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Chaining Tasks, Redefining Work: A Theory of AI Automation

Author

Listed:
  • Mert Demirer
  • John J. Horton
  • Nicole Immorlica
  • Brendan Lucier
  • Peyman Shahidi

Abstract

We study the automation and productivity effects of AI when production is a sequence of interdependent steps with a Leontief structure. Each step can be executed manually by labor, augmented with AI, or automated within contiguous AI-executed steps called “chains.” By characterizing the firm’s cost-minimizing assignment of steps to humans and AI, we show that chaining can overturn comparative advantage logic, since human-advantaged steps may be optimally assigned to AI as part of a chain. Chaining also makes gains from automation depend on how AI-exposed steps cluster in the workflow beyond just how many there are, and can make the returns to improving AI quality non-monotone in a pattern resembling the productivity J-curve. Empirically, we show that existing automation patterns are consistent with the model’s predictions: a step is more likely to be AI-executed when its neighbors are, and AI execution is more common where AI-exposed steps cluster in the workflow.

Suggested Citation

  • Mert Demirer & John J. Horton & Nicole Immorlica & Brendan Lucier & Peyman Shahidi, 2026. "Chaining Tasks, Redefining Work: A Theory of AI Automation," NBER Working Papers 34859, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:34859
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    Cited by:

    1. Mihai Codreanu & Alex Imas & Juan Mateos-Garcia & Joseph Emmens & Evalyne Muiruri & Arthur Turrell & Julian Jacobs & Atoosa Kasirzadeh & Ana Trisovic & Yiyuan Chen & Tanya Rodchenko & Catherine Pollar, 2026. "AI in Science: Early Insights," Papers 2609.28504, arXiv.org, revised Sep 2026.
    2. Drew Johnston & David Holtz & Alex Martin Richmond & Christopher Ong & Prasanna Tambe & Aaron Chatterji, 2026. "The Shift to Agentic AI: Evidence from Codex," Papers 2606.26959, arXiv.org.
    3. David M. Rothschild & Jake M. Hofman & Markus Mobius & Brendan Lucier & Eleanor Dillon & Daniel G. Goldstein & Nicole Immorlica & Aleksandrs Slivkins, 2026. "From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place," Papers 2605.29207, arXiv.org.
    4. Campbell Lund & Thomas Euyang & Zanele Munyikwa & Marzieh Fadaee, 2026. "AI Exposure Scores: what they measure, what they miss, and what comes next," Papers 2606.23633, arXiv.org.

    More about this item

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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