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arXiv · 2610.08264

Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles

Donghang Li·Yunhan Zheng·Alok Prakash·Shenhao Wang·Jinhua Zhao
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Abstract

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.

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Donghang Li, Yunhan Zheng, Alok Prakash, Shenhao Wang, Jinhua Zhao. 2026-10-06. Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles. https://arxiv.org/abs/2610.08264

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