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

Summary

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

Podcast: Excess Returns
Host: Excess Returns (Jack Forehand, Justin Carbonneau, Matt Zeigler)
Guest: Edward Chancellor (with guest host/interviewer Kai Woo)
Date: May 12, 2026

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Episode Overview

In this episode, financial historian, investment strategist, and celebrated author Edward Chancellor joins Kai Woo for a deep dive into the lessons derived from centuries of capital cycles and technology-driven investment booms—and what they may portend for the ongoing AI-driven infrastructure frenzy. Ranging from 19th-century railways to the present AI capex arms race, the discussion unpacks patterns of overinvestment, “anti-bubble” opportunities, pitfalls of forecasting technological demand, and philosophical questions around bubbles as engines—or destroyers—of long-term value. Chancellor also expands the discussion into intangible capital, China, consumer brands, and his contrarian view on gold.

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Key Discussion Points & Insights

I. Capital Cycles and Technology Booms: Echoes from History

The Classic Capital Cycle Pattern

• New Technology Emerges → Investor Euphoria → Heavy Investment (Capex) → Overcapacity/Oversupply → Profitability Collapses → Market Shakeout/Crash → Eventual Consolidation.
• Key Insight: Capital cycles are driven partially by a “prisoner’s dilemma” dynamic, where companies invest aggressively to avoid missing out on a possible monopoly—often to their collective detriment.

Examples from the Past

• UK Railways Mania (1800s):
  • Early railways were highly profitable; the mania of 1843-45 saw projected capex at 10% of GDP (far higher than AI capex today).
  • Overinvestment led to duplicative infrastructure, stock market collapse, and, surprisingly, better relative returns in “obsolete” canals immediately after the bust.
• Automobiles & Aircraft (Early 20th Century):
  • 2,000+ US car companies; only GM, Ford survived (after failures and recapitalizations).
  • Warren Buffett’s quip: even Nebraska had three aircraft makers; most failed.
• Dot-com/Telecom Bubble (Late 1990s):
  • Massive capex by telecom companies, which ultimately weren't the main winners.
  • NASDAQ lost ~78%-79%; Amazon fell over 90%.
  • “Markets have no trouble investing tons of money during these technology transitions but they do have trouble spotting the winners.” – Edward Chancellor, [12:49]

The Exception

• Monopolies (Bell Telephone, Western Union Telegraph):
  When technology allows for rapid monopoly consolidation, booms can avoid excess investment and destructive competition.

• “If you’ve got a revolutionary new technology and for reasons of inherent monopolistic, what do you want to call it, sort of environment, there is a case that the new technology can arrive without a huge overinvestment.” – Edward Chancellor, [13:56]

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II. The AI Capex Boom: How Familiar is This Cycle?

Today’s Tech Titans & the AI “Prisoner’s Dilemma”

• Microsoft’s investment in OpenAI spurred Google and others into action:
  “If you don’t go in, there’s a possibility the others will come out with a dominant monopoly and crush you.” – Edward Chancellor, [16:40]
• The arms race extends beyond US hyperscalers (Google, Meta, etc.)—Chinese firms and VCs also pouring capital in.

Demand: Overestimation, Hype, and History’s Lessons

• Historical Pattern:
  Investors repeatedly overestimate future demand, as with:

  • Railroads
  • Dot-com (data traffic myths, telecom fiber)
  • AI today (expectations for AGI and universal disruption)

• Physical vs. intangible depreciation:
  Chancellors notes gaming depreciation schedules for AI chips, postponing recognition of sunk costs—echoing how profits can spike during booms as capex is booked and not yet expensed ([21:00]).

• Cautionary Tale:
  “There is this view put out that, you know, that AI is—we’re on the cusp of artificial general intelligence, even singularity, and that this AI… will be able to do almost every conceivable human function… I’m very skeptical of that view.” – Edward Chancellor, [23:19]

Error Rates and the AI “Hindenburg Moment”

• Intrinsic limitations (hallucinations, error rates):
  Some LLMs have 2-20% hallucination rates—limiting risk-sensitive applications.
• Analogy to self-driving cars:
  Human and machine both err, but society is less tolerant of machine error; a spectacular AI accident (“Hindenburg moment”) could stall enthusiasm.
• Principal Risk:
  “The ruling principle of Menlo Park is fake it till you make it. And the amount of hype… There’s never ever, ever been as much hype as we see around AI.” – Edward Chancellor, [34:04]

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III. Navigating the Bubble: Anti-Bubbles and Investment Opportunities

Don’t Short Bubbles—Find Neglected Value

• Shorting bubbles is risky—even if right, potential drawdowns are large.

• Bubbles crowd out capital from other sectors:
  Old economy stocks in 1999-2000; energy stocks during the EV/SPAC boom.

• “Where are the anti-bubbles today?” – Edward Chancellor, [43:51]

  • Look for sectors sold-off on overblown fears of disruption (e.g., some software, travel, financials).
  • Caution: Some companies truly will be disrupted (e.g., educational tools replaced by AI).
  • Perform due diligence—many SaaS companies, even if down a lot, still may not be cheap.

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IV. Capital Cycles Beyond AI: China, Brands, and Intangibles

China: Growth ≠ Returns

• Despite stellar GDP growth, shareholders in China fared poorly due to low returns on capital and repeated equity dilution.
• “If your returns are low and you’re growing in an environment that is growing quickly, it’s actually particularly negative.” – Edward Chancellor, [49:51]
• Lesson: Don’t rely solely on valuation; combine it with capital cycle analysis.

