Jack Forehand (2:36)

Yeah. And we're going to focus on what people can take from it. You know, we will talk about current events, we'll talk about what's going on. I mean, our first couple of clips here are about the war in Iran. But we're going to try to say, like, what, what can we take from this beyond, like, what's going to happen next week in the market? Because that's. There's plenty of YouTube channels out, as you know, Matt, who have that covered. We're going to try to cover more. We get to talk to people like we talked to Andy Constant this week. We talked to Rob Arnot. I sat down with Tai Woo to talk about the Citrini piece. We had a great last call where we had Rer Mitchell and Ben Hunt and Brent Kachuba and Meb Faber. So we've got tons of insights from tons of people. And so we're going to bring you the best of it. Right now.

Matt Ziegler (3:10)

We have some really cool friends on excess returns. Now they're your friends, too. Let's dive through these clips because. Yeah, an embarrassment of riches in the networks we're keeping up here.

Jack Forehand (3:22)

So I want to start with Andy Constant and we talked about the war and what's going on. But I think his best take had nothing to do with the war. His best take was whenever anything happens, you know, we had Covid. We can have a war. Whenever anything happens in the market. People are going to come out of the woodwork on Twitter and on everywhere in the news and especially in this polarized world. They're going to tell you what you need to know, what are the facts. And most of that is going to be opinion, like hidden as fact. And so Andy has a framework here in terms of how he thinks about who to listen to during a time like this. And here's what that is.

Andy Constant (3:54)

I find there are a lot of people that have no basis for expertise, who express that expertise with high confidence. And those are the people you run across them all over Twitter. Those are the people that you're really probably better off not reading anything about. And then there are experts who deserve to be heard but are heavily political biased. And those are red flags to me as well, because you're not getting truth, you're getting information which is better than somebody who has no expertise. But at the same time, you're not getting truth, you're getting something, some bias. And then of course there's the people that have no prior experience and are heavily politically biased and express their view with high confidence. Now that's the worst. You know, those are, those are the people that you really don't need to spend even a moment thinking about their contribution on Twitter or, or in, in your reading in any way. So those are the things that I find are really useful in just streamlining the information content I'm getting. And then there are things that I actually really value and I think the things that. So experience is a two edged sword. I think we've talked about this before on one of the other programs that we've done. Experiential learning is not necessarily a good thing because what people do when they are learning something from somebody who has had experience or a person who has had experience is looking out into the world. They start with I've seen this before. And then they conclude based on decisions they made before and how those went, what to do going forward. And where that fails is they typically do the things that went well and don't do the things that didn't go well. And that could have been luck. They could have made a horrible decision based on the information they had and done something that just happened to work out. You know, luck. You know, they bet, they bet a 30% odds thing and a 50% needed payoff and just got lucky. And so that's not, you don't want that repeated, that sort of failure to understand whether you did it right the first time. And then there's of course, are they right that they saw this before? Like maybe it's different. So experiential learning to me is a crutch that people use. And it's just as dangerous as having no information at all. Sometimes, sometimes it's worse. Why? Particularly when you're dealing with factual situations and want a good sense of what history is about. I value people with experience because they've been there, they've done that. But the synthesis of what that experience is is less certain to me. So I look for people with experience who have low confidence in their views that recognize, hey, it might not be the same. I also think people, there are people out there that are just great thinkers that think through things with no experience at all. You drop them in the middle of a problem that they've had no experience with and they consider lots of possibilities and come to solutions and make judgments. And that thought process can be high quality. And you can learn a lot for how they think through the problem. They're almost like your colleague as they're thinking through a problem. You both don't have information. So I value that person. But ideally I find a person that has good thinking ability and good experience and has low confidence in their views. So that's what I'm looking for when I look out on the world. And it's very hard to find because mostly because the rest of the people are all trying these other things, which are very noisy.

Jack Forehand (8:08)

So I thought this was great. You know, he. I'll put up the tweet on the screen here that we were talking about in, in the clip because this is, this is really great in terms of like thinking about, like, what is someone's experience in this and how much confidence are they expressing in their views? And maybe using those two, two things to say, should I take this person's opinion into account or should I not?

Matt Ziegler (8:28)

I think by the time this is out, I have a way that I always frame this to people. I finally wrote it down, so there's probably a cultish creative post that's corresponding to this. It's the idea that you constantly have to separate social media and social mediums in your brain. And in this age of AI, I think it's ever more so important. And what Andy nails is something that I didn't articulate clearly enough, so I might have to go back and revisit this piece in the near future. Is the idea of confidence and experience. And so social media, which is the plural, are the things that scale. And the way that you get a message out across social media is you express a high degree of confidence, even if you don't have experience or anything else. The more confident are, the more enragement you can trigger, the more engagement you can get and people going back and forth with you on it. But it's all about confidence signaling. To get the broadest amount of scale social Medium, which is the singular, is like me and you having this conversation now. Me and you having this conversation. I can come in hot and be ultra confident about something. That's a terrible way to be a friend. It's a terrible way to be a spouse. It's a terrible way to have a professional relationship with anyone where there's trust on the line. Because confidence, unless you come to it with a certain amount of experience and it's earned through repeat interactions with individuals like that does not scale and it's not scaling is a feature and not a bug. So, Andy, being able to navigate back and forth between social media and the social mediums and extract signal from here, if you haven't learned to curate the curators yet, play this clip 18 times. It's so frigging good.

Jack Forehand (10:00)

Yeah, and I like the two highest categories he picked here. One was tons of experience, low confidence. And so that's saying like, if, if I have an opinion on the Iran war, you probably should ignore that if a general is coming out and telling you he has a low confidence opinion. But here are the different things that might happen. That's probably more worth listening to. But the other one I really liked is this idea. He likes to talk to people who don't have any experience in that domain but are really good critical thinkers. And I think that's a really, really interesting thing to think about. Like, you don't necessarily have to know a lot about war to take a step back and sort of dissect this and think it through.

