Data-Driven Automation

Maryam Farboodi, Andrew J. Koh and Anchi Xia

No 35320, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.

JEL-codes: J31 O33 O4 (search for similar items in EconPapers)
Date: 2026-06
Note: EFG
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