We invite graduate students and early-career researchers who are interested in studying the economics of AI to apply, regardless of prior experience. Fellows receive a grant of at least $10k, participate in conferences with leading economists and technologists, and have the potential to access unique data via Stripe and its customers. Our initial cohort will include 15-20 fellows, and will form the foundation of a community at the bleeding edge of economic research.
Basil Halperin is 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 and completed his undergrad at the University of Chicago. He previously worked as a data scientist at Uber and as a quant at AQR Capital Management.
Andrew Koh is a PhD Candidate in Economics at MIT. He works on game theory, mechanism design, and behavioural economics with a focus on how learning, information, and beliefs shape equilibria. He has applied these tools to understand (i) how policymakers should regulate new and uncertain technologies; and (ii) how to foster cooperation among AI agents in multiagent environments;
Arjun Ramani is a PhD student in economics at MIT. His research focuses on the economics of AI, technological change and innovation, often in spatial or organizational settings. Previously, he was a correspondent for The Economist in London and India where his reporting on the economics of AI was shortlisted for Wincott Journalist of the Year in 2023. Arjun holds a BA with honors in economics and an MS in computer science from Stanford. His research is supported by the Carl (1976) Shapiro and Paul and Daisy Soros Fellowships.
Berkay Saygin is a Ph.D. student in Economics at the University of Chicago, specializing in Economic Growth and Industrial Organization. His research investigates the micro-level dynamics of innovation, examining how innovative firms emerge, their innovation processes, interactions with scientific research, and the resulting productivity improvements. Prior to his doctoral studies, Berkay served as a pre-doctoral researcher at the University of Chicago, collaborating with Professor Ufuk Akcigit on science of science, business dynamism and innovation. He also contributed to the World Development Report 2024.
Bryan Seegmiller has been an Assistant Professor of Finance at the Northwestern Kellogg School of Management since 2022, and he received PhD in Financial Economics from the MIT Sloan School of Management. His research interests are in labor and finance, technological innovation, macroeconomics, and asset pricing. Much of Bryan's current research focuses on the consequences of technological change, with an emphasis on the labor market. His work in this area has examined the technological origins of new labor tasks since the mid-20th century; the worker-level skill displacement effects of new technologies emerging from 1980 to 2010; and, most recently, the effects of initial investments in artificial intelligence on firms' occupational labor demand.
Emma Wiles is an Assistant Professor of Information Systems at Boston University’s Questrom School of Business and a Digital Fellow at MIT's Initiative on the Digital Economy. Her research explores how artificial intelligence influences hiring markets and worker's skill development. She holds a PhD from MIT Sloan, with a dissertation focused on how AI impacts labor market matching. Her work has been featured in journals such as Management Science, Journal of Public Economics, and AEJ: Economic Policy.
Fiona Chen is a Ph.D. Candidate in Business Economics at Harvard. Her research is in labor economics, with a focus on the effects of new technologies and labor market institutions. Her primary work examines the effects of generative AI tools among software engineers, using data from a worker management platform on coding activity, task assignment, and organizational structure. She received her B.S. in Mathematics and in Economics from MIT.
J. Zachary Mazlish is a fifth-year PhD student in the Economics department at Oxford. In the fall of 2025, he will be a visiting student at NYU. His research is in macroeconomics, with projects on the implications of transformative AI for real interest rates, the biases in macroeconomic forecasts at different horizons, explaining the three eras of US real exchange rate and stock market movements over the last 30 years, using LLMs to identify monetary and fiscal shocks, and explaining why sovereign debt spreads co-move across countries more than fundamentals.
Katelyn Cranney is an economics PhD student at Stanford University and a National Science Foundation Graduate Research Fellow. Katelyn’s research uses insights from behavioral, labor, and development economics to better document and close gender gaps in global labor markets. Her current work focuses on understanding the gendered adoption of generative AI and how to leverage new technologies to improve labor market opportunities for disadvantaged groups. Before coming to Stanford, Katelyn worked as a research assistant at Harvard Business School under the Tech For All Lab at the Digital Data Design Institute.
Leon Musolff is an assistant professor in Wharton's Business Economics and Public Policy group. He specializes in empirical industrial organization, focusing on antitrust and the digital economy. His prior work include studies of algorithmic pricing, self-preferencing in e-commerce and how inertia and information frictions shape competition in the web search market. More recently, he has studied the productivity effects of generative AI. Before Wharton, he was a Postdoctoral Researcher at Microsoft Research New England and obtained his PhD in Economics at Princeton University.
Lindsey's work examines how digital technologies and artificial intelligence are reshaping work, firm behavior, and market competition. Currently, she is a postdoctoral researcher in the Economics and Computation Group at Microsoft New England and will join Harvard Business School in 2025 as an Assistant Professor. She earned my PhD from MIT in 2024 and her undergraduate degree from Yale University. From 2021 to 2022, she served at the White House Council of Economic Advisers.
