A Mathematical Framework for AI-Human Integration in Work
This work addresses the practical problem of AI-human integration in the workforce, providing insights for job design and policy, though it is incremental as it builds on existing models of skills and job fit.
The paper tackles the problem of understanding how Generative AI complements or replaces human workers by developing a mathematical framework that models jobs and skills, showing that combining workers with complementary subskills significantly outperforms single workers and explaining phenomena like productivity compression where GenAI yields larger gains for lower-skilled workers.
The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework' s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.