理解企业间AI人才流动网络：基于在线职业档案的分析

中文摘要 AI 辅助

人工智能能力通常被衡量为企业内部积累的资源，然而，这些能力也通过员工流动跨越组织边界进行传播。基于一个涵盖58个国家约5.35亿条就业记录的数据集，我们重建了2010年至2022年间企业间AI人才流动网络。我们发现，AI人才流入日益集中于少数领先企业，其优先连接效应强于一般劳动力流动。然而，网络核心仍具有可竞争性：AI网络中的核心位置更替频率远高于非AI网络。网络位置还承载着超越员工规模的信息。这一网络位置具有经济后果：在控制员工规模、资产和固定效应后，AI网络中心性与更高的企业价值相关。事件研究估计进一步表明，网络位置的显著改善会带来相对于具有相似事前轨迹的匹配企业更高的企业价值。综合来看，这些发现表明，企业AI优势不仅取决于企业积累了多少人才，还取决于其在人才流动网络中所处的位置。

英文摘要

Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Drawing on a database of approximately 535 million employment records across 58 countries, we reconstruct the inter-firm network of AI talent flows over 2010--2022. We find that AI talent inflows are becoming increasingly concentrated among a small set of leading firms, with stronger preferential attachment than in general labor mobility. Yet the network core remains contestable: central positions in AI networks change hands far more often than in non-AI networks. Network position also carries information that goes beyond workforce size. This network position is economically consequential: AI network centrality is associated with higher enterprise value, after accounting for workforce scale, assets, and fixed effects. Event-study estimates further indicate that sharp improvements in network position are followed by higher enterprise value relative to matched firms with similar pre-event trajectories. Together, these findings suggest that corporate AI advantage depends not only on how much talent firms accumulate, but also on where they sit in its circulation.

发表机构

• Massachusetts Institute of Technology（麻省理工学院）
• Peking University（北京大学）
• Singapore-MIT Alliance for Research and Technology (SMART)（新加坡-麻省理工研究与技术创新联盟）
• University of Florida（佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。