Matt Ziegler (10:30)

The Phil Tetlock thing from Superforecasters, where they were doing exactly that type of stuff. It's like you might not understand anything, you might have no working knowledge or experience in this domain, but you're an expert problem solver and we're going to drop you into do into this can result in false confidence, but it's really good to think through those things. I also think, I'm sure you have this experience too in client work where it's obviously I have not become a straight of Hormuz expert in the last like five days. Thank the gods. I've read enough Peter Zion books and other things to at least have some familiarity with shipping routes and things like that. But at the end of the day it's like that low confidence in being able to bring people down to that level, bring them in, into the individual space, away from social media and say,

Jack Forehand (11:14)

you know, you might have some really

Matt Ziegler (11:15)

interesting thoughts on this too, even though you don't have any experience there. How does this Apply back to you, your situation, your portfolio, your plan, whatever. It's a great organizing principle. I'm not surprised Andy came up with all this.

Jack Forehand (11:26)

It's funny, I find myself saying to people like now, like, oh, don't you know, it's obvious that 25% of the world's shipping goes through the Strait of Hormuz, when in reality, I didn't even know that yesterday. Like, I now know it. And now, like, I gotta tell everybody that I know this.

Matt Ziegler (11:37)

I got to tell everybody that I know exactly what's going on with these routes. And did you know about the insurance company? Fully felt.

Andy Constant (11:44)

I learned about that.

Matt Ziegler (11:45)

Fully, fully felt.

Jack Forehand (11:47)

So this next thing is from Andy too. And this is. This sort of takes this and applies it. Because one of the things you see immediately whenever anything like any of these conflicts start is you see the chart on Twitter. Here's what The S&P 500 does a year after the conflict starts. It is this bullish chart and you always see it. And here's Andy talking about the two major problems with that.

Andy Constant (12:04)

So I'm really negative about sort of data mining of that nature. Like for one, the market's always higher. So no matter what you have, the market's always higher. That's because beta is a good thing. You should always own assets in a diversified way to deal with these sort of things. They pay returns. It's good to own. So mostly that bias is always going to be in the data and people are going to show it absent the general greatness of owning assets. Listen, I worked at Bridgewater. My. The. The funny story I like to tell there is. I got. I was interviewed by Bob Elliott, who's now all over Twitter, a friend of mine, Greg Jensen and Bob Prince and Karen Carnoil Tambor at the time.

Ben Hunt (12:54)

And they.

Andy Constant (12:55)

I was a coming in to do talk about Vol. And listen, I was an experienced Vol guy. Like that was my life. And I came up with them and showed them a type of systematic trading strategy I wanted and had back tested till 1981. And they said, this is garbage. Can't you. Don't you have more data? I said, well, you know, index options were invented in 1981, so there wasn't any data prior to 1981, like legitimately. And they said, of course there was. You just have to come up with what it would have been if it had existed. So I was like, oh, okay. And what they were basically saying is sample size is just too small. Like you just can't, you can't have experience if you're trying to draw a picture of everything in the world and you only have 30, 40 years of data, you're not going to get a clear picture. You need hundreds of hundreds of thousands of years of data to get a clear picture. And it just doesn't exist.

Matt Ziegler (14:02)

Markets go up. Did you know markets go up?

Jack Forehand (14:06)

Yeah, and that's the, that's the thing. Like the first thing you have to look at this, by the way, this is true of anything that goes on in the world. When it's like, here's what happens and the market goes up a year later, you've got to do what he said, which is you've got to say the market always goes up a year later. So first of all is that chart showing that the market goes up more than say 8 to 10%. That's the first question you have to ask yourself. Because if it's like the market goes up 10% a year later, the market always goes up 10% a year later. So that's useless.

Matt Ziegler (14:33)

Stripping out these baselines. This is such an important part of like scenario planning that I think people often forget. And there's a difference. There's. When we're thinking about public market assets and we're thinking about what the likely long term outcomes are, you got to bake this assumption in and then you look at the differences. You can't make the same assumptions when it comes to like, things that you know have an end. Like, don't mistake this when it comes to your own life expectancy. Don't mistake this when it comes to how long you're going to work at the company or run the business or whatever else. But in domains that are basically like Evergreen, you have to start with the assumption that they always come back, that they always recover. I used to keep on my office a chart and the cutoff was like sometime in maybe the early 2000s, but it might have even been the late 90s. But it basically like showed the 87 crash and like zoomed out confidence from like, you know, 20 years ago at the time. And the great part about this was like, that 87 crash is such a blip. And I kept that chart on the wall, this giant blown up Dow Jones industrial Average, just so I could like say to people like, do you see that funny little squiggle on like right there, like 2/3 of the way over? Like, yeah, that's 1987. Like, you see like the left and the right of this? Like, you need the reminder of the context to neutralize this from the point As a data guy, was there any particular. How do you think about this as a data guy?

Jack Forehand (15:53)

Yeah. Well, the other thing is, as the quan, I always have to say N is such an important number, which is the number of observations. And, you know, that's the second thing he was saying. And like, we are not. If you, if you think about, like, yeah, there's been some wars in history, but the actual number of wars could use in your data set to say what happens to the stock market is so much smaller than it would actually be. Need to be. To be for you to have any confidence whatsoever in that. And, you know, one thing I hope as a quant is that I never get enough data on wars so that we can say for sure, you know, what the impact is in the stock market. Because. But you've got to see that all the time. You see that all the time. As people will say, here's what happens to the stock market. And you dig behind the scenes and you realize there aren't that many instances of this. And if there aren't that many instances, you can't draw any conclusions whatsoever.

Matt Ziegler (16:36)

Back to confidence over and over and over again. It's going to keep coming up.

Jack Forehand (16:39)

By the way, I don't, I don't want to go into Bridgewater enough to be interviewed by the people he was being interviewed, but I think I feel like they would get me out of that room in like two seconds. Like, it was like, who was it? It was like Bob Prince, Bob Elliott, Greg Jensen, and Karen Tarmiel Tambor, I think is her name. Yeah, the. It's just a crazy, like a crazy insane interview. That must be.

Matt Ziegler (16:58)

That is a pressure cooker. That is a pressure cooker. And it's also funny when it's like, oh, we only have the data back to 81. And I'm thinking to myself, like, I was born in 81. Like, this is. I am the data.

Jack Forehand (17:08)

So, so next, one of the big topics of the podcast this week has obviously been the Citrini piece. You know, there was all kinds of reaction on both sides of that. And I sat down with Kai Wu and we, we just tried to take both sides of it. We tried to just break it down. Not saying, like, the. This AI is going to be the

Matt Ziegler (17:21)

end of the world.

