He Wrote the Book on Bubbles | Edward Chancellor on If AI is Different

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[00:01:28] So one of the features of the CapEx booms is that they, as you know, they actually produce profits. Because if someone invests and the other person, the buyer doesn't actually immediately depreciate what they require, then aggregate profits rise.

[00:01:55] They have no trouble investing tons of money during these technology transitions. But they do have trouble spotting the winners. So the question is, can you get overinvestment in intangible as in any other type of physical overinvestment? I see the answer pretty obviously yes.

[00:02:22] Hi everyone. Welcome to The Intangible Economy. Our guest today is Edward Chancellor, who is a financial historian, journalist and investment strategist. He is the best-selling author of Devil Take the Hindmost, A History of Financial Speculation, one of the classic books on investment bubbles. At least in my opinion, he's also the world's foremost authority on capital cycles, having written two books on the topic.

[00:02:45] In addition, he's the author of The Price of Time, The Real Story of Interest, and most recently helped our former boss, Jeremy Grantham, write his autobiography. Ed, welcome to the show. Nice to see you, Kai. What you did mention is that you came to GMO as my analyst back in 2008. We go back to the financial crisis together, don't we? Yeah.

[00:03:10] And the other piece being that, to add to your many accomplishments, helping advise me on my economics thesis on credit cycles and bubbles at Harvard. And if I'm allowed to blow our trumpet together, you remember how we were working in early 2009, and I got you to do a piece of research that showed that quality stocks,

[00:03:36] that GMO was heavily invested in at the time, tended to deliver alpha or high performance during boom period, during bust periods, simply because they had a lower beta to the market.

[00:03:51] In which case, the answer was that when you thought the bust was coming to an end, you wanted to get out of quality as quickly as possible, which was actually, we delivered that research in February 2009, just in a couple of weeks before the market turned.

[00:04:15] And I credit you because you actually redefined quality in the various markets that we looked at historically as volatility or low vol stocks, because we didn't have data for full quality back then. Still, that, when I think of the sort of good pieces of research that I'd been involved in in my life, I'd say that was definitely one of the top five pieces.

[00:04:44] Yeah, very timely. And yeah, it was definitely a pleasure working with you at GMO. And that's why I'm so excited to have you on this podcast today to go through some of your research and your work. Okay, let's move ahead. Let's start. Okay, so the topic I wanted to start with is, I guess, the topic of the day.

[00:05:04] U.S. big tech companies are investing trillions of dollars into AI data centers in what is set to perhaps be the largest investment infrastructure boom of history. Setting aside any view on the technology itself, what does the history of capital cycles from the Railway Mania to the Japan bubble to the dot-com boom in the late 90s teach us about how the current cycle may play out?

[00:05:30] Well, it's hard to leave out the efficacy of the technology. So we're going to have to get back to that in a minute. But the general principle looking at past technology booms.

[00:05:52] And as you know, I wrote a paper with Jeremy Grantham on this in February this year, which is available on the GMO website. But the general pitch is a new technology arrives. Sometimes people get very excited about the impact of that new technology.

[00:06:14] Sometimes the new technology doesn't attract too much attention in its early years. I'm thinking, for instance, of when the railways came to Britain in the 1820s. 1825 was the launch of the first passenger railway in the U.K., which is called Stockton and Darlington.

[00:06:44] And the first few railway companies had dominant positions, no competition. They were pretty profitable and their technology was proven. And then we had two successive waves of investment. One a decade later, sort of 1835-36. That led to boom in the stock market.

[00:07:10] A bit of overbuilding came down, but it wasn't too much damage. The real problem or mania came in sort of 1843 to 1845. And that is really the period in which there was a massive launch of many, many railway schemes across Britain.

[00:07:40] And in terms of capital employ or projected capital expenditure, it was running to about 10% of U.K. GDP, actually much higher than AI today. And there were too many, as a result.

[00:08:02] Not all the schemes got actually went ahead, but the upshore was far too much duplicative investment. And famously, as I would say, three railway lines between London and Peterborough, which is in East Anglia. Three railway lines between Leeds and Manchester.

[00:08:25] And obviously, if you have three lines running between two places, they could be less profitable than if you have one line. And the upshot of that is that the railway index, I think, lost about 60% of its value. And ironically, I just looked at this the other day. You know, canal stocks, canals were the most obvious losers from the railway mania.

[00:08:54] And they did lose in the end. But actually, you did better investing in canal stocks to 1845 and 1850 than you did in railways. Because canal stocks had been beaten down a bit, whereas the railways still have plenty to fall. Now, look, in the very long run, let's say 20 to 30 years, that investment was pretty benign for the economy.

[00:09:21] And, you know, although the railways were never quite as profitable as they'd been in their early years, it did and it didn't. It was good for the economy. And if you got into railway stocks in 1850, once the bubble had burst, it was perfectly fine. Now, then, you know, we have other sort of new technologies that attract much more competition.

[00:09:49] I mean, you think, you know, motorcars in the late 19th century. I think the U.S. had something like 2,000 motorcar companies. And the upshot of that was, you know, General Motors, which was a winner, had to be recapitalised twice. And actually, GM was really a sort of roll-up of failing car companies.

[00:10:17] And Henry Ford, I think he only – his Ford Motor Company wasn't listed, but Ford only succeeded on his third attempt. It was hugely over, you know, over-invested air. Aircraft was much the same. I think – I mean, Warren Buffett points out that there were actually three aircraft companies in Nebraska,

[00:10:46] believe it or not, in Omaha, Nebraska. That, you know, again, far too many aircraft companies were founded. Huge, huge, you know, very loss-making. And that, you know, that pattern has really continued, you know, obviously into the dot-com era, where, you know, again, massive capex.

[00:11:13] And that's interesting – what's interesting about the, you know, what we used to call the TMT bubbles, technology, media and telecommunications. What's interesting is that the capex largely done by telecoms companies.

[00:11:38] And they – they weren't – they did the heavy spending, but they really weren't the winners. And so one of the points I made in a recent column of mine was that actually markets are – you know, they have no trouble, you know, investing tons of money during these technology transitions. But they do have trouble spotting the winners.

[00:12:04] And then, you know, after the dot-com bubble burst, you know, NASDAQ loses 85%, 95 – sorry, 78% to 79% of its value. Amazon goes down more than 90%.

