2026/04/07 by Seok Joon Kwon · 1 voice
#physics.soc-ph
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 γ = 2.0, review acceleration δ = 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 δ > γ; 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.