The current AI boom, driven by large language models and generative AI, can feel unprecedented. However, a look back at AI history reveals a pattern of dramatic boom and bust cycles. This article traces AI's journey from its theoretical birth in the 1950s through two major 'AI winters'—periods of collapsed funding and shattered expectations. The first winter in the 1970s followed overpromises on machine translation and neural networks. The second, in the late 1980s, came after the failure of expert systems to deliver on commercial hype. Each time, AI was declared dead, only to re-emerge with new techniques and more modest claims. For today's technical founders and engineering leaders, this history is a crucial cautionary tale. It suggests that while the current wave is real, the hype cycle is not new. Understanding these patterns can help in making sober investment decisions and building sustainable AI products that survive the inevitable next winter.
A retrospective on AI winters, exploring the cyclical nature of AI hype and its implications for today's developers and investors.