The Widest Valuation Gap in History | Rob Arnott on What Investors Are Missing About AI

Summary

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Podcast Summary: The Widest Valuation Gap in History | Rob Arnott on What Investors Are Missing About AI

Podcast: Excess Returns
Episode Date: March 5, 2026
Host(s): Jack Forehand, Justin Carbonneau, Matt Zeigler
Guest: Rob Arnott (Founder & Chairman, Research Affiliates)

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Episode Overview

The hosts welcome back Rob Arnott—recognized for his quantitative investing insights and historical market perspective—to discuss seismic shifts in market valuations, the ongoing implications of AI, and what investors might be getting wrong about both. Topics include the current valuation gap between large cap growth and small cap value, interpreting global conflict through a historical investing lens, how AI is reshaping the economy and investment bubbles, and Arnott's ongoing innovations in indexing.

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Key Discussion Points and Insights

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1. Geopolitical Turbulence and Markets

Timestamps: [03:25]–[07:50]

• Impact of War on Markets: Arnott stresses that, historically, war hasn’t been particularly damaging to stock and bond investments—except in the losing country. Markets react to economic fundamentals, not just conflict headlines.

  • Notable Quote [03:53]:
    “Historically, war has not been particularly damaging to stock and bond investments except in the losing country… The markets focus on what's happening in the global economy.”

• Energy, Oil, and Iran: The present Iran conflict is significant, as Iran is a major oil producer with the ability to disrupt 25% of the world’s oil supply via the Strait of Hormuz—a much bigger potential market mover than Ukraine.

• Opportunity in Tumult: Arnott invokes John Templeton’s maxim:

  • Notable Quote [07:25]:
    "Trouble is opportunity... Tumult creates opportunities. Oftentimes tumult creates market reactions that are sometimes overreactions."

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2. Record Valuation Gaps & Portfolio Implications

Timestamps: [07:50]–[13:47]

• Valuation Extremes: The US market is running at roughly twice the valuation multiples of the rest of the world—across CAPE, dividend yield, and book value metrics.

  • Notable Quote [08:15]:
    “US is at roughly twice the valuation multiples... as the rest of the world... I do view that as an extreme opportunity.”

• Growth vs. Value, Large vs. Small Cap: The spread between growth and value, and large vs. small cap, is at or near all-time extremes—having been wider only post the COVID-19 value crisis and at the dot-com bubble peak.

• Expected Returns: Small cap value is historically cheap versus large cap growth; Research Affiliates' models suggest small cap value could outpace large cap growth by 700 basis points annually over the next decade.

  • Notable Quote [61:03]:
    “Small would have to double relative to large cap in order to be back to historic norms of relative valuation... our work suggests that small cap value will beat large cap growth by on the order of 700 basis points a year.”

• Index Flows & Small Cap Underrepresentation: Index fund flows have disproportionately favored the largest companies, neglecting small caps which have seen faster underlying business growth but remain at massive valuation discounts.

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3. Rethinking Exposure: US vs. International

Timestamps: [39:12]–[44:49]

• International Exposure: Arnott reiterates his previous strong call on emerging market value, noting that while recent events have been rough for international markets, diversification and mean reversion advantages are compelling.

• Practical Allocation Advice:

  • Use broad-based international exposure unless willing to do deep research.
  • "Fundamental Indexing" is especially potent in less efficient markets, giving a value tilt and a source of "rebalancing alpha" (historically ~2%/year in developed markets, 3-4%/year in emerging).

• Currency Risk & Mean Reversion: Mean reversion operates in currencies, too; recent dollar weakness has boosted international returns, but long-term currency moves balance out.

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4. Artificial Intelligence: Revolution or Bubble?

Timestamps: [15:18]–[27:31]

• Technological Disruption and Jobs:

  • Every technological revolution (from the wheel, to the internet, to AI) has displaced jobs but created new ones. The transformation is especially disruptive to those currently employed, but a generation later those jobs are not missed.
  • Notable Quote [16:19]:
    “Every technological revolution in the history of mankind has killed millions of jobs... A generation later, those jobs aren't missed."

• AI as a Tool, Not a Threat:

  • Learning and adopting AI tools will define career resilience; “People who learn to use [LLMs] could replace you, not LLMs themselves.”
  • Notable Quote [21:17]:
    "LLMs are not going to replace you, but people who learn to use them could."

• Personal Anecdotes on Productivity Gains: Arnott illustrates AI utility through programming tasks solved with ChatGPT and notes internal preference for Anthropic's Claude among his research team.

• Bubble Dynamics in AI Stocks:

  • Arnott's definition of a bubble: when current asset prices require “implausible” (not impossible) growth assumptions in a DCF model to justify them.
  • AI (the technology) is not a bubble; but many AI stocks are—a difference that matters.
  • Notable Quote [27:31]:
    “I don't think AI is a bubble. I think AI stocks are a bubble. There's a difference.”

• Winners & Moats: The tech leaders of today (e.g. the “Magnificent Seven”) may not be the leaders in 10–20 years, as moats erode and disruption accelerates. Historical analogies: Only 1 of the 10 most valuable companies in 2000 (Microsoft) is still in the top 10.

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5. Margins, Moats, and Mean Reversion

Timestamps: [29:51]–[39:12]

• Profit Margins & Mean Reversion:

  • High margins attract competition; mean reversion is inevitable but its timing is unpredictable.
  • Notable Quote [30:14]:
    “If you have a stupendous profit margin, you're going to attract competitors galore... it doesn't matter how wide your moat is.”

• Innovation, Regulation, and Moat Erosion:

  • Even the most dominant firms are vulnerable; Google’s search moat is being challenged by AI, and pioneering AI models can themselves be rapidly disrupted.

