Generative AI

Authors:     Alex Bick, Adam Blandin, David Deming, and Tyler Schumacher

Latest Draft:     06/2026

Occupation and Task Adoption Indices

Generative AI Adoption Tracker.

Abstract:     We measure how workers use genAI for their jobs in a nationally representative survey linking genAI adoption to detailed occupations and tasks. Our data provide the first task-level genAI adoption indexes, which we show can inform analyses of genAI's labor market impact. Exposure scores explain some, but far from all, of the variation in adoption across occupations and tasks. We also distinguish our indexes from measures based on genAI platform chat logs, which differ conceptually and tend to over-classify chats into generic activities spanning many occupations. Finally, we highlight that current adoption is widespread but shallow: genAI is used across many occupations and tasks, yet within most of them, fewer than half of workers adopt. This indicates substantial variation among workers doing very similar work, suggesting that understanding who adopts may matter as much as understanding which tasks genAI assists.

Authors:   Alex Bick, Adam Blandin, David Deming

Publication: Management Science (2026)

Latest Draft:     10/2025 (St. Louis Fed Blog Post)

Generative AI Adoption Tracker.

Summaries:  VoxEU, The Project on Workforce

Abstract:     Generative artificial intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from a series of nationally representative U.S. surveys of generative AI use at work and at home. As of late 2024, nearly 40% of the U.S. population age 18-64 uses generative AI. Among employed respondents, 23% used generative AI for work at least once in the previous week: 9% used it every workday, and 14% on some but not all workdays. Relative to each technology's first mass-market product launch, work adoption of generative AI has been as fast as the personal computer (PC), and overall adoption has been faster than either PCs or the internet. Generative AI and PCs have very similar early work adoption patterns by education, occupation, and other characteristics. Between 1 and 5% of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4% of total work hours. This suggests that substantial productivity gains from generative AI are possible.

Authors:     Alex Bick, Adam Blandin, David Deming, Nicola Fuchs-Schundeln, Jonas Jessen

Latest Draft:     03/2026 (St. Louis Fed Blog Post)

Generative AI Adoption Tracker.

Abstract:     This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.