The Labor Market Effects of Generative Artificial Intelligence

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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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Does tokenized intelligence retain genuine value through exchange-based systems? How should human oversight be integrated with autonomous AI systems? Can language model RL training avoid reward hacking and misalignment? Why do persona-level simulations fail to predict individual preferences accurately? How does AI adoption affect human skill development and labor equality? How do language models inherit human biases from training data? How do interface design choices shape consciousness attribution? When does optimizing for quality undermine the value of diversity? When should tasks involve human-AI partnership versus full automation?