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6. Rethinking Indices and Fundamental Indexing

Timestamps: [44:49]–[54:25]

• Fundamental vs. Cap-Weighted Indexes:

  • The traditional approach of weighting by market cap ensures overexposure to overvalued stocks.
  • Fundamental Indexing weights companies by the scale of their business (sales, profits, etc.), breaking the distorted price link.

• The “Trifecta” Concept:

  • Applying fundamental methods to value, cap-weighted, and growth indices.
    • Fundamental selection improves even cap-weighted and growth-oriented portfolios.
    • Combining fundamental value and fundamental growth strategies (excluding expensive/slow-growers) can yield much higher historical returns (~2–2.5% annual alpha vs. standard indices).
  • Notable Quote [45:03]:
    “We have a trifecta: a world class value strategy (RAFI), a world class cap-weighted index (RAC), and a world class fundamentally selected and weighted growth strategy (RAFI Growth)... and it works everywhere in the world.”

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7. Conglomerates, Narrative, and Premium/Discount

Timestamps: [55:23]–[61:03]

• The “Conglomerate Discount” Revisited:

  • Historically, conglomerates (firms with a broad range of unrelated businesses) shifted from a market premium (for flexibility/opportunity) to a discount (for inefficiency and bloat). Today, several tech giants trade at hefty “conglomerate premiums"—but the risk of overextending and squandering capital is ever-present.

• Investor Implications:

  • Many large tech firms are trading on narrative and perceived ability to reallocate capital, rather than on proven synergies or effective scale.

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8. Market Leadership and the History of Top Companies

Timestamps: [64:04]–[66:20]

• Market Turnover:
  • The top 10 companies in the market almost always change over the course of a decade; most underperform the market subsequently.
  • Notable Quote [65:14]:
    “The companies in the top 10, on average seven or eight of them are gone within 10 years... staying a top dog is really hard.”

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Notable Quotes & Memorable Moments

• “Trouble is opportunity... Tumult creates opportunities.” [07:25], Rob Arnott
• “The spread between large cap and small cap is the widest ever, no exceptions, the widest in history.” [10:01], Rob Arnott
• “I don't think AI is a bubble. I think AI stocks are a bubble. There's a difference.” [27:31], Rob Arnott
• “Every technological revolution... killed millions of jobs... But a generation later, those jobs aren't missed.” [16:19], Rob Arnott
• “LLMs are not going to replace you, but people who learn to use them could.” [21:17], Rob Arnott
• “The companies in the top 10, on average seven or eight of them are gone within 10 years... staying a top dog is really hard.” [65:14], Rob Arnott

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Key Takeaways for Investors

• Valuation Gaps Create Opportunity. The extremes in valuation (large vs. small, growth vs. value, US vs. ex-US) are at or near record levels; history favors mean reversion, especially over multi-year horizons.

• Diversification and Contrarianism Matter. Overconcentration in US large cap growth today carries substantial risk; investors should be considering value, small cap, and international allocations.

• AI is Transformational (and Disruptive). The technology will upend industries, but stock prices assume rates of growth and profitability that are likely unattainable for most. The biggest beneficiaries of AI may be outside of tech.

• Narratives Set Prices, but Reality Wins. Market narratives (US exceptionalism, conglomerate efficiency, tech moats) justify lofty valuations—until they don’t. Caution is needed when valuations depend on “implausible” outcomes.

• Indexing is Evolving. Fundamental (not just price-based) indices can improve returns and risk profile, especially in less efficient and international markets.

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Recommended Action Items

• Rebalance exposure toward small cap, value, and international equities.
• Approach “AI exposure” with skepticism—focus on the transformative capability, not just on the current narrative leaders.
• Explore fundamental index products for enhanced diversification and return potential.
• Regularly question market narratives and the durability of current leaders.

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For more, see Research Affiliates' forthcoming papers ("Trifecta: A Fundamental Revolution in Indexing," etc.) and visit researchaffiliates.com.

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Transcript

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[Rob Arnott]

I don't think AI is a bubble. I think AI stocks are a bubble. There's a difference. Markets in the short run are voting machines and in the long run are weighing machines. The voting is American. Exceptionalism is real. Let's bid this this thing up because it's got nowhere to go but up. The weighing machine would say US is at roughly twice the valuation multiples. The narrative is AI will change everything, and it will. But the narrative also says the companies leading the AI charge today are poised for extraordinary growth. And that one or two of them might be. But there's also the issue of transforming AI into profits is proving to be rather difficult. Our work suggests that small cap value will beat large cap growth by on the order of 700 basis points a year. On a 10 year horizon, that's enough to double your money relative to sticking with growth.

[Ben Felix]

Rob welcome back to Excess Returns.

[Rob Arnott]

Glad to be here.

[Ben Felix]

It's great to have you on again. Our audience is very familiar with you. Hopefully you've been on multiple times and we always appreciate having you on. You're known in the investment world as, I think one of the most influential speakers on quantitative investing, fundamental indexing, and really just a student of of market history that we can always learn a lot from you on. And so today we have a wide variety of topics. I think some may be more evergreen than others that we want to talk with you today. That includes looking at some of the recent research that the firm has put out, talking about how you're looking at market valuations and future returns and then maybe to start we'll get into, I think some of the more maybe you can call them short term, but things that are going on in the market that I think investors are thinking about, including what's happening over in Iran and how investors should be thinking about that AI and some other stuff. So really appreciate you joining us. You can always learn more about research affiliates and access the research that these guys are putting [e-mail] and we'll put links to some of these research papers in the show notes as well. So anyways, thanks, Rob. Hopefully that was not too long of an intro, but I want to give you that because it's important.

[Rob Arnott]

Yeah.

[Ben Felix]

So yeah, to start, let's, I mean, let's, let's talk about what's sort of on the news and what's front and center with this Iran conflict. I mean, a lot of investors are sort of asking themselves, how is this going to affect my portfolio? Should I be making adjustments? But what I wanted to ask you what's important is to try to look at what does the study of market history tell us about the impact of conflict and whether or not investors should be letting stuff like this influence their portfolio.