Branded Consumer Goods: The “Lindy” Effect

• Spirits companies (e.g., Diageo, Remy Cointreau, Campari) saw COVID-driven surge then post-pandemic drag due to overstocking and weight loss drugs.
• The Lindy Principle: Long-lived consumer brands tend to endure (often undervalued after sell-offs).
• “My guess is that these brandy companies and spirit companies, they’ve been around for a couple of hundred years… Eventually people will drink down their alcohol in their houses and they’ll go back to the bottle.” – Edward Chancellor, [57:16]

Can Capital Cycles Happen in Intangibles?

• “Capital is capital, isn’t it?… The answer’s pretty obviously yes.” – Edward Chancellor, [60:24]
  • Examples:
    • Pharma’s “blockbuster” R&D cycle (1990s)
    • The SaaS stock bubble (2022)
    • Brand overvaluations (Coca Cola, Gillette, late 1990s)
• Human capital is included: Where Harvard MBAs/undergrads go is often a contrarian indicator ([65:56]).

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V. Are Bubbles Actually Good? (Society vs. Investors)

• The book Boom, Bust, and the End of Stagnation claims bubbles are productive because they accelerate the development of transformative technologies.
• Chancellor’s answer: It may be socially beneficial, but disastrous for the investing public, producing misallocation, market losses, and subsequent adverse macro effects (e.g., the US dot-com bust led to ultra-low Fed rates and the next cycle of bubbles and crashes).
• “It’s sort of irresponsible to argue that bubbles are good for society. It’s very tip—One of the things you see during bubble periods is a sort of lightheadedness, hey 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 they’re nursing 90% losses.” – Edward Chancellor, [72:10]

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VI. The Interest Rate and Liquidity Regime

• Major interest rate cycles can last 30-40 years; post-2021/22, Chancellor believes the upward trend has begun—but central bank liquidity and post-COVID excess savings mean “easy money” has not fully left the system ([74:39]).
• Some real estate and lending sectors (private credit, private equity) are now “percolating” with risk.

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VII. Contrarian Viewpoint: Gold as a Portfolio Asset

• Chancellor is an acknowledged “gold bug”—unlike many disciplined value investors:
  • “I think I do like gold’s hedging aspect… I like the way that gold is an asset without a liability. And I think that in a world where equities… look overvalued and bonds might be on a 30-40 year downward trend in value, as well as really severe debt problem dynamics, I hold the view that having a decent allocation to gold is a good portfolio position.” – Edward Chancellor, [79:32]

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Notable Quotes & Memorable Moments

• On AI and Bubbles:
  “There’s never really… ever, ever been as much hype as we see around AI. And frankly, if you take… the amount of hype relative to the proven efficacy of the technology, that ratio is just more extreme than anything before.” — Edward Chancellor, [34:04]

• On Demand Overestimation:
  “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… the actual accurate data was available in real time and no one paid attention to it.” — Edward Chancellor, [21:14]

• On Learning from China:
  “The lesson from China, as far as I’m concerned, is… when you’re looking at markets, you don’t want to look at valuation alone. You want to look at valuation and returns on capital and the capital cycle.” — Edward Chancellor, [49:49]

• On “Anti-Bubble” Opportunities:
  “Value stocks as a factor were very depressed, small cap was very depressed, emerging was very depressed. So there were a whole load of anti-bubbles by 2000. And those were areas where they offered really great investments…” — Edward Chancellor, [41:39]

• On the Broader Purpose of Bubbles:
  “You’re under no compulsion to make a loss-making investment for the benefit of society.” — Edward Chancellor, [68:59]

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Important Timestamps

| Time | Topic | |----------|--------------------------------------------------------------------------| | 03:01 | Chancellor & Kai’s research on capital cycles and market timing | | 05:04 | Historic tech booms: railways, autos, telecom, and their aftermath | | 15:03 | The “prisoner’s dilemma” in capital investment strategies | | 20:39 | Overestimating demand—a recurring trait in booms | | 23:19 | Chancellor’s skepticism on AGI and error rates in LLMs | | 31:03 | The “Hindenburg moment” risk for AI | | 34:04 | Unprecedented hype-to-efficacy ratio in AI | | 41:39 | "Anti-bubbles": Value and neglected sectors during tech booms | | 49:49 | Lessons from the Chinese capital cycle | | 54:55 | The Lindy effect and alcohol/spirits stocks post-pandemic | | 60:24 | Can capital cycle theory apply to intangibles? | | 66:28 | Harvard MBAs as a contrarian indicator | | 67:39 | Are bubbles good for society? Disputing bubble boosterism | | 74:39 | Interest rate regimes, liquidity, and possible future risks | | 78:36 | Chancellor’s contrarian “gold bug” stance |

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Flow and Tone

• The conversation is insightful, rich with historical anecdotes, and irreverently witty—balancing academic rigor with skepticism about hype and human nature.
• Chancellor’s tone is measured but sardonic, repeatedly cautioning listeners not to extrapolate technological promise into inevitable investment success (or societal good), and urging a critical look at cycles, incentives, and narratives.

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Summary Takeaways

• History doesn’t repeat—but it certainly rhymes: Every major technology investment boom is, in part, a behavioral and capital cycle phenomenon; most investors lose, some sectors collapse, but the real opportunity is to find “anti-bubbles.”
• AI is not different: The current AI capex arms race has strong historical parallels; demand will almost certainly be less than enthusiasts expect, and error-prone technology will constrain its total market.
• Intangible assets are not immune: Overinvestment can and has occurred in R&D, software, brands, and human capital—not just physical infrastructure.
• Don’t short bubbles—look for neglected value: Watch for “crowded out” sectors or anti-bubbles where pessimism is overdone.
• Being a contrarian can pay—if you do the analysis: Don’t just react to narrative or price, but scrutinize underlying fundamentals and competitive dynamics.
• Bubbles may build things—but beware the long tail of risk: The costs of financial excess ripple across decades, through recessions, crises, and regime changes.

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Recommended for:
Investors, economic historians, technologists, and anyone curious about the intersection of human psychology, market cycles, and the fate of brave new technologies.

