Chaining Tasks, Redefining Work: A Theory of AI Automation

2026-06-14

AI Summary

This study examines the division of labor between humans and artificial intelligence (AI) in production processes and its implications for job design and productivity. By decomposing workflows into steps executable manually, with AI augmentation, or fully autonomously, the authors develop a task-chain–based theoretical model to analyze how firms optimally cluster steps into tasks and roles, balancing gains from specialization against coordination costs. Key contributions include demonstrating that traditional comparative advantage logic may break down under chained AI execution, identifying nonlinear productivity gains from improvements in AI quality, and deriving an aggregate constant elasticity of substitution (CES) production function. Empirical tests confirm core predictions: AI-executed steps tend to appear in contiguous chains; the more dispersed AI-exposed steps are within a job, the lower the likelihood of AI adoption; and steps adjacent to AI-automated ones are more likely to be automated themselves.

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.

Problem

• AI automation
• task allocation
• production chains
• job structure
• comparative advantage

Innovation

• AI chaining
• task bundling
• comparative advantage
• non-linear productivity
• job structure