THE AI FLOOD: CONGESTION COLLAPSE IN SCIENTIFIC PEER REVIEW

Scientific peer review functions as the primary quality control mechanism in academic publishing. For decades the system remained manageable partly because producing a full manuscript required significant human effort which naturally limited submission volume. Generative artificial intelligence has changed this constraint by reducing the time and cost required to generate academic style text and enabling large scale submission growth. This introduces a new infrastructure level risk where the critical threat is not only low quality writing but sustained overload of editorial and reviewer capacity. This study frames AI-enabled submission flooding as a system stability problem and models the peer review workflow as a two stage queueing pipeline consisting of editorial screening and peer review. A stress testing approach is applied by increasing manuscript arrival rates above baseline levels and evaluating system utilization and backlog formation and expected review delay. The results show a tipping point behavior where moderate increases in submission volume can push the system beyond its stability boundary causing persistent backlog growth and rapid inflation of review timelines from weeks toward months and years. This collapse occurs when baseline peer review utilization is already high, such that a moderate proportional increase in submissions is sufficient to push reviewer demand beyond available service capacity. The findings indicate that content-based AI detection tools alone cannot prevent collapse under high volume conditions because congestion emerges when arrivals exceed service capacity regardless of manuscript origin. Therefore long term resilience requires defenses that reduce adversarial scaling and restore the balance between attacker cost and defender capacity through stronger identity verification and submission throttling and proof-of-personhood style controls.