Endogenous Task Bundling, Skills and Automation

Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape.We argue that this assumption hides much of the action.When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle them into one broad role or split them into specialist roles changes which surviving skills the labour market actually rewards.A skill that played no role in the pre-AI wage can become the dominant component of the post-AI wage, while a skill that anchored the pre-AI wage can disappear from the schedule.We develop an assignment model in which the priced human bundle is endogenous, and we use it to show that a fixed-bundle wage regression can mis-sign the effect of AI exposure.In general, the omitted-redesign bias has no unconditional sign: it is the residual covariance between exposure and role-specific redesign terms.Under explicit sufficient conditions, exposurecorrelated unbundling loads specialist comparative-advantage premia onto the exposure coefficient, while exposure-correlated rebundling loads a different, often opposite, omitted term.The sign must therefore be measured from local post-AI partition changes rather than assumed from exposure alone.