[00:12:25] So even though Amazon emerges as an eventual winner, there is a massive, you know, massive loss. And it's not – it's dangerous to say in hindsight that you can spot Amazon as the winner and that it's – you know, because there could be – you know, there were other businesses around, WebVan, this and that, you know,

[00:12:54] that might have actually taken the prime place. So anyhow, so that's the general picture. The general picture is a new technology. Everyone gets very excited about it, a huge amount of investment. And investors tend to anticipate the profits flowing more quickly than they actually tonight to be the case.

[00:13:21] And, you know, there is – there has always been – I think there's always been a shakeout. Possibly the one exception is the arrival of the telephone, another sort of revolutionary technology where the Bell telephone system sort of began to insert itself

[00:13:47] and got a pretty early monopoly. Telegraph also moved to – a bit earlier moved to monopoly very quickly. So, you know, you could say that if you've got a revolutionary new technology and for reasons of inherent monopolistic – what do you want to call it? Sort of environment.

[00:14:14] There is a case that the new technology can arrive without, you know, without a huge overinvestment. But if the barriers to entry are relatively low and therefore you can have more than one dominant player, then you're likely – historically you're likely to get – historically you have had overinvestment. And that's all we know.

[00:14:40] You know, we can't say that the, you know, that the future will resemble the past. But it should sort of guide our judgments, say the least. And then, you know, then we can – then we'll concess current circumstances and say, you know, to what extent is this time different or not? Right. So if I'm hearing you correctly, you are saying that, you know, over the course of history there have been many successive waves of capital cycles,

[00:15:06] which generally start due to the advent of a new technology, which, of course, attracts investment in capital. In a few cases, such as the telephone, the industry has consolidated into monopoly pretty early and has been relatively stable. But the vast majority of instances end up with an influx of capital and a fragmented market structure whereby profits are competed away to zero.

[00:15:31] And that leaves investors in these companies with, you know, a pretty rough outcome. Wow. And so then – I'll add something. You remember one of the sort of key precepts of the sort of capital cycle theory is that it draws on the prisoner's dilemma being a theoretical problem,

[00:15:56] which is that, you know, in the prisoner's dilemma, you've got two prisoners and the question is, you know, do they keep quiet and both serve a sort of moderate sentence? Or do they both rat on each other and get very long sentences? In other words, it's suboptimal to rat. But the way the game – you know, the prisoner's dilemma being structured is there's an inducement for both to rat.

[00:16:25] It doesn't work by not – you don't benefit by not ratting. And I think it's the same, you know, during a – you know, the capital cycle during a new investment. You see other people going into this, into the new area. And if you don't go in, there's a possibility that they might come out, you know, dominant monopoly and crush you. And so you go in.

[00:16:54] But if you go in, you may come for – you know, the end result may be suboptimal for, let's say, the investment world as a whole. It may actually – you may actually hang on in there. So it's not necessary – you know, it may be a sort of agency problem. It's not necessarily irrational for the individual.

[00:17:16] And I think that, you know, with AI, you know, there's one narrative – I don't know whether you want to – you know, how robust it is. But one narrative is that when OpenAI came out with its – you know, ChatGBT in November 22, Microsoft saw this as a way to team up with OpenAI to break open the Google search monopoly.

[00:17:45] And then, you know, Google and started, you know, how to get, you know, respond. And then, of course, you know, then everyone else standing by the waistline said, you know, this is a great technology. We want a bit of the action too. So of course, of course we're going to get a huge CapEx. As Gründerinnen und Gründer willst du vor allem eins – schnell vorankommen. Egal ob beim Product Market Fit, deinem nächsten Schritt oder deinem ersten großen Enterprise Deal.

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[00:18:42] Und für 1,99 Euro gönnst du dir danach noch eine kleine Eiszeit. Bei Aldi Nord findest du immer das Passende. Klingt gut? Dann probiere die Snacktime Sushi Box. Ab 205 Gramm für nur 2,99 Euro. Oder Mookie Sandwich Eis. Je 8 Stück für nur 1,99 Euro. Das ist Gutes für alle zum Aldi Preis. Jetzt in deiner Filiale. Aldi. Gutes für alle. Ja, I mean, it's interesting to think about how the different big tech companies have responded

[00:19:09] differently to the game theory, right? Because we have Apple, of course, as a famous example of a company that has largely abstained from the arms race and is kind of waiting to see how things shake out. Whereas amongst the hyperscalers, you see ever-increasing investment in quotes from CEOs being like, I'd rather go bankrupt than lose this race, right? So there's clearly a, you know, this dynamic is clearly happening amongst at least some subset. Maybe your point is just you only need a few players to be bought into the idea in order to drive the entire cycle.

[00:19:39] Yeah, and you may have been pulling this more closely than I have, but, you know, it's not just, as you know, it's not just the way, you know, the top, you know, the largest cap, U.S. tech companies are doing it, but there's a whole load of other competition. You've got, you know, you've got the, what is it, DeepSeek in China. You've got a, I mean, they had listed a few Chinese AI companies recently,

[00:20:05] and then you've got, you know, what, you know, vast amount of, you know, of VC capital going into the same space. So the tech, you know, the big tech cap X into AI gets most of the, most coverage, I suppose, in absolute terms. Relatively, it's the largest section, but there's a lot of other investment going on at the same time. Right.

[00:20:34] So I guess the key question then is, so we know that there's a lot of supply coming online, a lot of investment coming into the AI sector. I guess the question goes, is ultimately, will there be enough demand, right? Will there be enough demand to meet supply? How should we think about this side of the equation? So, you know, it is often the case, or it has been the case in these tech groups that people overestimate demand.

[00:21:03] And I think, in fact, the railways in 1845, the CapEx spending at that time would have required, you know, within a spec, if someone's supposed to crunch numbers, it would have required passenger rail traffic to increase by threefold over the next five years. And given that, you know, that there were a fair number of railways already by that time in the UK, it wasn't going to happen.

[00:21:31] During the dot-com bubble, there was this sort of urban legend going around that data traffic was doubling every two months, when in fact it was only doubling every six months.

[00:21:56] And, you know, this little factoid, I mean, it actually originated with some company that was later taken over by WorldCom, which later went bust. And it was signed actually everywhere, you know, all immediately, all the brokers picked it up, even the, you know, US government picked it up. So everyone believed it. But in fact, we actually have data. There's this guy I know called Andrew of the Leaco, who was at the time at Bell Labs.

[00:22:26] And in 2000, just around the time that the tech bubble was peaking, he put out a, you know, a paper saying, give you the true demand growth. And, you know, and no one, so the, you know, the actual, you know, accurate data was available in real time. And no one paid attention to it.

