The Race between Academia and Industry for AI Researchers

Francesca Miserocchi, Savannah Noray and Alice Wu

No 26106, RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin)

Abstract: The advances of artificial intelligence (AI) are built on the groundwork laid by researchers. We study the labor market competition between academia and industry for AI researchers and its consequences for public knowledge production. Using data on 150,000 computer science researchers, we document a major reallocation of AI talent toward top technology firms between 2005 and 2020. Publications at AI conferences predict transitions to top firms more strongly than to academia. Exploiting acceptance decisions at a leading AI conference, we compare accepted authors with similar rejected authors and find that a publication increases the probability of moving to a top firm by 2-6 percentage points in the next 1-3 years. Sorting to top firms is stronger for male researchers, whereas female students and postdocs are more likely to get tenure-track positions following a publication. Researchers who move to top firms subsequently publish fewer papers, resulting in approximately 1,000 fewer AI papers and 2,000 fewer papers in other computer science areas per year in the public domain.

Keywords: Sorting; Productivity Signals; Labor Market Concentration; Innovation (search for similar items in EconPapers)
JEL-codes: J23 J24 O31 (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-ain, nep-lma and nep-sbm
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