Jack Forehand (17:22)

Not saying this is the dumbest piece of all time, which was a lot of the bad reactions we got on both sides. But we tried to, to break down what the biggest takeaways are. So here's Kai talking about that.

Kai Wu (17:30)

I think going back to. I Mean, the whole point of the article was is it possible to be an AI bull and think that AI is a technology will be so dominant yet to think that that could actually lead to a worse outcome for investors? I think that overall premise is quite interesting. And I've argued this in the case of the hyperscalers, where I've said they're kind of inventing the technology, but they're kind of positioning themselves as utilities. Right. Which is not where the profits accrue historically. In a way, what Citrini is doing is making that same argument, but even more broad across the economy. And again saying that you can believe in AI as a technology, but not necessarily think that it'll be good for financial markets and for the economy in general. And again, I think that the argument's being made very strongly, but that general view isn't necessarily wrong just as a framing and is a potentially an interesting way of thinking about things.

Jack Forehand (18:19)

Yeah, and that gets kind of into the second argument I had here for his piece, which is the idea that AI will replace jobs rather than enhance workers. And I think that's, that's maybe the big question here is how much of this AI is going to enhance workers and make us all better off and how much of this is going to take over for workers. I, I think that's a huge question we all have to try to answer and the answer is probably somewhere in between. Right?

Kai Wu (18:38)

Yeah. I think again this is something we've discussed, which is that a job is a bundle of tasks, right? So on a day to day basis, I do a few different things. I send out emails, I like, do some coding, I'll like do some research, I'll talk to some clients, I'll hop on and talk to you. Right. And like, you know, it's. If you think about it that way, then like, you know, certain tasks that I spend time doing, you know, I no longer have to do or I can do it in a fraction of the time. Like coding is a lot more efficient now. You know, when I used to spend my time like scrubbing data or scraping websites, that's pretty much a solved problem too. Other things I have to spend more time on, like it's not saving me time talking to clients. Right. In fact, all else equal, I should spend more time talking to clients because that's one of the things that, you know, I as a human have a comparative advantage in. So I think again, a combination of both substitution and augmentation, both at the economy level, at the job level, but also within the job level, at the task level, where our jobs, whatever you want to call them, will look very different. Our day to days will look very different in 10 years than today because a lot of the things we spend our time doing now will go away and then new things will come into play or we'll just do more of certain things, which isn't necessarily bad. That's just the nature of the world. And in many ways it's good. Like a lot of things I spend my time doing that I can now automate away with AI are things I hated doing anyways. So it's actually kind of maybe a net benefit for me.

Jack Forehand (19:57)

So two quick things here. One is, I think the importance of separating the technology from the market and the economic impact is really important because the technology can be a massive success. But that doesn't necessarily mean what, you know, tell us what the impact is going to be on those other two things. It tells us some about that. But you have to separate those two things. And I think that was a big lesson from the Citrini pieces. This was obviously an extreme scenario that I don't think even Citrini expects to play out. But this idea that the technology doing better and better and better can be bad for the markets and the economy is something that I think all of us at least have to consider.

Matt Ziegler (20:30)

Yeah, this goes back to. We've talked about this a million times. It's like, have your base case, have your bull case, have your bear case, and many and many ideas have multiple of each. All the spaghetti noodles go in the pot. You don't get to just like pick one single noodle out, but you can, and you go like, oh, this is the bad outcome. This is the good outcome. This is the amazing outcome. This is the mediocre outcome. They're all in there and you have to think about it. When you're doing your analysis and looking at a range risk means more things can happen than will happen. Just keep saying it over and over again till it sets in. Inside of this too, the idea of being bullish, of being upward biased in the technology taking. I'll go back over and over again to the Jonathan Hickman X Men run, which I know sounds horribly unrelated, but it's still my favorite piece on this in like 2015 or so to 2019, when he was writing this run of X Men, Comets, House of X, Powers of X, I think. And it's this idea of AI when he introduces it as, don't treat it as a tool. Don't treat it as like a dis as an invention, treat AI as a discovery. With the idea here being that, like, we're going to come up with a bunch of tools, we're going to build fireplaces, we're going to build internal combustion engines, we're going to build all this stuff with it. But the core idea is that this is a discovery, much like discovering fire, and it's going to drive all these other things that are going to put us through a tremendous amount of change that you can't even start to extrapolate out into the future and then even out into the future. Hickman's genius. So he takes us through multiple paths the way it can turn out. But in all of them, it's like this discovery event of we've introduced something that might have a lot of bearish short term outcomes, but you're probably not going to erase going forward. We're not going back to the before times. And that reminder of how to think through this in context. What Kai's doing, that whole episode was remarkable.

Jack Forehand (22:18)

And also, when you find a piece that challenges your view that you think is ridiculous, learn from it, don't attack it. And that's something I am much smarter about what the potential negatives are of AI because I read that piece. So you could take that piece as, wow, he's saying this is going to happen and this is a doom and gloom thing. Or you could take it as, let's look at the facts behind this and what's driving this and let's learn from it. And I think that's something we all struggle with. And like 90% of the reaction to this was either like bearish people piling on or other people saying, this is the worst piece of all time. And those are both the wrong reactions.

Matt Ziegler (22:50)

We'll take that back to the idea. They wanted confidence, they wanted to confirm some prior or find a confident thing they could latch onto, run with on social media where the message gets scale and the scale gives them the exposure. And anybody you saw who just carried it over that way without discussing the nuance is back to that idea. I want low. I want a lot of experience or no experience with like low confidence and high curiosity. Those are the people you want to pay attention to on this piece because yes, we learned a ton from do that from doing this. We learned a ton from Back to the Hickman idea. If you know, you know Sam Rowe, you're out there. You know what I'm talking about here. Like the Moira X story, we find out all the mistakes she makes in all these Timelines and all these things along the way and it's recursive and it's like, no, now that I know this happens, I can try something else later. Or as an investor, as an allocator, as an advisor, I can be aware of like, oh, this is, this is a risky direction that we're going to. All the stuff Kai's written about the balance sheet, the utility aspect, the stuff like that, understanding when these companies change, let this all inform your current opinion. You can't know the future. You can be a little bit more smart about what's going on right now.

Jack Forehand (23:54)

And also just briefly before we go the next one. When I saw a job as a bundle of tax tasks, I'm like, this is right down Matt Ziegler center field here. Like I can't, I can't have Matt not respond to a job as a bundle of tasks.

Matt Ziegler (24:04)

That's all we are. It's just, it's jobs to be done all the way down. And then the trick is the things that are complete, finite games, extra Luca Delana inside of this and which ones are the infinite games that are trust based and across systems. If you can understand how to parse those things, you've probably got enough smarts to be able to parse and use AI to help you out with the tasks it should help you with and keep them as far away as possible from the stuff that they have no business being involved in.

Jack Forehand (24:30)

You know, and also not to look at this from the perspective of AI will eliminate X Job. It's more to look at what are the different things that X job does and how can I learn what of those are, you know, subject to AI and which ones aren't like that's a much better way to look at this than to just say, you know, make a blanket statement like AI is going to eliminate this job, right?

Matt Ziegler (24:47)

It's that substitute and augment or whatever the way is that Kai put it, which I think is such a brilliant reframing of it. And I mean I think about this in my own life. There's a whole part of the task stack, it's hard to say the task stack that I have in my day where it's like, can't do, can't do, can't do. Whoa, big chunk of stuff it can help me with. It can augment smaller piece that it can totally substitute. And then at the end on the, on the, the other tail, it's like, oh, here's a whole other thing where it's, it can't pick up the phone and call the client and walk them through the situation where there's concerns or whatever else can't connect on that empathetic level. And maybe it will someday, maybe it will in certain domains. But it's a stack, it's always there. And you always have to think in

Jack Forehand (25:32)

that nuanced way when the robot picks up the phone. Matt though, that's coming.

Matt Ziegler (25:36)

I mean, let's be honest, it's already happening. I can tell you from the sheer amount of spam calls I've gotten since we record on this thing. Just ridiculous. Just ridiculous.

Jack Forehand (25:48)

So to wrap up this idea, I wanted to talk to. We had Rob Arnott on recently and I love his definition of bubbles. And so whenever we have him on and we're a period where people are talking about bubbles, I like to ask him the definition. So here's Rob talking about that.

Rob Arnott (26:01)

I don't think AI is a bubble. I think AI stocks are a bubble. There's a difference. Our definition of bubble is very simple and that is that if you're using a discounted cash flow model, kind of a Gordon equation type thing, to value an asset, that you would have to use implausible growth assumptions to justify the current price. Not impossible, but implausible. So Amazon, as one example, in the year 2000, was priced at levels that required what was then reasonably thought to be implausible growth assumptions. And sure enough, it was a disaster for the next decade. And then it got its mojo and it was no longer priced at levels that reflected implausible growth expectations. And sure enough, it became a wonderful stock, one of the most successful stocks of the last quarter century, but not in the first decade of that quarter century. So there are companies that go on to achieve growth greater than what you would need to justify the current price. Amazon and Apple are two vivid examples. The growth required to justify the price in 2000 has been exceeded for a quarter century. Cool. But those are the exceptions that prove the rule. The vast majority. We've talked about this in the past. Of the 10 most valuable companies on the planet in the year 2000, only one Microsoft is still in the top 10. Only one Microsoft has come anywhere near me. Beating the S&P 500 and it's only beat it by a couple percent a year. Of the 10 most valuable tech stocks in the world, the median result has been a negative return over the last quarter century. Over half of them have had negative returns. The ones that have been wildly successful. Qualcomm has seen 60 fold growth in sales in the last quarter century, 60 fold. And yet it's behind the S&P 500. Why? Because it was priced to achieve that in 10 years, not 25.

Jack Forehand (28:20)

So I just like this idea of being able to look at this in real time. And so this idea that you need implausible assumptions to justify the current stock price, I think that's just something you can kind of use in real time. To say again, implausible is subject to interpretation, but you can at least use that in real time to say, like, is this specific thing in a bubble? Like, can I come up with a reasonable case that this should trade where it does and if it doesn't, you know, it's probably a bubble.

Matt Ziegler (28:41)

I like to think about this out of sample back to like the discovery idea. Can you imagine the caveman bubble in Fire?

Jack Forehand (28:49)

Like it was out of control.

Matt Ziegler (28:51)

I'm sure it was out of control. It was out of control. They were going wild with all like the, the startups, the startups and things where they have the, the great fire bubble of, you know, 16,000 BCE or something. Right?

Jack Forehand (29:04)

Yeah, I would assume, you know, I don't know, we have to, we don't have market data back that far, but going back to Andy Constance part, you know, if we talked to Bridgewater, they'd be like, well, go back and get, you know, get data on that, on Fire and bubbles, because we need to have it.

Matt Ziegler (29:15)

Get those fossil records. Still, Rob's point, and I agree with you completely, like Rob's definition of the bubble and being able to separate the idea is different from the companies around this and you should parse those two into two separate categories, then to reframe it as what is impossible, what is implausible. And parsing that nuance is incredibly important. And if you have a bunch of this stuff in your portfolio or whatever else, you should be asking these questions. Mobison and base rates, all, all these are really, really timeless and really, really valuable in a time like this.

Jack Forehand (29:47)

And the other thing we talked about a lot on these podcasts is this idea that software, I mean, software stocks have obviously gotten killed recently and they sort of all gotten killed. And so I talked to Kai about this idea of differentiating them. So here's what Kai had to say about that.

Kai Wu (29:59)

Imagine two types of companies. Company A is they're basically a, a software company where their only moat is their software. In other words, they produce really nice software and that's why people buy it. But that's the only thing they have going for them. Well, then yes, of course it will Be the case that if someone else can vibe code an app that looks exactly the same as what they have built, that their moat is basically gone. Whether that happens to be an in source, whether it's a competitors, sorry, their customers competing against them or new startups that are kind of, or existing incumbents encroaching on their territory using, you know, their moat is basically gone. So I think we can agree that those companies are imperiled. But now imagine the second type of company which, you know, large enterprise, let's say a CRM company, right? And they have these big Fortune 500 companies as their customers. Well, it's not that they have the best user interface. In fact, most of these, you know, kind of larger incumbent firms have pretty poor user interfaces, especially compared to the startups that try to compete with them. Right. These larger firms, their advantages are not their code. It's actually the other stuff. I focus a lot on intangible assets. I think a lot of intangible moats. It's the brand equity of these firms. You don't get fired for hiring Salesforce. It's the customer relationships, the distribution, the lock in, the switching costs, the network effects in some cases that make these firms so powerful. And if that's the case, it's almost the opposite where it's like, well, these guys will have their customers no matter what because of these other reasons. And hey, guess what, you know, they used to have to spend all this money on software engineers in order to support and maintain and upgrade the software. Well, you can now cut your workforce in half and that's, that's helpful, right? That means that their cost base is much lower and they manage to maintain their revenues in this hypothetical scenario. So I think you have to be like, you have to think a little bit more about like the exact competitive dynamics of each industry because I think both these scenarios are plausible. But again, it's going to be very case by case depending on, you know what the kind of, I guess the competitive dynamics of each of these businesses.

Jack Forehand (32:00)

Yeah, I love this idea of what is the moat. And Kai's really good at that kind of stuff. You know, if the moat is just the software, then you've probably got a problem with AI. If the moat is something else, then you're probably in much better shape than maybe the stock price reflects at this point.

Matt Ziegler (32:14)

I was talking to a person who does marketing type work and it's a lot of like marketing and sales stuff and they were getting asked by one of the people they, they consult with if they should Basically like abandon Salesforce for something else or Vibe code their own CRM or one of these things. And, and this was interesting to me because I keep hearing about from like investment finance markets people, it's like, oh, they're going to replace this. And this is the bear case for SAS stocks and whatever. And even Rupert Mitchell, who's been like two years early on this story, has been the one to say like, not so fast for a couple of these reasons. So what this marketing person told me, he said, we build, we build stuff that integrates this for entire sales funnels and works way down the process. Whatever the layout is like the behind the user interface, like layout that Salesforce uses. He was like, do you know how much easier it is to pull the data from that to do everything you do if you have Salesforce? My life is infinite easier for all the stuff that I can build there versus, like. And I forget what he related to HubSpot and something else. He was like, salesforce is actually maybe not the best user experience or maybe not the best like design. You might be tempted to think you could do better. I can extract more of that backend to drive the process so much faster. So point number one is there's still details here that nobody knows or understands. Point number two that I think you were just making inside of this and like Kai's made is like understanding the integration levels of the Salesforce that Salesforce has. Like, there's customer relationships, there's people who go to a conference and then went out to drinks and feel forever loyal to somebody and it makes no frigging sense. But the idea that you're just going to upset some of those relationships, especially at the level where it's the nobody ever got fired for buying IBM or like using Salesforce or whatever stuff. At that level, where there's social status risks, you're not just going to tip that Apple cart over in one shot.

Jack Forehand (34:11)

Yeah, I was thinking about like I was visiting an investment advisor the other day and like there's an. I don't use it, but there is, there's a software that a lot of advisors use where you can maybe make notes on your calls, like in the software and then you go back and you can talk to it. You know, when you have another call with that client to update you and there's probably a lot of private information that goes into that app and so you know you're not going to be on your next SEC audit being like, well, I just Vibe coded, you know, a new version of this and who knows what's going on with it, but it's, you know, you can't do that. So like covering yourself is a huge part. Using the brand name, using the, the place that's doing everything right, the place you can trust. That's a huge part of this stuff. So I think we're taking a little far with some of these, like thinking

Matt Ziegler (34:45)

we could just vibe code everything, especially in regulated industries. The bane of my existence is how many things we can do behind the scenes for the podcast stuff and whatever else that I can't do in the rea side of my life. And it's just like figuring out which things can go over but there's so much where there's security or other risks, where it's like this is a hard no until you can answer these 20 questions. Because when the SEC comes knocking, you better have all of these answers with footnotes. It's another part that's a friction that slows the adoption curve down in various spaces. We'll get there. It's moving very fast. There's a whole bunch of stuff Claude just rolled out a whole thing in the last like month, for example, with financial planning. I don't know if you saw any of this stuff, not yet. But it's like there's a bunch of integrations to make stuff pirate private or loop it in under whether it's under your co pilot license or another license to make it more of a closed ecosystem. But run these models. But it's those frictions will slow us down. And they are real. And they're not bad. They're not bad.

Jack Forehand (35:44)

So our last one on AI, one of the things I've been thinking about a lot is AI does have the potential to maybe be the most disruptive technology of our lives. And like that, that probably has both positive and negative implications. So I asked Rob Arnott about that. One of the things that I've been thinking about a lot is is this. We can learn from all the other innovations of the past, but is this kind of innovation on steroids because it's intelligence? And do we think about maybe we magnify both the long term value of this in terms of what's going to create, but we also maybe magnify the short term pain in terms of because it is intelligence, it can replace more human jobs. I mean, do you think that's a fair way to look at it?

Rob Arnott (36:15)

I think that is spot on. I think that it will be more disruptive perhaps than any technological innovation since computers, since the railroad. I mean back in 1825, to get a message from Washington D.C. to New York, it had to be on horseback. And it took two to three days. Even if you were replacing horses every, every 25 miles and just kept going. 100 miles a day was your maximum. 20 years later, it took one day because of the railroad, and 10 years after that it took milliseconds because of the telegraph. So there have been some humongous technological innovations. AI, I think, will be one of those. Now, the leaders of AI today may not be the leaders of AI in 20 years. That's why they have massive capex spend, because they want to secure their place in the pantheon of leaders and feel that they have to spend hundreds of billions. I mean, Zuckerberg said as much. He said the cost of spending a quarter trillion on capex is horrific. The cost of not spending it may be much more horrific. And there's a lot of truth in that. But the question of is, is it going to change our lives? Yeah, in more ways than we can possibly imagine. It'll be massively disruptive. The most, I think the most disrupt, technological disruptive disruption of my lifetime and I've been around for a while.

Jack Forehand (38:20)

This is the interesting thing to me, and this gets back to the Satrini piece, is this idea that if AI is probably because it's intelligence, if it's more disruptive than other things we've seen, you probably can make an argument that the long term benefits are probably better than anything else we've seen, but the short term pain also might be worse in terms of, like, it's going to take a while if we're going to find new jobs, which we always do with these new technologies. It may take a while to work through that and we may have more pain in the short term than we think. I mean, that's just, I think, an interesting thing to me, and it was one of my takeaways from the Citrini article.

