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.

翻译：生成式人工智能（GenAI）是首次大规模波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在很大程度上仍属未知。利用北京2018—2024年间500万条招聘信息，我们通过整合五个领先大语言模型的任务级评估，构建了邻里层面的GenAI暴露指数。我们考察了这一冲击的空间、结构与因果机制。研究发现，GenAI暴露高度集中于城市核心区，加剧了城市内部的人工智能鸿沟。自2023年以来，高暴露社区尽管持续吸引高技能劳动力，却经历了工资停滞——形成“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拥挤加剧。以ChatGPT发布为中心的双重差分设计支持了因果解释。这些发现挑战了既有的技能偏向型技术变革理论，并为全球科技枢纽的包容性人工智能治理提供了依据。