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AI productivity paradox
also AI-productivity paradox or artificial intelligence productivity paradox
Definition of AI productivity paradox
-
: the apparent mismatch between rapid improvement and widespread adoption of artificial intelligence and weak, delayed, or difficult-to-measure productivity gains at the level of an organization, industry, or economy
- The company bought AI tools for every employee, but rework and unchanged processes contributed to an AI productivity paradox.
- Economists cautioned that the AI productivity paradox may reflect implementation delays and complementary investments rather than an absence of long-term value.
Origin & history
Erik Brynjolfsson, Daniel Rock, and Chad Syverson used Artificial Intelligence and the Modern Productivity Paradox in a November 2017 NBER working paper. They compared strong expectations for AI with disappointing measured productivity and emphasized diffusion delays, organizational change, skills, and complementary innovation.
Editorial references
Sources & further reading
- Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics↗ NBER / MIT Initiative on the Digital Economy · 2017-11
- New Research Addresses AI-Productivity Paradox↗ MIT Initiative on the Digital Economy · 2018-01-05
- The AI productivity paradox: Why AI is everywhere except in the productivity numbers↗ Fast Company · 2026-10-18
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Cite this entry
"AI productivity paradox." AI Dictionary, Dadgogo, https://dadgogo.com/dictionary/ai-productivity-paradox/. Accessed 23 Oct. 2026.
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