Matt Ziegler (38:49)

Yeah, one of my takeaways too. Also. The reminder that it's basically like between Battlestar Galactica, Idiocracy, the Hickman Weapon, or Powers of X, House of X series, like Silicon Valley, the show. These are the things that feel like the most descriptive of both the now that we're living in and the potential futures in front of us, which are we have this discovery, we have this amazing thing that's in front of us that will probably change the way that we as humans interface with reality in ways we can't even imagine in fifty, let alone a hundred years, let alone five years. And so like, if that's the scale of the disruption, you just have to sit back, look at some of the stuff, look at how you can use it or utilize it in your own life, practice, career, whatever you're doing. And just understand that this pace of change is to the Citrini piece. It's going to be eye opening. But that is just one noodle out of the pot of spaghetti here and nobody's picking. They don't have the answer. The confident guy on Twitter doesn't have the answer on which path this is going to take. It's just big opportunity, long term.

Jack Forehand (39:54)

And another topic we've been talking about a lot is how international stocks have done much better, obviously in the last few days with, with this conflict. They're not doing that anymore. But they, they have bounced back and they've, they've had really, really good returns relative to U.S. stocks. And one of the things I thought was just an interesting one to insert, like when I was talking to MEB favor about this, like so many people are saying now that international stocks have had a pretty good run. The easy money has been made in international stocks. And here's MEB talking about why that's not true.

Meb Faber (40:17)

As we look around the world, my least favorite phrase is the easy money has been made. You hear this on CNBC all the time. They're like, the easy money has been made. And I'm like, bro, there has never been easy money been made in markets. In fact, I just use the opposite. I'd be like, the hard money has been made. So when you look at like global deep value stocks last year, up 50, up another 15 to 20 this year, I say, the hard money has been made. And people are like, what are you talking about? I say, well, who owned those at that point, right? Because if you own deep value stocks, ex us, that meant since 2009, you underperform year after year after year after year after year after year. I could keep going about five more years, right? So the hard part was holding, buying, re upping. I actually went on CNBC and Bloomberg a couple years ago and I was trying to make this example and I say very quietly, look, I love Yalls TV show, but all you do is talk about Nvidia all day long. Nvidia, Nvidia, Nvidia. And as you should like record profits. Execution at this scale is astonishing. Maybe the world's first $5 trillion stock. We'll see.

Jack Forehand (41:25)

But I said, how many times do

Meb Faber (41:26)

you guys mention the stock Hanmi Semiconductor? And they're like, what are you talking about? Did you just sneeze? Like, what are you doing? And I say, well, this is another semiconductor stock that's outperformed Nvidia over the last one, three, five years, but it just happens to be located in South Korea, right? Like, like nobody's paying attention because it's somewhere else. That stock has been like, I think a 20 bagger or something, but very quietly, right? Like, no one's. So when you look at kind of a lot of these trends, if we did this show a year or two ago, I think, you know, we were still in this, like to the moon forever, period. But very quietly, underneath the surface, you're now seeing the rotation and into lots of other things. So maybe the bull market and diversification has begun.

Matt Ziegler (42:10)

I frequently get the sense with MEB it's like the Simpsons did it first thing on South Park. Like, MEB Favors had the conversation first. He's seen the thing first. He's got the snippy quote, and he's done it first. This, this idea that the, the easy money has been made is, is a complete myth. I rate, rate from my veins, straight out of the center of my heart. There's no easy time. It always sucks. It always sucks.

Jack Forehand (42:37)

You know, I'm a manager who has a small cap bias, has a value bias, has an international bias. Like, I can tell you for sure, this is, this hasn't been easy money. I mean, for, for the better part of a decade, you've been trying to explain to people, why are my tilts in those three areas? Why are none of them working? Like, over and over and over again? You know, you lose clients because of it. Like, this is not. There's nothing at all that's easy about this. Just because it went up in a short period of time. And this was the same thing, by the way, in 2000. Like, sure to value at this crazy run from 2000 to 2003. But you had to sit through so much pain to get there that like, that three year run can look like easy money because you're going up a lot in the market's going down. Like, that was the hardest money of all time because no one was left. Like, by the time you got to 2,000, like, who was left invested in small cap value? Basically nobody.

Matt Ziegler (43:21)

There's no such thing outside of, like the lottery ticket stuff, outside of effectively the dumb luck and good fortune of winning the lottery that results in, quote, unquote, easy money. Everything else takes the frictions, the struggles, the understanding where a bottleneck is before the rest of the market figures out what that is. And I don't care if that's starting a landscaping business in the rural suburbs of some city or if that's going out and finding us betting on Nvidia, you know, 15 years ago or whatever. Both of those are really hard. There's a lot of friction along the way, and you just have to appreciate it. The only thing that looks easy is, in hindsight, the rest of it. Outside of a scratchy lotto that you win the mega millions or something, nothing is easy. Welcome to human suffering, the human condition. This is where you want to take it, right, Jack?

Jack Forehand (44:08)

Yeah, exactly. And now that we've dispelled this easy money myth, there potentially is some extremely, extremely hard money to be made in the fact that value might be a good opportunity for the next decade. So here's Rob Arnott talking about that.

Rob Arnott (44:19)

I think we are likely to see a pivot back to value. Value is very nearly the cheapest it's ever been. You'd have to see value stocks beat growth stocks by a hundred percent. They'd have to double relative to growth stocks just to get back to historic norms for relative valuations. Small would have to double relative to large cap in order to be back to historic norms of relative valuation. Now, I'm not saying small cap is going to double or value is going to double, but some sort of mean reversion where they move in that direction is certainly possible. If you look back again at the dot com bubble, as a wonderful example, the narrative coming into the dot com bubble was, get on board. This Internet thing is huge. And it was. And these Internet companies are going to be stupendously successful. And some of them were, but they were also priced as if they were going to be even more successful. And so the narrative was, these companies are where you got to invest, because that's the future. Well, the first two years after the bubble burst, let's say March, let's choose March of 2000 as the bubble bursting. The first two years after that, NASDAQ was down a little over 50% by March of 2010, 2 on its way to a drop of just under 80% s. And P was down 27% on its way to a 46% drop. Russell value was down 4. Russell 2000 was up 4. And Russell 2000 value was up 53%. So you literally tripled your money if you pivoted from Nasdaq into Russell value at that moment. If you had the prescience or the luck to choose that moment on a ten year horizon. Our work suggests that small cap value will beat large cap growth by on the order of 700 basis points a year. On a 10 year horizon that's enough to double your money relative to sticking with growth. I'm not saying get out of growth partly because who knows what the right timing is on this, but also because people will get cold feet if they make the move at the wrong time. What I am saying is fade some of your winners, buy into what's out of favor and cheap and just kind of lightly average in to increased exposure to what's newly cheap.

