iceberg. Right? Because there are limits to, if you’re a Bermuda reinsurer, I mean, you’re kind of, you’re, you’re stuck with treasuries, right? Right. I mean that, that

01:15:11 [Speaker Changed] You’re, you could buy some gold to offset it, but you can’t sell a hundred billion dollars worth of treasuries. No, you

01:15:16 [Speaker Changed] Can’t. You can’t. So it’s a melting iceberg. But we can clearly see that the dog is not barking yet and maybe never will is capital flight. What we see in 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 saw, started seeing a lot earlier where immigration as a thing is actually pretty darn popular in the United States.

01:15:47 [Speaker Changed] Yeah. But so are, so is restricting assault rifles and we can’t get any change on that. I that’s 75, 80% or the actual

01:15:57 [Speaker Changed] Large. I, I gotta tell you, I gotta tell you that depends very much in how that question is asked.

01:16:01 [Speaker Changed] Well, that’s true for all polling. Well,

01:16:04 [Speaker Changed] This is the benefit of what I’m trying. So, so when, when we’re doing these seman, 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, in media.

01:16:20 [Speaker Changed] 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?

01:16:35 [Speaker Changed] I think there’s an impact on the election, the midterms next year.

01:16:40 [Speaker Changed] So we’re talking, we’re talking literally 12 months from now.

01:16:45 [Speaker Changed] Yeah. ’cause that’s how this, the stuff gets ca on the political front narratives and opinions. They get cashed out in elections. Right. Mark are different. You, you cash stuff out every day. The right, the market’s

01:16:59 [Speaker Changed] Open every day. The feedback loop is so rapid with markets.

01:17:02 [Speaker Changed] Ex Exactly. Politics is a different story. So Right. It gets cashed out in the elections.

01:17:08 [Speaker Changed] 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. Yeah, I,

01:17:26 [Speaker Changed] I saw that.

01:17:27 [Speaker Changed] And, and that’s an amazing swing. And I’m, I’m saying to myself, you know, listen, the out of power party usually picks up, you know, 10, 15 seats. Yeah. 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.

01:17:55 [Speaker Changed] It’s not,

01:17:56 [Speaker Changed] It’s not what, so it’s the economy, it’s immigration. What else? What else are you seeing? That’s interesting. That’s

01:18:01 [Speaker Changed] It. That’s, that’s pretty. Now there are other aspects though, where the, the stories, it, this is, this is people talking about immigration as a thing. Now, whether, now whether that gets translated into a political party position. ’cause I gotta tell you, the Democratic party I is enormous enough. But there, there are no, there are no narratives that are being put forward by the Democrats that are powerful. Or, or, or, or popular. So I

01:18:31 [Speaker Changed] I I don’t, other than the mayoral contest in New York. Yeah,

01:18:34 [Speaker Changed] Yeah.

01:18:35 [Speaker Changed] Right. That what I, that’s this

01:18:36 [Speaker Changed] One. What I’m saying is that the, is that the 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,

01:18:58 [Speaker Changed] Tariffs aren’t really popular amongst, but people perceive that as a tax increase or, or a pressure on small business

01:19:07 [Speaker Changed] Government shut down. Right? So both also not popular. And also in this, it, it’s, it’s that ’cause the, 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

01:19:19 [Speaker Changed] Pay, pay and by the out and listeners should know. It is a long way off. You are not a hard, you’re not a Democrat. Oh my

01:19:25 [Speaker Changed] God. No.

01:19:26 [Speaker Changed] Like, like I know you and your politics and the stuff you’re saying, I, I could hear people shrugging and saying, oh, that Ben Hunt is just a liberal Democrat. I’m like, no, no, no. That’s not who Ben is.

01:19:37 [Speaker Changed] No, no, it’s not.

01:19:39 [Speaker Changed] You are just talking about here’s what I see in the data.

01:19:42 [Speaker Changed] I’m saying I’m observing. I don’t, I’m observing. And I think that a lot of times, if we are like me very online and on Twitter too much, right? You,

01:19:53 You don’t see the broader picture of what is happening on blog posts and local newspapers and, and, and everything else. So I think having the ability to read everything and track these stories that, you know, they don’t, they, they, like I say, they wax and wane, but they don’t, they don’t ever go away. The, it’s, it’s, it’s something we’re very excited about to track the stories of America. I’ll, 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? That, that that

01:20:48 [Speaker Changed] Global standard of living,

01:20:49 [Speaker Changed] Global standard of living, that, that America has been this powerful force to, and, and capitalism and America have been this powerful force to raise people outta poverty. That’s a story that in other periods of time has been out, has been prominently talked

01:21:08 [Speaker Changed] About. Very resonant.

01:21:09 [Speaker Changed] That story is non-existent today.

01:21:11 [Speaker Changed] Well, you cut things like HIVA few million dollars to inoculate all of Africa from HIV. That’s gonna have repercussions.

01:21:21 [Speaker Changed] Well, it, it’s, it’s not just that, that that has the con those those repercussions that you’re describing. Right. What I’m saying is that the story that would support that, it’s gone.

01:21:37 [Speaker Changed] Is it gone forever? Has no, has

01:21:39 [Speaker Changed] No, that’s, these things are never gone. These stories, stories have, they never die. Right? They’re, they’re, they’re waiting for a new force to give them life.

01:21:49 [Speaker Changed] The next version of it, if us is not seen as a force for raising people out of poverty around the world, do people see China as that force?

