A Mathematical Framework for AI-Human Integration in Work

TL;DR

This paper introduces a mathematical framework to model AI-human collaboration in work, analyzing how subskill complementarity affects job success and productivity gains, supported by real-world data.

Contribution

It presents a novel decomposition of skills into decision and action subskills, providing insights into when AI complements human work and improves productivity.

Findings

GenAI assistance benefits lower-skilled workers more

Conditions for significant productivity gains are identified

Framework validated with real-world data from O*NET and Big-Bench Lite

Abstract

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.