Racing the Rumor: Proprietary AI Models and the Ethics of Scientic Priority

This paper examines the ethical implications of proprietary AI systems participating in open, priority-based scientific competition, using the 2026 Navier–Stokes episode as a case study. It identifies three distinct but mutually reinforcing concerns: asymmetry of computational means, self-verified provenance, and institutional epistemic asymmetry in the allocation of credibility and scientific credit. Drawing on Robert Merton’s sociology of science and Miranda Fricker’s theory of epistemic injustice, the paper distinguishes between the validity of a scientific result and the fairness of the process through which priority is established. It argues that formal verification of an AI-generated proof does not, by itself, resolve questions concerning provenance, competitive fairness, or attribution. The paper therefore proposes a principle of separation between legitimate scientific competition and non-public computational advantage. It concludes by recommending public-parity requirements, mandatory disclosure of relevant system access histories, and independent pre-announcement adjudication for contested priority claims.