Chaining Tasks, Redefining Work: A Theory of AI Automation

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.