LLM-assisted proposal writing in competitive R&D funding: Evidence from Horizon Europe
JRC Working Papers on Territorial Modelling and Analysis No 02/2026
Large language models (LLMs) can lower the cost of producing complex text, potentially reshaping competition for research and development (R&D) funding to private firms. We provide the first evidence on this issue using data covering the universe of firm applications to a major competitive R\&D funding program: Horizon Europe. We find that LLM-assisted writing rises sharply following the public release of ChatGPT in late 2022, with around 40% of proposal abstracts exhibiting LLM-modified content by the end of 2024. Adoption is heterogeneous across applicants and is more common among younger, and less innovative firms, as well as among firms located in countries with lower levels of English proficiency, economic development and R&D intensity. In cross-sectional analyses, proposals that rely extensively on LLM-generated text are associated with lower evaluation scores and funding probabilities, whereas partial LLM assistance is only weakly related to such outcomes. However, analyses exploiting repeated submissions of the same proposals do not indicate that adopting LLM-assisted writing causally worsens evaluation results. Overall, the findings suggest that generative AI may reduce barriers to participation in competitive funding without clear evidence that LLM-assisted writing itself alters evaluation decisions.
SANTOLERI Pietro;
RENTOCCHINI Francesco;
LELLI Francesco;
2026-03-18
European Commission
JRC146131
https://publications.jrc.ec.europa.eu/repository/handle/JRC146131,
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