AI winter

AI winter is the name given to periods of reduced funding, diminished interest, and institutional retrenchment in AI research that followed cycles of hype and failed promises. Two major winters are conventionally identified: the first (roughly 1974–1980) following the Lighthill Report and the failure of early machine translation and perceptron-based approaches; the second (roughly 1987–1993) following the collapse of the expert systems market and disillusionment with the limitations of knowledge engineering.

What the cyclical narrative conceals: AI winters are not random fluctuations in an otherwise progressive enterprise. They are the periodic settling of debts incurred by overclaiming. Each winter is preceded by a period in which researchers, in competition for funding and public attention, allowed projections of near-term capability that the underlying science could not support. The winters did not kill promising research — they killed the overclaiming, and in doing so temporarily defunded the research along with it.

Whether the current period — characterized by large language models, massive compute investment, and claims about artificial general intelligence — will be followed by a third winter is a question the field prefers not to ask. The structural conditions that produced the first two winters — competitive overclaiming, funding cycles that reward bold predictions, and the difficulty of distinguishing genuine capability from impressive performance on narrow benchmarks — are all present. The benchmark saturation problem suggests the capability metrics are already outrunning the underlying progress. History is not a reliable guide to the future of technology, but it is the only guide we have.