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Transcript

[Edward Chancellor]

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Jobs we are excited to announce the launch of a new podcast, the Intangible Economy with Kai Woo. AI and the broader technology revolution are changing how we live, work and create value. In each episode, Kai will sit down with investors, researchers and other experts to discuss how innovation and other intangible forces such as brands, human capital and network effects are transforming markets and investment outcomes. In this episode, Kai sits down with Edward Chancellor, one of the world's leading experts in capital cycles. They discuss lessons from past capital cycles and how they can be applied to the current AI cycle. If you would like to continue receiving new episodes of the Intangible Economy, you can subscribe on all major podcast platforms using the links in this episode. Description thank you for listening. We hope you enjoy the new show.

[Edward Chancellor]

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 what they've acquired, then then aggregate profits rise. They have no trouble investing tons of money during these technology transitions, but they do have trouble spotting the winners. So then the question is, can you get over investment in intellectual, as in any other type of physical, over investment. So the answer's pretty obviously yes.

[Kai Woo]

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 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. 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.

[Edward Chancellor]

Nice to see you, Kai. Well, you didn't mention is that you came to GMO as my Analyst back in 2008. We go back to the financial crisis together, don't we?

[Kai Woo]

Yeah. 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

[Edward Chancellor]

I'm allowed to blow our trumpet together, do you remember how we were working in early 2009 and I got you to do a piece of research that showed that quality stocks that GMA 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. In which case the answer was that when you wanted to get out of 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 09, just been a couple of weeks before the market turned. And I credit you because you actually redefined quality in the various markets that we looked at historically as volatility or low volume stocks because we didn't have data for full quality back then. So when I think of the sort of good pieces of research that I'd been involved in my life, I'd say that was definitely one of the top five pieces.

[Kai Woo]

Yeah, very timely. 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.

[Edward Chancellor]

Okay, let's move ahead.

[Kai Woo]

Let's start. Okay, so the topic I wanted to start with is, I guess, the topic of the day. US 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 railroad mania to the Japan bubble to the dot com boom in the late 90s, teach us about how the current cycle may play out?

[Edward Chancellor]

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, 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. People get very excited about the impact of that new technology. 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 uk, which is called Stockton and Darlington. And the first few railway companies had a. Had dominant positions, no competition, they were pretty profitable and that 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. A bit of overbuilding came down, but it wasn't too much damage. The real problem or mania came in 1843-1845. And that is really the. A period in which there was a massive. The launch of many, many railway schemes across Britain. And in terms of capital employed or projected capital expenditure, I think was running to about 10% of UK GDP. So actually much higher than AI today. And there were too many as a result, not all the schemes got actually went ahead. But the upshot was far too much duplicative investment. And famously, as I say, three railway lines between London and Peterborough, which is in East Anglia, three railway lines between Leeds and Manchester. Obviously, if you have three lines running between two places, there 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, canal stocks. Canals were the most obvious losers from the railway mania and they did lose in the end. But actually you did better investing in canal stocks between 1845 and 1850 than you did in railways, because canal stocks had been beaten down a bit, where the railways still have plenty to fall. Now look, in the very long run, in the very long run, let's say 20, 30 years, that investment was pretty benign for the economy. And although the railways were never quite as profitable as they'd been in their early years and did. It didn't, it was good for the economy. And if you got into railway stocks in 1850, once the bubble burst, it was perfectly fine. Now then, you know, we have, we have other sort of new technologies that attract much more competition. I mean, you think, you know, motor cars in the late 19th century, I think the US had something like 2,000 motorcar companies. And the upshot of that was General Motors, which was a winner, had to be recapitalized twice and actually GM was really a sort of roll up of failing car companies. And Henry Ford, I think he only Ford Motor company wasn't listed but Ford only succeeded on his third attempt. It was hugely over, you know, over invested aircraft was much the same I think, I mean Warren Buffett points out that there were actually three aircraft companies in Nebraska, believe it or not, in Omaha, Nebraska. That again far too many aircraft companies founded. Huge, huge, very loss making. And that pattern has really continued obviously into the dot com era where again massive cap, massive capex. 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 the capex largely done by. Telecoms companies and they, 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 actually markets, they have no trouble investing tons of money during these technology transitions but they do have trouble spotting the winners. And then you know, then as you know after, after the dot com bubble bursts, you know, NASDAQ loses 85%. 95, sorry, 70, 78, 79% of its value. Amazon goes down more than 90%. So even even though Amazon emerges as, as an eventual winner, there is a massive, massive loss. And it's dangerous to say in hindsight that you can spot Amazon as the winner and that it's, there could be. There were other businesses around webvan this and 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. Investors tend to anticipate the profits flowing more quickly than they actually turn out to be the case. And 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 began to insert itself and got a pretty early monopoly. Telegraph also moved to a bit earlier, moved to monopoly very quickly. So you know, you could, you could say that if, if you've got a revolutionary new new technology and for reasons of inherent monopolistic, what do you want to call it sort of environment, there is a case that the new technology can arrive 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. We can't say that the future will resemble the past, but it should sort of guide our judgments, say at least. And then one can assess current circumstances and say to what extent is this time different or not.

