This paper examines the spatial and temporal dynamics of artificial intelligence (AI) adoption in the United States, leveraging county-level data on AI-related job postings from 2014 to 2023. We document significant variation in AI intensity across counties with tech hubs like Santa Clara, CA, leading in adoption, but rapid growth occurring in unexpected, suburban, and remote-friendly areas such as Maries, MO, and Hughes, SD, particularly following the lockdown era. Controlling for county and year fixed effects, we find that higher shares of STEM degrees, labor market tightness, and patent activity are key drivers of AI adoption, while manufacturing intensity and turnover rates hinder growth. Our results point to the uneven distribution of AI’s economic benefits and the critical role of local education, innovation, and labor market dynamics in shaping adoption patterns. Furthermore, they suggest the potential of place-based policies to attract AI talent and investments, providing actionable insights for policymakers aiming to bridge regional disparities in AI-driven economic growth.
That is from a new paper by Eleftherios Andreadis,Manolis Chatzikonstantinou, Elena Kalotychou, Christodoulos Louca and Christos Makridis. Via the excellent Kevin Lewis.
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