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AI Adoption in America: Who, What, and Where

Kristina McElheran, J. Frank Li, Erik Brynjolfsson, Zachary Kroff (), Emin Dinlersoz, Lucia Foster and Nikolas Zolas

No 31788, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more-educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups’ adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist.

JEL-codes: M15 O3 (search for similar items in EconPapers)
Date: 2023-10
New Economics Papers: this item is included in nep-ain, nep-bec, nep-cmp, nep-ent, nep-sbm and nep-tid
Note: PR
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Published as Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, vol 33(2), pages 375-415.

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