[Rob Arnott]

Historically, war has not been particularly damaging to stock and bond investments except in the losing country. And so the markets, the global markets shrugged off Ukraine. To this day, the tragedy of what happens in war notwithstanding, the markets focus on what's happening in the global economy. Now, one of my favorite economists, Charles Gov, a brilliant French economist, sounds like an oxymoron, but it's true. Is fond of saying that GDP is energy transformed. That is to say, you take energy, usually oil, and you turn it into prosperity. And yes, over the course of decades, economies become more energy efficient, that is to say, more dollars of GDP per barrel of oil, but it's still the dominant source. Fossil fuels are still the dominant source of energy powering the entire planet. And so a major oil producer that's 5% of world supply, that also has potential to close off the Straits of Hormuz, through which 25% of world oil supply passes, has the potential to be massively disruptive. So this is not Ukraine. This is potentially a big deal, which is why very aggressive strikes, very fast and obliteration of Iran's navy so that they have less chance to close off the Straits are important elements here. The other issue that I think is equally important is what's the exit strategy? And if the exit strategy is to leave a collapsed economy run by unknown zealots or dictators or whomever, then what you've probably done is take much of 5% of world energy supply offline for a fairly long period of time. So we don't know what the end game is. We don't know how it'll play out. I do have a plaque. You were kind enough to share that question with me ahead of time. I do have a plaque that used to be on the desk of John Templeton. John Templeton was a legendary investor from the 1940s and he had a plaque on his desk that said trouble is opportunity. It was gifted to me about a decade ago and it's a useful reminder. People worry about trouble and they should, but they should also think in terms of what are the investment implications because tumult creates opportunities. So oftentimes tumult creates market reactions that are sometimes overreactions. So you can have opportunities to contra trade. The Liberation Day April 2nd was beautiful example of that. The market severely overreacted to tariffs that were of unknown eventual magnitude. That turned out to be more of an economic than anything.

[Ben Felix]

Tremendously influential is the higher than, you know, average valuations here in the US does that at all or could that at all play into maybe this being a little bit more of a delicate situation given what's going on? I mean, how do valuations sort of, or do they at all sort of factor into how you might kind of be looking at this?

[Rob Arnott]

Well, Ben Graham famously said that the markets in the short run are voting machines and in the long run are weighing machines. The voting is American exceptionalism is real. Let's bid this, this thing up because it's got nowhere to go but up. The weighing machine would say US is at roughly twice the valuation multiples, whether you're using cape ratios or dividend yields or price to book value, roughly twice the valuation multiples of the rest of the world. And so I do view that as an extreme opportunity, not necessarily horrifically bearish for the US but very interesting opportunity to build positions and diversification and risk reduction by deploying money elsewhere in the world. Elsewhere in the world is also likely to be more affected than US markets by the tumult in the Middle east. And we certainly saw that the last few days. But I view US expensive markets in the US as being vulnerable, but more from a perspective of opportunity. Best opportunities lie elsewhere.

[Ben Felix]

And when you're looking at expected returns, you guys have a tool on the site where you do asset class and market expected returns. I think 7 to 10 year returns. Are there any where in the US looks attractive, I think like large cap growth, probably long term returns less than historical average. But are there Any pockets in the US that do look more attractive to you based on your estimates?

[Rob Arnott]

Well, firstly, the spread and valuation between growth and value is historically extreme. It's been wider than this only in the aftermath of the COVID value meltdown in the summer of 2020. The spread between growth and value in various valuation metrics, price to sales, price to book, dividend yield, price to cash flow, various metrics have the spread between growth and value roughly as wide today as it was at the peak of the dot com bubble. Okay, well, that's a little alarming. The narrative is AI will change everything, and it will. But the narrative also says the companies leading the AI charge today are poised for extraordinary growth. And one or two of them might be. But there's also the issue of transforming AI into profits is proving to be rather difficult. The CapEx outlays are stupendous. Estimated to be $600 billion next year. Well, that's a lot of money to spend when you don't know how to get that money back. So. So one of my colleagues, Chris Brightman, is writing a paper that's coming out in a few days on exactly this topic. The spread between large cap and small cap is the widest ever, no exceptions, the widest in history. And we have a paper coming out. CFA Institute has a research foundation, so like Financial Analyst Journal that publishes academic articles. The research foundation publishes more in depth monographs longer than the papers. And we have a monograph coming out shortly. I think it comes out in maybe next month that looks at the active side of indexing. Indexes are thought to be passive. They're not. They mostly are. If they have 5% turnover, you could think of it as 5%, 95% passive, blissfully indifferent to what's going on in the individual companies or the economy or the market just along for the ride. The 5% looks like a crazed growth investor, buying stocks that have soared at lofty multiples, selling stocks that have tanked at deep discounts. Now, why do I mention this in the context of small cap? Well, small cap isn't in the big indexes, whether you're looking at S&P 500 or Russell. And as money flows into index funds, it flows out of things that aren't in index funds. So the spread in valuation is now better than 2 to 1 between S&P 500 and Russell 2000 in terms of relative valuations. And here's the fascinating thing. The companies that aren't in the index have had 2% per annum. Faster growth in the underlying business over the last 30 years than the companies that are in the top indexes. 2% per annum. Faster growth and they're priced at a 50% discount today. That's fascinating. So if your audience, anyone in your audience, doesn't have a toehold in small cap, put it in place. It should be a decent slug of your portfolio.

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[Rob Arnott]

and

[Host/Interviewer]

it has been nice. As a factory investor myself, you know, for years we've been talking about value, we've been talking about international, we've been talking about small cap. And actually when I tell people about it now, they're actually excited about it. You know, in past years, for many years, they were like, you're an idiot. What are you talking about? Now it's actually working to some degree. So it is, it is, it is nice to at least see some results.

