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AI Winters: Cycles of Hype and Funding Collapse
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Throughout AI’s history, enthusiasm has repeatedly outpaced capability, followed by funding contractions. This sub-article expands the recurring-theme discussion in History.
What Is an AI Winter?
An AI winter is a period of reduced funding, interest, and expectations in AI research and industry, typically after a “spring” of hype.
Major Winter Episodes
- First winter (1970s–1980s): early overpromising and the LISP machine market crash (1987).
- Second winter (1990s): expert-system decline and reduced corporate and government investment.
- Pattern: hype → underdelivery → disinvestment → later breakthrough.
Causes
- Overstated claims (“AI will soon match human intelligence”).
- Brittle systems that fail outside narrow settings.
- Economic downturns and shifting government priorities.
- Benchmarks and demos that do not generalize.
Lessons for the Present
- Modern AI (e.g., large language models) may face similar cycles of expectation.
- Distinguish capability from deployment.
- Fund research on fundamentals, not just demos.
Sources
- Crevier, D. (1993). AI: The Tumultuous History of the Search for Artificial Intelligence.
- Nilsson, N. (2009). The Quest for Artificial Intelligence.
Back to History.