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

TL;DR

This paper models human-AI interaction to understand how AI assistance can paradoxically reduce productivity and cause skill polarization, highlighting mechanisms behind these effects.

Contribution

It introduces a model capturing the interplay of skill, effort, and AI assistance, explaining productivity paradoxes and skill polarization in human-AI collaboration.

Findings

Increased AI assistance can decrease productivity due to endogeneity and unreliability.

Skill polarization can emerge over time due to heterogeneity in AI literacy.

Simple measures can predict when productivity paradoxes and polarization occur.

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. 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.