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

2026-03-05

AI Summary

This study investigates how generative AI simultaneously compresses individual skill disparities while exacerbating aggregate economic inequality through asset concentration. By developing a task-based model that integrates endogenous education, employer screening, and heterogeneous firms, the paper uncovers a dual mechanism—skill homogenization and asset value concentration—through which the structure of AI technology and labor market institutions jointly shape inequality. The analysis combines theoretical modeling, simulated method of moments (MSM) for scenario evaluation, and empirical validation using regression on occupational data from the Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (BLS OEWS). Calibration results indicate that inequality dynamics are driven by key structural parameters, and existing occupation-level data prove insufficient to test task-level predictions, highlighting the need for novel panel datasets.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Generative AI
• Skill Homogenization
• Asset Concentration
• Economic Inequality
• Task-based Model

Innovation

• Generative AI
• Skill Homogenization
• Asset Concentration
• Task-based Model
• Inequality Regimes