How I learned to love financial bubbles, by the author of a book on them

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Byrne Hobart explains the difference between losing your shirt and losing your mind

Tech stocks have sold off this week over fears of frothiness in artificial intelligence (AI). Some investors were no doubt surprised by this, but for others the question is what took so long. The signs of an AI bubble have been there for some time. Cluely, whose original product was a tool for using AI to cheat during Zoom job interviews, raised $15m—and then dropped its “cheat on everything” tagline and pivoted to being a more benign AI meeting assistant. More serious AI labs have been able to raise ten-figure sums at eleven-figure valuations, not just pre-revenue, but pre-product. Individual researchers have reportedly been offered nine-figure signing bonuses. And in the past year the spending commitments made by a single AI company, OpenAI, total about $1.4trn, a sum equal to 1.2% of global economic output.

A frenzy like that is enough to make you long for the relatively sane and responsible days of the Pets.com sock puppet or the synthetic CDO-squared.

But bubbles are tricky things. The default school of thought is that they’re driven by irresponsible speculators who aren’t trying to invest in great companies, but to buy something they can flip to someone more gullible. A more benign theory is that they’re a wealth transfer from rich investors to everyday consumers—people who bought telecoms firms’ junk bonds in the late 1990s lost their shirts but the rest of us were blessed with bandwidth cheap enough to support the likes of YouTube and Netflix. There is some truth to this: to this day, America and Britain benefit greatly from rail networks whose construction turned out very badly for the original investors.

But there’s another way to look at bubbles: the participants in the AI race are all building products that are economic complements to one another—you need the turbines that power the grids, that power the chips, that run the models, that power the products. And you need firms to build their growth and hiring plans around the expectation that ever more of their work will be done by AI, but that every company—and every employee—will be automating different sets of tasks.

If TSMC builds hugely expensive chip factories, but the big AI labs all decide they’ve spent as much as they need to, those factories are a stranded asset. But when asset prices are loudly signalling that the technology is real and that the economics will be compelling, it encourages those complementary investments that actually make it happen.

There are countless historical examples of this: the car industry’s growth implicitly subsidised oil exploration, and vice versa. Electrification followed a similar path: appliance manufacturers had to operate on the assumption that utilities would wire up more households—and those utilities had to bet that, once power was available, GE, RCA and the like would give people something to plug in. During the heyday of Moore’s Law, chip companies raced to build ever more powerful chips, and software companies rushed to ship products that would use them.

It’s hard for any of this to happen without entrepreneurs getting excited about a business based on its hypothetical future rather than its present profits, and it’s impossible for this process to keep going unless investors, too, get excited. Naturally, one side or the other will overshoot. There hasn’t been a technological revolution in history that didn’t at some point get overhyped.

That’s always obvious in retrospect, but less so when we are in the cycle. An investment researcher once circulated an essay called “A Home Without Equity Is Just a Rental With Debt”, warning that house-price appreciation was driven by loosening underwriting standards and would inevitably lead to a collapse—but it was dated June 2001. Even at the post-crisis low, a decade later, the Case-Shiller index of American house prices was still 18% above its level when that piece was published. Similarly, media coverage of dotcoms described trading as “nutty” and quoted an investor saying “I don’t really know anything about the company.” But that article, the Wall Street Journal’s on the Netscape IPO, was published in the summer of 1995. At its post-dotcom low in 2002, the Nasdaq 100 was still 40% higher than it had been then.

Signs of a bubble aren’t necessarily signs that it’s time to sell, because they precede the peak of the mania by an unpredictable amount. Anyone who read the (quite cogent) arguments against buying a house in 2001 or buying tech stocks in 1995 would have benefited financially from completely ignoring them.

The famous dictum, apocryphally attributed to John Maynard Keynes, is that markets can remain irrational longer than you can remain solvent, but this presupposes that everyone has the same information and that irrational traders are simply ignoring it. It’s more in the spirit of Keynes to argue that economic growth is partly a matter of believing that it will happen. Recessions end when people and companies start to spend as if they’re over, and booms persist when some participants are building the infrastructure that others need to make that boom happen.

When OpenAI announces a splashy new scale-up, or Meta declares that it has found yet another opportunity to raise its planned capital expenditures, they’re signalling to AI users—coders, lawyers, writers, whoever—that they’d better be prepared for smarter models. The more people and organisations gear their behaviour towards a world in which AI is even more powerful and more ubiquitous, the more they’re locking in the demand that justifies all of those eye-popping expenditures.

In the end, a bubble functions like an industry cluster that exists in time rather than space. If you want to be a movie star, you move to Los Angeles; if you want to start a hedge fund, you move to New York; and if you want a part of being first to something in AI—first to build, first to use, first to profit from—asset prices are insisting that now’s the time to act. ■

Byrne Hobart is a partner at Anomaly, an investment firm, and the author of “Boom: Bubbles and the End of Stagnation”. He writes a newsletter at thediff.co