An up-to-date monitor of AI’s impact on our economy
Artificial intelligence is advancing faster than our ability to understand its economic consequences, and the pace of change is likely to accelerate as adoption spreads across sectors and new capabilities emerge. Yet we lack timely, trusted ways to measure how these changes are affecting work, productivity, and value creation in the economy. Decision-makers currently rely on limited evidence to plan for and react to AI’s impacts–this expanding series is born out of an imperative to remedy that.
The AI Economic Indicators at the Stanford Digital Economy Lab is an expanding series of economic dashboards that connects policymakers, business executives, and individual workers to timely and reliable information on the economic impact of AI. We believe that a better understanding of the technology’s effects will lead to better decisions.
Learn more about the project
Selected Results #
See Canaries We group workers by their AI exposure score, comparing employment trends across these groups. We see modest differences between the five exposure groups, although employment growth is lowest for the most exposed occupations.
See Canaries We group employees by their age and AI exposure scores, comparing employment trends across these groups. For early-career workers (22-25), the two most exposed groups of occupations see noticeable declines since the introduction of ChatGPT, while the other three occupation groups see growth. These patterns become less stark, and ultimately disappear, as we consider older workers.
See Canaries We contextualize the AI exposure results with aggregate employment trends by age group, pooling all occupations. Consistent with the pattern in the previous chart, early-career workers (22-25) and the next-youngest group (26-30) see modest gains or slight declines. Given the narrow age range for early-career workers (22-25), this group accounts for just 7.0% of employment in our sample at baseline.
See Takeoff We summarize our 12 indicators of takeoff, assessing the extent to which each points towards explosive economic growth. By tracking these series over time, we can contextualize recent changes against longer-running economic trends. We see no decisive evidence of takeoff at present.
See Adoption Self-reported adoption rates of generative AI for work diverge in recent surveys: Hartley et al. report a decrease in adoption, while Gallup and Bick, Blandin, and Deming report continued increases towards 50% adoption.
Adoption Monitor U.S. firms lead adoption of AI technologies, but show little gap between current and expected use. In contrast, firms from the UK, Germany, and Australia expect to increase adoption.
The AI Economic Indicators is made possible through the generous support of Schmidt Sciences, the Siegel Family Endowment, and other individual donors.
For a complete list of DEL supporters, please visit our support page.
Cutting through noise and anecdote with up-to-date measurements of AI's impact
Contemporary discourse on the economic impact of AI often relies upon recent news and combining signals across disparate sources, many of which are not designed for this purpose. The Indicators gathers research by scholars from the Lab and other institutions in one location, providing a comprehensive picture of the economic impacts of AI. Additionally, the project regularly updates this work, presenting a collection of low-latency, rich indicators of labor market transition, growth effects, and more.
We combine and regularly update data from government statistical agencies, researchers, and private companies.
The Indicators is organized around five core questions:
*Employment and wages:*How does AI affect employment and wages across occupations and worker groups?*Aggregate economic outcomes:*Are these effects reflected in broader measures like productivity and GDP?*Consumer surplus:*Are the benefits of AI captured in traditional economic metrics, or are new measures needed?*Hiring and skills:*How are worker skill requirements changing?*AI usage:*How and to what extent is AI being used to complement or replace human labor?
The AI Economic Indicators is designed to be an economic observatory for the AI era. Our aim is to turn scattered signals into a shared, evidence-based understanding of how AI is reshaping work, productivity, and prosperity.
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The Indicators Team #
Erik Brynjolfsson is one of the world’s leading experts on the economics of technology and artificial intelligence. He is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
One of the most-cited authors on the economics of information, Brynjolfsson was among the first researchers to measure productivity contributions of IT and the complementary role of organizational capital and other intangibles.
Read more Connacher Murphy is a research manager at DEL, where he works with lab scholars to turn their research on the economic impacts of AI into low-latency, regularly updated measures of the economic impacts of AI. He also pursues new research partnerships for this work. These efforts are housed under the forthcoming Stanford AI Economics Observatory.