01:22:00 [Speaker Changed] Haven’t looked at that. We’ve been looking at the, the US store. But I, but I I, my my personal sense is, is that we’ve essentially seeded the field

01:22:09 [Speaker Changed] CEDD with a C not

01:22:10 [Speaker Changed] An S Yeah. With a C. Not in s particularly in South Asia and Africa to, to, to China. I, I mean, that, that seems, that seems pretty,

01:22:20 [Speaker Changed] You know, pretty clear. This is, I’m glad I asked the question about the end of Pax Americana. ’cause I, I, I’ve 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, sure. All of our cl all of our guests. But before I do, there’s a question that I, 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 are just starting to sniff out? That most of the world, or most of the country, or most of Wall Street and finance hasn’t seen yet?

01:23:15 [Speaker Changed] It’s a story that people are talking about since tricolor and first brands went out,

01:23:23 [Speaker Changed] Right? The, the,

01:23:24 [Speaker Changed] So it’s the story on, on credit, story of credit, and

01:23:29 [Speaker Changed] I mean, first brands is legitimate. It sounds like felonies were happening there. So we’ll have to see what, how that plays out. I

01:23:37 [Speaker Changed] Probably try color as, as as well. But my point is that every day you have a new bank, CEO come out and say, no, no, everything’s fine. Right? 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

01:24:09 [Speaker Changed] The, you think thou death protested too much. Is it that

01:24:12 [Speaker Changed] Well, I’m, I am I, all I’m saying is I am observing. I’m not predicting, I am observing the level of volume for Hmm. Alternative asset managers, their exposure to private credit. This is problematic. Oh, I’m wondering what’s gonna happen when the music stops. Oh, I think that these, 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. Hmm. At any point, including during COVID during when you had similar concerns over, oh my god, private credits.

01:24:51 [Speaker Changed] I was easy to rationalize everything. Hey, listen, we’re all frozen. Just ignore it.

01:24:55 [Speaker Changed] But I, 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. Right.

01:25:13 [Speaker Changed] It’s immediately what I thought of as soon as you

01:25:15 [Speaker Changed] Mentioned it. Absolutely. It does not get fixed by people saying, don’t worry, there’s no problem.

01:25:21 [Speaker Changed] Jim Grant said he was right. It was contained to Earth. Yes. Yes. The rest of the solar system was fine. Which turned out to be a very witty and clever observation. This

01:25:31 [Speaker Changed] Story, this worry, this concern absolutely has legs. And we’re seeing no signs of it easing off in financial media and press. So

01:25:43 [Speaker Changed] The, the big question is, is it systemic? Or I don’t, is it the specific, like

01:25:49 [Speaker Changed] I say, I, I don’t know the truth, right? I don’t know reality. All I can tell you is

01:25:55 [Speaker Changed] We’re seeing more of this.

01:25:56 [Speaker Changed] Well, we’re seeing this is, this is the story.

01:25:59 [Speaker Changed] Huh. Really, really interesting. Let, let’s jump to our favorite questions, starting with who are your mentors who helped shape your career?

01:26:09 [Speaker Changed] Hmm. That’s good. Well, I, you know, I mentioned that the people both in undergrad and graduate school, who turned to 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 now.

01:26:34 [Speaker Changed] Well, Cialdini, of course.

01:26:36 [Speaker Changed] So the, the notion of inference taking large data sets and pulling out,

01:26:41 [Speaker Changed] Oh, inference. I thought you said influence. No, no, no, no. Inference. Inference.

01:26:46 [Speaker Changed] So the science of inference, the, I learned that from Gary, you know, 30 years ago. And it’s so interesting to see that come full circle because that’s at the core of, you know, Jensen Wong’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.

01:27:14 [Speaker Changed] Let’s talk about books. What are you reading now? What are some of your favorites?

01:27:19 [Speaker Changed] I’m a science fiction guy.

01:27:21 [Speaker Changed] Okay. I really am. You’re talking to another sci-fi guy. So let’s, so I I I give us something new and something classic. Well,

01:27:27 [Speaker Changed] Something classic would be Lu Hin and the Three Body Problem. Right.

01:27:32 [Speaker Changed] By the way, the Netflix show on that was surprisingly watchable.

01:27:36 [Speaker Changed] I thought it was excellent before that. And you know, I named the company after him was the Foundation Trilogy

01:27:44 [Speaker Changed] Asimov with

01:27:44 [Speaker Changed] Asimov. Those books hold up Less well, honestly. And I thought the, the series was, it had its moments. I

01:27:50 [Speaker Changed] Didn’t love the series. Yeah.

01:27:51 [Speaker Changed] It was, it was so pretty soso to me. Current science fiction. Rebecca goes, she goes by RF kwong and she wrote a book called Babel, and she’s got a new one out two B or

01:28:07 [Speaker Changed] One BBAB.

01:28:08 [Speaker Changed] Like Tower of Babel. Right. B-A-B-E-L. And she’s got a new one out Caba I think it is, but science fiction dealing with language and linguistics. I love that stuff.

01:28:19 [Speaker Changed] That, that, I love that stuff. That sounds like that’s right in your, your sweet spot. Let’s talk about streaming. What are you watching or listening today? Podcasts or Netflix or whatever? So,

01:28:29 [Speaker Changed] Honestly, I don’t listen to any podcasts. Isn’t that terrible to admit?

