📝 SummarXiv

Abstract

Generative artificial intelligence (AI) has begun to reshape academic publishing by enabling the rapid production of submission-ready manuscripts. While such tools promise to enhance productivity, they also raise concerns about overwhelming journal systems that have fixed acceptance capacities. This paper uses simulation modeling to investigate how AI-driven surges in submissions may affect desk rejection rates, review cycles, and faculty publication portfolios, with a focus on business school journals and tenure processes. Three scenarios are analyzed: a baseline model, an Early Adopter model where a subset of faculty boosts productivity, and an AI Abuse model where submissions rise exponentially. Results indicate that early adopters initially benefit, but overall acceptance rates fall sharply as load increases, with tenure-track faculty facing disproportionately negative outcomes. The study contributes by demonstrating the structural vulnerabilities of the current publication system and highlights the need for institutional reform in personnel evaluation and research dissemination practices.