Connacher is interested in the economic and social impacts of AI, both for their relevance to policy and as strong proxies for capabilities.
Read more Christie Ko is a leader in the field of academic research on AI and the digital economy. As the Executive Director of the Stanford Digital Economy Lab, Christie is at the forefront of guiding the Lab’s planning, strategy, and program development. Her commitment to building diverse, multi-disciplinary, impact-oriented research programs drives the Lab’s groundbreaking research on the economic and social implications of AI
Read more Susan Young leads strategic initiatives at the Stanford Digital Economy Lab, working closely with faculty, researchers, and other stakeholders on projects focused on the economics of artificial intelligence and the future of work. Her work centers on shaping and guiding the Lab’s priorities and collaborations — including interdisciplinary research agendas, educational programs, and public-facing essay volumes such as The Digitalist Papers — that move ambitious ideas from concept to real-world impact.
Susan holds an MA in international relations from New York University and a BA in political science from the University of Chicago.
Read more Dr. Nela Richardson is ADP’s Chief Economist and ESG Officer. Nela is the head of the ADP Research Institute (ADPRI), where she leads economic research and provides reliable and timely analysis for the public, global and local businesses, and policymakers. Her background and expertise cross many industries, including finance, technology, housing and labor.
Nick Bloom’s research interests focus on measuring and explaining management practices across firms and countries.
His research includes collecting data from thousands of manufacturing firms, retailers, schools and hospitals across countries to develop a quantitative basis for management research. Recently, Nick has also been running management field experiments in India to identify clearly causal links between management and performance.
Read more Bharat Chandar is a labor economist working on understanding AI’s impact on work. His recent projects include work with Erik Brynjolfsson and Ruyu Chen tracking “canaries in the coal mine” for entry-level employment changes in jobs exposed to AI. He also recently surveyed the state of knowledge about AI and labor markets.
His ongoing work has focused on three areas. The first asks, how will workers adjust if we see AI-driven changes in hiring? Which workers will have an easier or more challenging time if displaced, and where should we target support? The second asks, how can we use AI to make it easier for people to learn new things and pursue new forms of work? Third, how will impacts of AI differ across the world?
Read more Ruyu Chen is a research scientist at the Digital Economy Lab and the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Her research lies at the intersection of the economics of innovation, information systems, and business strategy.
She focuses on two main areas: information technology adoption and firm performance, where she examines the drivers of IT adoption within firms and its impact on innovation and market performance; and AI and the future of work, where she leverages large-scale payroll data to study how emerging technologies, particularly generative AI, are reshaping employment, wages, skill demands, and organizational structures. Her work has been published in leading academic journals, including the Strategic Management Journal.
Read more Phil is an economics postdoc working with Erik Brynjolfsson and Chad Jones (of Stanford GSB) on questions related to economic growth and AI. He’s mainly working on theoretical questions regarding the consequences of building machines intelligent and dextrous enough to automate essentially all work. With Erik and others at the lab, Phil is thinking about the macroeconomic trends that we should expect to observe at the beginning of such a transition, and about the extent to which we are starting to observe these trends today.
Read more Andrew Wang is a research scientist at the Stanford Digital Economy Lab.
He is interested in technology, innovation, productivity, and the workforce. His prior work experience includes program evaluation and R&D project management in federal government at the National Institute of Standards and Technology, and in public-private partnership programs for early-stage R&D, where he interacted with both start-ups and large corporate R&D centers.
Andrew received a BA in history and economics from the University of California, Berkeley, and a PhD in economics from Harvard University.
Read more Matty is a writer, director and editor based in Los Angeles. Over the past decade, he’s directed national commercial campaigns, created, directed and written for television, and written advertising copy for some of the largest brands in the world. At the Lab, he works to connect the brilliant work of the research team to policymakers, business leaders, and workers who can use that information to build better working and living environments.