01:28:31 [Speaker Changed] Well, if you host, I will say this. If you host a podcast, you’re either preparing for a podcast Yeah. Doing a podcast or ordering a podcast. Alright. Right. So it’s like, all right. That’s three or four.

01:28:43 [Speaker Changed] That’s all it is. Yeah.

01:28:44 [Speaker Changed] Right. Every now and then I’ll, I’ll catch something. ’cause I wanna either listen to this, a specific guest, like IWI will not listen to any podcasts that you are on before we do our podcast. Right. ’cause I don’t wanna steal anybody else’s Yeah. Or narrative or questions or line of thinking. So I, that’s purposeful. But every now and then, so I listen to, the things I listen to are like John Pirelli’s Radio Deluxe. Yeah, yeah. Which is sort of like a podcast slash music series. Something

01:29:17 [Speaker Changed] A little different. Yeah, exactly Right. What,

01:29:20 [Speaker Changed] What about streaming? What are you watching? Well,

01:29:22 [Speaker Changed] You, you and I are both Big Godfather fans. So we love all the, the mobster. I Have you seen Mob Land yet? With, with No. Alright. Worth your time. Okay. Pierce Brosnan eats up every scene. Tom Hardy is awfully good. I just love him in anything. If you, I don’t know if you ever saw Peaky Blinders, right. Started as a modern

01:29:40 [Speaker Changed] Version of Pinky Blinders. I started, I kind of fall, so couldn’t get into it. So my issue is I have to watch something that my wife tolerates. Like I have, I have friends

01:29:49 [Speaker Changed] That’s a struggle

01:29:50 [Speaker Changed] 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. Like it’s, she’ll watch some stuff without me. I will watch some stuff without her. Right.

01:30:02 [Speaker Changed] You gotta have your own space.

01:30:02 [Speaker Changed] But 80% of what we watch, so I have fallen that’s hard down the British upstairs. Downstairs Gilded Age. Yeah. So we watched The Crown, we watched the Gilded Age. We finally, let

01:30:15 [Speaker Changed] Me give you one, lemme lemme

01:30:16 [Speaker Changed] Give you one. We went back to Downton Abbey, which I missed when I first rolled out.

01:30:20 [Speaker Changed] Check out. So my guilty pleasure, and I do watch this with my wife, is a diplomat.

01:30:25 [Speaker Changed] Ju the new series, just the new season just dropped new season. Yeah. And that’s teed up for this weekend. I’m looking forward to that. Excellent. That the first, was this season two or season three? The first season was great.

01:30:36 [Speaker Changed] Yeah.

01:30:37 [Speaker Changed] Yeah. And I’m gonna give you, if you like that. So my wife finds these really interest. 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, oh,

01:30:52 [Speaker Changed] That’s the one I’ve gotta see. Yeah.

01:30:54 [Speaker Changed] It’s really, that’s the one. So the first season is great and people have told me the most recent season, it, it, some people will say, I don’t like the second kinds in the middle. I, I know people who loved it all the way through. But it’s, it’s really, it, it’s really an interesting well told beautiful cast. Just fantastic. Yeah. You’ll, you’ll love that. Our final two questions. Yeah. 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? Hmm.

01:31:35 [Speaker Changed] Don’t, and I, I say that tongue in cheek. I I,

01:31:41 [Speaker Changed] You had a lot of fits and starts going back seven, eight years

01:31:44 [Speaker Changed] For, for sure. And I, I tell you what I, I think’s I important you have to, what, what you, 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 the issue is though, is that once you enter, particularly the investment world where particularly if you’re responsible for managing other people’s money, brother, that’s it. Right? I mean, you’re, you’re, there’s, there’s not, you’ve, that’s got to be your, your total focus. You are spending your intellectual capital, you’re not gaining intellectual capital once you take on a role like that. So I, my first advice is find a a path where, and that’s often in, in, in academia where you’re building intellectual capital. Because once you leave that environment,

01:33:02 [Speaker Changed] Then you’re spending it down.

01:33:03 [Speaker Changed] You’re spending it down.

01:33:05 [Speaker Changed] Makes sense. You’re spending it down. Our, our final question. What do you know about the world of, fill in the blank in investing data analytics? Narrative storytelling today would’ve been helpful 25, 30 years ago

01:33:22 [Speaker Changed] In investing. I wish, I wish I had understood the role of, lemme step back a second. I, I think that whether you’re talking about data or whether you’re talking about investing, and 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, 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.

01:34:00 [Speaker Changed] And you have learned since then,

01:34:03 [Speaker Changed] Ain’t no such thing. Right now, there is magic and there are patterns, but it’s, it’s, 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, you know, the stochastic element, right? The, the, 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 process with a capital

01:34:43 [Speaker Changed] PII 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. Yeah. 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,

01:35:21 [Speaker Changed] 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?

01:35:31 Which is, which is really interesting because it’s this, this business of investing. It’s a wonderful life. You were solving puzzles, we meet interesting people. We get to have these sort of conversations. It’s a, it’s a career for a lifetime. And the two pieces of advice are really, it never go all in. And also, reputation is absolutely the most important thing. And again, especially when you’re young, you think, oh, well that’s not the most important thing. You know, 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 reput reputation

01:36:16 [Speaker Changed] Is never go all in by never go in. You mean never put everything at risk so that if it doesn’t work out, you’re, you’re outta the game. You’re done, you’re out.

