E buon Natale! <https://ash.harvard.edu/resources/watching-the-generative-ai-hype-bubble-def...> Only a few short months ago, generative AI was sold to us as inevitable by AI company leaders, their partners, and venture capitalists. Certain media outlets promoted these claims, fueling online discourse about what each new beta release could accomplish with a few simple prompts. As AI became a viral sensation, every business tried to become an AI business. Some even added “AI” to their names to juice their stock prices,1 and companies that mentioned “AI” in their earnings calls saw similar increases.2 Investors and consultants urged businesses not to get left behind. Morgan Stanley positioned AI as key to a $6 trillion opportunity.3 McKinsey hailed generative AI as “the next productivity frontier” and estimated $2.6 to 4.4 trillion gains,4 comparable to the annual GDP of the United Kingdom or all the world’s agricultural production.5 6 Conveniently, McKinsey also offers consulting services to help businesses “create unimagined opportunities in a constantly changing world.”7 Readers of this piece can likely recall being exhorted by news media or their own industry leaders to “learn AI” while encountering targeted ads hawking AI “boot camps.” While some have long been wise to the hype,8 9 10 11 global financial institutions and venture capitalists are now beginning to ask if generative AI is overhyped.12 In this essay, we argue that even as the generative AI hype bubble slowly deflates, its harmful effects will last: carbon can’t be put back in the ground, workers continue to face AI’s disciplining pressures, and the poisonous effect on our information commons will be hard to undo. Historical Hype Cycles in the Digital Economy Photo by Museums Victoria, Unsplash Attempts to present AI as desirable, inevitable, and as a more stable concept than it actually is follow well-worn historical patterns.13 A key strategy for a technology to gain market share and buy-in is to present it as an inevitable and necessary part of future infrastructure, encouraging the development of new, anticipatory infrastructures around it. From the early history of automobiles and railroads to the rise of electricity and computers, this dynamic has played a significant role. All these technologies required major infrastructure investments — roads, tracks, electrical grids, and workflow changes — to become functional and dominant. None were inevitable, though they may appear so in retrospect.14 15 16 17 The well-known phrase “nobody ever got fired for buying IBM” is a good, if partial, historical analogue to the current feeding frenzy around AI. IBM, while expensive, was a recognized leader in automating workplaces, ostensibly to the advantage of those corporations. IBM famously re-engineered the environments where its systems were installed, ensuring that office infrastructures and workflows were optimally reconfigured to fit its computers, rather than the other way around. Similarly, AI corporations have repeatedly claimed that we are in a new age of not just adoption but of proactive adaptation to their technology. Ironically, in AI waves past, IBM itself over-promised and under-delivered; some described their “Watson AI” product as a “mismatch” for the health care context it was sold for, while others described it as “dangerous.”18 Time and again, AI has been crowned as an inevitable “advance” despite its many problems and shortcomings: built-in biases, inaccurate results, privacy and intellectual property violations, and voracious energy use. [...]