[Kai Woo]

Right. So if I'm hearing you correctly, you are saying that over the course of history there have been many successive waves of capital cycles which generally start due to the advent of a new technology which of course attracts investment capital. In a few cases, such as the, the, the telephone, the industry has consolidated into monopoly pretty early and has been relatively stable. But the, the vast majority of instances end up with an influx of capital and a fragmented market structure whereby profits are competed away to zero. And that leaves investors in these companies with, you know, pretty, pretty rough outcome. Well,

[Edward Chancellor]

I'll add something. You remember one of the key precepts of the capital cycle theory is that it draws on the prisoner's dilemma being theoretical problem, which is that in the prisoner's dilemma you've got two prisoners and the question is, do they, 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 game is structured is there's an inducement for both to rat. It doesn't work by not, you don't benefit by not ratting. And, and I think it's the same during a, you know, the capital cycle, during an investment in a new investment boom is 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 dominant monopoly and crush you. And so you go in. But if you go in, you may come for the end result may be suboptimal for let's say the investment world as a whole. You may actually hang on in there. So it's not necessary. It may be a sort of agency problem. It's not necessarily irrational for the individual. And I think that with AI there's one narrative, I don't know whether you wanted to how robust is, but one narrative is that when OpenAI came out with its ChatGPT in November 22, Microsoft saw this as a way to team up with OpenAI to break open the Google search monopoly and then Google and how did I get respond? And then of course then everyone else standing by the wayside saying this is a great technology. We want a bit of the action too. So of course we're going to get a huge capex There's a new way to sweetgreen Meat wraps handheld. Hearty and made for life on the move. With bold, chef crafted flavors, fresh ingredients and over 40 grams of protein, they're built to satisfy without slowing you down. Try wraps today in the app [e-mail] available at all participating locations.

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[Kai Woo]

Yeah, I mean, it's interesting to think about how the different big tech companies have responded differently to the, 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 it's 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.

[Edward Chancellor]

Right.

[Kai Woo]

So there's clearly a, 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.

[Edward Chancellor]

Yeah. And you may have been pulling this case, more cases than I have, but it's not, as you know, it's not just the way, you know, the, the top, you know, the largest cap US tech companies are doing it, but there's a whole load of other competition. You've got, what is it? Deep sea in China. You've got a, I mean they outlisted a few Chinese AI companies recently and then you've got vast amount of VC capital going into the same space. So the big tech capex into AI gets 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.

[Kai Woo]

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, will there be enough demand to meet supply? How should we think about this side of the equation?

[Edward Chancellor]

It is often the case, or it has been the case in these tech booms that people overestimate demand I think going back to the railways in, in the, in 1845 capex spending at that time would have required, you know, within a spec this someone's called Crunch the numbers said would have required passenger and 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 wasn't going to happen. During the, 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. And this little factoid made it actually originated with some company that was later taken over by WorldCom which later went bust and it was signed actually everywhere, needlessly all the brokers picked up, even the US government picked it up. So everyone believed it. But in fact we actually had data. There's this guy I know called Andrew Ozolico who was at the time at Bell labs and in 2000, just right around the time that the tech bubble was peaking, he put out a paper saying giving you the true demand growth and no one so the actual accurate data was available in real time and no one paid attention to it. And the upshot was WorldCom went bust and the host of those other so called alternative telecoms carriers, Altnets went bust and there was a massive overcapacity in fiber optic cable and all the telecoms equipment suppliers like Nortel and Ericsson and Lucently took, took big hits and actually there was a massive decline in profitability. So one of the features of the Capex booms is that as you know they actually produce profits because if someone invests and the other person, the buyer doesn't actually immediately depreciate what they've acquired, then aggregate profits rise and say well you see in the late 1990s going into 2000, massive search and 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 and so you have a collapse in profitability and something, you know we're seeing something very similar today in that as you know the depreciation schedules for these AI chips, GPUs has been extended 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 to six and a half years. And I understand that 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 don'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 even before you actually start using the chip. So yeah, so we'll see. But yeah, the market is being driven as far as I see by strong economy on the back of a lot of capex and very strong earnings growth. But those are contingent on the investment turning out to be profitable and the demand being there. Now can I just say going back on the demand question and look, you and by all means conflict me if you have a different view. Is there is this view put out that, you know, that AI is, we're on the, we're on the cusp of artificial general intelligence, even singularity, and that this AI, whatever that might be, is just, we'll be able to do, you know, almost every sort of conceivable human function apart from merely physical, physical work, plumbing. And you know, I'm very, very skeptical of that view because I don't, I mean when I, if I was talking about AI as a Large language models. Large language models work through sort of inference, through probabilities of what is the most likely next token to pull out. I can't see A, how that can lead to really genuinely original thinking or activity and then B, as you know, sometimes it's going to make the wrong selection and therefore you're going to get the hallucinations and I think the hallucinations, 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, I really don't think that the demand that is mooted currently is going to be there. And I don't know if you picked this up. Do you see last week or so a story about some company that provided software for car rental businesses? Did you see this? They used everyone's vaunting, the Claude, the new Claude Vibe coding. They use Claude to update their software and it wiped out their entire customer database and it did some other sort of quirky things too. So the whole business crashed and the companies that we're using, as far as I understand the car rental companies were using the software, were suddenly frozen. A couple of months ago, the winner of the Royal Society's Faraday Prize, sort of top scientific price. I called Michael Wooldridge, gave the Faraday lecture, which I think is worth listening to. He's making These sort of points that I'd made, in other words, I've just taken my points from him and he then thinks it might be what he calls a Hindenburg moment. When the Hindenburg moment was, I don't know when it was in 1937 when one of these being Zeppelin, everyone was excited about these zeppelins and the Hindenburg Zeppelin blew up somewhere over some US airfield and everyone had second thoughts about the technology. So I may have this wrong, but 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 breakthrough in agentic AI. And as I said, well, we'll see. I mean it's not, you know, it's not uncommon for investors to, I mean investors both in companies and in markets to imagine that technology is more advanced 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 faster Internet connection at the time people were on by and large working on sort of 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 AI development. Maybe someone fix merges the large language model technology with I think these so called reinforcement learning technology model and then maybe they can harmonize them and get something much better. But at the moment they don't seem to be quite there.