[00:22:55] The upshot was, you know, WorldCom went bust, and a host of those other, you know, so-called alternative telecoms carriers, altnets went bust. And there was, you know, massive AVA capacity in fibre optic cable, and all the, you know, telecoms equipment suppliers, like, you know, Nautil and Ericsson and Lucent, you know, took big hits.

[00:23:20] And actually, you know, there was a massive recline in profitability. So one of the features of the, of the capex booms is, is that they, as you know, they, they actually produce profits. Because if someone invests and the other person, the buyer doesn't actually immediately depreciate what they're, what they've acquired,

[00:23:50] then they, then aggregate profits rise. And so what you see in, you know, in the late 1990s going into 2000, massive surge in reported profitability. And then because that capital turns out to be misallocated, then you have, then new capex is immediately curtailed. And then you have to depreciate past capex.

[00:24:18] And so you have a collapse in profitability. And something, you know, we're seeing something very similar today. And that, as you know, the depreciation schedules for these AI chips, GPUs has been extended. And, and I think you probably know better than I, but I think from sort of roughly an average of three to three and a half years to six, six and a half years. And I understand that they,

[00:24:47] because if you buy a GPU and, you know, keep it in a warehouse, because you haven't actually built your, your data center yet, you didn't actually start depreciating the GPU until it's actually in the warehouse. But there is a sort of technological depreciation that is going along, going on, you know, even before you actually start using the chip. So, yeah.

[00:25:16] So we'll see. But yeah, you know, the market is being driven, as far as I see, by, you know, strong economy on the back of a lot of capex and very strong earnings growth. But, you know, these are contingent on the investment turning out to be profitable and the demand being there. Now, can I just say, going back to,

[00:25:46] on the demand question, and you, and by all means, contradict me if you have a different view, is there is this, there is this view put out that, you know, that AI is, we're on the, we're on the cusp of, you know, artificial general intelligence, even singularity, and that this, you know, the, you know, the AI,

[00:26:17] whatever that might be, is just, you know, it will, we'll be able to do, you know, almost every sort of conceivable human function, apart from, you know, merely physical, you know, physical work, plumbing. And, you know, I'm, you know, I'm very, very skeptical of that view, because, because I don't, I mean, when I, if I was talking about AI as a large language models,

[00:26:46] large language models work through sort of inference, through probabilities of, you know, what is the most likely next token to provide that. I can't see, A, how that, it can lead to really genuinely original thinking or activity. And then, B, as you know, it's, you know, sometimes it's going to make, you know, the wrong selection. And, and therefore, you're going to get the hallucinations. And I think the hallucinations,

[00:27:16] because these machines aren't sort of genuine reasoning models, are inherent to the, to the large language model technology. And if that's the case, then, if that's the case, you know, I really don't think that the demand that is mooted currently is going to be there. And I, I don't know if you picked this up.

[00:27:45] Do you see that last week or so, a story about some company that provided software for car rental businesses? Do you see this? I don't know. They, they used, you know, everyone's wanting the Claude, the new Claude Vibe, Vibe coding. They used Claude to update their, their software. And it wiped out their entire customer database. And it did some other sort of quirky things too. So, so the,

[00:28:15] you know, the whole business crashed and the companies that were using, as far as I understand, the car rental companies were using the software were suddenly frozen. the, a couple of months ago, the, the winner of the, of the Royal Society's Faraday Prize, sort of top scientific prize, a guy called Michael Wardridge, gave, gave,

[00:28:43] gave the Faraday lecture, which I think is worth listening to. He, he's, you know, he making these sort of points that I've made. In other words, I've just taken my points from him. And, and he, he then thinks it might be, you know, what, what he calls a Hindenburg moment. You know, when the Hindenburg moment was, I don't know when it was, in 1937, when one of these, zeppelins, everyone was excited by these zeppelins. Um, and, and the Hindenburg zeppelin, you know,

[00:29:13] blew up somewhere over some US airfields. And everyone had second thoughts about the technology. So, I, I, I, I mean, I may add this wrong, but I, as far as I can understand, the CapEx into large language models is not, you know, about simply improving search and helping with research, but actually is posted on, you know, a great,

[00:29:43] um, you know, breakthrough in, in, into agentic AI. And, um, as I say, I, well, we'll see, I mean, it's not, you know, it's not uncommon for investors to, or, you know, I mean, investors both in, in, in companies and in markets to, to imagine that technology is more advanced

[00:30:12] than it actually is. I mean, if you think about, a lot of those, you know, go back to the dot-com bubble. Some of those businesses would have been viable had we had the, um, faster internet connection. By the time, people were on, by and large, working on sort of, you know, incredibly slow dial-up connections that the businesses that would have been viable 10 years later were not viable then. So, yeah, I don't know what's going to happen to, you know, AI development. Maybe someone fix,

[00:30:42] you know, merges a, you know, the large language model technology with, um, I think, you know, these so-called reinforcement learning technology model. And then maybe they can harmonize and get something much better. But at the moment, they didn't seem to be quite there. So, Ed, so you believe that the, you believe that these models can be error-prone and that will limit the total addressable market of these models. I guess where I would, I think an interesting analogy would be that of, like,

[00:31:11] cell-driving cars, right? So we know, for example, that Waymo is not perfect, but that, and its error rate is not, is not 0%, but so, so too are human drivers imperfect, right? And so one of the interesting phenomenons we've seen is that for whatever reason, politicians and, and drivers have a, a bias against AI, whereby, you know, the, the tolerance threshold is so much lower. That if there's just one big, um, you know, kind of headline about a bad Waymo accident, suddenly, you know, the,

[00:31:41] the interest in, in the technology, you know, wanes significantly. And so what, with your Hindenburg moment, is that kind of what you're saying here, that we all know that AI is not perfect. We all know it's a, it's a nascent technology. the AI bulls will say it's going to be getting better over time. This is the worst it'll ever be. I mean, it's kind of hard to argue with that. How much better it will become? We don't know. But you're saying that the, the, there is a kind of tail risk for the AI thesis around if just one really bad thing happens that could potentially put a pause on AI development.

