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Government spending on artificial intelligence (AI) has surged across the world. Quantifying the return on research investments is notoriously difficult, especially in newly emerging economic sectors. Here, we propose a novel way to describe and analyze where AI ideas are being used and how they spread—by tracing the people and academic communities involved in AI research as they transition from government-funded research labs to private sector companies, carrying cutting-edge “AI know-how” with them. Linking existing university administrative data with state employment records allows several quantifiable inferences about the value of AI research to be drawn from these academia-to-industry migrations. Here we describe a pilot implementation of this system, which is being developed in the State of Ohio. It offers a template for governments and policy makers all over the world. Importantly, the metrics discussed below offer a way to measure the economic impact of scientific research in general, with implications for critical and emerging technologies that go far beyond AI. 

Abstract There is a well-documented gap between the observed number of works produced by women and by men in science, with clear consequences for the retention and promotion of women 1 . The gap might be a result of productivity differences 2–5 , or it might be owing to women’s contributions not being acknowledged 6,7 . Here we find that at least part of this gap is the result of unacknowledged contributions: women in research teams are significantly less likely than men to be credited with authorship. The findings are consistent across three very different sources of data. Analysis of the first source—large-scale administrative data on research teams, team scientific output and attribution of credit—show that women are significantly less likely to be named on a given article or patent produced by their team relative to their male peers. The gender gap in attribution is present across most scientific fields and almost all career stages. The second source—an extensive survey of authors—similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source—qualitative responses—suggests that the reason that women are less likely to be credited is because their work is often not known, is not appreciated or is ignored. At least some of the observed gender gap in scientific output may be owing not to differences in scientific contribution, but rather to differences in attribution.