[Kai Woo]

So Ed, so 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 self driving cars. So we know for example that Waymo is not perfect, but that and its error rate is not 0% but, but so too are human drivers imperfect. And so one of the interesting phenomenons we've seen is that for whatever reason politicians and drivers have a bias against AI whereby the tolerance threshold is so much lower that if there's just one big kind of headline about a bad Waymo accident, suddenly the interest in the technology wanes significantly. And so 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 nascent technology, the AI bulls will say it's going to be getting better over time. This is the worst it'll ever be. It's kind of hard to argue with that how much better it will Become. We don't know. But you're saying that 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?

[Edward Chancellor]

No, I'm, I'm being, I'm being a bit more adamant, namely that if given the problems of hallucinations and, and, and I, you know, if there are these people who test models for hallucinations, I looked at, looked at one report from, I think October of last year. Finally, you know, the best performer was 2% hallucinations. And I think 20th performer was out at, I don't know, it might have been 20 cents a bit higher, but seems to say 20. You're not going to. There are certain activities where you cannot tolerate that degree of error. You can't. Yeah, that. Well, you know what I mean? There are certain areas where you can tolerate a certain amount of error.

[Kai Woo]

Yeah. 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 nuclear weapons. Right. But in the case of, say, doing R and D and doing research into a completely greenfield technology where it's like the alternative is to do nothing, then I think the bar is much lower. Right. So then the question becomes what percentage of economic activity falls into category one versus Category two? Because that, as I said, will constrain the total addressable market of a technology like this. And what I guess is being implied is that when the demand forecasts are being written by the AI companies, it's to an extent baking in both categories. And you're saying maybe only one category is actually available, in which case the potential demand curve is a little bit less enticing than is currently being priced by the market.

[Edward Chancellor]

Yeah. Or put it simply, the ruling principle of Menlo park is fake it till you make it. And the amount of hype in that, you know, needless to say, by definition, all these technology manias have large doses of hype. And you know that 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 as we see around AI. And frankly, if you take, you know, you take the efficacy of the amount of hype relative to the proven efficacy of the technology, that ratio is just more extreme than anything before. I think Thomas Edison, He hyped the incandescent bulb technology before he'd really fixed the problem, and it worked out in the end. Then. I think what's happened is that in these speculative markets we've lived through in recent years. The reality distortion field that Steve Jobs popularized and then Elon Musk's taking it into the public markets of Tesla. Always suggesting you're going to do something or about to achieve something when you haven't achieved it. And that in itself can become some extent a self fulfilling policy in Musk's case because it gives you the capital to then make the development. But if everyone is doing it and there's a huge amount of great competition and I think that that just increases the prospect of of it coming undone.

[Kai Woo]

So I want to 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 kind of AI investment boom you are negative that you kind of see a repeat of history, these historical booms and busts in the making and that would generally make you bearish on I would assume all the companies in the investment value chain or are there particular you know that you find to be areas where you think that the prospects may be less dim.

[Edward Chancellor]

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[Edward Chancellor]

Look, I mean first of all we all have our own sort of specialities. Perhaps some people look at markets where the investors or strategists can cover many different bases. I'm not saying I'm solely a capital cycle investor, but if you are a capital cycle investor, Your principal bank to step back whenever you see a surge of investment across many companies. Capital cycle investor they can rationalize investing in a company that is itself investing a huge amount, providing the industry or sector that they're operating in is not investing massively too. But when a whole sector is plunging in, then the capital psych investor prefers to step back. And yes, at the moment you've got a lot of the picks and shovels, makers of AI have all been boiled up. The gram companies and the corning. A lot of the same companies that actually were involved in the, in the TMT who have now come back and I mean I own some copper stock and copper is as you know also beneficiary. So I'm sort of unwitting beneficiary. But the reason I invested in the copper stuff was not because I anticipated the AI thing but because I thought that have been under investment in copper. And so everyone, this is one of those very big investment booms where everyone really gets caught up in it whether they like it or not.

[Kai Woo]

Right. So you actually wrote a piece recently for Reuters where you said markets have poor scorecards for spouting AI losers. This is the piece on the anti bubbles. And so maybe you could walk me through this because I think you talked in this piece about some cases where obviously the market got ahead of itself saying hey, 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 are trading kind of at 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.

[Edward Chancellor]

Okay, well first of all, as you know we used to have discussions about this at GMO that problems of shorting bubbles. Yeah. Because even if you're accurate you can have massive drawdowns and it's probably not worth shorting a bubble even if you have perfect foresight then and I hadn't, I think at a sort of, from a sort of top down level I think there is this, I think bubbles do have a sort of crowding out effect. So capital gets drawn into one sector and it gets pulled out of other parts of other parts of the markets. And we saw that again during the TMT bubble with what we'll call the old economy stocks. Companies that were deemed to be, were deemed not to be affected by. Negatively affected by the arrival of the Internet. They were. Sorry, I rephrase that. The old economy stocks were businesses that were deemed not to partake in the new Internet economy that were old economy that was soon about to be replaced. Now those old economy stocks sold off and that offered very good, you know, very good companies were selling at cheap multiples even while the market itself was on a high all time, all time high. Now and then, you know, as you know, value stocks as a factor were very depressed. Small cap was very depressed, emerging was very depressed. So there were a whole load of anti Bubbles by 2000. And those were areas where they offered really great investments so you could make money in the anti bubbles and while the Nasdaq was losing 80% of its value. And then I like, you know, I drew attention in that same piece to the, the excitement over the energy transition. And if you remember the SPAC boom of 202021 was largely around, you know, stuff relating to electric vehicles and you know, all those sort of lidar lidar companies and EVs and so forth. But in 202021 the traditional energy stocks were very beaten up. I think the energy sector went down to about 2% the S&P 500 from its average level of around sort of 8 to 10% waiting and Tesla was worth more than the entire listed North American energy universe. To me it didn't take huge amount of analysis to see that this, you know, that oil companies weren't going to disappear overnight, that their investments weren't going to be these sort of so called stranded assets and that EV growth was largely driven by subsidies and that so on. And so that was a really nice anti bubble. You didn't have to short the SPACs, but it had created a wonderful investment opportunity. I mean for instance, a friend of mine, Jonathan Tepper, who runs a company called Brevit Capital, he was pointing out that there was this company called Garrett Motion that provides the turbos that go into internal combustion engines that had a 40% market share, 60% of incremental market share selling on 7 times PE. I mean the stock's up 150% over the last year or so. That's an anti bubble. And where are the anti bubbles today? I mean Tepos shares the same skepticism about agentic AI thinks that he owns his stock site, sort of car auction business or online travel. He doesn't believe that these online businesses, whether in sort of whether to real estate market or travel, whatever, are going to be disrupted. He also mentioned, I think the London Stock Exchange group that not only does provides the market for the stock exchange, but also a lot of other financial data, whatever. He doesn't believe that their moats are interrupted by or fatally compromised. And the market, I don't, I think the market has revised its opinion somewhat. But definitely two or three months ago in this, you know, the market was I think sort of overblown. It was selling everything. There was a sort of massive overblown sell off. The only company I saw that really has been impacted and genuinely impacted was one of these sort of Californian educational technology companies. That provided sort of essay notes for lazy students and you know, yeah, AI's going to replace that. Business stocks down 95%. I've got no argument with that. Straightforward AI may make one or two errors in a student's essay, but that, you know, we all make mistakes, Neil. It's not going to be the end of the world if it does. But 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.

