AI Winters & Hype Cycles

Twice in AI's history, the gap between promises and reality grew so wide that funding dried up, researchers moved on, and the field entered what's known as an "AI winter." The first came in the mid-1970s after early enthusiasm hit the wall of limited computing power and overly ambitious timelines. The second arrived in the late 1980s when expert systems failed to deliver on their commercial promises and the specialised hardware built for them became obsolete. Both winters followed the same pattern: researchers and companies overpromised, governments and investors poured in money expecting near-term results, those results didn't materialise, and the backlash was severe. The field didn't die - important work continued - but funding, talent, and public attention moved elsewhere. This history matters today because we're in the middle of the biggest AI hype cycle ever. Billions are flowing into AI companies, some with no clear path to profitability. Whether this leads to another winter depends on whether the technology delivers enough real value to justify the investment. The honest answer is that nobody knows yet.