Meghana Gaur is a PhD Candidate in Economics at Princeton, specializing in macroeconomics and labor economics. Her research focuses on the impacts of technological change on labor markets and the role of labor markets in the innovation process. Her current projects investigate the potential impacts of AI-driven automation of tasks performed within career ladders, as well as the consequences of AI assistant technologies for the skill accumulation process in fields such as software development. She received her undergraduate degree in mathematics and economics from Rice University and previously worked at the Federal Reserve Bank of New York as a Research Analyst.
Mihai Codreanu is a fourth-year Economics Ph.D. candidate at Stanford. His research uses applied microeconomic methods and large, novel datasets to study the micro-foundations of business creation, technology adoption and performance. Most recently, he studies how technological change and generative AI affect entrepreneurship. He previously received the Hewlett Stanford Graduate Fellowship, Sean Buckley Memorial Award, NBER I3 Fellowship, and ERMAS Young Romanian Economist Prize. He holds an M.Phil. in Economics from Oxford and graduated first in class at the University of Manchester.
Pedro Aldighieri is a third-year Economics PhD candidate at Northwestern University specializing in the economics of science and innovation. His current research examines how the introduction of early digital computers transformed scientific practices, providing historical insights into the impacts of general-purpose technologies that parallel today's AI revolution. Pedro’s work applies LLM embeddings to map scientific knowledge in ways that reveal connections between ideas and identify promising research directions. He brings a distinctive interdisciplinary perspective to economics, having previously published policy analyses, recorded music albums, and authored a poetry collection before dedicating himself to understanding technological progress and scientific advancement.
Sami Petersen is a PhD student in economics at the University of California, Berkeley. His interests are in microeconomic theory, especially aspects of incentive design and foundations. He received an MPhil in economics from the University of Oxford in 2025. Previously, Sami was a Research Scholar in the ML Alignment & Theory Scholars (SERI MATS) programme and studied philosophy at LSE.
Thomas Houlden is a first-year PhD student at Columbia University interested in growth and welfare economics. His current research focuses on amplification of AI progress through feedback loops and the labor market implications of transformative AI. Before pursuing his PhD, Thomas worked in policy at the Commonwealth Treasury of Australia and the University of Oxford's Global Priorities Institute. He holds a Master's degree from the London School of Economics.
Tianyu Fan is a Ph.D. candidate in Economics at Yale University, whose research investigates the causes and consequences of inequality in international trade, development, and economic growth. His most recent work examines the labor-market impacts of automation technologies and artificial intelligence. Tianyu's work has been published in a leading economic journal, and he is committed to charting a path toward more equitable prosperity.
Todd Lensman is an Assistant Professor in the Entrepreneurial Management Unit at Harvard Business School. His research explores how new technologies drive economic growth, and in particular how firms shape the innovation and adoption of new technologies. Todd received a PhD in economics from MIT and a BA in economics and mathematics from Cornell University.
Beyond financial support of at least $10k (see next FAQ), the fellowship includes:
1. Community: A community of like-minded researchers studying the economic implications of AI.
2. Conference: Funding to attend an exclusive conference in San Francisco (early summer 2025), featuring leading economists and technologists in this space. Fellows will be asked to present a brief project proposal, and invited to participate in follow-up conferences to present and get feedback on preliminary results.
3. Opportunity for data: The opportunity to gain access to unique datasets through Stripe and its customers.
Fellows receive a baseline grant of $10,000. Applicants may request additional funding with justification, for example if funding for a data purchase would accelerate research progress. Graduate students are encouraged to request sufficient funding to buy out a unit of teaching or research assistantship, if they have a specific project idea which a buyout would provide time to work on.
Our goal is to support rigorous, innovative research on the economics of AI – broadly defined – that advances the field and merits publication in top journals. We are particularly interested in high-impact research that:
1. is focused on the economics of transformative AI
2. is forward-looking
3. is expected to be of durable importance, and
4. moves fast.
By “transformative” AI, we mean AI technology that precipitates change on the scale of the industrial or agricultural revolutions (see here for more detail and a list of related papers, from Stanford’s Digital Economy Lab).
• We expect most of the initial fellows to be grad students or early-career researchers (including postdocs, assistant professors, etc.) in economics.
• Researchers from any country are eligible to apply.
• Prior experience in AI economics is welcome but not required; exceptional candidates without any background in the field are encouraged to apply, including researchers in departments outside of economics.
• Fellows are not required to have a project proposal to apply.
Please direct any questions to Basil Halperin (basilh@stanford.edu), who is leading the fellowship program with Stripe.
The economics of AI remains surprisingly understudied, even as technical progress in artificial intelligence continues rapidly. The Stripe Economics of AI Fellowship aims to help fill that gap by supporting foundational academic research in the area.
We invite graduate students and early-career researchers who are interested in studying the economics of AI to apply, regardless of prior experience. Fellows receive a grant of at least $10k, participate in conferences with leading economists and technologists, and have the potential to access unique data via Stripe and its customers. Our initial cohort will include 15-20 fellows, and will form the foundation of a community at the bleeding edge of economic research.