[00:32:10] No, I'm, I'm being, I'm being a bit more adamant. Mainly that if, given, um, the problems of hallucinations and, and, and I, you know, if there are these people who test models for hallucinations, I looked at, I looked at one report from, I think October of last year and finally, you know, the best performer was 2% hallucinations and, um, I think, you know, 20th,

[00:32:41] performer was, was out of, I don't dare you, it might have been 20 cents, a bit high, but seems to say 20. I, you know, you're not going to, there are certain activities where you cannot tolerate that degree of error and you, you, you won't, you can't, yeah, so I'm, and that, well, you, you see, you, you know what I mean. There are certain areas where you can tolerate a certain amount of error. Right. Yeah, Yeah, so I think,

[00:33:10] so I think what that means is that certain things you would not want to give AI. Like, you don't want to make AI in charge of the, the, the nuclear weapons, right? Um, but in the case of, say, doing R&D and doing research into a completely greenfield technology where it's like, you know, the alternatives to do nothing, then I think the bar is much lower, right? So then the question becomes what percentage of economic activity falls to the category one versus category two because that, you know, as I said, will constrain the total addressable market of a technology like this. And what, you know, I guess is being implied is that,

[00:33:41] you know, when, when, um, the demand forecasts are being written by AI companies, um, it's in, to an extent baking in both categories. And you're saying maybe only one category is actually available in which case the, um, potential demand curve is a little bit less, um, enticing than is currently being priced by the market. Yeah. Or, or put it simply, the, uh, the ruling principle of Menlo Park is fake it till you make it. And, um,

[00:34:10] the, the amount of hype in that, you know, oh, need, need is safe. By definition, all these technology manians have, have large doses of hype. And, you know, that's not, in many cases, that hype over the long run is, you know, is factored. But there's never, really never, ever, ever been as much hype, um, as we see, uh, around AI. And, frankly, if you take, you know,

[00:34:40] you take the, um, efficacy of, the amount of hype relative to the proven efficacy of the technology, the, the, the, that, that ratio is just more extreme, uh, than, than anything before. And, I think the, you know, Thomas Edison, um, he, he, he, he, he hyped, you know, the, uh, incandescent bulb

[00:35:09] technology before he'd really fixed the problem and it worked out in the end then. But, but, you know, and, and, you know, and I think what, I think what people, what's happened is, is that in these, in expected markets we've lived through in recent years, the, the, um, reality distortion field that, that, that Steve Jobs, um, authorizing, and then, you know, um, you know,

[00:35:37] Musk's taking it into the public markets of Tesla, who was suggesting you're going to do something. We're about to achieve something when you, you haven't achieved it. And that in itself can become some extent self-fulfilling policy in Musk's case because it gives you the capital to then make the development. But, you know, if everyone is doing it and if, and that, you know, and that there is, and the, and there's a huge amount of, uh, of, of great competition, and I think that, you know,

[00:36:06] that just increases the prospect of, of, um, of it coming undone. So, I want to like, um, kind of take the flip side of this. So, it sounds like on the, you can feel free to interject if you disagree, but it sounds like on the, um, kind of AI investment, um, boom, you are, you know, negative, that you kind of see a, a repeat of history, these historical, um, booms and busts, um, in, in the making. And, you know, that would generally make you bearish on, you know,

[00:36:35] I would assume all the companies in the, um, in the investment, um, supply, uh, value chain, or are there particular pockets, um, you know, that you find to be, you know, areas where you think that the, um, prospects would be less dim? Um, look, I mean, first of all, you know, we all have our own sort of specialities. So, no, um, I mean, it perhaps, it's, some people look at markets, whether investors or strategists, can cover many different bases. So,

[00:37:05] I'm not saying I'm solely a capital cycle investor, but if you are a capital cycle investor, you, you know, you, you're, you're sort of, it's your principal bank to step, step back whenever you see a surge of investment, um, across many companies. And you, you capital cycle investor can, if you, they can rationalize investing in a company,

[00:37:32] that is itself investing a huge amount, providing the, the, um, industry or sector that they're operating in, uh, is not investing massively too. But when a whole sector, uh, is plunging in, then, you know, the capital cycle investor, you know, prefers to step back. And, and I, you know, yes, you know, at the moment you've got all the, you've got a lot of, uh, the picks and shovels makers of the, um,

[00:38:02] or, or, or, or, or AI have all, or, or been void up. You know, the crap, you know, the tram companies, and then Corning, a lot of the same companies that, um, actually were involved in the, in the TMT, who have now come back. Um, and I, you know, I felt, I'm eyeing some, being copper stock, and, and copper is, as you may, also beneficiary.

[00:38:31] So I'm sort of unwitting, uh, beneficiary. But the reason I invested in the proper stuff was not because I anticipated the AI thing, but, because I thought that, um, like, that there'd be another investment in, in copper. And, and, so, you know, everyone's, this is one of those, you know, very big investment teams where everyone really, you know, gets caught up in it, whether they like it or not. Right. So you actually wrote a piece, um, recently for, uh, Reuters, um,

[00:39:01] where you said markets have poor scorecards for spouting AI losers. This is the piece on the anti-bubbles, right? and, and so maybe you could walk me through this because I think, you know, you talked in this piece about some cases where obviously the market got ahead of itself saying, hey, this, they're going to be the long term winners and they were not. But there are also cases of the market pockets of the market, perhaps today, even that, um, are trading kind of at disparate depressed multiples on this idea that maybe they'll be disrupted by AI. So maybe walk me through this argument, which I think is pretty interesting. Okay. Well,

[00:39:32] um, first of all, as you know, uh, we used to have discussions about this at GMA that, you know, problems of shorting bubbles. Yeah. Because you, you can, you can, even if you're accurate, uh, you can have massive drawdowns. Um, and this is probably not worth shorting a bubble, even if you have perfect foresight. Uh, then, and I hadn't, I think as a sort of, um,

[00:40:02] from a sort of top down level, I think there is this, I think bubbles do have a sort of crowding out effect. The capital gets drawn into one sector and it gets pulled out, uh, of other parts of, uh, of other parts of the markets. And we saw that in the, um, in the, again, during the TMT bubble, uh, with what we'll call the old economy stocks, um, the companies that were deemed to be, um,

[00:40:33] were deemed not to be, um, affected by, um, negatively affected by the, uh, arrival of the internet. They were, they, sorry, I rephrase that. Uh, with the old economy stocks, the businesses that were deemed not to partake in the, uh,

[00:41:03] in, a good company. It was selling cheap multiples, even while the market itself was on a high, uh, all time, uh, all time high. Now, and then, you know, as you know, value stocks, uh, as a factor, you know, were very depressed. Small cap was very depressed. Emerging was very depressed. So, you know, there were, there were a whole load of anti-bubbles in, um, by 2000. Uh,

[00:41:33] and those, those were areas where they offered, you know, really great investments. So you could make money in the anti-bubbles and while the Nasdaq was losing, you know, 80% of its value. And then I like, in that true tension, you remember too, in that same piece to the, um, the excitement over the, uh, you know, energy transition. And if you remember, you know, the, the SPAC boom of 2020,

