Automation Atlas

About

Public companion to a research project on task-level automation exposure.

Authors

Prashant Garg

Prashant Garg recently completed his PhD in Economics at Imperial College London. His research covers science, innovation, production, and media using machine learning, causal inference, and network science. He joins Bocconi University as a Postdoctoral Researcher in September 2026.

Imperial College London

Tommaso Crosta

Tommaso Crosta is a PhD candidate in Economics at Bocconi University. His research sits at the intersection of development and labour economics, with additional interests in Bayesian statistics applied to microeconomics and meta-analysis.

Bocconi University

Jasmin Baier

Jasmin Baier is a doctoral candidate in Behavioral Economics and Public Policy at the Blavatnik School of Government, University of Oxford. Her research sits at the intersection of behavioral and development economics, focusing on skills, AI and labor markets, and human–AI interaction.

University of Oxford

Measures

Task exposure, not outcomes

Each task is scored against a structured rubric for what today's tools can plausibly do. The atlas does not predict wages, employment, or adoption.

Data

124 countries, 18,797 tasks

Country-conditioned benchmark from the current paper release. All files are public.

Data →

Paper

Full academic manuscript

Methodology, empirical findings, and limitations in detail.

Paper →