The Rise of Industrial AI in America: Microfoundations of the Productivity J-Curve(s)

Abstract

This study examines the productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the U.S. Census Bureau to collect detailed large-scale data for 2017 and 2021, we find significant initial productivity losses preceding medium-term gains from industrial AI deployment. We attribute this to costly adjustment (not just mismeasurement), which we observe directly via increased work-in-progress inventory, investment in industrial robots, and labor shedding. Over time, however, early AI adopters exhibit stronger growth on average, conditional on weathering the initial "dip." Losses vary considerably across firms and establishments. A key contingency is age, with young firms faring better than older incumbents---particularly startups with growth-oriented business strategies. Management practices and production-process design significantly shape both the uptake and effects of industrial AI use. Firm structure also matters, as we observe significant cross-establishment spillovers inside large, multi-unit firms. Overall, our detailed findings provide novel evidence regarding AI-related "J-curve" effects, unveiling key mechanisms and extending our understanding of this emerging General Purpose Technology.