[00:42:03] 21 was, you know, largely around, you know, stuff relating to electric vehicles and, you know, if you remember all those sort of LIDAR, LIDAR companies and EVs, um, and so forth. Um, and, um, but, you know, in, in, in 2020, 21, you know, the, the traditional energy stocks, uh, were very beaten up. I think they, you know, the, um,

[00:42:32] energy sector went down to about 2% of the S&P 500 from its average level of the Rhyne, sort of 8%, 10%, um, weighting. and Tesla was worth more than the entire listed North American energy, um, uh, universe. and they, to me that, I mean, it, it, it took, you know, it, it, it didn't take huge amount of analysis to see, uh, that, that this,

[00:43:02] you know, that, that, that, that oil companies weren't going to disappear overnight, that their investments weren't going to be sort of so-called stranded assets, uh, and that EV growth was largely driven by, um, subsidies and that so on. And, and so that was a really nice anti-bubble. You, you didn't have to short the SPACs, but you, it had created a wonderful investment opportunity. I mean, for instance, friend of mine, Jonathan Tepper, who runs a company called Prebit Capital, he,

[00:43:32] he was pointing out that you could, there was this company, um, called, called Garrett Motion that, that provides the, turbos that goes on, go into internal combustion engines that had a sort of 40% market share, 60% of incremental market share, selling on seven times PE. I mean, it, it's, again, the stocks are up 150% over the last year or so. It's, that's an anti-bubble, yeah? Um, and, you know,

[00:44:02] where are the anti-bubbles today? I mean, so, um, yeah, Tepper shares the same skepticism about agentic, AI, thinks that, you know, he owns, you know, stocks, sort of car auction business, or, you know, online travel. He doesn't believe that these online, um, businesses, whether in sort of, you know, whether, you know, real estate market, or, or travel, whatever,

[00:44:32] are going to be disrupted. Um, and he owns, and he also mentioned, I think the London Stock Exchange Group, that not only, you know, does, you know, does provide, you know, the market for, you know, stock, the stock exchange, but also a lot of, a lot of other financial data, that, whatever, he doesn't believe that their, that their, their moats are, are, are interrupted by, or, or, or fatally compromised. And the market, um,

[00:45:02] I don't, I think the market has revised its opinions somewhat, but definitely, you know, two or three months ago, uh, in this, you know, the market was, I think, sort of overblown. It was sort of selling everything. There was a sort of massive, overblown sell off. The, the only company, um, I, I saw that, that really has been impacted, and genuinely impacted, um, was one of these sort of, you know, California and educational technology companies that provided sort of, um,

[00:45:32] essay, essay notes for, uh, lazy students. And, you know, the stock, yeah, AI's going to replace that business. Stock's down 95%. I've got no argument with that. Straight forward. You know, that AI may make, you know, one, one or two errors in a student's essay. Uh, but that, you know, that, you know, we, we all make mistakes. They knew it's not going to be the end of the world if it does. But, right.

[00:46:01] You don't want AI to be booking you a ticket to London and then find that it's flying you to Timbuktu. You know, that, that is a real problem. And unraveling that problem is going to be very costly for a travel company that went down that route. Got it. Got it. So, so you and Tepper believe that a lot of what sounds like software companies or other companies that, you know, are selling down 50 plus percent on this idea that they will be disrupted by AI,

[00:46:29] that those are interesting places to at least look to the extent that, you know, you don't want to short a bubble because as we know, that can be quite challenging. So where areas where investment has been crowded out, and you're saying potentially the perceived losers of the AI disruption, which, you know, as you write in your article very well, historically, the market has a pretty bad scorecard when it comes to identifying who will actually, in the long term, the losers, not always the folks who initially sell off. Yeah. And I mean, it requires a bit of, you know, a bit of analysis because what, you know,

[00:46:59] people who hold that view, you know, also point out that the software as a service companies, you know, were trading it with a tremendous bubble, you know, three or four years ago. And so part of their selling off is really just to do with getting back to what was fair value. And the other issue is that they, you know, they have, you know, they, they, they love stock options.

[00:47:29] They pay out most of their earnings to, to their employees. So, you know, those are businesses that everything else being equal, you want to be, you want to be pretty cautious of. And when I last looked, those, some of those companies were really not, you know, they may have, it may have sold off a lot, but they weren't, they weren't, we weren't optically cheap. And I think once you take into base, into account the stock based compensation,

[00:47:58] they were probably worth it avoiding. So, yeah, I think, you know, look for the anti bubble, but, you know, but, but a duper type of analysis. Right. Of course, because there will always in these cases also be companies that are truly being disrupted by technology. And those will be zeros, right? You don't want to, you know, but be buying Blockbuster into its downfall. Okay. So let's switch gears now from technology to other capital cycles. You know, when we were working together at GMO, I know you were spending a lot of time on,

[00:48:28] this was in the early 2010s, spending a lot of time on China. So I think this is a really interesting example of a case where, you know, I think the capital cycle did a good, good job kind of explaining what ultimately happened with China in terms of the fact that, you know, shareholders have not received a good return on their investment in Chinese stocks, despite a pretty robust economic growth. So maybe walk me through that episode and kind of, and then also bring me to the present and where we are now in terms of the capital cycle with China and other emerging markets, if you can.

[00:48:58] I wouldn't say, you say investors and well, despite robust economic growth, I'd say because, because, because of the growth, because the growth was faster than I knew was, was higher than the returns on, on capital. And then actually the companies to grow had to issue, had to raise more capital. So in fact, actually,

[00:49:24] if your returns are low and you're growing in an environment that are growing, is growing quickly, it's actually particularly negative. And I think my lesson from China, you know, was this, you know, the lesson from China, as far as I'm concerned, is this, is, you don't want to, when you're looking at markets, you don't want to look at valuation alone, you want to look at valuation and, you know,

[00:49:50] returns on capital and the capital cycle. And, you know, China had, you know, was the largest investor, you know, went through the greatest investment boom ever. And, and, and they, and, and, and so just, as you say, you know, had relatively strong economic cravetail off a bit, you know, over the last few years, but still very strong growth and absolute miserable returns.

