GenAI

/ Adoption Tracker

Explore the trajectory of genAI adoption
The impact of generative artificial intelligence (genAI) on the economy depends on the speed and intensity of its adoption. This tracker reports results from a series of nationally representative U.S. surveys of genAI use at work and at home.
The figure shows the share of adults ages 18–64 who use genAI for any purpose, for work, and outside of work. In the overall view, results can also be viewed by frequency: any use, use in the last week, and daily use. The education and earnings views compare use across groups for any use only. Work estimates and all earnings views are limited to employed respondents.

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Usage frequency

Adoption rate (%)

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GenAI usage
60.8%
of the U.S. population aged 18-64 uses genAI
August 2026
GenAI adoption by workers
40.5%
of the employed respondents used genAI for work
August 2026
Work hours using genAI
5.3%
of total work hours spent using genAI
August 2026
Work hours time saving
2.1%
time saved out of total work hours due to the genAI adoption
August 2026
Computer, Internet and genAI: Speed of adoption
3x
GenAI reached similar levels of adoption at work (~44%) in a third of the time personal computers did
August 2026
Media, Reports & Policy

Axios

AI agents have a normal-people problem

2026-09-30
Read

VoxEU

Measuring what work generative AI does: Survey evidence versus chat logs

2026-09-25
Read

The New Republic

A New Report Reveals Why Robots Aren’t Taking Your Job Anytime Soon

2026-09-11
Read

Forbes

Why Knowing When To Use AI Is The Next Leadership Challenge

2026-09-08
Read
Our Funders
Walmart Logo
Harvard Kennedy School Mossavar-Rahmani Center for Business and Government logo
The research included in this tracker was made possible through funding from Walmart. The findings presented in this tracker are those of the authors alone and do not necessarily reflect the opinions of Walmart.
This tracker is supported in part by the Harvard Business School AI Institute.
About Us
Alex Bick
Alex Bick
Senior Economic Policy Advisor, Federal Reserve Bank of St. Louis
Disclaimer: The findings in this tracker are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.
Adam Blandin
Adam Blandin
Assistant Professor of Economics, Vanderbilt University
David Deming
David Deming
Faculty Co-Director, The Project on Workforce at Harvard. Danoff Dean of Harvard College and Isabelle and Scott Black Professor of Political Economy at Harvard Kennedy School.
Kerry McKittrick
Kerry McKittrick
Executive Director, The Project on Workforce at Harvard.
Methodology Background
We visualized the combination of 9 surveys and over 40,000 respondents.
This tracker visualizes data from the first nationally representative U.S. surveys of genAI usage at work and at home. Our data come from the Real-Time Population Survey (RPS), a national online labor market survey of working-age adults aged 18-64 that has run since 2020.

RPS is designed and weighted to be nationally representative and to complement existing government surveys, such as the Current Population Survey or the American Community Survey, by carefully replicating core sections of those surveys while still leaving room for novel questions.

Read more about the methodology and see additional insights from the survey data at https://doi.org/10.1287/mnsc.2025.02523 (Alexander Bick, Adam Blandin, David J. Deming (2026) The Rapid Adoption of Generative AI. Management Science).

Disclaimer: The findings in this tracker are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.
Cite the GenAI Adoption Tracker
GenAI Adoption Tracker: Harvard Project on Workforce, Alex Bick, Adam Blandin. (2026, May). GenAI Adoption Tracker. https://www.genaiadoptiontracker.com/
@misc{genaiadoptiontracker,
    author = {{Harvard Project on Workforce} and Bick, Alex and Blandin, Adam},
    title = {GenAI Adoption Tracker},
    year = {2026},
    urldate = {2026-05-01},
    url = {https://www.genaiadoptiontracker.com/}
}