Generative AI impacts on intra-urban inequality and skill premium in Beijing
2026-05-25 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors studied how generative AI affects job opportunities across different neighborhoods in Beijing from 2018 to 2024. They found that neighborhoods in the city center are more exposed to AI, which has led to wage stagnation despite these areas still attracting skilled workers. This happens because AI simplifies some tasks and increases competition for jobs, creating a "high-skill trap." Their analysis suggests these effects are likely caused by AI, challenging common ideas that tech always benefits skilled workers more. These insights can help guide fair AI policies in big tech cities.
Generative artificial intelligenceIntra-urban inequalityTask de-skillingLabor-market crowdingSkill-biased technological changeDifference-in-differencesLarge language modelsWage stagnationAI governanceJob postings analysis
Authors
Xiliu He, Haoxiang Zhao, Mingyi Ma, Edward Wen Chuan Lai, Koei Enomoto, Anni Hu, Jiatong Li, Lingyun Chu, Yuan Lai
Abstract
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.