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Data-Driven Automation

Author

Listed:
  • Maryam Farboodi
  • Andrew Koh
  • Anchi Xia

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.

Suggested Citation

  • Maryam Farboodi & Andrew Koh & Anchi Xia, 2026. "Data-Driven Automation," Papers 2606.10127, arXiv.org.
    • Handle: RePEc:arx:papers:2606.10127
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    File URL: http://arxiv.org/pdf/2606.10127
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