Pay and Beyond: The Evolving Rewards for AI Skills

Alejandra Castaneda, Matthew Bone and Fabian Stephany

Papers from arXiv.org

Abstract: This study examines the monetary and non-monetary rewards advertised in job vacancies requiring artificial intelligence skills in the U.S. labor market. Using approximately ten million online job vacancies posted between 2018 and 2025, we examine the development of AI demand, wage premia, and non-monetary benefits, including tuition assistance, paid leave, health and well-being programs, parental leave, inclusive workplace language, and remote work, across firms of different sizes. We find that demand for AI skills increased sharply following the introduction of generative AI in late 2022 across small and medium-sized enterprises (SMEs), large firms, and S&P 500 firms, while AI wage premia rose most strongly among SMEs. AI vacancies are significantly more likely than comparable non-AI roles to advertise non-monetary benefits, particularly remote work, inclusive culture, and parental leave. This premium is strongest among SMEs and large firms, but weaker among S&P 500 firms. Finally, we show that the relationship between wages and benefits shifted after the spread of generative AI: whereas complementarity between high wages and benefits was previously concentrated among S&P 500 firms, and substitution among SMEs, the difference across firm sizes has attenuated. Our findings indicate that the increased demand for AI skills has been accompanied by changes in compensation patterns beyond pay, as employers across the size distribution combine monetary and non-monetary rewards to recruit AI-skilled workers.

Date: 2025-07, Revised 2026-11
New Economics Papers: this item is included in nep-ain, nep-hea and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2507.20410 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2507.20410

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().