When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality

TL;DR

This paper develops a model to understand how generative AI can both homogenize individual skills and increase overall inequality, depending on technological and institutional factors, with implications for future data testing.

Contribution

It formalizes the mechanisms behind AI-induced skill homogenization and inequality regimes, highlighting the role of technology structure and labor market institutions.

Findings

Two inequality regimes depend on AI technology and market institutions.

Model matches six empirical targets using Method of Simulated Moments.

Occupation-level data cannot currently test task-level predictions.

Abstract

Generative AI compresses within-task skill differences while shifting economic value toward concentrated complementary assets, creating an apparent paradox: the technology that equalizes individual performance may widen aggregate inequality. We formalize this tension in a task-based model with endogenous education, employer screening, and heterogeneous firms. The model yields two regimes whose boundary depends on AI's technology structure (proprietary vs. commodity) and labor market institutions (rent-sharing elasticity, asset concentration). A scenario analysis via Method of Simulated Moments, matching six empirical targets, disciplines the model's quantitative magnitudes; a sensitivity decomposition reveals that the five non-$\Delta$Gini moments identify mechanism rates but not the aggregate sign, which at the calibrated parameters is pinned by $m_6$ and $\xi$, while AI's technology structure ($\eta_1$ vs. $\eta_0$) independently crosses the boundary. The contribution is the mechanism -- not a verdict on the sign. Occupation-level regressions using BLS OEWS data (2019--2023) illustrate why such data cannot test the model's task-level predictions. The predictions are testable with within-occupation, within-task panel data that do not yet exist at scale.