How AI Is Changing the Economy: New Evidence on Work, Growth, and Economic Transformation

Professor Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University.

Rishi Bommasani is a PhD Student in Computer Science at Stanford University, advised by Percy Liang and Dan Jurafsky. He is also the Society Lead at the Stanford Center for Research on Foundation Models. His research centers on the societal impact of AI, especially foundation models. Prior to joining Stanford, he received his B.A. and M.S. from Cornell University. Rishi’s research with the RegLab addresses governance and policy for foundation models.

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.

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?

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.

Basil Halperin an assistant professor of economics at the University of Virginia.. His research focuses on topics in monetary economics, macroeconomic growth, and AI.

Basil received his PhD in economics from MIT in 2024. He previously worked as a data scientist at Uber and as a quant at AQR Capital Management. He completed my undergrad at the University of Chicago.

Sophia Kazinnik is a Research Scientist at Stanford’s Digital Economy Lab (HAI), where she builds generative AI systems to explore how language and behavior shape economic outcomes. Her work turns economic questions into computable experiments, using LLM-powered agents and multi-agent simulations to study financial fragility, policy communication, and market expectations. In some of her recent projects, she has modeled bank runs, simulated FOMC deliberations, and evaluated how today’s AI interprets central bank language.

Abhishek Nagaraj is an associate professor in the Management of Organizations (MORS) and the Entrepreneurship and Innovation group at the Haas School of Business, UC Berkeley. He is also a faculty research fellow at the National Bureau of Economic Research (NBER) and the director of the Data Innovation and AI Lab (DIAL). He earned his PhD in Management at the MIT Sloan School of Management, in the Technological Innovation, Entrepreneurship and Strategic Management (TIES) group. Nagaraj also holds an MBA from the Indian Institute of Management (IIM) Calcutta and a B.Tech in Computer Science from the College of Engineering, Pune (COEP).

David is an expert on economic measurement, statistics, and indicators related to the digital economy. His work aims to provide a better evidence base for policy and business. David is also a Research Associate at the Economic Statistics Centre of Excellence (ESCoE) which has been created to address the challenges of measuring modern economies. He published several papers and reports in the fields of economic measurement and digital economics and previously worked as an Economist at the OECD in Paris and NIESR in London. David received his PhD from the London School of Economics in 2018.

Daniel Rock is an Assistant Professor of Operations, Information, and Decisions at the Wharton School of the University of Pennsylvania. His research is on the economic effects of digital technologies, with a particular emphasis on the economics of artificial intelligence. He has recently worked on studies addressing the types of occupations that are most exposed to machine learning, measuring the value of AI skillsets to employer firms, and adjusting productivity measurement to include investments in intangible assets. His research has been published in various academic journals and featured in outlets such as The New York Times, Wall Street Journal, Bloomberg, Harvard Business Review, and Sloan Management Review. He is also a cofounder of Workhelix, and is a Schmidt Sciences AI2050 Early Career Fellow.

Professor Rock received his B.S. from the Wharton School of the University of Pennsylvania, and his M.S. and Ph.D. from the Massachusetts Institute of Technology.

Christopher Tonetti is an associate professor at Stanford Graduate School of Business and a research associate at the National Bureau of Economic Research. His research is in the area of macroeconomics, with a focus on growth, and household finance. Christopher received a BA in economics-mathematics from Columbia University and a PhD in economics from New York University.

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.

Gabriel Unger is a research scholar at the Stanford Digital Economy Lab. Unger’s main field is macroeconomics. His research broadly attempts to understand how technological advances, like the IT Revolution, change our understanding of important macroeconomic questions, such as the mechanics of productivity growth, the rise of industrial concentration, or the transformation of the business cycle.

He holds a PhD in Economics from Harvard and a JD at Yale Law School, and undertook his undergraduate education at Harvard.

Jenn is a PhD student in Computer Science at Stanford, co-advised by Diyi Yang and Erik Brynjolfsson. Her research explores how AI is shaping the future of work and how to design AI systems that preserve and enhance human knowledge production. Her current project investigates new approaches to building a next-generation O*NET —a database of occupations and the tasks —to better capture how work is changing.

She holds a BS in Computer Science from Brown University. Her interest in the societal implications of technology has led her beyond computer science into civic tech and AI policy, with experience at the White House Office of Science and Technology Policy and the U.S. Census Bureau. Across these settings, she has been drawn to how research and policy can help ensure that advances in AI serve the people and communities they affect.