Weak Bundle, Strong Bundle: How AI Redraws Job Boundaries

This paper studies how the effect of AI on an occupation depends not just on which tasks AI can perform but also on how costly it is to unbundle those tasks from the job. Much of the discussion of AI and labor markets starts from task exposure: if AI can perform more tasks in an occupation, that occupation should lose employment or earnings. This is incomplete because labor markets price jobs, not tasks. Jobs bundle tasks together, and the effect of AI depends on how costly it is to break the bundle. We build a two-task model in which AI can either assist one task inside a bundled job or supply that task autonomously while a human supplies the residual task. We show that, in weak-bundle occupations, AI automates some tasks and narrows the boundary of the job, activating the standard task-substitution channel once product demand is sufficiently inelastic. In strong-bundle occupations where tasks are not independently reallocable, AI improves performance inside the job, but does not remove the human from the bundle. Thus, bundling provides a force that protects jobs and workers' share of downstream revenue.