Forty-Five Years to a Trillion: David Booth and the Answer Nobody Wants

An older investor stands alone at the end of a long corridor, its walls lined with framed stock certificates of bankrupt companies, warm light pouring in from a distant window

He who has stayed low a long while will fly the higher; he who blooms first will be first to fall.

—— Hong Yingming, Caigentan (Ming dynasty, c. 1600; translation mine)

What This Episode Is About

The August 21, 2026 episode of The Meb Faber Show features David Booth, founder of Dimensional Fund Advisors. The firm crossed a trillion dollars in assets this past February. It opened its doors in 1981 — forty-five years ago.

The interesting part isn’t the number. It’s how he tells it. Asked about the most memorable moment of his career, he says it was the day in February when he learned they’d crossed a trillion. He called Gene Fama — the Nobel laureate who, by Booth’s account, never gets excited about anything — and Fama swore into the phone. Booth says he’d only seen the man that animated once before: the day the Nobel committee called.

A man who oversees a trillion dollars, and his most memorable moment is his old professor cursing. That’s the tone of the whole conversation: a kid from Kansas spending forty-five years proving something he believed on day one and nearly didn’t survive believing.

The Main Points

The index fund wasn’t anybody’s grand vision. Marketing saved it. Booth joined Wells Fargo in September 1971 — the same month they launched their first indexed portfolio, for a Samsonite pension account. He says the team had designed something fancier, engineered to beat the market’s average return by a bit. Then someone with a marketing brain pointed out the obvious: what you actually want is an index fund, because clients can judge the quality instantly. Did you track the index or didn’t you? And professional managers seem to have a hard time beating that line. The two outside consultants on the project were Fisher Black and Myron Scholes, who happened to produce the options pricing model while working on it. Everything was new. Booth calls it the Wild West.

For the first nine years, all they had was a small-cap fund — and those nine years were the worst stretch small caps had ever had relative to large caps. Through 1990, the portfolio compounded at 2% a year while the S&P 500 compounded at 14%. Clients were furious. The scene he remembers best: walking down the hall at one of their biggest clients, the assistant treasurer grabs him by the arm and says, “I just want you to know, you’re the worst performing manager we have in any asset category.” Booth thought he was joking. He wasn’t. And Booth’s response is the part worth writing down. He didn’t defend the performance. He turned the anger into an answerable question: which part of the argument are you no longer comfortable with? That small caps are riskier than large? That risk and return are related? The man had no answer, stayed, and was amply rewarded.

Investors do enormous work on the buy decision and almost none on the sell. The host says that when they ask clients what criteria they’ll use to eventually sell, more than nine out of ten say some version of “I don’t know, I’m winging it.” And the temptation is always the same one: you’ll sell it when it starts to trail — the index, the benchmark, your neighbor, whatever your yardstick is. That might be this year. It might be twenty years from now.

Booth adds the behavioral layer. When results disappoint, the instinct is to find someone to blame — yourself, the manager, anyone — because somebody must have screwed up, otherwise the outcome wouldn’t be this bad. Almost nobody says “I made a good decision and it just didn’t work out.” He uses football: you called the right play, you threw the pass, it just wasn’t executed. That happens. So if you can get people to judge themselves by the quality of the decision rather than the outcome, they’ll be far better off — you can’t control the outcome, you can control the decision. As for how long to give a strategy, his answer is: at least one year longer than you’re willing to give it. Elsewhere in the episode, Fama’s answer to “how long statistically to know if an active fund is any good” comes up: sixty-something years, without blinking.

The birth of the three-factor model plays out more like a road trip than a paper. September 1991: Booth calls a big client about something else, asks what he’s working on, and hears “value and growth research — do you know anything about that?” Funny you should ask, Booth says; Gene and Ken just sent me a draft. He flies to Atlanta, does a poor job explaining it, and asks whether the client ever gets to Chicago. They meet, drive down to Hyde Park, sit in Fama’s office while he waves his hands at a laptop and walks through the research, then eat a greasy hamburger at a college hangout. The following week the client calls to move money into value strategies — so the funds launched before the paper was published. What made it a breakthrough, Booth explains, was that multi-factor theory had been floating around academia for years with no empirical model to match it. Fama and French split returns into market, size, and value, and explained away a pile of anomalies at once. Then he adds a line worth chewing on: in some ways that’s still the last major breakthrough investing has had, and it’s been over thirty years.

