Chaining Tasks, Redefining Work: A Theory of AI Automation

Mert Demirer, John Horton, Nicole Immorlica, Brendan Lucier and Peyman Shahidi

No 34859, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Production is a sequence of steps that can be executed (1) manually, (2) augmented with AI, or (3) fully automated within contiguous AI-executed steps called “chains.” Firms optimally bundle steps into tasks and then jobs, trading off specialization gains against coordination costs. We characterize the optimal assignment of humans and AI to steps and the firm’s resulting job structure, showing that comparative advantage logic can fail with AI chaining. The model implies non-linear productivity gains from AI quality improvements and admits a CES representation at the macro level. Empirical evidence supports the model’s key predictions that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.

JEL-codes: D24 J23 J24 O33 (search for similar items in EconPapers)
Date: 2026-02
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