AI-Accelerated Research and University Labor: A Simple Model of Metric-Driven Substitution

This paper models how increasingly capable, low-cost AI research systems interact with metric-driven universities. We formalize a simple production economy in which labs allocate human and AI effort to maximize publications, citations, and grant dollars. AI ``effective IQ'' (research capability) doubles every 16 months and can be rented as a service. With a constant-elasticity-of-substitution technology, the relative demand for human research labor decays exponentially when AI and human work are gross substitutes. A grant tournament with prestige multipliers amplifies concentration toward already advantaged principal investigators (PIs). We derive role-specific theorems for tenured and tenure-track faculty, graduate students, and professional research support staff. The model highlights one central comparative static, the elasticity of substitution, and shows how policy levers that reduce substitutability or raise human-oversight floors materially change the trajectory.