From journalist Brian Merchant:

Since ChatGPT’s viral success in late 2022, which drove every company within spitting distance of Silicon Valley (and plenty beyond) to pivot to AI, the sense that a bubble is inflating has loomed large. There were headlines about it as early as May 2023. This fall, it became something like the prevailing wisdom. Financial analysts, independent research firms, tech skeptics, and even AI executives themselves agree: We’re dealing with some kind of AI bubble.

But as the bubble talk ratcheted up, I noticed few were analyzing precisely how AI is a bubble, what that really means, and what the implications are. After all, it’s not enough to say that speculation is rampant, which is clear enough, or even that there’s now 17 times as much investment in AI as there was in internet companies before the dotcom bust. Yes, we have unprecedented levels of market concentration; yes, on paper, Nvidia has been, at times, valued at almost as much as Canada’s entire economy. But it could, theoretically, still be the case that the world decides AI is worth all that investment.

What I wanted was a reliable, battle-tested means of evaluating and understanding the AI mania. This meant turning to the scholars who literally wrote the book on tech bubbles.

Sandra Upson channels the spirits of an entire generation of AI founders.
In 2019, economists Brent Goldfarb and David A. Kirsch of the University of Maryland published Bubbles and Crashes: The Boom and Bust of Technological Innovation. By examining some 58 historical examples, from electric lighting to aviation to the dotcom boom, Goldfarb and Kirsch develop a framework for determining whether a particular innovation led to a bubble. Plenty of technologies that went on to become major businesses, like lasers, freon, and FM radio, did not create bubbles. Others, like airplanes, transistors, and broadcast radio, very much did.

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Goldfarb and Kirsch’s framework for evaluating tech bubbles considers four principal factors: the presence of uncertainty, pure plays, novice investors, and narratives around commercial innovations. The authors identify and evaluate the factors involved, and rank their historical examples on a scale of 0 to 8—8 being the most likely to predict a bubble.

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It’s worth reiterating that two of the closest analogs AI seems to have in tech bubble history are aviation and broadcast radio. Both were wrapped in high degrees of uncertainty and both were hyped with incredibly powerful coordinating narratives. Both were seized on by pure play companies seeking to capitalize on the new game-changing tech, and both were accessible to the retail investors of the day. Both helped inflate a bubble so big that when it burst, in 1929, it left us with the Great Depression.

So yes, Goldfarb says, AI has all the hallmarks of a bubble. “There’s no question,” he says. “It hits all the right notes.” Uncertainty? Check. Pure plays? Check. Novice investors? Check. A great narrative? Check. On that 0-to-8 scale, Goldfarb says, it’s an 8. Buyer beware.

The explanations given for each of the four heuristics were insightful. I left those out, but you can find them back in the original article.