01:36:25 [Speaker Changed] Stay in the

01:36:25 [Speaker Changed] Game was the Gerald Loeb’s book, the Battle for Investment Survival? Stay

01:36:29 [Speaker Changed] In the Game, avoid that risk of ruin You, you, you just, or don’t ever risk ruin.

01:36:36 [Speaker Changed] That makes sense, right?

01:36:37 [Speaker Changed] Because once

01:36:37 [Speaker Changed] You, people don’t think in those terms, but that’s really, they don’t, they don’t all in means risk of ruin.

01:36:42 [Speaker Changed] It’s the risk of ruin. And it also connects with reputation because once you,

01:36:50 [Speaker Changed] Once you blow up, it’s tough to go back

01:36:53 [Speaker Changed] And you start saying, oh, I can fix it by taking this shortcut or doing this other thing. Right? And once you do that never ends Well, it never ends well. Right? And you tell yourself, oh, just this one time. It’s never this one time,

01:37:07 [Speaker Changed] Ben, this has been absolutely delightful. I’m so glad we’ve finally me too gone around to doing this. I, I 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 ’cause it forces me to say, well, he’s not just making this up. I know how you, your brain works. And it’s like, all right, if Ben is saying this, then I’m gonna make this my worst case than I’m, ’cause it’s easy to dismiss it. Everybody gets out seeking, confirming information and rather than being dismissive of it, alright, 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. Well, thank you. And it really, it really helped me figure out how to think about, especially in that week between April 2nd and ninth, where everyone was losing their mind. It’s like, oh no. So they’re losing their mind. ’cause hey, this could happen. But maybe something good comes out of it. Trying

01:38:26 [Speaker Changed] To avoid tunnel vision. I think it’s so important in our business or any business, but especially our

