生成式人工智能对北京市内部不平等与技能溢价的影响

摘要

生成式人工智能（GenAI）是首个大规模触及高认知任务的自动化浪潮，但其对城市内部不平等的影响仍鲜为人知。基于北京2018至2024年间的500万条招聘信息，我们整合了五大主流大语言模型的任务级评估，构建了社区层面的生成式人工智能暴露指数。我们分析了这一冲击的空间、结构与因果机制。研究发现，生成式人工智能的暴露度高度集中于城市核心区域，加剧了城市内部的人工智能鸿沟。自2023年以来，高暴露度社区在持续吸引高技能劳动者的同时，却出现了工资停滞现象——即“高技能陷阱”。这种工资惩罚源于任务去技能化与劳动力市场竞争加剧。围绕ChatGPT发布构建的双重差分设计支持了因果解释。这些发现挑战了技能偏向型技术变革的主流理论，并为全球技术中心的包容性人工智能治理提供了依据。

English

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.

生成式人工智能对北京市内部不平等与技能溢价的影响

Generative AI impacts on intra-urban inequality and skill premium in Beijing

摘要

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