A Short History of Fads

In February 1637, a single bulb of the Semper Augustus tulip sold in Amsterdam for the price of a house on a canal. If you had bought one of those bulbs in November 1636 and sold it in January 1637, you would have tripled your money. If you held it until March, you were ruined.

Tulip mania is the most entertaining entry in the catalog of financial bubbles because it involves flowers. But the mechanics are identical to what happened in 1720 with South Sea Company shares, in the 1840s with British railway stocks, in the 1980s with Japanese real estate, in 1999 with dot-com IPOs, in 2006 with Florida condos, and in 2021 with NFTs. The first thing to understand about a bubble is that it is not entirely irrational while it is happening. Tulips were genuinely scarce and genuinely fashionable. Railway technology was genuinely transformative, and the internet did change commerce. Every bubble grows from a real phenomenon, which is precisely what makes it so effective at separating people from their money.

The second thing is the “greater fool” theory. You do not have to believe that a tulip bulb is worth a house. You only have to believe that someone else will pay you more for it next month than you paid for it today. This is rational behavior when prices are rising, but disastrous the moment they stop.

Mackay documented this pattern in 1841, which remains readable and depressing because every pattern he identified is still with us. Isaac Newton, one of the smartest people who ever lived, lost the equivalent of several million dollars in the South Sea Bubble of 1720. He reportedly said afterward that he could calculate the motions of heavenly bodies, but not the madness of men. He had already sold his shares at a profit, watched the price continue rising, bought back in near the peak, and then watched it collapse.

Robert Shiller, who won a Nobel Prize partly for studying this problem, argues that markets are driven less by rational calculation than by contagious stories. He calls this narrative economics: when a compelling story spreads through a population, it changes behavior, and changed behavior changes prices, which seems to confirm the story, so it spreads further. This feedback loop is difficult to interrupt because the people caught up in it are not obviously doing anything wrong. They are listening to their neighbors, watching prices, reading the news, and making bets based on the best available information. The information just happens to be mostly about what other people are betting.

The British Railway Mania of the 1840s combined technological innovation with genuine investment needs to produce something close to collective insanity. Between 1844 and 1846, Parliament approved over 400 new railway projects. Engineers, lawyers, and promoters collected fees regardless of whether the lines were ever built. Landowners extracted fortunes from right-of-way negotiations. Ordinary investors poured their savings into railways that had no surveyed routes, no locomotives, and sometimes no identifiable board of directors. When the bubble collapsed, it wiped out a significant fraction of the British middle class, destroyed several major banks, and left behind a network of railways that, once consolidated by the surviving companies, actually worked.

That last part matters. Bubbles are destructive for investors, but sometimes productive for infrastructure. The dot-com crash of 2000 that wiped out trillions of dollars in market value left behind fiber-optic cable in the ground, server farms, and the engineering knowledge to run large-scale internet services cheaply. Most of the companies that failed deserved to fail: their business models assumed that selling pet food online at a loss, while paying for national television advertising, would somehow eventually produce a profit. The crash was the market performing its nominal function of reallocating capital away from bad ideas. The problem was that the reallocation happened after several hundred thousand people lost their jobs and several million lost their retirement savings.

The housing bubble that collapsed in 2008 followed a different structure because it was built on debt rather than equity. Banks lent money to people who could not repay it, packaged those loans into securities that were sold to pension funds and insurance companies, bought insurance against those securities from companies that could not pay out, and collected fees at every stage of the transaction. The economists who designed the risk models assumed, based on historical data, that housing prices never declined nationally at the same time. They were right until they were catastrophically wrong.

Crucially, the losses did not fall on the banks that had made the bad loans—the US government covered those. The losses fell on homeowners who lost their houses and workers who lost their jobs when the broader economy contracted. John Kenneth Galbraith noted in his history of the 1929 crash that the capacity of human beings to ignore evidence that contradicts a profitable belief is essentially unlimited. The 2008 crisis added the observation that when enough money is at stake, the strongest defenders of free markets suddenly become socialists in all but name.

Another feature of speculative bubbles is that they tend to be ahistorical. The dot-com bubble was the first internet bubble; there were no previous internet bubbles to learn from. The mortgage securities market of the 2000s was large enough that its collapse was qualitatively different from anything that had happened before. The phrase “this time it’s different” has become shorthand for the moment just before a collapse, but it is genuinely true in a narrow sense: the “why” of a particular collapse is usually novel enough that people who want to believe can point to real differences from previous disasters.

Which brings us to AI. Large language models are genuinely impressive. The companies building them have produced tools that fundamentally change how people write and program. But the valuations of AI companies have, since roughly 2023, been running well ahead of their revenue, their profit margins, and any plausible estimate of the addressable market. The story driving those valuations is that AI will soon be able to do everything, which is a story that has been told before about nuclear power, and about previous generations of expert systems and neural networks.

This doesn’t mean that AI companies will collapse to nothing, any more than railways did. The real question is who is going capture the value and who is going to absorb the cost when reality catches up with the hype. Historically, the engineers and founders who cashed out early do well. The pension funds, retail investors, and laid-off workers who arrived later in the story do not, while the people who collected fees at every stage of the transaction do best of all.

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Galbraith1954

John Kenneth Galbraith: The Great Crash 1929. Houghton Mifflin, 1954, 978-0395859995.

Kindleberger2005

Charles P. Kindleberger and Robert Aliber: Manias, Panics, and Crashes: A History of Financial Crises (5th ed.). Wiley, 2005, 978-0471467144.

Lewis2010

Michael Lewis: The Big Short: Inside the Doomsday Machine. W. W. Norton, 2010, 978-0393338829.

Mackay1841

Charles Mackay: Extraordinary Popular Delusions and the Madness of Crowds. Richard Bentley, 1841.

Shiller2015

Robert J. Shiller: Irrational Exuberance (3rd ed.). Princeton University Press, 2015, 978-0691166261.