[Kai Woo]

Got it. So you and Tepper believe that a lot of what sounds like software companies or other companies that are selling down 50 plus percent on this idea that they will be disrupted by AI, 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 be the loser. It's not always the folks who initially sell off.

[Edward Chancellor]

Yeah. And I mean it requires a bit of analysis because what people who hold that view also point out that software as a service companies with a tremendous bubble three or four years ago and so part of their selling off is really has to do with getting back to what was fair value. And the other issue is that they have, you know, they love stock options, they pay out most of their earnings to their employees. So you know, those are businesses that everything else being equal, you want to be pretty cautious of. And when I last looked those, some of those companies were really not you. They may have made 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, they were probably worthy of avoiding. So yeah, I think you know, look for the anti bubble but you know, but, but do a tiny bit of analysis.

[Kai Woo]

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 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 are spending a lot of time on 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.

[Edward Chancellor]

I wouldn't say you say investors end up well despite robust economic growth. I'd say because, because of the growth, because the growth was faster that was higher than the returns on capital. Then actually companies to grow had to issue, had to raise more capital. So in fact, actually if your returns are low and you're growing in an environment that is growing quickly, it's actually particularly negative. I think my lesson from China was this, 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 returns on capital and the capital cycle. And China was the largest investor, you know, went through the greatest investment boom ever. And, and they, and, and, and so just as you say, you know, had relatively strong economic grave tailed off a bit, you know, over the last few years, but still very strong growth and absolutely miserable returns. And, and, and the shareholders were diluted partly because the Chinese have a tendency to, to add companies to the index at very high valuations and then people imported at the high valuations. The index gets diluted by these new issuances and therefore gives a poor return. So I think the capital cycle has been, capital cycle Nas is very well vindicated there. And as you remember, we did a lot of work on the real estate and you know what we've indicated. Yeah, I mean, yes and no. I saw the other day a chart showing real, real Chinese house prices below where they were in in 2010. You know, a lot of those big, we used to be short some of this big Chinese real estate companies like Evergrande, you know, they've all gone, many of them have gone bust. It took, it took longer to play out than I expected. But as you know, however long we've been in this business, you know, if you, you might make a sort of a fundamentally sound observation and then be, you know, surprised by how long it took goods taken to play out. So yeah, I, I don't, you know, I'm not following China so close today. I know some people think that, I mean I do some works, you know with the marathon asset management and they, you know, their emerging markets. People think that some of the Chinese real estate developers now, you know, particularly in the Tier 1, Tier 2 cities are in a relatively good state. I'm not following the Chinese story close enough to tell you where we are now. Just from 15 years ago, I'd say that our positions have largely been vindicated.

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[Kai Woo]

Got it. So here's another fun one. So this is from one of your Reuters columns entitled Big Booze can Sweat off its Multi year hangover. I'll let you tell the story, but basically in the COVID boom, people were stuck at home drinking booze. These stocks did well and then following the 2022 unwind, they have 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. So what does that mean and how are you thinking about these stocks today?

[Edward Chancellor]

So the Lindy effect is a sort of joke. It's interesting. It was a notion that urban legend has derived from a Broadway cafe and where someone at the bar saying how long do you think this show is going to run for. And 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, if the show's been going for one night, it will go for one more night, then it'll close. If it's been going for five years, it'll run another five years. And then it turns out actually that as a sort of rule of thumb that was actually turned out to be reasonably accurate. And what I would suggest is probably the case for spirits companies, I think particularly spirits, because of the companies that were beaten up are companies like Pano Ricard, Campari, Diageo, Remi Cointreau Brandy sales really took off during the pandemic because people were drinking and they were speculating and they went hand in hand and they were stuck and apparently they loaded up their drinks cabinets. So by the end of the pandemic there was no space in the drinks cabinet and they've been slightly drinking off the experience ever since. So you could say that was a stock problem. And then the other issues, the GLP1s came along, these sort of weight loss drugs and they apparently put people off alcohol. And then there might be an issue that sort of younger generation is taking ketamine rather than drinking. I don't know. But my guess is that these brandy companies and spirit companies, they've been around for a couple of hundred years and you've got countries like India getting richer. I don't really like to play the sort of emerging consumer demand story because it's often what the brokers do, but I just. I think it's obviously not hard on the fast rule and one can find exceptions, but I think it's as a sort of good principle it was. And where they are now, again, not following it particularly. I saw that Diageo recently sort of picked up because it had better than expected results, whereas Campari still built out. I liked, you know, I particularly liked the story that Remy Contre where you could. Where the company was valued at less than the market value of. Of the brand in its vaults. And that. That sort of reminded me of. There was a story during the German hyperinflation when the. When the Daimler Bentz company was worth less than the. Than the cars in the factory lots. I thought that was a good story. I wouldn't surprise me at the GLP1s if that sort of. It wouldn't surprise me if that had. That diminishes somewhat over which years and eventually people will drink down their alcohol in their Houses and they'll go back to the bottle anyhow, that's what I sort of. That's another antibub.