[Rob Arnott]

Yeah. The look for that monograph when it comes out, because there's a wonderful graphic in it that shows the divergence between relative valuation and relative performance of the underlying businesses. It's relentless.

[Host/Interviewer]

So I want to switch and ask you about AI and I don't know, did you get an opportunity to read the Satrini piece? It sort of took the world by storm here. Okay.

[Rob Arnott]

It's a really interesting paper and I think there's a lot of truth in it, but I think the truth is somewhat overstated.

[Host/Interviewer]

Yeah. And it was meant as a thought exercise, which I kind of enjoyed it from that perspective. Like, I think people who took it as, this is the way the world's going to be, you know, we're attacking it and going crazy. Like, for me, I feel like I know a lot more about AI. Because I read it and because I thought about the concepts within it. And one of the things I thought when I read it is I thought we have Rob coming up. And I want to ask Rob, because you're really good at thinking about things from the long term. And one of the things that struck me about the paper is we have this idea that in the long term AI is going to create a lot of jobs, which I think is probably true. It's going to create a lot of jobs. But we also have this push and pull with this idea that in the short term it's going to both maybe enhance us in our careers, but it also might destroy a bunch of jobs. And if it's a more transformative technology, it might destroy more jobs than other technologies have. So how do you think about like that balance between all that.

[Rob Arnott]

Yeah. Every technological revolution in the history of mankind has killed millions of jobs. If they had job statistics back when fire and the wheel were invented, I bet you would have seen lots of jobs displaced. But, but, you know, whether it's the steam engine or the cotton gin or railroads or telephone and telegraph or cars and trucks and, and airplanes or computers or the Internet or the AI revolution, every one of these killed millions of jobs. And that's devastating to the people who are displaced and find, oh, I have skills that are no longer useful, I gotta find something totally different to do. But a generation later, those jobs aren't missed. Computers displaced. Computers. It used to be a computer was a job description. Somebody who was very quick and facile with math and could do complex equations faster than anybody else in the office. Okay, that was a computer. Does anyone want that job now? I don't think so. Computers can do all that stuff a billion times faster with absolute accuracy. So when it comes to social interaction, social media, that sort of thing, lots of jobs displaced. The newspaper industry upended. That. Create new jobs, you bet. And the same will hold true for AI. Anyone who's not thinking about how can I use AI to do my work better and faster is an idiot. It is very powerful. Fun little anecdote. I'm kind of old fashioned. I like to do initial research in Excel because it's so quick and easy. And so I'll have research team put together data into an Excel file and then I'll play around with it. We got a. I had them put together a file of all US companies for the last 70 years with what was their return for the year, what were their valuations, ratios and construct Rafi Fundamental Index rac. We are cap weighted index, a cap weighted construct to compare attributes, and so forth. And I asked one of our programmers, doing this for 70 different pages on the Monster spreadsheet is time consuming. Can you set up a macro for me? And he, he said, I don't know, I haven't done macros. And he said, but let me take a look. He got back to me less than an hour later and said, it's done. And I said, how did you do that? You found a. You learned how to do macros in less than an hour? He said, no. I asked Chat GPT to write it

[Ben Felix]

and

[Rob Arnott]

IT put it together, I tested it, works fine. So here you go. So cool. That would have been a day or two or three of his time if he had to learn how to do macros. It would have been three or four hours of my time. Even though I know how to do macros. Chat GPT just flung it together and it worked. So cool.

[Host/Interviewer]

It's funny too, because I just, I haven't used it a ton yet, but I just. Claude has a plugin that you just install straight into Excel, and I think that's probably pretty promising too, is just to have these working for you right inside the spreadsheet too, if you prefer to continue using Excel, Notwithstanding the

[Rob Arnott]

other war going on right now between the administration and Anthropic, IT bears mention that we polled our IT people and our programmers with which tool they most like to use and anyone relating to programming at all. It was unanimous. Claude, cool.

[Host/Interviewer]

Yeah, I do programming as well and I'm kind of the same way. Like I use Claude code all the time now to build interesting things. You know, one of the interesting things you said to us last time you were on, or maybe by the time before that I think relates to what you were just saying is this idea that you had once called an all hands meeting of research affiliates and you'd basically told people, LLMs are not going to replace you, but people who learn to use them could. And I think that's the idea. Like that stuck with me ever since. Like, I've been thinking about everything I try to do in my life. I'm like, how can this enhance what I'm doing? How can this make me better at it? And I think that's the attitude we all have to have with this.

[Rob Arnott]

Yeah, that's exactly right. Think of it as a tool. It's an extremely powerful tool. In fact, it's an astonishing tool and it interacts in the most complex programming language in the history of mankind. It's called English, the notion that you

[Ben Felix]

can

[Rob Arnott]

ask questions and get a reasonably reliable answer. Yeah, it still hallucinates. I asked what the all time high was for Cisco during the dot com bubble and it came back with a date in 1999. And I knew that was wrong so I said well, what was the high in 2000? And it came out with a totally different and higher number. So the first answer was a hallucination, which I, I knew enough to know it was wrong. But you know, you ask a human researcher, they're going to get things wrong too. So it's, it'll change everything. But one of the interesting challenges is how do you make money if you're creating AI? If you're creating the hardware that runs AI for the moment, that's super easy because you've got people on waiting lists wanting to spend tens of thousands of dollars per chip to buy your AI hardware. But AI software is, is currently run at a massive loss by everyone who's offering the AI tools. And so one of the fascinating things is we're going to get there, it'll be profitable. The question is when and how. And so we'll see.

