Skills, Not Scale: GenAI and Technology Adoption

Nuriye Melisa Bilgin and Gianmarco Ottaviano

No 21506, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Do the determinants of technology adoption depend on technological architecture? Using administrative data on Turkish firms from 2021 to 2024, we compare the adoption of traditional and generative artificial intelligence (GenAI). We show that GenAI adoption is driven by workforce skill intensity and is not positively associated with firm size, whereas traditional AI depends on both scale and skills. Firms that adopt both technologies are distinct and represent the most persistent adoption mode. Conditional on adoption, the skill-to-size ratio governs technology choice, and transition dynamics indicate a sequential process in which firms adopt GenAI before expanding to hybrid use. Exploiting the release of ChatGPT as a quasi-experimental reduction in access costs, we find that high-skill firms differentially increased GenAI adoption, while firm size played a limited role. These results suggest that the canonical size-based diffusion pattern is not universal but depends on the cost structure of technologies, with implications for innovation policy and productivity dispersion.

Keywords: Artificial; intelligence (search for similar items in EconPapers)
JEL-codes: D22 J3 L25 O14 O33 (search for similar items in EconPapers)
Date: 2026-05
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP21506 (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:cpr:ceprdp:21506

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP21506

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().