Summary
Google is expanding its AI & Economy Research Program with economists Philippe Aghion, Ajay Agrawal, Anu Madgavkar and Daniel Rock. The programme will study AI adoption, productivity, labour-market change and scientific discovery.
Google is expanding its AI & Economy Research Program with new academic advisers, a visiting fellow and two research directors. Announced on 18 September 2026, the programme will examine how artificial intelligence is adopted across economies and organisations, and how that adoption affects work, productivity, business growth and scientific discovery.
The programme follows Google's launch of AI & Economy ATLAS v1.0, described by the company as an interactive, open-access site for examining how people use Google's AI tools at work and in daily life. Google says the expanded research effort will combine detailed adoption data with economic analysis rather than treating the spread of AI as an instantaneous technological shift.
Contents
What Google is expanding
Google says the research programme will measure and analyse the development of AI adoption in real time. Its stated areas of work include the future of work, productivity and economic growth, the global diffusion of technology, and AI's effect on scientific discovery.
This approach links two kinds of questions. Adoption research examines where and how AI tools are being used, while economic research evaluates how those changes relate to organisations, workers and output. The programme is intended to support research and discussion among organisations, workers, academics and policymakers.
The expansion adds external academic expertise and dedicated leadership. Google presents the group as a collaboration involving academia, industry and policymakers, with future work aimed at identifying organisational practices, public-policy frameworks and training programmes that could help workers gain skills as AI spreads.
The new research leadership
Philippe Aghion, the 2025 Nobel Laureate in Economics and Kurt Björklund Chaired Professor at INSEAD and the Collège de France, joins as an Academic Advisor. Google says he will contribute research on innovation-led growth and creative destruction to the programme's work on AI's long-term macroeconomic trajectory. He joins an advisory group that includes Nobel Laureate Michael Spence and Cambridge's Dame Diane Coyle.
Ajay Agrawal, Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management, joins as a Visiting Fellow. His work with the programme will cover the economics of AI and scientific discovery, AI and robotics, and the possibility that AI could expand the frontier of human welfare. He will collaborate with David Autor, a current fellow and head of MIT's Department of Economics.
Anu Madgavkar, formerly a partner at the McKinsey Global Institute, becomes a research director. Google says she will lead empirical work on global AI diffusion, small-business ecosystems and the workforce effects of generative AI, drawing on her previous research into labour markets, technology adoption and structural economic transitions.
Daniel Rock joins from the Wharton School of the University of Pennsylvania as the other research director. His work will focus on AI and labour, enterprise productivity, labour restructuring and scientific discovery. Google says Rock will connect data from frontier AI models with econometric analysis, a statistical approach used to estimate relationships between economic variables while accounting for other factors.
Madgavkar and Rock will lead the programme alongside Alex Imas, Director of AGI Economics at Google DeepMind, and Zanna Iscenko, AI & Economy Lead in Google's Chief Economist's Office.
Research priorities and practical aims
The programme's central task is to connect evidence about AI use with economic outcomes. That includes tracking how widely technologies spread, studying whether organisations change their processes, and examining how workers and businesses experience those changes. In this context, productivity refers to the amount of economic output generated from given inputs such as labour, capital or time.
Google says the expanded team will inform future ATLAS updates and empirical research. Its stated longer-term aim is to help determine which workplace practices, public policies and training programmes can support worker upskilling, widen access to expertise and distribute the benefits of AI more broadly.
The announcement establishes the programme's new leadership, research agenda and connection to ATLAS. Findings from the planned empirical studies on jobs, productivity, business growth and scientific discovery will emerge through the programme's subsequent research and data releases.