[Host/Interviewer]

On this treaty piece like one of the things I've been thinking about a lot is is this we can learn from all the other innovations of the past. But, but is this kind of innovation on steroids because it's intelligence? And do we think about maybe we magnify both the long term value of this in terms of what it's going to create, but we also maybe magnify the short term pain in terms of because it is intelligence, it can replace more human jobs. I mean, do you think that's a fair way to look at it?

[Rob Arnott]

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

[Host/Interviewer]

What do you think the most important things are for investors to think about with this? Like when I talk to clients they're always asking questions like what is my exposure to AI? I want exposure to AI. But I think maybe the lessons from past booms may teach us that that may not be the right way to approach this. So how do you think people should think about invest AI from the perspective of an investor?

[Rob Arnott]

Well, you can have firstly, the narrative that drives the bubble is a narrative that says this is going to change everything. And that narrative goes on to say that the key players, the dominant players, have a moat that'll protect them and it'll be very difficult to displace them. Difficult doesn't mean impossible, especially if you're looking at a multi year horizon. It also goes on to suggest that the change will happen very fast. I mean AI that can relate to you in English has been with us since late 22 when ChatGPT was introduced. Boy, what an innovation. But how many people spend more than a few minutes a day trying their hand at exploring what AI can do for them? I would venture to guess that at least 90% of the population doesn't sit down and try using AI to explore what it can do for them more than a handful of times a week if that there's 2% of the population that spends hours every day if they exploring it and they're going to own the future. So we all owe it to ourselves to, to learn.

[Host/Interviewer]

You have a great practical definition of what a bubble is, one of the more practical ones I've seen and I'm just wondering if, if you could talk about what that is but also how are you thinking about, do you think AI is a bubble based on your practical definition right now?

[Rob Arnott]

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

[Host/Interviewer]

I want to ask you about margins because that's something we've been talking about for a long time. Like as a value guy, I've been one of these people who's been saying margins have to mean revert and they really haven't been mean reverting for a long time. So I'm just wondering, like, first of all, do you have any thoughts on that idea in terms of why margins haven't been reverted? But I also want to bring it back to the idea of AI. And like, do you think AI could lead margins to continue, maybe to go to higher levels than we think they

[Rob Arnott]

could go to mean Reversion has been central to my career, central to everything I've done. Valuation multiples tend to mean revert. That is to say, if they get stretched, they're more likely to mean revert than to go further in the same direction. Doesn't mean they can't. It just means that they're not likely to go further in the same direction. One of the areas where mean reversion operates most powerfully is profit margins. Why? Because if you have a stupendous profit margin, you're going to attract competitors galore. And it doesn't matter how wide your moat is, somebody's going to figure out a way to get past it. Intel was the fourth most valuable company on the planet in the year 2000. It had a moat nobody could touch them on. Making chips and doing it inexpensively with huge margins, and nobody could touch them until they did then. Nvidia, Taiwan, Semi amd, asml all of these wound up surpassing intel over the subsequent 25 years. But it took time. I mean, intel was still the dominant chip maker 10 years after in 2010, and just slowly but surely frittered away its its advantage. Another interesting warning sign is the more dependent you are on government largesse, the more likely you are to not have a successful mode. So when the CHIPS act was passed, I thought, what horrible news for Intel. A $50 billion bailout from the government. What horrible news. And of course, they've gone on to achieve great disappointment.

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[Host/Interviewer]

it's funny too, because thinking about these moats in real time is so hard to do. Like for instance, if you had asked me a few years ago about Google's moat in search, I would have said there's no chance like or before 2022. I'm like, they've got such a strong moat. And like, when you're living through it These companies seem so dominant, but now we've got ChatGPT. So now I've seen a way in which Google's moat could be challenged. And I think that's the case with a lot of these big companies in history. At the time, it's hard to see it. But then in the future something comes you didn't think of.

[Rob Arnott]

Yeah, we talked about this last time, that using AI as a search engine. Google's entire business model was predicated on sponsored links and sponsored pop ups. And without those it has no profits. And then all of a sudden people realized, oh, I can use AI as my search engine. I'll get no sponsored links and no pop ups. How cool is that? So Google itself had to decide we're going to disrupt our own business model and erode our own margins by introducing AI as part of our search engine. Because if we don't disrupt ourselves, were going to be disrupted out of business. And then ChatGPT itself was disrupted by Deep Seq, supposedly created on 1/100 the resources of Chat GPT. And the initial re. The initial version of Deep SEQ was rated to be as powerful as ChatGPT 4.5. All right. The other thing that's interesting is how fast AI is evolving. GPT5.2 is phenomenally more powerful than 3.5 and the hallucinations are way down. It's just, it does feel like you're talking to typing, to a person who has multiple PhDs and has intimate knowledge of more or less all of human knowledge. It's so cool.

[Host/Interviewer]

Do you think on the idea of mean reversion, do you think there's a case that it's been slowing down? I mean, I think we, you and I both believe mean reversion is still a very powerful force in markets. But you could argue margins haven't mean reverted like you'd think. You could argue some of these big tech companies have grown at rates that you've thought were impossible. Valuations have taken longer to come back than people think. Do you think you could argue that the process of mean reversion has just slowed down for some reason?