Jack Forehand (47:15)

Yeah, I mean this, this is interesting to me. I just want to bring this in there because not, not because I have any idea what's going to happen over any period of time, because I don't. But I, I do think it's interesting to look at these spreads and to look at opportunity going forward. And you know, now that we're starting to see some of that reflected, I think it's just important to recognize that if we do get this value run, which who knows if we will. I mean if we do get it, there's a lot of room. Know what went on for the past decade created a lot of room in terms of value. So although value and international and everything have done well in the past year, if spreads are going to go back to where they were, which who knows if that's going to happen but you know, history would tell you they may if they're going to. There's a lot of room to run here.

Matt Ziegler (47:53)

This is one of those things, mean reversion, when it shows up, it's violent, it's crazy. It seems obvious in hindsight whether or not we get there, whether or not the numerators and the denominators act a slightly different way in this scenario, tbd, we could see that if you're looking at small versus big or value versus growth doesn't mean that the stuff that's worked really well could just catch down and the stuff on the bottom doesn't really do so much and we normalize that way. There's a lot of paths, but I think acknowledging the size of these gaps is really important. When something starts to move and there's a shot at mean reversion. Hard money to make, but I think it's really valuable to call out these extremes when we see them. I really appreciate Rob's work and context on this.

Jack Forehand (48:39)

So this next tip is from last call and this is just a phenomenal. I don't Know if I used it or not, but this is a phenomenal YouTube title, Matt. When the lenders say no more. Come on now. It's not gonna be better than that.

Matt Ziegler (48:49)

I mean, we are one nevermore away from, you know, pure po on this poetry from Ben Hunt.

Jack Forehand (48:56)

But there has. And this sort of relates back to the Satridi thing. So I'll play the, I'll play the clip from Ben and then we'll, we'll

Ben Hunt (49:01)

talk about it more. There comes a time in every credit cycle where the money, the lenders, the investors where they say no more. Where they get skittish about whatever. Maybe it's the economy, maybe it's AI is coming, maybe they're, they think there's some threat and they say, you know what? I, I would rather take a manageable certain loss today and move on than re up for more money that could be potentially existential loss tomorrow. Every credit cycle, this happens where the money says I'm out. I think that's where we are. I think that's where we are. And there are lots of reasons for it and there always are. AI is going to undermine the ability of these companies to make as much money in the future as they've done in the past. Oh, the consumer is really stretched. And so we're not, we don't want to lend money to these companies that are facing a stretched consumer.

Andy Constant (50:17)

All these reasons, and they're all true.

Ben Hunt (50:21)

But what's really true, and this is what I mean by it's the constant boom, bust credit cycle is that we had basically free money for a long time and then the world just awash in money. And now that tide's going out and the cost of capital is going up. And this is what always makes people with money say, I'm out. That's what I think's happening.

Jack Forehand (50:50)

So this does sort of relate back to the Satrini thing because one of the things Citrini said in the piece is sort of these problems in these software stocks eventually end up spilling into the private markets and, you know, the private credit and private equity and a bunch of other places. And so this idea that, you know, it sort of ties the two things together.

Matt Ziegler (51:08)

It ties the two things together. It signifies the, this becomes the event that we all focus on. But it's boom, bust as a cycle, which means it's boom, bust, boom, bust. And we have different stories that push the boom, and then we have different stories that push the bust, and then we have another story that pushes the next boom and another story that, like it just keeps going like this. And so what's interesting and part of the broader conversation with Ben from the Last Call episode is just you're gonna have a boom bust cycle. This just seems to be part of the thing that's careening us towards the next bust. And with or without Citrini, you've got a whole bunch of leverage in the system that's going to clear one way or another in the next bust. Can't tell you when it'll be. Can't tell you the Citrini piece or AI is going to be, tips us over the edge. But the boom comes before the bust and then the, the bus comes after the boom.

Jack Forehand (51:56)

And it's just interesting to me too, like, how much like narrative and perception plays into this. Like I was thinking about like, so back in, in Covid, like a lot of the commercial properties had all kinds of problems, but the lenders were willing

Kai Wu (52:08)

to work it out.

Jack Forehand (52:09)

You know, the lenders kind of realized like, we've got a systematic problem here. We're not gonna, if we let everybody default on these properties, we got, we've got a problem and they worked it out. But like, if you get to a situation where they're not willing to work it out, it's just, it's, it's a very different thing. And we're not saying, and even Ben's not saying we're necessarily there now, but, but it's one of those things that's just interesting like how that behind the scenes thought process plays into this whole

Matt Ziegler (52:29)

thing and you can't predict when it's going to show up. You can predict the starting conditions, you can see what's there. You can see how the story is starting to get narrative momentum into a common knowledge moment, as Ben would call it, where all of a sudden we realize the emperor has no clothes and we don't find that agreement. I think the Silicon Valley bank thing, I still think is one of the most useful experiences of the last couple of years. And probably that right next to some of the political stuff like the Biden presidency nomination and like stepping down and all that. So you have these moments where you see everyone is suddenly aware in the Silicon Valley bank thing that we have a real banking problem. And oh no, what does this mean for regional banks? What does this mean for commercial real estate? What does it mean for all these knock on effects? And then they brought the boil back down to a simmer and like, we all forgot about it in like three months versus, like in the Biden situation that we did on Breaking News with Ben where it was like, we have this event, everybody knows this isn't going to work the way that they were telling us it was going to work. And that agreement set in and then you see actual change that shows up in the perception of what this is. A new narrative gets room in the gap to take over. Citrini may or may not cause that tipping point in this boom bust cycle, but it is worth saying like we saw the type of market reaction that indicates a lot of these things are, are moving and are afoot not to predict the outcome, but just to say it plays an important role in the furthering of this narrative in markets.

Jack Forehand (54:00)

And this is what's so cool about Ben's data is like he's now quantified narrative so we can see this in data that we otherwise wouldn't like. When the lenders actually start saying no more and that shows up in like standard data, it's going to be well past the, you know, the breaking point of this whole thing. But he can kind of see this like, you know, their data can see this in real time, which is very, very cool.

