Google Recruits Top Economists to Analyze AI's Economic Impact
Google is significantly expanding its economic research division by hiring Nobel laureates and senior analysts to measure how artificial intelligence is reshaping labor markets and global productivity.
Google has announced a major expansion of its AI & Economy Research Program, bringing in high-profile economists to study the real-world effects of artificial intelligence on work and daily life. The initiative follows the launch of the ATLAS v1.0 tool, which tracks how people use AI in professional and personal settings. The company states that simple adoption tracking is insufficient and that a deeper, multidisciplinary approach is needed to understand the structural changes occurring in the global economy.
According to reporting by GN technics/ai (en-US), the new team will focus on four core areas: the future of work, productivity growth, the spread of technology across borders, and AI’s role in scientific discovery. The goal is to provide rigorous data that can guide policymakers, businesses, and workers as they navigate this technological shift. This move signals that tech companies are increasingly stepping into the role of economic researchers, aiming to define the narrative around AI's societal impact.
Nobel laureates join advisory board
Google has appointed Philippe Aghion, the 2025 Nobel Laureate in Economics, as an Academic Advisor. Aghion, who holds positions at INSEAD and the Collège de France, will apply his expertise in innovation-led growth to model the long-term macroeconomic effects of AI. He joins an advisory group that already includes Nobel laureate Michael Spence and Cambridge economist Dame Diane Coyle.
Ajay Agrawal, a professor at the University of Toronto’s Rotman School of Management, has also joined as a Visiting Fellow. He will collaborate with David Autor, head of MIT’s Economics Department, to explore the economics of AI and robotics. Their work aims to understand how these technologies can expand human welfare, moving beyond simple efficiency metrics to consider broader societal benefits.
New directors lead empirical research
The program has also added two new directors to steer its empirical projects. Anu Madgavkar, formerly a partner at the McKinsey Global Institute, will lead research on global AI diffusion and its impact on small businesses and the workforce. Her two-decade career advising governments and multilateral institutions brings a policy-focused perspective to Google’s internal research efforts.
Daniel Rock, an economist from the Wharton School at the University of Pennsylvania, will oversee the integration of frontier model telemetry with rigorous econometrics. His work will focus on analyzing enterprise productivity and labor restructuring. Together, they will work alongside Alex Imas and Zanna Iscenko to ensure that the data collected is both technically sound and economically relevant.
Trade-offs in industry-led analysis
While the addition of renowned economists lends credibility to the research, a significant trade-off remains. The data originates from Google’s own platforms, meaning the findings may reflect the usage patterns of its specific user base rather than the broader global market. Critics of industry-led economic studies often point out that such data can be limited by the platform's design and user demographics.
Furthermore, the rapid pace of AI development means that today's economic models may become obsolete quickly. The program aims to measure these changes in real time, but the complexity of technological diffusion makes long-term predictions difficult. The catch is that while the team has elite expertise, the scope of their data is inherently tied to the ecosystem they are measuring, potentially limiting the generalizability of their conclusions.