[Rob Arnott]

I'm not so sure about that. I, I, I think mean reversion has always been choppy and sluggish and feels like a random walk, but it's a mean reverting random walk. And when a company's at frothy multiples, one of two things has to happen. The fundamentals have to catch up with the price or the price has to revert back to the fundamentals. So you're going to get mean reversion on the valuation multiples for sure. But it can happen one of two ways. And so the question on defining a bubble is would I have to assume implausible growth for it to close with the fundamentals? Catching up that graph in the upcoming paper that that looks at the relative valuation and relative growth of fundamentals for members of the indexes and non members is just a fascinating case study. Does this mean that we're going to see mean reversion in the next year, next five years for small cap and value to mean revert towards large cap and growth? Not necessarily on a short horizon. I would say high odds on a long horizon and that's because we've seen it happen again and again and again. The Nvidia has vast margins, so does Palantir, so do a host of others. But they're com to some extent they're competing with one another. Not so much Palantir and Nvidia. One is a user, the other's a supplier. But they wind up in having competition and competitors coming in that if your horizon is three to five years, there will be competitors. I don't care what your Mode is. Within five years, if your margin is 50%, you're going to have competitors. And on a 10 year horizon, the likelihood of your margin being remaining as far away from industry norms as they are at the start of the decade is really remote. So I do see mean reversion coming in the profit margins, very high odds. And, and the interesting thing is people think that I'm poo pooing the AI revolution. I'm not. I think the AI revolution is very real and is going to continue to surprise us for years and years to come. I'm just questioning who's going to come in onto the scene that will have better ideas and displace some of the leaders and who are the end users who are going to benefit in ways that can't be anticipated. Ultimately, I think the big winners may be organizations way outside of the tech arena. I mean, if you're running a trucking company, if your dispatcher isn't using AI to figure out what trucks to move, where you're behind the curve and things like that are coming.

[Ben Felix]

You had mentioned international stocks before and maybe we've kind of hit on a little bit of this, but when we had you on previously and we were asking about how you were investing your personal portfolio and long term assets, you know, you were pounding the table on emerging market value stocks and that ended up being A, a phenomenal call. And so you know, what I want to ask you related to international is obviously the last couple days it's been kind of painful. If you're new to allocating to international.

[Rob Arnott]

Yes, yeah, little, little.

[Ben Felix]

But that's kind of into your point. You know, those, those international markets could be more expected, more impacted from things like this and maybe the US at least initially here. But what I want to sort of ask you is if you were trying to convince someone, you know, if you look across most US Investors portfolios today, strong home bias, very little international. Like if you were trying to make the case for allocating towards international, you know, how would you do that? How would you emphasize sort of the long term market history, the risk and diversification that international gets you and then kind of going one level deeper, like where would you be focusing that? Would it be broad based international exposure? Would you be a little bit more developed?

[Rob Arnott]

I mean, unless you want to take the time to dive in and study individual markets. Broad based exposure is, I think, the right answer. Fundamental index has been more powerful in emerging markets than in developed markets, I think because they're less efficient. Fundamental index, for those of your viewers who aren't familiar with it, simply says instead of creating a portfolio that looks like the stock market, let's create a portfolio that looks like the publicly traded macro economy. Meaning that you choose companies based on how big they are. You weight them on how big they are, not on how expensive they are, not on how big their market cap is, but how big are their sales, how big are their profits, and so forth in an inefficient market. Well, firstly, in any market, if you cap weight, stocks that are trading above their eventual fair value are overweight in your portfolio. So you're overweight. The overvalued companies, you don't necessarily know which ones they are. That's the, that's the problem. But if you break the link with price, then companies that are overvalued might be overweight or they might be underweight. Companies that are undervalued might be overweight or might be underweight, the errors cancel. And so there's this rebalancing alpha that's worth about 2% a year. And that's, that's not speculation or theory that that's actual observed results. In emerging markets it's more like 3 or 4% a year. And so you can buy fundamental index based ETFs and mutual funds that will give you broad diversification that won't pull you into putting most of your money in the countries that are the most expensive. It'll put you into the countries and companies that are most important to the global macroeconomy. And if you do that, you get broad diversification, you get risk. That's not dissimilar to conventional cap weighted indexes, but you capture that mean reversion alpha if a stock soars or if a country soars, country stock market soars and its underlying fundamentals don't. A RAFI based portfolio will say thanks for the nice gain. I didn't see any fundamental validation for it. I'm going to trim it. And that rebalancing alpha is just really powerful.

[Ben Felix]

Is there anything with the long term trend in your opinion on the strength or weakness of the US dollar and how that might play into that as well? I mean, I know you know, most of these strategies don't hedge currency or anything like that, but I'm just curious on if you have any feelings on, on that.

[Rob Arnott]

You know, it's interesting. I was at a conference just a few days ago where there was a panel discussion on the dollar. And when I went to this meeting a couple years ago, the, the question was why invest anywhere else when the dollar's clearly the strong currency and going to continue to soar? And then this go around it's why would you invest in the US when the dollar is going to continue to tank? And mean reversion happens in currencies too, they can get ahead of themselves. I think the dollar looks a little weak. One of the things I like to do is use a CPI adjusted currency and then you can compare them around the world. And the US dollar is a little on the cheap side relative to where it's been in the past. So that's one very minor argument in favor of maintaining that US focus. But part of the tremendous strength of international and emerging stocks in the last few months has been dollar weakness.

[Host/Interviewer]

You wrote a great paper recently, Trifecta A Fundamental Revolution in Indexing. And you just talked about the first leg of the trifecta with Raffi here. But before we talk about the different legs, I just wanted to maybe see if you could talk at a high level. What were you trying to accomplish with this paper?

[Rob Arnott]