Matt Ziegler (54:18)

I'm about to publish a little piece. It'll go up on Panoptica, so maybe by the time this is out, it'll be out there too. And one of the things, so the weekend happens, we're all talking about like texts are flying, emails are flying, whatever else, looking at markets, what's going to happen post the Iran announcement and then Monday morning as the actual calls and the pre market stuff and the investment committee meetings and notes are flying around. One of the things that happened in like three of the conversations I had pre market open on Monday was like the head scratch over the 10 year yield being up. Did you have this experience at all that this like. Yeah, okay, so I took note of this because this is a conversation I feel like I've been having with Ben. Ben's been writing about for like over a year now, which is basically that and Grant Williams who we had on the show talking about this explicitly this moment. Like since the Russian Swift thing, international investors don't treat US Assets the same way that they did post The Russian Swift 2020 22, I think event. And so like the, the idea here is that US Treasuries are no longer being seen as like a safe asset in the sense of like the flight to safety. And so what was interesting is like Iran happens. We can look at the narrative density of those storyboards of those signatures in the, the Persian data set and go like the rest of the world and most of media does not consider U.S. treasuries to be the flight safety asset anymore. And so when Iran happens and the bond prices go down and the yield ticks up even just a tiny bit and everybody's confused because they think war safety trade, flight to quality, it's like, no, no, no, we haven't been talking about this in a couple years and it has different ramifications for the rest of the data set than we have from the prior ones. And that change in narrative, that stuff really, really matters when stuff gets brought to a head.

Jack Forehand (56:08)

So our last clip is from Rupert Mitchell, also from Last Call. And this was interesting from the perspective of you've got governments abroad now spending money and they weren't for a long time. So here's Rupert talking about the implications of that.

Rupert Mitchell (56:18)

Well, I have a bias for a weaker dollar generally, but that's, you know, that's become a consensus view that normally would bother me a lot but I actually see how it, it benefits everyone, you know, for the dollar to weaken slightly and you know, while, while they can't say it out loud, you know, this is, you know, this is, this is more suited to achieving the administration's aims. Having, having, having a weaker currency, it's just not good for the ego. And meanwhile, you know, real or not real, I think marginal dollars are staying at home in less liquid indices that are cheaper and there is a kind of forced RE rating going on. And you've got the rest of the dirty little secret is that equity markets go up where governments are spending money. Right? I mean that's the reality and governments are spending money all over the world now. And it used to be a sort of unique, unique, unique achievement by the U.S. so I mean, you know, I guess Japan's the poster child for that. You know, China's going to have to carry on doing fiscal and they are now cheerleading their own equity market as well. That's, that's a, that's a double, double winner. And you know, there are plenty of reasons to be excited about a Europe that's starting to spend money properly.

Matt Ziegler (57:50)

I love the openness to MPT of especially the trader friends. I love like that Rupert's adopted this approach, Kevin Weir's adopted like this, like it's set into all their brains to look for where governments are spending money and go, this is, they're flow machines, they're giant flow machines and you have to baseline, be aware of where that's going on in the world and reversing in the world because it's a huge part of the flow mechanism. On how you think about it, the macro trading people all embracing MBT, I did not see that coming 15 years ago.

Jack Forehand (58:22)

And I also love the chart of truth. Like every single time, you know, the chart of truth is basically just us versus international. But like every time Rupert comes on, we get to see an update of the chart of truth and it's, it's continued to show, you know, international coming back more and more and more against the us.

Matt Ziegler (58:36)

I sometimes think it takes to talking to somebody like a Rupert Blind squirrel macro. Check that out. Subscribe if you haven't already. It takes somebody who's got that out of rest of world experience to be able to shine this light on it. So obviously back to the Andy Constant idea of you want the high level of experience with the low level of confidence. I think between the investment banking experience that Rupert had, like all around the world cracking emerging markets open for 20 years, when somebody writes about that relationship to capital flows in and outside of the US from his seat in Australia, it's really interesting to hear him think this way and constantly go back to that chart of truth because he's outside of the US thinking about these allocation decisions. This is a very, very real and very poignant chart for him and the money that he runs.

Jack Forehand (59:21)

And also when like a narrative becomes dominant, when you believe something so strongly, it's so important to look at the other side of that. And this idea that the US dominates everybody else is something that's been drilled into our heads for such a long time. So I find a lot of value in talking to people like Rupert who understands like what's going on behind the scenes. And maybe that allows you to maybe take that strong belief and put it in a different context.

Matt Ziegler (59:42)

Yeah. And much like, much like the value of the Citrini piece that we've been talking about this whole time and what everything with everything that's going on, much like the value that brings to the table of giving you nuance in the conversation to just take your confidence down low. Because if all you did was talk to, I don't know, like Cathie Wood or something, you'd have a video, a warped, literally a warped version of the world because you only see it through that lens. If you're not doing the eye doctor test, when you're swapping like this one better, that one better, if you're not doing the eye doctor test of like checking out all these different lenses, you lose the nuance and you lose the perspective. And, hey, I hope we do more of this show because candidly, I think this is the value of being forced to reflect on a bunch of these. This is amazing. This is all from the last week of our lives.

Jack Forehand (60:26)

Yeah, exactly. And if you're still with us, definitely let us know in the comments whether you like this. You know, feel free to put personal attacks about me or Matt. We certainly get those occasionally. But I think what's cool about this is, like, when I. When we participate in all these interviews, like, the wheels start turning in your head. It's like, oh, I tie this from Raul or not over to this from Andy Constant. And, like, what we're trying to do with this show is we're trying to do what went on in our head, but, you know, present it for people. Like, a lot of these clips are here because they tied together in the way we thought about things. And, you know, you're in a lot of the interviews, and I edit the ball, so, like, just when I'm editing them, I'm always thinking about this. So hopefully people find value in this. But definitely let us know one way or the other.

Matt Ziegler (61:00)

Let us know. Say hi in the comments. We're in there, too, one way or another.

Jack Forehand (61:04)

I was supposed to say, what, like, subscribe and comment. Like, what's your. What's your ending? You could do your ending.

Matt Ziegler (61:07)

You always do. Like you're watching Excess Returns. Like, comment, subscribe, all the things below. Jack forehand, don't roll any more clips because we're out.

Podcast Host (61:15)

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Rob Arnott (61:33)

Com.

Jack Forehand (61:34)

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Matt Ziegler (61:36)

construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.