Publish and Perish: How AI-Accelerated Writing Without Proportional Verification Investment Degrades Scientific Knowledge

TL;DR

This paper models how AI-driven manuscript writing accelerates submissions faster than review capacity, leading to degraded verification quality and long-term knowledge loss, with policy implications for maintaining scientific integrity.

Contribution

It introduces a formal differential equation model linking AI adoption, review queue dynamics, and verification quality degradation, highlighting the paradoxical effects on scientific knowledge.

Findings

Knowledge output peaks around 2026 before declining.

Degradation to 40% loss in long-term knowledge output.

Combined policy interventions can restore positive knowledge production.

Abstract

Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize this asymmetry through a minimal two-variable ordinary differential equation model coupling review queue evolution and verification quality degradation via an endogenous, queue-pressure-driven review AI adoption mechanism. The causal chain is: writing AI adoption increases submissions, growing the review queue, which drives reviewer AI adoption under pressure, degrading verification quality and reducing net knowledge output. Under empirically informed parameters (writing acceleration {\gamma} = 2.0, review acceleration {\delta} = 0.5), the model predicts a deceptive honeymoon where knowledge output peaks at 1.10K0 (circa 2026), followed by paradox onset at t = 6 years (2028) and long-term degradation to 0.68K0 (32% loss), approaching a steady state of 0.60K0 (40% loss). The critical condition for net benefit is {\delta} > {\gamma}; the current operating point lies deep in the paradox regime. Empirical validation against NeurIPS, ICLR, arXiv, and bioRxiv submission data shows qualitative consistency with observed post-ChatGPT acceleration patterns. Policy analysis reveals that only combined interventions such as review infrastructure investment paired with institutional quality standards can restore positive knowledge production.