01:38:31 [Speaker Changed] Business. In our business in particular. Well, thank you for being so generous with your time. We have been speaking with Ben Hunts. He is the co-founder and president of Prescient. 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 behaviors that destroy wealth and how to avoid them at your favorite bookseller. I would be remiss if I did 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. Short Sage Bauman is the head of podcasts here at Bloomberg. I’m Barry Riol. You’ve been listening to Masters in Business on Bloomberg Radio.

~~~

 

 

 

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My morning train reads:

• Bill Pulte: Agent of Chaos: In an administration hardly known for competence, coherence and subtlety, Pulte stands out. When Treasury Secretary Scott Bessent reportedly threatened to punch Pulte “in your fucking face” and to “fucking beat your ass”, it was mostly notable for how many people both inside and outside the administration were seemingly #TeamBessent. (Financial Times)

• Is the Iranian Regime About to Collapse? Five conditions determine whether revolutions succeed. For the first time since 1979, Iran meets nearly all of them. (The Atlantic) see also Weakened by War, Iran’s Regime Faces Its Toughest Challenge Yet: Iranian leaders’ last claim to legitimacy was shattered in the war with Israel (Wall Street Journal) see also Death Toll in Iran May Already Be in the Thousands: To account for what it called a “significant” death toll, the Islamic Revolutionary Guard Corps on Sunday raised the specter of ISIS, claiming in a statement that slain protesters were terrorists hired by Israel and the U.S. (Time)

• How Google Got Its Groove Back and Edged Ahead of OpenAI: After ChatGPT dominated early chatbot market, Google staged comeback with powerful AI model; biggest search-engine overhaul in years. (Wall Street Journal)

• GE’s CEO Has Saved the House That Jack Welch Built: Shares of General Electric Co. have jumped out of the gate this year faster than the gains of the S&P 500 Index. That doesn’t sound like a stellar achievement unless you consider that the stock has already surged sixfold in the last three years. The fact that GE’s turnaround still has legs is testament to the talent of Chief Executive Officer Larry Culp, who took over a legendary company that was in deep trouble in 2018 and rescued it. (Bloomberg)

• Big Oil Knows That Trump’s Venezuela Plans Are Delusional: The president’s thinking is stuck in the 1980s. (The Atlantic)

• Americans Predict Challenging 2026 Across 13 Dimensions: Only the stock market evokes optimism from a majority in the U.S. (Gallup)

• Why Trump wants Greenland and what’s standing in his way: Denmark’s leader warned that any use of force by Washington to seize Greenland, as Trump officials have suggested, would render the postwar NATO alliance defunct. (Washington Post)

• My Playdate With the Watch Nerds: 48 Hours at the Watch Convention: A Casio man in a Rolex world crashes the convention where guys trade six-figure grails for fun. (The Cut)

• Grok Is Generating Sexual Content Far More Graphic Than What’s on X: A review of outputs hosted on Grok’s official website shows it’s being used to create violent sexual images and videos, as well as content that includes apparent minors. (Wired)

• How Amy Poehler’s ‘Good Hang’ broke through celebrity podcast fatigue: The market was saturated with stars chatting with other stars. But the ‘SNL’ veteran channeled the old magic of late-night TV talk shows. Now she’s a favorite in a new Golden Globe category. (Washington Post)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media.

 

Sign up for our reads-only mailing list here.

 

 

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* Hopefully…

 

This is likely the week the Supreme Court issues a ruling on the IEEPA tariffs in place since April 2025 (excluding the 90-day suspension, and other sundry modifications, revisions, pauses, exemptions, etc.). As I noted from the start, this is a deeply flawed policy of questionable legality that was implemented haphazardly (my priors are below).

During the SCOTUS this past November, I explained why the “Tariffs are likely to be overturned.” No new information has come out since then that has changed my perspective.  Indeed, the Customs department was updating its website and added a way for companies to apply for tariff refunds, revealing their expectations about the SCOTUS decision.1

There has been too much “opinionating” about the tariffs, much of which is misguided and/or misinformed. If you are NOT an attorney, perhaps it is best that you do NOT play an attorney on TV.

For investors, the key question is whether a SCOTUS decision affirming the appellate court’s rejection of the tariffs will be easy for the administration to circumvent. The quick answer is that if it had been quick and straightforward to achieve legally, they would have done so initially. Instead, many of the IEEPA tariffs will require congressional approval; others need the type of administrative action that this government has not demonstrated as its strong suit.

Specifically, I surmise it will be more difficult to navigate around these tariffs if the Supreme Court invalidates all or most of these.2

The other question for investors is exactly how much of the tariff repeal is already reflected in market prices. It’s always difficult to assess what is in the collective minds of traders, but consider that since the April 2nd tariff announcements (markets since April 3rd), the S&P 500 and the Industrial sector are up about the same — 29.1% vs 28.7%. However, since the November 5th argument at the Supreme Court, the S&P500 is up 2.4%, while the SPX manufacturing sector is up more than double that at 5.7%.

 

I can imagine that a resounding rejection of the tariffs would cause a healthy rally in the broad indices, led by manufacturing companies, retailers, and any firm deeply involved in importing or exporting physical goods. Perhaps that is my wishful thinking, reflecting how RWM’s clients (including myself) are positioned.

A few reminders that provide additional insight to those who non-lawyers discussing tariffs in the media:

-President Trump’s second‑term tariffs have pushed the average effective US tariff rate to the highest level in more than a century.

-The legal question is whether IEEPA, which does not mention tariffs, gives the executive branch powers that have previously been reserved to Congress.

-The House IEEPA report stated: “Emergencies are by their nature rare and brief, and are not to be equated with normal ongoing problems.”

-Does the word “Emergency” mean what common usage assumes, or is there some other legislative interpretation as to what an “Emergency” is?

-50 U.S.C. § 1702 allows the President to “investigate, regulate, or prohibit” certain transactions and the “importing or exporting of currency or securities;” this does not give the president the authority to “impose unlimited tariffs on goods from nearly every country in the world.”

-In his first term, President Trump applied Section 232 and Section 301 broadly to both rivals and allies; those tariffs were authorized by legislation and used as a central, recurring instrument of economic/foreign policy.

