Payrolls to Prompts: Firm-Level Evidence on the Substitution of Labor for AI

2026-01-28

AI Summary

This study addresses the lack of credible empirical evidence on the firm-level displacement of human labor by generative AI. Leveraging granular payment data from a major U.S. expense management platform and exploiting the release of ChatGPT as a quasi-natural experiment, the authors employ a difference-in-differences framework to examine shifts in firm expenditures between online labor and AI services. The paper provides the first direct causal evidence at the firm level that generative AI is partially substituting human labor. It introduces a novel exposure metric based on firms’ pre-existing share of online labor spending. Results indicate that highly exposed firms increased their AI expenditure share by 0.8 percentage points relative to less exposed firms by Q3 2025, with every $1 reduction in online labor spending associated with an average $0.03 increase in AI spending, revealing significant cost-saving potential.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Generative AI has the potential to transform how firms produce output. Yet, credible evidence on how AI is actually substituting for human labor remains limited. In this paper, we study firm-level substitution between contracted online labor and generative AI using payments data from a large U.S. expense management platform. We track quarterly spending from Q3 2021 to Q3 2025 on online labor marketplaces (such as Upwork and Fiverr) and leading AI model providers. To identify causal effects, we exploit the October 2022 release of ChatGPT as a common adoption shock and estimate a difference-in-differences model. We provide a novel measure of exposure based on the share of spending at online labor marketplaces prior to the shock. Firms with greater exposure to online labor adopt AI earlier and more intensively following the shock, while simultaneously reducing spending on contracted labor. By Q3 2025, firms in the highest exposure quartile increase their share of spending on AI model providers by 0.8 percentage points relative to the lowest exposure quartile, alongside significant declines in labor marketplace spending. Combining these responses yields a direct estimate of substitution: among the most exposed firms, a \$1 decline in online labor spending is associated with approximately \$0.03 of additional AI spending, implying order-of-magnitude cost savings from replacing outsourced tasks with AI services. These effects are heterogeneous across firms and emerge gradually over time. Taken together, our results provide the first direct, micro-level evidence that generative AI is being used as a partial substitute for human labor in production.

Problem

• generative AI
• labor substitution
• firm-level evidence
• online labor marketplaces
• AI adoption

Innovation

• generative AI
• labor substitution
• firm-level evidence
• difference-in-differences
• exposure measure