AI winter

A period when AI research funding and public optimism dropped sharply after early hype exceeded practical results.

An AI winter refers to one or more historical slumps: roughly the mid-1970s and again around late 1980s–early 1990s: when progress in symbolic AI and expert systems disappointed funders and industry. Critics argued many tasks remained out of reach; budgets shrank despite earlier promises of human-level reasoning soon.

Recoveries came partly from expert systems in narrow domains, then from renewed machine learning and deep learning when data and compute caught up.