[Kai Woo]

Well, I guess one thing is that the AGI crowd, they don't like to drink. So maybe that's two sides of the same coin. And maybe it is the anti bubble.

[Edward Chancellor]

Yeah, if AGI were ready to take over, there'll be a lot of idle hands. So they might actually start drinking again. Who knows?

[Kai Woo]

Right. So maybe it's a head against AGI. We have nothing better to do since the machines are doing all our work. Okay, so one question I had that's kind of more personal based on my own curiosity is, you know, as you know, my, my area of focus is often on intangible investment, such as an R and D and software advertising, human capital. As you know, in the U.S. for example, in 1995, intangible intangible investment were both about, you know, 12% of GDP. Since then, intangible investment has increased to about 16% of GDP, while tangible investment has fallen to 10%, you know, over the past couple decades. And so capital cycle theory, of course, having originated, you know, many years ago, tends to be more focused on investment in physical capital. And you talked about the energy cycle, emerging markets, AI data centers. So my question is, is it fair to assume that the capital cycle also applies to intangible capital? And if so, where are some notable examples that we can point to either in the past or today where we may have seen capital cycles in mostly or purely intangible assets?

[Edward Chancellor]

Well, I mean, capital is capital, isn't it? So I don't think if we believe in the concept of intangible capital, which I think we probably should do, because the reason is really to do with accounting convention. Tell me if I'm largely to do with accounting conventions that we don't put that we tend to expense R and D and we don't tend to put brand values or brand values on the book unless there's been an acquisition. Is that correct? And then yes. So then the question is, can you get over investment in intangible as in any other type of physical over investment. So the answer is pretty obviously yes, probably. And you might be able to give me some better examples. But I remember that back in the early 1990s, huge excitement when Glaxo 18 and now GlaxoSmithKline had this great blockbuster ulcer drug called Zantac that encouraged huge amount of investment in big Pharma, in R D, that the cost of blockbuster drug development soared over that period until the blockbuster drugs that they came out with were not delivering a decent demo on equity. So yeah, I think that's, that's one that, that comes to mind I think. Can you, can you name do you think of other intangible. I mean as I say, anything, anything really what we're seeing today in the AI space we've been talking about, you've got, you've got obviously huge physical capex in the data centers and then a huge valuation placed on the, on the AI scientists meta going out and I don't know hiring people for AM. Right. You know 100 million. Is it there like or something. And so that would look, you know that, that would look to be the case. I mean there are AI companies here. The Mira Murati when she had former chief technology company or of Open Air chief technology officer of OpenAI she left what a couple of years ago to form her own company and they, they raised, they, they raised it rate money at a valuation of $12 billion. Even though she had, she certainly wasn't going to tell investors what they're going to put there, what, what, what the company was going to do. So that, that seems like a, an intangible capital boom. I, I frankly. And then you know, yeah Kai, they get think of brand valuations. This, this is not so much capex but just brands getting over. Do you remember how in the late 1990s Buffett who normally talks a lot of sense, got a bit carried away with the likes of Coca Cola and Gillette and started calling them the inevitables. And I don't think that either Coca Cola or Gillettes will attract a huge amount of capex but they definitely or capex competition but they definitely went their intangibles or brands were definitely overvalued at that time and the likes of Gillette was eventually swallowed up by Procter and Gamble. But Coca Cola had a very poor decade after 2000.

[Kai Woo]

Yeah, I think that's right. Obviously an asset's an asset. Capital is capital whether it takes the form of a factory or the IP embedded in an Nvidia GPU which is at least half the value of the actual data centers. It's embedded in the value added of the semiconductor supply chain.

[Edward Chancellor]

And Kai, we've been talking about this software as a service, might as well draw it out. I did write a piece back in 2022 about how these software as a service business were at absurd valuations, attracting capital inflows and so forth. I remember one of the capital cycle metrics is when you know, you're really in favor because someone creates an index and then update the index to prove how well it's historically performed. And then you can pretty much guess that from the moment the inception of the new index, it's going to do poorly. I think the SaaS bubble of 2022 is another very good example of an intangible bubble.

[Kai Woo]

Another interesting related topic is if you follow human capital, just talent, where's the Harvard MBA indicator? Where are all the best and brightest going? 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 peaked in 07 08, everyone wanted to go work for Wall street until they didn't and then it was tech. So, you know, 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.

[Edward Chancellor]

Yeah, well, Harvard, I mean, it's just down. I mean not just hbs, but the undergraduate, you know, the graduating body, they're very, very reliable contrarian indicator.

[Kai Woo]

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.

[Edward Chancellor]

So you got out of value investing at the time, we'd have done much better.

[Kai Woo]

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, 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. And 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?