[00:50:20] And, and, and the shareholders were diluted, partly because the Chinese have a tendency to, you know, to add companies to the index at a very high valuation. And then, and then, and then, and then people who bought it at the high valuations, and the index sort of gets diluted by these new issuances and, and, and therefore gives a poor return. So I think the capital cycle, you know,

[00:50:49] has been capital cycle. NASA is very well indicated there. And as you remember, we did a lot of work on the real estate. Um, and I, you know, well, we've indicated it. Yeah. I mean, yes and no. Um, you know, the, I saw the other day, a chart showing real, real Chinese house prices below where they were in, in 2010. Um, you know, a lot of those big, you have, we used to be short some of this,

[00:51:19] um, big Chinese real estate companies like Evergrande, you know, they, they've all gone, they, many of them have gone bust. Um, it, it took, it took longer to play out than I expected. But as you know, however long you've been in this business, you know, if you, you might make a sort of, uh, fundamentally sound observation and then be, you know, surprised by how long it's taken to play out. So,

[00:51:49] um, yeah, I, I don't, you know, I'm not following China so close today. I know some people think that, um, I mean, I do some works, you know, with the marathon asset management and they, um, you know, that their emerging markets, people think that, um, some of the Chinese real estate developers now, you know, particularly in the tier one, tier two cities, um, are in a relatively good state. Um, I, yeah, I'm,

[00:52:17] I'm not following the Chinese story to us enough to tell you where we are. Now I just from, from 15 years ago, I'd say that our positions have largely been vindicated. Got it. So here's another fun one. Um, so this is from one of your Reuters columns. Um, entitled big booze can sweat off its multi-year hangover. Um, I'll let you, um, tell the story, but, you know, basically in the COVID boom, um, people were stuck at home drinking booze. these stocks, um, did, did well. And then, you know,

[00:52:47] following, uh, the 2022 unwind, they have, you know, since collapsed in price and their valuations have fallen as well. But you and your article from July last year argue that these companies are Lindy. Um, so what does that mean? Um, and kind of, how are you thinking about these stocks today? Um, so the, the Lindy effect is a, um, sort of joke. it's, it's, it's interesting. It was a notion, uh, the,

[00:53:17] you know, the urban legend has derived from a Broadway cafe. And he, where someone at the bar saying, how long do you think this show is going to run for? And, um, the person says, the show is going to run for as long as the show has run. In other words, if the show's been going for two weeks, you know, if the show's been going for one night, it will go for one more night, then it'll place. If it's been going for five years, it'll run another five years. Um,

[00:53:46] and then it turns out actually that as a sort of rule of thumb, that was actually turned out to be recently, accurate. And what I would suggest is, um, it's probably the, you know, probably the case for the spirits companies. I think, you know, particularly spirits, because of the companies that were beaten up are companies like, you know, Pano Ricard, Campari, Diagio, Remy Cointreau. I,

[00:54:14] brandy sales really took off during the pandemic because people, you know, people were drinking and they were speculating. And I say, when they went hand in hand and they were stuck, you know, and then, and apparently they, you know, they loaded up their drinks cabinet. So by the end of pandemic, you know, there was no space in the drinks cabinet and they'd been slowly drinking off the, um, the experience. So you could say that was a,

[00:54:42] that was a stock problem. And then, you know, then the other issues, these, um, you know, the GLP ones came along, along these sort of weight loss drugs. And, and they, um, apparently put people off alcohol. And then there might be an issue that, you know, sort of younger generation is, um, taking ketamine rather than drinking. I don't know. Um, but my guess is that, you know, these brandy companies and spirit companies,

[00:55:09] they've been around for a couple of hundred years. And you've got, you know, you've got, you know, countries like India getting rich. I, I don't really like to play the sort of emerging consumer demand story because it, it's a, um, it's often what the breakers do. Um, but yeah, I, I just, you know, I think it's, it's obviously not hard. And one can find exceptions, but I think it's,

[00:55:39] as a sort of good principle, I, it was, and, and where they are now, again, not following it particularly. I, I saw that the ad show recently sort of picked up because it, you know, it had better than expected results, whereas Campari still, still died. I liked, you know, I particularly liked the story that, uh, read, read me country where you could, um, where the company was valued at, uh, less than the market value of,

[00:56:09] of the branding is false. Um, and that, that sort of reminded me of, there was a story, you know, generally, um, German hyperinflation when the, when the Daimler-Benz company was worth less than the, um, the, the, the cars in the factory lots. And so, I, I thought that was, you know, a, you know, a good story. I, I, I wouldn't surprise me at the GLP ones, you know, if that sort of, that's,

[00:56:38] it wouldn't surprise me at that, that, that, um, that diminishes somewhat over, which years, and eventually people will drink down their, with alcohol, in their houses, and they'll go, you know, they'll go back to the bottle. that, that's my, that, that I, anyhow, that, that's what I sort of, there's another anti-bubble. Well, I guess one, one thing is that the, um, the AGI crowd, they don't like to drink. So maybe it's, maybe that's two sides of the same coin.

[00:57:09] And maybe it is, it is the anti-bubble. Yeah. Um, If AGI, you may, were, um, ready to take over, there'd be a lot of idle hands. Um, so that they might actually, um, start creeping it. Who knows? Right. So maybe it's a head against AGI. We have nothing better to do. Right. since the machines are doing all our work. Um, okay. So one, one question I had, that's kind of more personal, um, based on my own, uh, curiosity is, you know, as you know, my,

[00:57:38] my area focuses often on intangible investment, such as an R&D and software, advertising, human capital. As you know, in the U S, uh, for example, um, in 1995, intangible, intangible investment were about, you know, 12% of GDP. Since then intangible investment has increased to what? 16% of GDP while tangible investment has fallen to 10%, um, you know, over the past couple of decades. And so capital cycle theory, of course, having originated, um, you know,

[00:58:05] many years ago tends to be more focused on investment in physical capital, right? And you talked about the energy cycle, emerging markets, AI data centers. So my question is, you know, is it fair to assume that the capital cycle also applies to intangible capital? And if so, you know, where are some notable examples that we can point to either in the, in the past or today, um, where we may have seen capital cycles in, you know, mostly or purely intangible assets? Um, well, I mean,

[00:58:36] capital is capital, isn't it? So I don't think, you know, if we believe in the concept of intangible capital, um, which I think we probably should do because it, the reason, you know, is really, uh, it's really to do with accounting convention. Tell me if I'm, well, largely to do with accounting conventions that we don't put, um, you know, that we tend to expense R and D and we don't tend to put brand values or, or we, uh,

[00:59:05] brand values on, on, on the book and let, and unless there has been an acquisition. Is that correct? Yeah. Um, and then, um, yes. So then the question is, can you get a reinvestment in intangible, uh, as in, um, as in any other type of physical, uh, overinvestment? I, so the answer pretty obviously, yes. Um, probably, and you might be able to give me some better examples, but,

[00:59:35] but I remember that back in the, um, early 1990s, huge excitement, uh, when, uh, Glaxo, actually, you know, now GlaxoSmithKline, um, had, you know, this great blockbuster ulcer drug called Zantac. That encouraged huge amounts. Oh, of, of investment in, by big pharma, in, in, in R&D, that the cost of,

[01:00:05] you know, of blockbuster drug development, thawed, um, over that period, until the blockbuster drugs that they came out with were, you know, not, you know, didn't, weren't delivering a decent tunnel. Um, on equity. So, yeah, I think that's, that's one that, that comes to mind. Um, I think, can you, can you name up, can you think of other intangible? I mean, as I say,

[01:00:36] anything, anything really, what we're seeing today, uh, in, in the AI space, we've been talking about, you've got, um, you've got obviously huge physical capex in the data centers, and then a huge valuation placed on the, um, on the AI scientists, that you knew, meta going out and, you know, I don't know, hiring people for, um, am I right? You know, a hundred million.

