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-ΔGini moments identify mechanism rates but not the aggregate sign, which at the calibrated parameters is pinned by m6 and ξ, while AI's technology structure (η1 vs. η0) 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.
Figures & tables
Figure 1: Model-implied mechanism chain from AI capability to inequality. The equalizing channel operates through within-task compression. The concentrating channel operates through rising returns to complementary assets. Net inequality depends on AI’s technology structure (proprietary vs. commodity) and labor market institutions ( ξ , Gini(K) ).
Value
SE
Description
Panel A: Structural Parameters
α/(ϕμh)
0.526
(0.096)
AI/human output ratio
σ
1.166
(1.639)
Task substitution elasticity
η0
0.142
(0.058)
Pre-AI asset elasticity
η1
0.323
(0.035)
Post-AI asset elasticity
Gini(K0)
0.909
(0.132)
Asset concentration (Gini)
Table 1: Method of Simulated Moments: Calibrated Parameter Values
Table 7: AI Exposure and Within-Occupation Wage Dispersion: Levels vs. First Differences
H
Prediction
Source
Data
Outcome
H1
Within-occupation output dispersion declines; largest gains for low-ability
Prop. 1
Task-level productivity
CV, 90–10 gap
H2
Education premium declines in high-AI occupations for codifiable skills (requires σ>1 ; see text)
Prop. 3
CPS-ASEC × AIOE
Education-wage gradient
H3
Credential requirements rise in high-AI occupations, controlling for tasks (untested prediction; requires post-2023 Lightcast data linked to AI adoption)
Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.
Xiliu He, Haoxiang Zhao, Mingyi Ma +6
School of Architecture, Tsinghua University, Beijing, China · ZODA LAB, Hangzhou, China · Technology Innovation Center for Smart Human Settlements and Spatial Planning & Governance, Ministry of Natural Resources, Tsinghua University, Beijing, China
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.
Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled experiment in which participants-analogs of early-career knowledge workers-were assigned to self-study a technical domain using either traditional resources or large-language-model (LLM) assistance. On average, GenAI access significantly increased task performance, but the distribution of gains was highly uneven. Improvements were not predicted by GPA or prior knowledge, but by AI Interaction Competence (AIC) -- the ability to elicit, filter, and verify model outputs. High-AIC participants realized outsized gains; low-AIC participants saw limited or even negative marginal returns. A scaffolding intervention (conceptual maps) reduced outcome variance, indicating that standardized workflows can mitigate inequality in AI-mediated performance. We interpret these findings through the lens of human-AI complementarities: GenAI raises mean productivity while introducing a new axis of capability inequality. Managerially, firms should pair GenAI access with short AIC micro-training and simple standard operating procedures to capture value consistently and avoid uneven adoption outcomes.
Lihi Idan, Bharat Anand
Industrial and Systems Engineering Department, Texas A&M University · Leonard N. Stern School of Business, New York University