[Edward Chancellor]

Well, it depends from what perspective one's looking at it from. I tend, and I think you do too, we tend to view the world from the perspective of an investor who's trying to. It maximizes gains, minimize his risk and I, you know, from the perspective of society. Yeah, if you can bring forward the technology, whether it's railways or cars or aircraft or Internet telecommunications or you know EVs or batteries or AI. That's all well and good sometimes, you know, sometimes it might not. The whole technology might be a sort of dead end but perhaps not the end of the world. But so yes, I think we talked, I mentioned the railroads earlier. They tended to come in surges of capex both in US and uk. I think probably good thing in the end for both economies but really disastrous for the investors who partook in them. And so I think that you have to bear that in mind. You're under no compulsion to make a loss making investment for the benefit of society. The other issue is, as I've already mentioned, investment booms tend to lead to a misallocation of capital and over investment and that tends to slow rather than increase economic growth. And even if the dot com bubble because of that fiber optic cable that was laid, it meant that you could get Netflix up and running and all the video conferencing, whatever, that's all well and good but actually if you remember that the downside of that of the dot com bust with the market gate down the S and p down what, 40% as I remember it required the we didn't require as a response the Federal Reserve slashed interest rates to 1% to fend off deflation and then we got a housing boom, we got a bust. In fact the upshot, the way I see it is the upshot of the dot com bust was the cable financial crisis. The upshot of the global financial crisis with 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 for the companies that end up as winners and so on but actually the very long tail of these bus can be quite severe. And so I think it's sort of irresponsible to argue that bubbles are good for society. It's very tip. One of the things you see during bubble periods is a sort of lightheadedness, hey 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 when they're nursing sort of 90% losses on yesterday's high flyers. So this is a big what's going on in the AI space is a big capex boom as you know taking place with very weak consumer confidence. It's so called K shaped economy and problems happening elsewhere in the financial system, private credit and private equity and really a lot of legacy VC stuff that was badly invested that you didn't really hear too much about the whole sort of private alternative investment world is pretty seems to be in a pretty bad place. So I would have thought that if and when this AI boom ends and turns to bus, a whole load of problems will emerge and as the valuations come down and people just simply won't be saying that that type of commentary is a sort of giddy commentary that invariably accompanies the bubble.

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[Edward Chancellor]

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[Kai Woo]

That's really interesting. So one point you made that that was kind of interesting in particular is this idea that the kind of like long term consequence of the dot com bust was a Fed mindset that led to ultra low interest rates for a long period of time. Which Fed if you were, and this is in your book, the price of time was the original sin behind a lot of the speculative excesses that came afterwards. So that something can happen and it looks fine, we can patch it over, but that's just sowing the seeds for the next cycle. Now obviously rates have increased over the past few years from their Covid lows. Do we think that the regime has actually changed or does the fact that we now are seeing a massive capex boom in AI investment suggest that the money never left the system?

[Edward Chancellor]

Well, I mean do like to hedge my bets, but I'd say a bit of both. I think that the interest rate cycle turned in 2021, 22 for long term yields and historically that those interest rate cycles have tended to be multi decade, often 30 to 40 years. And if you remember back at GMA that was another thing that we did a lot of work on which is like why were our bond models so wrong? Because we always kept on talking about mean reversion and what we found is that these very long bond cycles and so I always say we can't. I always say you can't predict anything about long term rates because they're not mean reverting like, say like equities. But you know, they do go on these very long cycles. And I do think that we are, that we have entered into a prolonged period of an upward trend in long term rates. However, there was clearly a lot of liquidity left over from that sort of COVID lockdown, quantitative easing splurge, you know, I don't know what $8 trillion of money printed by the world central banks and you know, a lot of that and there's sort of excess savings, a lot of it was still held on the Fed balance sheets and that has been drawn down. Interest rates remain low relative to nominal GDP growth. And so the question is what's going to happen? Our interest rate is going to go up or the world GDP growth going down or perhaps, you know, possibly the worst and GDP goes down and interest rates go up. So yeah, I think there was some liquidity, a lot of liquidity left over from that period. You know, it's always difficult to know what's going on in the plumbing of the financial system. But yeah, there was perhaps more liquidity than I expected left over. And there are, as you know, these problems in private credit, which three just the financing arm of private equity that is still percolating away and I expect that will continue being the case. And the real estate market, as I think in America it's fairly moribund, isn't it? Because house prices really haven't come down enough to, you know, in response to the shifted interest rates. So that seems to be stuck in its interest. I haven't done work on it, but I think it's an interesting case. US residential real estate because there hasn't been much investment there recently, but valuations seem probably too high relative to interest rate regime. So that's, I mean that's either a threat or an opportunity. I think it's not going to sort of middle of the line, it's going to happen. Something good is going to happen or something bad is going to happen. I'm not quite sure which.

[Kai Woo]

Fair enough. Yeah. Okay, so I just got one more question for you, which is our standard closing question. What's the one thing you believe about investing that most of your peers would disagree with?

[Edward Chancellor]

I, I, at least one area where I would, you know, differed from a lot of, 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 you people, you know, like, you know, our old boss Jeremy Grantham, and, and you know the team head Ben Ink, who would always say I don't like gold has no dividend, this and that. And I will, you know, I've always, yeah, I think I've always felt that I was really. Well, yeah, I 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, difficult value, but I like the way that gold is an asset without a liability. And I think that in a world where equities take in the US are look overvalued and bonds might be on a 30 to 40 year downward trend in value valuations as well as really severe debt problem dynamics, I hold the view that having a decent allocation to gold is a good portfolio position and that. I mean look, I may have this complete right in the last 10 years actually having gold, you're having a sort of equities gold profit, you know, has been obviously a lot better than having bond. So I might be, I might be touting a. Something that's sort of near the end of, near the end of its blue period. 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 one area where I might disagree.

[Kai Woo]

Interesting. Well, thank you, Ed. I know I've taken up lots of your time, so really appreciate you coming on.

[Edward Chancellor]

Good Kai and I'll see you in London soon.

[Kai Woo]

See you soon.

[Podcast Host / Narrator]

Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Returns [e-mail] if you have any feedback or questions, you can contact [e-mail] no information on this podcast

[Kai Woo]

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