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

2025-11-03

AI Summary

This study investigates how labor mobility drives the inter-firm diffusion of artificial intelligence (AI) knowledge and identifies organizational conditions that amplify productivity spillovers. Leveraging a firm-level AI talent mobility network constructed from 460 million job postings, and employing an empirical framework based on the Cobb–Douglas production function, we find that AI talent inflows boost total factor productivity (TFP) two to three times more than traditional IT talent inflows—yet this effect is significant only when the talent originates from flat, lean-startup organizations. The underlying mechanism is that such organizational structures cultivate AI specialists with greater generalizability and experimental capacity. This paper is the first to systematically incorporate organizational form into technology diffusion theory, demonstrating that AI knowledge spillovers critically depend on integrative, trial-and-error–oriented organizational environments at the knowledge-production stage—thereby extending beyond the boundaries of conventional IT diffusion models.

Technology Category

• Humans and AI: Teamwork, Team formation
• Intelligent Robots: Embodied AI
• Philosophy and Ethics of AI: AI & Jobs/Labor

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Examining AI knowledge dissemination through labor mobility across firms
• Identifying organizational conditions enabling productive AI spillovers
• Comparing AI spillover mechanisms with traditional IT spillover patterns

Innovation

• Labor mobility network for AI knowledge diffusion
• Flat lean organizations enable versatile AI generalists
• AI spillovers depend on experimental integrative environments