Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles

2026-10-06

AI Summary

This study addresses the limitation of existing research that predominantly conceptualizes AI capabilities as firm-internal resources, overlooking their dynamic inter-organizational distribution through talent mobility. Leveraging 535 million employment records, we construct a global AI talent mobility network and integrate complex network analysis with event study methodology to examine how network centrality influences firm value. Our findings reveal that while AI talent inflows are highly concentrated, core network positions remain contestable. Furthermore, network centrality exhibits a significant positive association with firm value, and upward positional shifts presage subsequent value enhancement. This research demonstrates that network structures encode economic information beyond mere labor scale, offering a novel perspective for understanding the cross-organizational spillovers of AI capabilities.

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.

Problem

• AI talent flow
• interfirm network
• labor mobility
• network centrality
• enterprise value

Innovation

• AI Talent Flow Networks
• Preferential Attachment
• Network Centrality
• Event Study
• Online Professional Profiles