On AI, his answer is a nineteenth-century analogy. He isn’t worried that AI won’t matter — it obviously will. What worries him is people assuming they have to judge which AI companies to buy. He thinks of the California gold rush: look back at who actually made money, and one of the big winners is Levi Strauss, selling jeans to miners. Knowing AI will be a big deal gives you almost no insight into which companies win and which lose. All you know is there will be big winners and big losers. There’s a wall in the Austin office covered with stock certificates from bankrupt companies — headstones for active investors. When you own the market, he says, you get all the winners and all the losers, and some of the losers become genuine catastrophes. Which is precisely the argument: individual stocks can go to zero. The market doesn’t.

The safe deposit box is the heaviest part of the episode. After his father died, the family opened the box and found fifteen dollars in cash. The host says his own father, on a Nebraska farm, left a briefcase with roughly the same. Booth’s parents lived through the Depression, his father fought in the war, and he describes them as wealthy without having much money — they knew exactly what mattered to them, which was family. But the deeper thing is that they never believed public markets were a place for people like them. They thought of themselves as outsiders, and assumed insiders made all the money and would take advantage of them. So they never invested. Booth ran the math twice: fifteen thousand dollars put into the market in 1945 would have been worth over a million by 1985, when he opened the box. And from 1985 to today — another forty years — fifteen thousand dollars at market returns is again worth over a million. Same arithmetic, run twice, same answer.

Going Further

”I’ve trailed the index for three years. Do I cut it or hold it?”

This is the question that keeps people up at night, and this episode answers it backwards from what you’d expect: don’t start with cut or hold. Start with whether the sentence you bought it on still holds.

When Booth got grabbed in that hallway, he didn’t defend performance. He translated the anger into an answerable question: which part of the argument do you no longer believe? That move has a hard prerequisite — there had to be an argument in the first place. If you bought because “I thought it would go up,” then three years later there’s nothing to examine, only feeling. But if you bought because “this layer bottlenecks first when demand doubles, so it gets pricing power,” you can go check. Did it bottleneck? Did prices move? If not — is it early, or was it never going to happen?

Which is why sell criteria belong to the day you buy, not the day you’re down. When you’re losing money your brain is compromised. That instinct Booth describes — somebody must have screwed up — pushes you to invent a story explaining the pain, and the story is almost always “I was wrong from the start,” which gets you selling at exactly the wrong point. Criteria written in advance aren’t smarter. They’re just written while you’re sane.

One layer deeper: what should the criteria look like? The host floats something counterintuitive — what if performance weren’t one of the reasons? That sounds absurd until you see what it separates. A falling price is the market’s opinion of you. A broken thesis is a verdict from the facts. The first happens constantly; the second is rare. What belongs in your criteria is usually the second kind: the bottleneck got routed around by a new process, the anchor customer brought it in-house, the policy tailwind expired, the company started funding cash flow by selling assets. Those are checkable events, not a percentage that drifts with your mood.

The boundary matters too. All of this assumes you had a thesis. If you bought a theme, a screenshot from a group chat, or a feeling that everyone else was buying, there’s nothing to test and no criteria to write. In that case the thing to fix isn’t the sell decision — it’s writing the sentence down before the next buy.

”AI is obviously huge. How do I actually participate?”

Booth’s answer is “own all of them,” because he’s Booth. But unpack the reasoning and it hands stock pickers a much harder homework assignment.

His logic isn’t that AI doesn’t matter. It’s that certainty about a trend does not transfer to individual stocks. The gold rush was completely real — people genuinely pulled gold out of the ground. But do the payoff math and the miners’ return distribution is enormously wide, while the people selling jeans and shovels take a cut of every miner’s revenue regardless of who strikes it. Those are two different exposures. You think you’re betting on whether AI works. You’re actually betting on whether this particular company wins, which is a far harder question.

