AI Spillover is Different: Flat and Lean Firms as Engines of AI Diffusion and Productivity Gain

TL;DR

This paper investigates how AI knowledge spreads through labor mobility among firms, emphasizing that organizational traits like flatness and lean startup practices significantly enhance AI productivity spillovers, unlike traditional IT.

Contribution

It introduces a comprehensive analysis of AI spillovers via labor mobility, highlighting the critical role of organizational context in facilitating productive AI knowledge transfer.

Findings

AI spillovers are 2-3 times larger than traditional IT spillovers.

Hiring from flat and lean firms yields significant productivity gains.

AI knowledge transfer depends on organizational environment, not just scale.

Abstract

Labor mobility is a critical source of technology acquisition for firms. This paper examines how artificial intelligence (AI) knowledge is disseminated across firms through labor mobility and identifies the organizational conditions that facilitate productive spillovers. Using a comprehensive dataset of over 460 million job records from Revelio Labs (2010 to 2023), we construct an inter-firm mobility network of AI workers among over 16,000 U.S. companies. Estimating a Cobb Douglas production function, we find that firms benefit substantially from the AI investments of other firms from which they hire AI talents, with productivity spillovers two to three times larger than those associated with traditional IT after accounting for labor scale. Importantly, these spillovers are contingent on organizational context: hiring from flatter and more lean startup method intensive firms generates significant productivity gains, whereas hiring from firms lacking these traits yields little benefit. Mechanism tests indicate that "flat and lean" organizations cultivate more versatile AI generalists who transfer richer knowledge across firms. These findings reveal that AI spillovers differ fundamentally from traditional IT spillovers: while IT spillovers primarily arise from scale and process standardization, AI spillovers critically depend on the experimental and integrative environments in which AI knowledge is produced. Together, these results underscore the importance of considering both labor mobility and organizational context in understanding the full impact of AI-driven productivity spillovers.