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AI Winter
The periodic cycles of collapsed expectations and funding in AI research, most famously 1974–1980 and 1987–1993 — moments when the gap between promised and delivered capability became too painful to sustain.
An AI winter is a period in which AI research loses public and institutional credibility, funding dries up, and capable researchers leave the field. The phrase was coined at the 1984 American Association for Artificial Intelligence meeting by analogy with "nuclear winter," as a deliberate warning about the expectations-disappointment cycle. Two canonical AI winters are generally recognized: 1974–1980 (following the UK Lighthill Report and ALPAC's pessimism about machine translation) and 1987–1993 (following the collapse of the expert-systems industry and the LISP-machine market). Whether a third winter is coming is a live question in every era of AI enthusiasm, including this one.
In The You On AI Field Guide
This matters historically because the same field that now claims imminent general intelligence has twice before claimed imminent general intelligence, raised billions of dollars, and had the claims fail in ways that hurt careers, funding, and public trust. Whether the current era is different is the question both camps are currently debating with increasing heat.