So if you’re going to pick, this episode raises the burden of proof: you need to say why this layer, not just why this trend. Three questions help. First, when demand doubles, which link in the chain jams first? Only the jammed link gets pricing power; the rest just pass orders downstream. Second, how many substitution paths exist around that layer? One path is a bottleneck. Five paths is a supplier. Third, whose pocket does the benefit actually land in — the vendor, its sole-source equipment supplier upstream, or does the customer downstream squeeze it all back out? Plenty of people get the first question right and die on the third.

And that wall of bankruptcy certificates is the counterweight. Individual stocks can go to zero — not theoretically, routinely. The universal retail response is “sure, but not mine.” If you choose the stock-picking path, you’re also accepting that sooner or later one of your positions becomes a piece of paper on a wall. That’s not failure; that’s the cost of the path. Whether you can absorb that cost determines how large any single position should be — and that’s arithmetic you do before you buy, not after it halves.

”History says the next twelve months are great for small caps. Can I trust that?”

There’s a subtle moment in the episode. The host says he pulled Ken French’s public data and found that, over the last hundred years, small-cap value is about to enter its best twelve months, with January 2027 as the single strongest month in the cycle. Then he immediately adds: I would never put money on this and would never use it as a forecast.

Saying it out loud while explicitly refusing to bet on it is itself the lesson — and it’s aimed squarely at anyone who scrolls past statistical screenshots all day. Before a number earns a bet, it has to clear three gates. First: is it mined? If you tested twelve months, four quarters, and seven ways of slicing market cap in one dataset, finding something significant was close to guaranteed. That’s not discovery, that’s enumeration. Second: is there a mechanism? “January has historically been strong” with no explanation sits somewhere near astrology. A version with a mechanism sounds different — year-end tax-loss selling ends, institutions rebalance in January, indices reconstitute on a schedule. Third: how independent is the sample? A hundred years sounds like a lot, but “January” happened a hundred times, and dozens of those shared the same macro regime.

Is such a statistic useless, then? No. Its correct use is as background, not as a trigger. It can make you slightly less anxious about entry timing on something you’d already decided to buy. It should not change your position size. The host modeled the line exactly: interesting to say, foolish to act on, and he kept the two well apart.

Incidentally, this is the general solvent for every “historically, after conditions like these, the market rose X% on average” headline. Ask one question first: how many times has “conditions like these” actually occurred? If the answer is seven, you’re looking at the average of seven numbers, and its error bars are probably wider than the average itself.

The One Thing to Take With You

One idea: judge yourself by the quality of your decisions, not by outcomes.

Outcomes are set by too much you don’t control — timing, luck, other people’s behavior, whichever regime happened to be running that year. Decisions aren’t. All you control is the information, the reasoning, and the criteria at the moment you decide. That sounds like a fortune cookie, but it has a very practical consequence: people who judge only by outcomes abandon an entire method after one good decision produces one bad result, and then adopt whatever method happened to be winning at the time — which is usually the method about to start losing. Booth survived nine years of 2% annualized not because he was braver, but because he held a yardstick that didn’t depend on the outcome.

An exercise you can do today, and it works far outside investing: pick one decision from the last three months that turned out badly. Write two paragraphs.

First: what I knew, what I didn’t know, and what I based the decision on.

Second: if I went back with only that same information, would I decide the same way?

If the answer is yes, you made a good decision that didn’t work out. Write that down and stop punishing yourself for it. If the answer is no, add one more line: what was the thing I could have checked at the time and didn’t?

That line is the only part you can actually change. It might be an email you never finished reading, a question you didn’t ask out loud, a document you assumed you remembered so you never confirmed it. The outcome doesn’t come back. The unchecked step does, next time.

Raw Markdown

This article is an educational discussion of investment method. It is not advice to buy or sell any individual security, offers no target prices, and does not analyze any current holding. Investing carries risk; make your own decisions or consult a qualified professional.