AI has faced boom and bust cycles. Learn what defines an AI winter, why past crashes happened, and whether today’s advances can prevent another freeze.
More than 70 years after British mathematician and computer science pioneer Alan Turing posed the question of whether machines can think, the world is investing billions to answer it. Artificial intelligence dominates headlines, venture capital portfolios and boardroom discussions. The possibility of another AI winter may sound far-fetched. However, history shows that AI’s trajectory has never been linear.
The slowdown was global. Japan’s ambitious Fifth Generation project, launched in 1981 to build machines that could converse, translate, and reason like humans, fell short of expectations. In the United States, DARPA’s Strategic Computing Initiative, which once funded more than 90 projects, also scaled back after leadership dismissed AI as “clever programming” rather than the next technological wave.
AI no longer relies on a handful of government agencies. Public funding still matters, but a well-developed venture capital ecosystem now drives much of the investment, spreading the risk across startups and private labs. This diversification makes a sudden collapse less likely. Together, these factors reduce the odds of another AI winter. The field is more resilient, diverse, and embedded in the economy than in past cycles.History shows that momentum can turn quickly if promises exceed results. Overstating the nearness of human-level intelligence or ignoring ethical, safety, and energy concerns erodes trust and triggers backlash.
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