You know, when we developed fundamental index back in 2004, we knew we were onto something important and it's become a. There's almost $200 billion invested in fundamental indexes around the world and probably a similar amount invested in imitating and otherwise similar products. So that's a lot of money. It's probably the most successful non cap weighted index in the world. When we developed that, we knew it was going to be important. We discovered, we thought if you choose companies based on how big they are and weight them based on how big they are, you're going to get a rebalancing alpha, you're going to get a value tilt. I thought this will probably add 50 or 100 basis points a year. No. In back testing it added over 200. Live, it's added over 200. When compared with an appropriate value oriented cap weighted index like the standard cap weighted value indexes, the outperformance has been relentless. What I didn't realize at the time was that one of the core attributes of rafi, it involves fundamental selection, not choosing companies based on how frothy and expensive they are, but based on how big their business is and fundamental weighting, weighting companies based on how big their business is. Fundamental selection also works for cap weighted indexing. The cap weighted indexes. Imagine I said to you, I've got a great idea, been working on this and I'm really excited. We will buy a stock, any stock that has soared past a certain threshold and on average it'll be priced at twice the market multiple. On average it will have beat the market by about 75% in the last year before we buy it. But it's clearly onto something and it's headed for great things. Now, some of them don't work out. My cell discipline is the opposite. We'll sell it if it tanks below that threshold, could be top 500, could be top thousand and on average, we'll sell it for half the market multiple and on average we'll sell it after it's underperformed by 7,000 basis points. But you know, most of these companies do go on to good things. What do you think you want to invest with me? Describe that way. It sounds like an absolute dithering idiot, but that's the way indexes trade. And it's because they buy into this mantra that markets are efficient, prices are efficient. And so yeah, if you want representative stocks that mirror the look of the stock market, you're going to cap weight because the market's cap weighted. And you might as well cap select because otherwise you're including companies that are too small in market cap to matter. Well, it turns out that you can use fundamental selection not just for creating rafi, but for creating a better cap weighted index and for creating a better growth index. So the essence of Trifecta is we decided to launch a better cap weighted index back in 2021. We did the work in 2020 and 21. We launched in 21. The ETF was launched last year. He started working on can this also be adapted to growth investing? And did that back in 2023-24. Index was launched in early 25. And I'm not allowed to speculate on whether ETFs might be in the pipeline or not, but it wouldn't be shocking. So you can use fundamental selection to choose better growth companies, to choose better representative representative companies for a cap weighted index and to have a better, better value strategy. That's a trifecta. If you have a world class value strategy, rafi, a world class cap weight index rac and a world class fundamentally selected and fundamentally weighted growth strategy, RAFI Growth. And it works everywhere in the world and they add value with statistical significance everywhere in the world. That's a trifecta. So I'm excited. I feel like I'm a septuagenarian getting ready to launch another revolution. It's just a hood.

[Host/Interviewer]

Would there be a way to combine all of these three things into one index?

[Rob Arnott]

Absolutely. But let's start with your core index fund. If your core index fund chooses companies based on their market cap, based on crossing that threshold, you're buying stocks that are frothy and expensive after they've soared. And by the way, you missed it. If it underperforms badly enough, you're going to drop it after it's underperformed. And by the way, you wrote it all the way down and you're selling cheap. If you can mitigate that by choosing companies where the fundamentals are now big enough to matter regardless of price level, then you have a more powerful core cap weighted index. And it has 99.9% correlation with the S&P or the Russell 1000 or for global investors, the ACWI. All right, 99.9% correlation. That's pretty cool. And ballpark of 50 to 100 basis points incremental performance. That's pretty cool. So that's your better core. Now what's another way to do things? We have a paper coming out in the next issue of the Financial Analyst Journal. I think it comes out also next month called Fundamental Growth that looks at our research in applying fundamental index principles to choosing growth stocks. And what we find is if you choose stocks based on how fast they're growing percentage growth and then weight them based on the dollar magnitude of that growth, so you're weighting them in proportion to their contribution to the growth in sales and Profits of the total economy. You wind up with something that over the last 30 years beats Russell growth by about four and a half percent a year. Very cool. Okay. Rafi beats the value indexes by two to two and a half percent. Rafi Growth wins. Historically it is a back test, but it's a very simple backtest. It's not data mined. Four and a half. Let's haircut that and say maybe it's going to be two and a half the same as Rafi in the future. You could put those two together and say instead of buying Racque, I'm going to put half my money in Rafi and half in Rafi Growth. Instead of adding 50 to 100 basis points, I'm going to add two to two and a half percent. That's really cool. But it's not an inclusive index. It doesn't span the market. It leaves big chunks of the market out. So you have companies that are cheap and and or growing slowly. Those would be considered standard value stocks. Well, we want cheap, but we don't want sluggish growth. If you have stocks that are expensive and growing fast, we want. We call that a growth index. It's treated as a simple linear binary choice. It's either growth or value. Throw that out the window and say if it's cheap, it's value. If it's growing fast, it's growth. Now you're leaving out companies that are expensive and growing slowly. That fourth of the market historically underperforms the market going back. And this we're able to test way back. We've tested it back over 50 years going way back. That quadrant of the market underperforms by 2% a year. If you're lagging by 2% a year for 55 years, guess what? You are less than a third as wealthy as you could have been. So leave those out and you wind up with a strategy. I'll call it a strategy rather than an index that captures the best of value and the best of growth. It's not a core index in the sense that it leaves a fourth of the market out.

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[Ben Felix]

[e-mail] 149 what I love about the research that you guys put out, Rob, is that there's always these little, like, I'm going to use the word nuances. It's probably not the right word, but like this conglomerate piece that your colleagues put out, like, it's like somebody had to think about, okay, what did conglomerates look like in the past and what type of valuation did they command or maybe not command? And how does that stack up, like, relative to today's conglomerates that investors sort of see in, in the index? And so, you know, I just think it's a very, it was, I thought it was a great paper. And I think, let's just kind of talk through. I think maybe some of the things that you found, they found and maybe some of the possible takeaways for investors. I mean, to start, based on the historical data, it looks like conglomerates have had this diversification sort of discount, as the paper called it. So can you kind of just help us work through what that actually means?