[01:01:07] Is it, they're like affairs or something. Um, and, um, so that would look, you know, that that would look to be the case. I mean, there are the AI companies here, uh, Mira Murati, when she had the former chief technology company, or, of open AI, take no, chief technology officer of open AI. She left, um, what, a couple of years ago, uh, to form her own company. Uh, and they, they, they,

[01:01:37] they, they raised it, raised money at a valuation of $12 billion. Um, even though, um, she had, she said, she wasn't going to tell investors what they're going to put that, what, what, what the company was going to do. Um, so that, that seems like a, an intangible capital boom. I, I frankly, um, and then, you know, yeah, Kai, they get, they get brand valuations. This, this is not so much CapEx,

[01:02:06] but just brands getting over value. I remember how in, in the late 1990s, you know, Buffett, who normally is, you know, talks a lot of sense, got a bit carried away with, you know, the likes of Coca-Cola and Gillette. We started calling them the Inervitables. And they, I don't think that, that either Coca-Cola or Gillette, sort of attracted to each amount of CapEx, but they definitely, or CapEx competition, but they definitely, you know, when, you know,

[01:02:35] their intangibles or brands were definitely overvalued at that time. and, and, and the likes of, of, um, you know, Gillette was eventually swallowed up by Procter and Gamble, but, but, you know, Coca-Cola, I mean, had a very poor decade after 2000. Yeah, I think that's right. Um, you know, obviously an asset's an asset, capital is capital, whether it takes the form of, or, you know, the,

[01:03:04] the IP embedded in a NVIDIA GPU, which is, uh, you know, there's half the value of the, um, actual data centers. It's embedded in the, in the value, the value added of the, of the semiconductor. And Kai, we've been talking about this software, this service, which might as well draw it out. That there, I mean, I did write a piece back in 2022 about, you know, how these software as a service businesses were, were, um, you know, track, you know, uh, absurd valuations,

[01:03:34] attracting capital inflows and so forth. I remember one of the, um, capital cycle metrics is when you, when you, you know, you're really in favor because someone creates an index and then at date, the index to prove how well it's historically performed. And then you can pretty much guess that from the main, the inception, the new index is going to do poorly. So, so that I'm, yeah, I think, yeah, I think, this, you know, the fast bubble of,

[01:04:01] of 2022 is another very good example of an intangible bubble. And another interesting, uh, kind of related topic is if you can follow human capital, just talent, where's, you know, the Harvard MBA indicator, where all these, where are all the best and brightest going? Right. It was big tech in the past cycle before that, if you remember, it was, you know, Goldman Sachs and finance, right? So in the, in the bubble that kind of picked, you know, seven, oh eight, everyone wanted to go work for wall street until they didn't. And then it was tech. So, you know,

[01:04:29] maybe there is an interesting element here where it's, you know, the capital cycle has both kind of human capital elements as well as the actual physical accounting capital. Yeah. Well, Harvard, I mean, it's just the, I mean, not just HBS, but the undergraduate, you know, the graduating body, then very, very reliable contrarian indicator. Well, I graduated from Harvard undergrad and went into to work as a value investor in 2009. So I think invested at the bottom of the capital cycle.

[01:04:59] So we got out of value investing at the time. We'd have done. All right. I got a couple more questions for you, Ed. So here's one that I like. So there's this idea in Silicon Valley that's popular today, that bubbles may actually be productive, that they may actually be good for the economy long run. There's a book that's been making the rounds called boom bubbles and the end of stagnation by Hobart and Huber. So the idea is that even if these bubbles form and they eventually end in tears for investors,

[01:05:28] they're ultimately productive to the extent that they accelerate the development of a genuinely transformative technology. So in this sense, the argument would go, even if AI ends up being a bubble, not that everyone there is conceding it, but to the extent it were, it would have still been a good thing to have happened. Do you find this argument at all compelling? Well, it depends from what perspective one's looking at it from. I tend, and I think you do too,

[01:05:56] we tend to view the world from the perspective of an investor who's trying to maximize his gains, minimize his risk. and, and, and, and I, you know, from the perspective of society. Um, yeah, if you can bring forward the technology, whether it's, you know, railways or cars or aircraft or, or,

[01:06:25] you know, internet telecommunications, um, or, you know, EVs or, or batteries or, or AI, that's all well and good. it, it, it, it, it, sometimes, you know, sometimes it, it might not be, the whole technology might be a sort of dead end, but perhaps not the end of the world. Um, but so, yes, I, I think, I think we talked to you,

[01:06:54] I mentioned the railroads earlier, you know, they, they tended to come in searches of CapEx, both, both in, in, in the U S and UK. It was probably, yeah, I think probably good thing in the end for both economies. Um, uh, but, but pretty, just really disastrous for the investors who partook in them. And, and so I think that's, you know, you have to bear that in mind. Um, you, you're under,

[01:07:23] we're under no compulsion to make a loss making investment for the benefit of society. The other issue is, and as I've already mentioned, um, investment booms tend to lead to a misallocation of capital and over investment. And that, um, that tends to slow rather than,

[01:07:53] than, um, increase economic growth. And, and even if, am I, even if the dot com bubble, because of that fiber optic cable that was laid, meant that you could get, um, you know, it meant that you could get, um, um, you know, Netflix, um, up and running and, and, and, and, and, and all the, you, um,

[01:08:22] video conferencing, whatever, uh, that, that's all well and good. But actually, you remember that the, um, the downside of that, of the dot com bust with, you know, the market came down, what, um, or the S and P Dan worked by 40%, as I remember, you know, was, um, it required the, that we didn't require, but as a response, the Federal Reserve slashed interest rates to 1% to fend off, you know, deflation.

