AI may not simply be “a bubble,” or even an enormous bubble. It may be the ultimate bubble. What you might cook up in a lab if your aim was to engineer the Platonic ideal of a tech bubble. One bubble to burst them all. I’ll explain.
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.
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.
Where many economists view markets as the product of sound decisions made by purely rational actors—to the extent that some posit that bubbles don’t exist at all—Goldfarb and Kirsch contend that the story of what an innovation can do, how useful it will be, and how much money it stands to make creates the conditions for a market bubble. “Our work puts the role of narrative at center stage,” they write. “We cannot understand real economic outcomes without also understanding when the stories that influence decisions emerge.”
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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