[Rob Arnott]

Back in the 60s, conglomerates were priced at a premium, substantial premium. And that was because, gosh, they can put their money anywhere. The opportunity is. ITT was not att. ITT was a famous conglomerate that was involved in telecom and transportation and retail and everything else under the sun. And the narrative was the management can steer company resources to wherever the growth is. And, boy, isn't that a powerful tool. And so what we found at the time was that they were priced at a premium. Then it turned out that running too many disparate businesses, you're not liable to not be very good at some of them, and you're liable to be reactive, chasing what's been successful, rather than anticipatory, putting money into businesses that are poised to take off. And so we went from a conglomerate premium to a conglomerate discount. Now you've got big businesses. Several of the magnificent Seven are diversified into a Wide array of businesses and the narrative is they've got it covered. So we're back to a premium and the question is, are some of them going to stub their toes and turn out to be not very good at choosing where to allocate their resources? For example, that $650 billion of anticipated CapEx next year on data centers and other infrastructure for AI, they're all chipping in. 100 billion here or 150 billion there. Adding up to those lofty numbers, are some of them going to turn out to have wasted their money? Probably. And then the diversification premium, the conglomerate premium, can go back to a discount. So I'm, that's not a projection, it's, it's a scenario that is possible that gets very little attention today. Yeah.

[Ben Felix]

One of the charts you had in here for Amazon, Apple, Alphabet, Microsoft, you kind of show the revenue makeup starting in 2015 and how, you know, these businesses have gotten more diversified, gotten in more sort of non core services over the last 10 years as they've looking to kind of expand, you know, their business model into some of these areas that maybe might not be as, they might not be as good at. They may be as profitable as what the core businesses are.

[Rob Arnott]

Yeah. If you're Apple, how are you going to move the needle on business the size of Apples with a new iPhone? Come on. If you're Microsoft with a new software package. No. And so the presumption is, well, we got to find something to do. You don't have to find something to do. You can return the money to the shareholders. You can say we've done a great job for you. Here's a bunch of money for you. You figure it out. Now companies don't like to do that because they have the hubris that we know way more than our shareholders do and they do in their core businesses, but they don't. When looking at the aggregate opportunity set in the macro economy.

[Ben Felix]

One of the sort of charts in here shows that this conglomerate premium and right now when you take those four, you know, mega cap tech companies, it looks like it's, you know, on average they're trading at a 70% conglomerate premium. What do you think sort of how, how would you. What explains that premium is narrative set prices.

[Rob Arnott]

The narrative that these companies know what they're doing and they are building our future and you better get on board or you're going to miss it.

[Ben Felix]

What do you think that sort of implies for, I mean, these are the largest companies in the market today. So what would that, what implications does that have for sort of market cap weighted investors?

[Rob Arnott]

I think, I think we are likely to see a pivot back to value. Value is very nearly the cheapest it's ever been. You'd have to see value stocks beat growth stocks by 100%. They'd have to double relative to growth stocks just to get back to historic norms for relative valuations. Small would have to double relative to large cap in order to be back to historic norms of relative valuation. Now I'm not saying small cap is going to double or value is going to double, but some sort of mean reversion where they move in that direction is certainly possible. If you look back again at the dot com bubble is a wonderful example. The narrative coming into the dot com bubble was get on board, this Internet thing is huge. And it was. And these Internet companies are going to be stupendously successful. And some of them were, but they were also priced as if they were going to be even more successful. And so the narrative was these companies are where you, where you got to invest because that's the future. Well, the first two years after the bubble burst, let's say March, let's choose March of 2000 as the bubble bursting. The first two years after that, NASDAQ was down a little over 50% by March of 2002 on its way to a drop of just under 80%. S P was down 27% on its way to a 46% drop. Russell value was down 4, Russell 2000 was up 4. And Russell 2000 value was up 53%. So you literally tripled your money if you pivoted from NASDAQ into Russell value at that moment. If you have the prescience or the luck to choose that moment on a ten year horizon, our work suggests that small cap value will beat large cap growth by on the order of 700 basis points a year. On a 10 year horizon, that's enough to double your money relative to sticking with growth. I'm not saying get out of growth partly because who knows what the right timing is on this, but also because people will get cold feet if they make the move at the wrong time. What I am saying is fade some of your winners, buy into what's out of favor and cheap and just kind of lightly average in to increased exposure to what's newly cheap.

[Ben Felix]

Just in closing on this, like the article, you know, started with the story of GE and it kind of came all the way to the late 90s when Jack Welch was, you know, doing all these roll ups and making GE into this, like, massive conglomerate. And I just remember, you know, in, in the late 90s, early 2000s, I think my timing's right here. Where GE was top of the food chain, you know, was the preeminent company, was maybe the most valuable company in the S&P 500. And, you know, then it sort of all came to somewhat crashing down. I mean, the company has turned around now. They split off different units and stuff, and it's been much better as they've kind of realized the value of the different parts of the business. But I think the point that stood out to me as I was reading this is you see those lists of, for each decade, the most valuable stocks in the S&P 500. And for his all of history that we have market history for, they've always been different and they've always been changing. And so that's kind of what really sort of made me think when I was reading this article. I'm like, you know, when we look out 10 to 20 years from now, the most valuable companies today are probably not going to be at the top of the list. I don't know if you agree with that or not, but that's kind of what it made me think of.

[Rob Arnott]

That's a reliable pattern. The companies in the top 10, on average seven or eight of them are gone within 10 years, no longer in the top 10. And on average eight or nine of the 10 underperform the market over the next 10 years. Now, sometimes 2000 and tens being a vivid example, there were only two winners, Apple and Microsoft, out of the top 10. But they beat the market in the 2010s by enough that the top 10 list actually did fine. But in most decades, no, they the top dogs, the very business practices that propel you to being a top dog are suddenly decried as predatory. And so regulators are all over you. Your competitors are gunning for you. Your customers are no longer fans of you because you're too big for your britches and you're too arrogant. So staying a top dog is really hard.

[Ben Felix]

Rob, thank you very much. We're looking forward to following all the great research that you guys are putting out. So please don't be a stranger. You're welcome back anytime. Thank you.

[Rob Arnott]

Thank you so much. This has been great fun.

[Host/Interviewer]

Thanks, Rob.

[Ben Felix]

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[Rob Arnott]

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