The Labor Market Effects of Generative Artificial Intelligence
In this paper we develop a new survey analyzing Generative AI use in the labor market to assist in measuring the economic effects of Generative AI. We find, consistent with other surveys that Generative AI tools like large language models (LLMs) are most commonly used in the labor force by younger individuals, more highly educated individuals, higher income individuals, and those in particular industries such as customer service, marketing and information technology. Overall, we find that LLM adoption at work among U.S. survey respondents above 18 has increased rapidly from 30.1% as of December 2024, to 43.2% as of March/April 2025. We also estimate Generative AI use at the intensive margins, its efficiency gains and its use in job search and seek to examine the effects of LLMs on productivity and the labor market using a number of additional datasets. These results have several implications for policymakers, businesses, and researchers navigating the evolving landscape shaped by the integration of Generative AI into the global economy.
Introduction. The advent of generative artificial intelligence (AI) tools such as large language models (LLMs), enabled by the In the past decade there have been statements and analyses about the recent decline in productivity and economic Meanwhile, others have suggested that we may be approaching a point in time (or "singularity") that unleashes Much of the labor market effect of generative AI will hinge on whether it is a complement or substitute for existing
Discussion / Conclusion. To summarize, in this paper we develop a new survey analyzing Generative AI use in the labor market to assist in The findings of this study highlight the uncertain and multifaceted nature of the impacts induced by LLM adoption. There are several avenues for public policy as it relates to Generative AI. One is the government subsidization for Policymakers, businesses, and educational institutions may want to consider such results to foster a resilient labor
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Research framings built by reading the notes related to this paper — the questions it feeds into.
What happens to knowledge when intelligence becomes tokenized like a commodity? How should designers communicate what AI systems truly are and can do? Can inoculation prompting prevent emergent misalignment after reward hacking? How well do AI systems understand human social norms? How does AI adoption across firms reshape employment and inequality?- Why would compute-replacement cost determine wages instead of productivity?
- Does deploying AI uniformly across task types increase or decrease workplace inequality?
- How does uneven access to AI tools shape who benefits from productivity gains?
- What policy levers can redirect AI deployment toward reducing rather than deepening inequality?
- How does concentration of AI capability across firms affect labor market outcomes?
- How should forecasting methods adapt to a post-AGI regime?
- How does occupational segregation affect who gains from AI productivity?
- How do institutions shape whether AI enables worker mobility or deepens hierarchy?
- Can AI narrow inequality or does deployment determine the outcome?
- How does concentrated AI exposure across workers affect firm-level employment demand?
- How do worker-side adaptation effects interact with firm-level substitution patterns?
- What mechanisms enable some firms to adopt AI more cheaply than others?
- Does codifying expertise into AI agents drive faster labor substitution?
- Which firms capture the cost advantages from labor-to-AI substitution?
- Why do firms substitute labor for AI faster than gig worker jobs disappear?