hype cycle
In plain wordsA pattern in which excitement about a new technology rises to a peak, crashes into disappointment, then slowly recovers as the technology finds real uses.
A curve describing how expectations for a new technology typically evolve, together with the research firm Gartner's method for placing technologies on it. After an Innovation Trigger, expectations climb to a Peak of Inflated Expectations, fall into a Trough of Disillusionment, then recover along a Slope of Enlightenment to a Plateau of Productivity. Gartner publishes annual hype cycles for emerging technologies and for AI, which are widely cited as shorthand for a technology's maturity: its 2025 hype cycle for AI showed generative AI entering the Trough of Disillusionment and AI agents at the Peak of Inflated Expectations. Critics note that many technologies do not follow the curve, that positions are analysts' judgments rather than measurements, and that the model's predictive record is weak. Loosely, the phrase names any boom-and-bust pattern of expectations, including those behind the AI wintersAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s..
Our board keeps asking where agents sit on the hype cycle, as if the curve could tell us what to build.
Origin
Introduced by Gartner analyst Jackie Fenn in 1995 and elaborated in Fenn and Mark Raskino's Mastering the Hype Cycle (2008). A related adage, Amara's law, attributed to futurist Roy Amara, holds that people overestimate a technology's short-run effects and underestimate its long-run ones.
Usage note
'Hype Cycle' is a Gartner trademark; capitalized, it refers to Gartner's published charts.
Related
- AI hypeAI hype, n.: Exaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers.
- AI bubbleAI bubble, n.: The worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000.
- AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s.
- AI washingAI washing, n.: Falsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors.
- productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce.
Sources
- Fenn, J. & Raskino, M. (2008). Mastering the Hype Cycle: How to Choose the Right Innovation at the Right Time. Harvard Business Press.
Mentioned in
AI bubbleAI bubble, n.: The worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000., AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s., AI hypeAI hype, n.: Exaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers., AI washingAI washing, n.: Falsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors.