Abstract:
I study how human validation requirements shape the response of wages and labor demand to AI adoption. I introduce fixed, recurring validation labor into a general equilibrium task model with endogenous AI adoption. It features a validation threshold determining whether the labor created by AI adoption offsets the productive labor it displaces after accounting for demand expansions. I estimate the model using U.S. occupation-task data on human versus AI involvement at work, and AI adoption rates. The estimated U.S. validation requirement lies below the threshold, implying that productive labor displacement dominates and wages fall. For the occupations in the estimation sample, a ten year projection of declining AI costs raises adoption from 18 to 62 percent and nearly triples output, while reducing employment by 13 percent and wages by 43 percent. Extending the exercise to the entire U.S. economy attenuates the employment and wage declines to 5 and 18 percent, respectively.
