The Labor Market Effect of Generative Artificial Intelligence on Artists

Abstract

Technological change has repeatedly disrupted creative labor markets, raising concerns about whether new tools substitute for artists or shift the organization of creative work. This paper studies how occupational exposure to generative AI (genAI) maps into employment and earnings outcomes for U.S. artists following the unanticipated release of ChatGPT. I combine an occupation-level LLM task exposure index with establishment-based occupational outcomes from the Occupational Employment and Wage Statistics (OEWS) and individual microdata from the American Community Survey (ACS), estimating event-study specifications that compare more versus less exposed artistic occupations from 2017 to 2024. Across datasets, I find little evidence of short-run earnings declines associated with LLM exposure through 2023, with point estimates near zero and in some specifications modestly positive. Evidence on employment is more mixed, with weaker employment growth in 2023 for more exposed artistic occupations in some specifications. To investigate mechanisms, I use the Gallup Workplace Panel from 2023 to 2025 to measure AI use directly and relate changes in AI use to job satisfaction and burnout. Within-person estimates show limited average well-being effects of adoption, but suggest heterogeneous responses for artists and a use pattern concentrated in ideation and creative-support tasks. The results are consistent with early task reallocation than immediate labor-market harm, while leaving open the possibility of medium-run adjustment as adoption deepens and complementary investments accumulate.

Keywords: artificial intelligence, arts, culture, creative economy, large language models, generative AI, skills dynamics, workforce

JEL Classification: J23, J24, L82, O33