Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance
This paper provides a theoretical framework explaining mixed empirical evidence on AI's impact on productivity for researchers and policymakers.
The authors propose a model of human-AI interaction showing that increased AI assistance can degrade productivity due to endogenous skill development or AI unreliability, and that skill polarization emerges from heterogeneity in AI literacy.
Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise.