Kristina McElheran is an Associate Professor of Strategic Management at the University of Toronto Scarborough and the Rotman School of Management. Trained in managerial economics and strategy, she examines the impact of digital technologies on firms and workers. Her research spans the rise of the commercial internet, big data and cloud computing, to recent A.I.-related technologies. Previous experience includes six years on the Harvard Business School faculty and stints at two Silicon Valley startups. Her research has been featured in Management Science, the American Economic Review, the Journal of Economics and Management Strategy, Harvard Business Review, Sloan Management Review, and Communications of the ACM, as well as leading news outlets in the U.S. and Canada.
McElheran received her PhD from Northwestern’s Kellogg School of Business and bachelor’s and master’s degrees from Stanford University.
Abstract
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI’s impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact—exemplified here by AI—may initially disappoint, particularly in contexts dominated by older, established firms.
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