This article critically examines the accelerating phenomenon of academic overproduction, tracing its roots from exponential publication growth in the late twentieth century to the contemporary landscape overwhelmed by digitization, global competition, shifting publication economics and now artificial intelligence. The surge of scholarly output, enabled by advanced digital infrastructures, open-access models, and mega-journals has fueled not only greater access and collaboration, but also mounting information overload, declining editorial standards, and the evolution of a research workforce that spends more and more time chasing metrics. Against this backdrop, the rise of generative artificial intelligence is poised to further intensify these dynamics through both “flattening” (the homogenization and proliferation of scholarly writing) and “enslopification,” defined as the mass production of low-quality academic content optimized for metrics rather than insight. These issues reflect deeper epistemological tensions within academic research, between cultures of “knowledge sharing” and “knowledge transfer”. Rather than simply blaming digital technologies or AI, we argue that quantification pressures, institutional incentives, and the commodification of research have primed the academy for a crisis of relevance and authenticity. It is thus imperative to reimagine research beyond compliance-driven production and superficial debates about AI integration, instead advocating for multimodal, participatory, and dialogical scholarship. Meaningful reform demands a shift from metric-driven output toward research that cultivates agency, reflection, and genuine public engagement, urging institutions and scholars to reclaim the value and purpose of scholarly inquiry in a post-AI world.