We could go deeper into the weeds, but instead, I will point you to my conversation with Neal Katyal, the attorney who actually won this case at the DC Court of Appeals and argued it before the Supreme Court.

As to my priors, here is my disclosure: Whatever you are reading here is influenced by my understanding of both the law and markets. That includes three major factors:

A) Bad Policy: From an economic standpoint, tariffs are a terrible idea; this has been proven repeatedly throughout history, but most especially through The Smoot Hartley Tariff Act in 1930, which deepened the Great Depression.

B) Illegal: The 2025 Tariffs are wholly and incontrovertibly unconstitutional.1 Article 2 Section 8 of the US Constitution reserves the power to tax, raise duties, and levy exclusively to Congress and not the Executive branch.

C) China has been a bad actor in international trade, engaging in theft of intellectual property, dumping, anti-competitive import rules, and other bad behaviors. That said, there must be a more productive and intelligent way to negotiate with China about its bad behavior.

There is no guarantee that SCOTUS will release its decision this week, but it did fast-track the case back in September. We shall soon find out exactly what this policy, its legality, and economic impact, means to the markets…

 

 

See also:
Trump’s tariff revenue tracker: How much is the US collecting? Which imports are hit?
By Gary Clyde Hufbauer and Ye Zhang
PIIE, December 15, 2025

New Trump tariffs collection hits $200 billion, Customs says
By Lori Ann LaRocco and Dan Mangan
CNBC Dec 15 2025

 

Previously:
Tariffs Likely To Be Overturned (November 5, 2025)

Which States Could Suffer the Most From Trade War Tariffs? (September 16, 2019)

Transcript: Neal Katyal on Challenging Trump’s Global Tariffs (September 8, 2025)

MiB: Special Edition: Neal Katyal on Challenging Trump’s Global Tariffs (September 3, 2025)

Are Tariffs a New US VAT Tax? (March 31, 2025)

The Muted Impact of Tariffs on Inflation So Far (July 17, 2025)

Might Tariffs Get “Overturned”? (July 31, 2025)

The Consequences of Chaos (April 7, 2025)

7 Increasing Probabilities of Error (February 24, 2025)

 

 

__________

FOOTNOTES:
1. “Automated Commercial Environment” (ACE) is a secure electronic portal allowing businesses to file import/export data, trade information, and comply with regulations. This is where companies apply for Tariff refunds.

2. Side note about this particular SCOTUS case: It is so painfully obvious to any student of constitutional law that these tariffs are unconstitutional. It should be a 9-0 or 8-1 decision, but is likely more likely to be 7-2 or 6-3 due to the most extreme and/or partisan Justices.

If it’s somehow not overturned, I will venture that marks the end of the Supreme Court’s credibility as we know it. Even a 5-4 decision will be problematic for the court. We will save that discussion for another time.

My guess? Justices Samuel Alito, Clarence Thomas, and maybe Neil Gorsuch will dissent…

3. Article I, Section 8, often called the Taxing Clause:

“The Congress shall have Power To lay and collect Taxes, Duties, Imposts and Excises … but all Duties, Imposts and Excises shall be uniform throughout the United States.”

 

The post It’s Tariff Week! * appeared first on The Big Picture.

My back-to-work morning train reads:

• Peter Navarro: ‘There’s No Softening on China’ Trump’s longtime trade adviser discusses tariffs, doing business with Beijing, and the Supreme Court case that could define his legacy. (Bloomberg free)

• US prosecutors launch criminal investigation into Federal Reserve’s Jay Powell: Central bank chair says grand jury subpoenas are retaliation for refusal to bow to demand to cut interest rates. (Financial Times) see also U.S. Prosecutors Are Investigating Fed Chair Jerome Powell: A criminal probe looks at his testimony to Congress over central bank renovations. (Wall Street Journal)

• How Google Got Its Groove Back and Edged Ahead of OpenAI: After ChatGPT dominated early chatbot market, Google staged comeback with powerful AI model; biggest search-engine overhaul in years. (Wall Street Journal)

• The Year of the $100 Million House: For the first time ever, every luxury property on the list of 2025’s 10 biggest sales traded at nine figures or more (Wall Street Journal)

• Ten Lessons the Market Taught Us in 2025. Lessons the Market Taught Us in 2025 (Larry’s Substack) see also Even Warren Buffett couldn’t keep beating the market without fail. Here’s why.Would you have invested in Berkshire Hathaway stock at the start of Buffett’s career? (Marketwatch)

• It’s One of America’s Most Successful Experiments, and It’s Coming to an End. Amid an astonishing wave of anti-Indian animus, it’s a question many Indian Americans are asking. In its crudest form, mostly expressed on social media, this antipathy shows up as gutter racism and religious bigotry — an endless stream of invective declaring that Indians have low I.Q.s, worship devils, cheat their way into the country and commit terrible crimes. (New York Times)

• More than 10% of Congress won’t return to their seats after 2026: More than a tenth of the current Congress has now indicated they will not return to their seats after the 2026 midterms, driven by redistricting, retirements and lawmakers running for different offices. (NPR)

• Cuba Is Already on the Brink. Maduro’s Ouster Brings It Closer to Collapse. Cubans speculating about whether their government will be next to fall, with crucial Venezuelan oil imports now in jeopardy. (Wall Street Journal)

• We are all le Carré’s people now: Look into his wilderness of mirrors and see our own world reflected all around. (New Statesman)

• Instructional Manual for Good Writerly Practice: An Interview with Rob Mclennan (Cleveland Review of Books)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media.

Wells Fargo’s economic chart of the year

Source: @MylesUdland

 

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Yes, the HNTI podcast series continues in 2026 (albeit at a much slower pace)

Barry Ritholtz, co-founder, chairman and CIO of Ritholtz Wealth Management, is out with a new book titled “How Not to Invest: The Ideas, Numbers, and Behavior that Destroy Wealth—and How to Avoid Them,” which he discusses along with his earlier book “Bailout Nation: How Greed and Easy Money Corrupted Wall Street and Shook the World Economy.”

Ritholtz is also creator of The Big Picture blog, and creator and host of the Bloomberg podcast Masters in Business. (01/2026)

Via Real Assets ASdviser

 

The post Real Assets: How Not to Invest appeared first on The Big Picture.

Avert your eyes! My Sunday morning look at incompetency, corruption and policy failures:

• Stop, Shop, and Scroll: Behind every influencer is an army of the influenced, many adrift in debt and mass-produced clutter. The platforms need influencers and influencers need audiences — but what the influenced need is not so simple. (The Verge)

• Prediction Markets Are Becoming a National Security Problem: The bets on Maduro’s ouster should be treated like a five-alarm fire. (Slate)

