北京研究发现生成式AI加剧城市内部不平等，高技能区反陷工资停滞。
Generative AI impacts on intra-urban inequality and skill premium in Beijing
- 用500万招聘数据构建社区级生成式AI暴露指数，评估任务层面影响。
- 2023年后高暴露区域出现工资停滞，即使吸引高技能人才，形成‘高技能陷阱’。
- 揭示任务去技能化与就业竞争加剧是主因，适合政策制定者和城市研究者参考。
生成式人工智能（GenAI）是首个大规模影响高认知任务的自动化浪潮，但其对城市内部不平等的影响尚不明确。基于北京2018至2024年的500万份职位招聘信息，本文通过五款主流大语言模型的任务评估，构建了街区级的GenAI暴露指数，探究该冲击的空间、结构及因果机制。研究发现，GenAI暴露高度集中于城市核心区域，加剧了城市内部的AI鸿沟。自2023年起，高暴露街区虽持续吸引高技能人才，却出现工资停滞现象，形成‘高技能陷阱’。这一工资惩罚由任务去技能化与劳动力市场拥挤加剧所致。基于ChatGPT发布事件的双重差分设计支持了因果推断。研究挑战了传统的技能偏向型技术变革理论，为全球科技枢纽的包容性人工智能治理提供了依据。
原文摘要 · Abstract (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.
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