[01:08:51] And then we got a housing boom, we got a bus. In fact, the upshot, the way I see it is the upshot of the dot com bust was the cable financial crisis. Upshot of the global financial crisis. With, you know, low interest rates and so on and so forth, is a collapse in productivity growth. So we've got the new technology, and that's all very well, uh,

[01:09:21] for the companies that end up as winners and so on. But, but actually the, the very long tail of these bus can be in a bit quite severe. And so, um, I, I think, I think it's sort of irresponsible to argue, uh, that, that, uh, that the bubbles are good for success. It's very tip.

[01:09:47] One of the things you see during bubble periods is a sort of lightheadedness. Hey, you know, my portfolio is up 50%. Everything's great. Hey, what does it matter? If there's a downside? But of course, people don't feel like that when, you know, when they're, you know, when they're nursing sort of 90% loss on, losses on, on yesterday's high flyers. So, and again, this a big, this is a big, you know, what's going on in the AI space is a big CapEx boom. As you know,

[01:10:16] taking place with very weak, um, um, you know, consumer confidence, as they call K-shaped economy. And, and problems happening elsewhere in the, you know, in the financial system, private credit and private equity. and really a lot of legacy VC stuff was badly invested that you don't really hear too much about. So the whole sort of private and, you know, private alternative investment world is pretty,

[01:10:45] sort of seems to be a pretty bad place. So I would have thought that if, and when, you know, this AI boom ends and turns to us, a whole load of problems will emerge. And, and, and the value, and as the valuations come down and people just simply won't be saying that it's, you know, that type of commentary is a sort of giddy commentary that invariably accompanies the, the bubble.

[01:11:15] That's really interesting. And so what, one point you made that that was kind of interesting in particular is this idea that the, the kind of like long-term consequence of the dot com bust was, you know, a, a fed mindset that led to ultra low interest rates for a long period of time, which, you know, fed, if you were, you know, and this is in your book, the price of time, you know, kind of was the original sin behind a lot of the speculative excesses that came afterwards. Right. So that, you know, something can happen and it looks fine. You can pass it over,

[01:11:44] but that's just sowing the seeds for the next cycle. Now, obviously rates have increased over the past few years, you know, from their, from their COVID lows. Do we think that, that the regime has actually changed or, you know, does the fact that we now are seeing, you know, a massive CapEx boom in, in AI investment suggest that the money never left the system? Well, I mean, I do like to hedge my bets, but I'd say a bit of both, you know,

[01:12:13] that I think that, I think that the interest rate, interest rate cycle turned in 2021, 22 for, for long-term yields. And, and historically, that, you know, those interest rate cycles have tended to be multi-decade, you know, often 30 to 40 years. And if you read that note,

[01:12:43] back at GMA, that was another thing that we did a lot of work on, you know, which is like, why were our bond models so wrong? Because we always kept on talking about mean reversions. And, and what we find is that these, these, these very long bond cycles. And I always say, we can't, I always say you can't, you can't critique anything about long-term rates, because they're not mean reverting, like, say like equities. Um, you know,

[01:13:12] they do go on these very long cycles. And I do think that we are, that we have entered into, um, you know, a prolonged period of an upward trend in long-term rates. However, there was clearly a lot of liquidity left over from, um, that sort of COVID lockdown, uh, quantitative easing splurge, you know, I don't know what,

[01:13:41] $8 trillion of money printed by the world central banks. And, you know, a lot of that, and there's sort of excess savings, uh, a lot of it was still, um, it held on the Fed, um, balance sheets. Um, and, and that has been drawn down, uh, interest rates remain low relative to nominal GDP growth.

[01:14:11] And so the question is, what's going to happen? Are interest rates going to go up? Or the world GDP growth going to go down? Or perhaps, you know, possibly the worst of it was, and GDP goes down and interest rates go up. So, I, yeah, I think there was some liquidity, a lot of liquidity left over from, from that period. Uh, you know, the, the, um, it's always difficult to know what's going on in the plumbing of the financial system. Um, the,

[01:14:41] um, but yeah, there was perhaps more liquidity than I, I expected, uh, left over. And there are, as you know, these, you know, problems in, in, in private credit, uh, which three, just the financing arm of private equity that is still fluctuating away. Uh, and I expect, uh, that will continue to be the case. And, and the real estate market, as I think in America,

[01:15:11] it's definitely moribund, isn't it? Cause the highest prices really haven't come down enough to, you know, in response to the shift of interest rates. So that seems to be, um, stuck in, you know, it's interest. It's, I haven't done work on it, but I think it's an interesting place. Yes. Residential real estate, because there hasn't been much investment there recently, but valuations seem, um, probably too high relative to interest rate regime. So that's 10,

[01:15:41] I mean, that's either a threat or an opportunity. It's sort of, it's not, I think it's not good. It's sort of middle of the line. It's going to happen. Something good is going to happen. Something bad is going to happen. I'm not quite sure which. Fair enough. Yeah. Okay. So, um, I just got one more question for you, which is our standard closing question. Um, what's the one thing you believe about investing that most of your peers would disagree with? I, it's one area where I would have, you know, differed from, um, a lot of,

[01:16:11] um, you know, a lot of, you know, of sophisticated investors, I suppose, is that I, I've always been a bit of a, a gold bug. And, um, you know, people, you know, like, uh, you know, our old boss, Jeremy Grantham and, and, uh, you know, the team head, Ben Inca would always say, you know, dividend, this and that. And I, you know, I always,

[01:16:39] I think I've always felt that I was, you know what? Yeah, I've always got this atavistic attraction to gold. And I think that it does, I think I do like gold's hedging aspect. I know it is difficult to value, but I like the way that gold is an asset without a liability. And I think that in a world where equities,

[01:17:08] particularly in the US, are not overvalued and bonds might be on a 30th, 40th downward trend in valuations, as well as, you know, really severe debt problem dynamics.

[01:17:32] I hold the view that having a decent allocation to gold is a good portfolio position. And that, you know, I mean, look, I may have this complete right. In the last 10 years, actually having gold, you know, having a sort of equities, gold portfolio, you know, portfolio has been obviously a lot better than having bonds. So I might be,

[01:18:01] I might be touting a, something that's sort of near the end of, near the end of its few bits. I, I, I, I'm drawn to gold in a way that the average, you know, person who's, you know, the average CFA is not. So I think that's probably the, the one area where I, I disagree. Interesting. Well, thank you, Ed. I know I've taken up a lot of your time, so really appreciate you coming on. Good guy. And I'll see you in London soon.

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