• Under Armour’s Kevin Plank built an empire. Now Rome is burning. The decade brought a stunning collapse. Once a budding rival to Nike, Under Armour’s sales stalled and its stock plummeted. Forbes figures the 53-year-old Plank has lost two-thirds of his net worth. The company fended off allegations of fraud. He stepped aside as CEO. (The Baltimore Banner)

• Peter Navarro, Trump’s Ultimate Yes-Man: The tariff cheerleader established the template of sycophancy for Trump Administration officials. (New Yorker)

• Small-Time Crypto Investors Are Facing Violent Attacks: Rising prices and the irreversible nature of crypto transactions have led to a surge of brutal home invasions and kidnappings. (BusinessWeek free)

• Living in the Lie And How Not To. I find the endless lies and lying often to be the most infuriating and demoralizing part of the present situation. In the wake of this terrible killing in Minneapolis, my first thought, after the initial shock, anger, and sadness, was the knowledge that the next few days, months, weeks, and years would be filled with endless lying about what happened from the regime and its stooges. This filled me, if not quite with despair, then at least with a strong sense of depression. I suppose it has something to do with my vocation as a historian, where I can accept tragedy and even evil as unavoidable parts of the human experience, so long as witness can be borne and the truth eventually discovered.  Unpopular Front)

• The Wrath of Stephen Miller: The man who turns President Trump’s most incendiary impulses into policy. (The Atlantic) see also The turbulent trajectory of Trump’s ‘Nazi streak’ acolyte: Paul Ingrassia’s almost Trumpian survival demonstrates how ideological affinity and personal loyalty can outweigh all other considerations in this administration. (Politico)

• Why are malnutrition deaths soaring in America? “We see it across the board. Every state, every education level, every race, every gender.”  According to data, malnutrition deaths have jumped in the United States, especially among those 85 and older. Here’s why the numbers suddenly started to rise. (Washington Post)

• DOJ admits it has still not released 99% of Epstein files, violating law: The DOJ said it is still reviewing millions of Epstein files, after previously claiming it completed the review in July. (Popular Information)

• The Oddest Couple in American Literature: Norman Mailer and Lawrence Schiller’s Marilyn: A Biography sold more copies than anything Mailer ever wrote. He also believed it cost him a Nobel Prize (Airmail)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media.

 

Inflation rates tended to rise in the 2 years after tariff increases and fall in the 2 years after tariff reductions
Source: Jon Hilsenrath, LinkedIn

 

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~~~

To learn how these reads are assembled each day, please see this.

 

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This week, I speak with Ben Hunt is president and co-founder of Perscient, an AI research firm and software company that pioneered the use of language models and unstructured data analysis for investment strategies. We discuss how emerging narratives move a market, and how AI is revolutionizing Perscient’s computing power.

He explains how the firm tracks the global media narratives, looking at unstructured data and language to identify the stories that are impacting investors.

A list of his current reading/favorite books is here; A transcript of our conversation is available here Tuesday.

You can stream and download our full conversation, including any podcast extras, on Apple Podcasts, Spotify, YouTube, and Bloomberg. All of our earlier podcasts on your favorite pod hosts can be found here.

Be sure to check out our Masters in Business next week with Nobel laureate Richard Thaler and his University of Chicago Booth School colleague Alex Imas on the update and reissue of his classic book The Winner’s Curse.

 

 

 

Current Reading & Favorite Books

 

Books Barry Mentioned

 

Ben’s Published Books

 

 

 

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The weekend is here! Pour yourself a mug of Danish Blend coffee, grab a seat outside, and get ready for our longer-form weekend reads:

• Dopamine Markets: Investing is entertainment, traders are celebrities, BREAKING news comes with live odds, everyone has inside information, the American dream is to get rich quick. (Dopamine Markets)

• The $2 Billion Experiment to Transform Restaurants With Robots and AI: E-commerce mogul Marc Lore’s Wonder is trying to do to takeout what Amazon did to shopping. Do people want food made in a culinary fulfillment center? (Businessweek)

• The Dawn of the AI Drone: In Ukraine, a New Arsenal of Killer A.I. Drones Is Being Born: As the war grinds on, sophisticated Russian defenses have pushed Ukraine to develop a frightening new weapon: semiautonomous killing machines. In the past year, drone warfare in Ukraine has undergone a chilling transformation. Most drones require a human pilot. But some new Ukrainian drones, once locked on a target, can use A.I. to chase and strike it — with no further human involvement. This is the story of how the battlefield became the birthplace of a powerful new weapon. (New York Times)

• The Mischievous Ex-Bankers Behind “Industry:” Konrad Kay and Mickey Down failed as financiers—but they’re making a killing by depicting the profession on HBO. (New Yorker)

• The Front-Runner: California’s Gavin Newsom would rather be wrong than weak. (The Atlantic)

• How to Do Great Work. If you collected lists of techniques for doing great work in a lot of different fields, what would the intersection look like? I decided to find out by making it. (Paul Graham) see also 37 Pieces of Career Advice I Wish I’d Known Earlier: I wouldn’t say I’ve done it all because that’s one thing you learn–how much you have left to learn–but I have done a lot. I’ve had to think a lot about how to be a good employee as well as how to be a good boss. I’ve seen what makes good companies succeed and bad companies fail. I’ve seen how people get ahead…and how people get stuff. (Ryan Holiday)

• How Amy Poehler’s ‘Good Hang’ broke through celebrity podcast fatigue: The market was saturated with stars chatting with other stars. But the ‘SNL’ veteran channeled the old magic of late-night TV talk shows. Now she’s a favorite in a new Golden Globe category. (Washington Post)

• Trump: The Anti-Lincoln: On emergency power, constitutional preservation, and the regime crisis now unfolding. (Notes from the Circus)

• A New Bridge Links the Strange Math of Infinity to Computer Science: Descriptive set theorists study the niche mathematics of infinity. Now, they’ve shown that their problems can be rewritten in the concrete language of algorithms. (Wired)

• Inside the Lost ‘Star Trek’ Movie That Would Have Rebooted Kirk and Spock at Starfleet Academy:  How this bold prequel plan unraveled amid studio politics, fan backlash and cast fears. (Woman’s World)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media

 

The US ended the year with an effective tariff rate of more than 10% — the highest since WW2

Source: Financial Times

 

 

