Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance

Abstract:Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed across different operating contexts. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setting, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating endogeneity in skill development or AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. We construct interpretable measures that characterize when the productivity paradox occurs. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state. We identify an effort-skill non-monotonicity condition under which skill polarization emerges.

Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH)

arXiv:2605.11350 [cs.GT]

(or arXiv:2605.11350v2 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2605.11350 Focus to learn more arXiv-issued DOI via DataCite

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