2026/02/19 by Martin Bulla, Peter Mikula · 1 voice
Decision Sciences · Medicine · #scientometrics and bibliometrics research #Academic Publishing and Open Access #Artificial Intelligence in Healthcare and Education
paper · doi:10.31222/osf.io/d8gcu_v1
openalex publication_date 2026/02/19 · openalex created_date 2026/02/20 · openalex updated_date 2026/07/15
Research funding schemes are increasingly struggling to reliably distinguish scientific merit through traditional scoring. Using the most recent evaluations of the EU Marie Skłodowska-Curie Actions postdoctoral fellowships as a case study, we show how the rapid institutional adoption of Large Language Models coincides with unprecedented score compression. With only ~5% of proposals now falling below the 70% quality threshold, down from ~20% in previous years. We argue that “excellence saturation” has reached a tipping point that exposes the structural limits of fine-grained peer review and alters reviewer decision-making dynamics where funding decisions resemble a lottery. This shift to AI-assisted grant writing effectively decouples a proposal’s form from its scientific substance, necessitating a transition from fine-grained ranking toward managing an abundance of excellence through alternative allocation mechanisms, such as funding lotteries.