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The post 10 Weekend Reads appeared first on The Big Picture.

My end-of-week morning train WFH reads:

• Even Permabears Have Portfolios. Where Jeremy Grantham Sees Value Now. The veteran investor and co-founder of GMO likes quality stocks, international value, and Japan. (Barron’s)

• Beauty Has Never Been More Cutthroat. Here’s How Sephora Plans to Stay on Top. Executives at the LVMH-owned retailer offer a rare look inside the company’s strategy. (Wall Street Journal)

• More Than 1,000 Companies Are Suing Trump Over His Tariffs. Companies are flocking to challenge the administration’s import taxes in court, after the Supreme Court started asking tough questions. (Bloomberg)

• The data center rebellion is here, and it’s reshaping the political landscape: As the buildout of AI infrastructure alarms communities, it is fast emerging as a potent electoral issue across the political divide. (Washington Post)

• 25 Lessons on Money and Meaning: Notes from everything I’ve learned so far about the deeper forces behind our financial lives (The Root of All)

• Rubio Has Long Dreamed of Changing Latin America. Embracing MAGA Opened the Door. Once a Trump critic, the secretary of state has become one of the president’s closest advisers. (Wall Street Journal) see also Dateline: Caracas: Venezuela’s Democratic Roots Deeper Than Trump Knows (or Cares) (Intrinsic Value by Roger Lowenstein)

• AI and the Human Condition. OpenAI may or may not be the most important company of the future. From the day it was founded — with a non-profit corporate structure that sought to build AGI and then control it themselves “to ensure artificial general intelligence benefits all of humanity” — this company has divided the audience and invited either passionate support or aggressive eye-rolls. (Stratechery)

• The most volatile group of voters is turning on Trump: There’s a new line dividing young Americans. (Vox) see also Is the Future of MAGA Anti-Israel? For 40 years, Christian Zionism was a powerful force in American politics. A new generation on the right is taking cues from elsewhere. (New York Times)

• Among the Prophets: Science fiction and the art of prediction. (The Baffler)

• Terrence Malick’s Disciples: Why the auteur is the most influential director in Hollywood (The Yale Review)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media

 

U.S. Dollar is slumping vs other currencies; 2025 was the biggest decline since 2017

Source: The Bulwark

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The post 10 Friday AM Reads appeared first on The Big Picture.

My morning train WFH reads:

• The Great Credit Convergence: How public and private credit are now one market (Paul Kedrosky)

• The Emperor’s New Oil Wealth: You may have heard that Venezuela has the world’s largest oil reserves — 300 billion barrels. You probably don’t know that Venezuela’s reported oil reserves tripled while Hugo Chavez was president. This increase, from roughly 100 billion to 300 billion barrels, didn’t reflect major new discoveries or exploration — it reflected the Chavez government’s decision to reclassify the country’s Orinoco Belt heavy oil as “proved.” It is not… (Paul Krugman) see also On the Legality of the Venezuela Invasion: Executive branch precedents can be garnered to support the action—which does not, of course, mean that it is lawful. (Executive Functions)

• A tale of two Ralphs — Lauren and the supermarket — shows the reality of a K-shaped economy: Wealthy shoppers browse Ralph Lauren on Rodeo Drive while struggling consumers hunt for bargains at Ralphs grocery, revealing America’s stark wealth divide this holiday season. The K-shaped economy shows high-income households thriving with rising pay and asset gains while lower-income families squeeze budgets amid inflation and stagnant wages. (Los Angeles Times)

• America Is Falling Out of Love With Pizza: Restaurant chains explore strategy changes as sales slow for food-delivery mainstay. (Wall Street Journal)

• A Risk of Cognitive Convenience: What ChatGPT and GPS have in common. (Range Widely)

• Disgust Over Jan. 6 Is No Longer Bipartisan. The aftermath of Jan. 6 made the Republican Party even more feckless, beholden to one man and willing to pervert reality to serve his interests. Once Mr. Trump won election again in 2024, despite his role in encouraging the riot and his many distortions about it, it emboldened him to govern in defiance of the Constitution, without regard for the truth and with malice toward those who stand up to his abuses. (New York Times) see also Five years later, these 10 corporations still aren’t funding election deniers. On January 6, 2021, a violent mob stormed the Capitol building. Following the attack, over a hundred major companies released statements condemning the insurrection and promising to stop donating to the 147 members of Congress who voted to overturn the 2020 election, or to halt all political donations entirely. (Popular Information)

• Why Trump wants Greenland and what’s standing in his way: Denmark’s leader warned that any use of force by Washington to seize Greenland, as Trump officials have suggested, would render the postwar NATO alliance defunct. (Washington Post)

• Millions of Kids Are on ADHD Pills. For Many, It’s the Start of a Drug Cascade. Powerful psychotropic drugs are often the next step, even though their combined effects in young children haven’t been studied closely. ‘I was living in a body hijacked by the medication.’ (Wall Street Journal)

• Nine science-backed ways to help you feel better in 2026. From channelling your anger to writing lists and singing more often – here are some science-backed tips to boost your wellbeing. (BBC) see also The New Rules Of Good, Deep Sleep Will Help You Feel More Rested And Energetic: Spoiler: Quality may be more important than quantity. (Women’s Health)

• The Worst Shot Ever Taken: The author and his closest basketball confidantes undertake a formal analysis of Steph Curry’s shot at the Paris Olympics as art object (The Believer)

Be sure to check out our Masters in Business interview this weekend with Ben Hunt, founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media

 

Why Trump wants Greenland and what’s standing in his way

Source: Washington Post

 

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How did a layoff lead to a revolution in financial data? Mike Bloomberg shares the story behind the Bloomberg Terminal, lessons from serving as NYC Mayor, and his philosophy on risk-taking and leadership:

Step inside Bloomberg’s New York headquarters for a conversation with the man who built it all. Nicolai Tangen sits down with Michael Bloomberg to explore his journey from Wall Street to founding Bloomberg LP, his approach to leadership and risk-taking, and the values that fuel his extraordinary philanthropic work. Bloomberg shares stories of creating the Bloomberg Terminal, transforming New York City as mayor, and why he still works almost every day at 83. An insightful look at a life devoted to building, improving, and giving back.

I have been trying to get Mike on MY Podcast now for many years, but he is reluctant to show up on his own air…

 

 

 

 

 

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