Hope, Signals, and Silicon: A Game-Theoretic Model of the Pre-Doctoral Academic Labor Market in the Age of AI

This paper develops a unified game-theoretic account of how generative AI reshapes the pre-doctoral "hope-labor" market linking Principal Investigators (PIs), Research Assistants (RAs), and PhD admissions.

We integrate (i) a PI-RA relational-contract stage, (ii) a task-based production technology in which AI is both substitute (automation) and complement (augmentation/leveling), and (iii) a capacity-constrained admissions tournament that converts absolute output into relative rank.

The model yields four results.

First, AI has a dual and thresholded effect on RA demand: when automation dominates, AI substitutes for RA labor; when augmentation dominates, small elite teams become more valuable.

Second, heterogeneous PI objectives endogenously segment the RA market: quantity-maximizing PIs adopt automation and scale "project-manager" RAs, whereas quality-maximizing PIs adopt augmentation and cultivate "idea-generator" RAs.

Third, a symmetric productivity shock triggers a signaling arms race: more "strong" signals flood a fixed-slot tournament, depressing the admission probability attached to any given signal and potentially lowering RA welfare despite higher productivity.

Fourth, AI degrades the informational content of polished routine artifacts, creating a novel moral-hazard channel ("effort laundering") that shifts credible recommendations toward process-visible, non-automatable creative contributions.

We discuss welfare and equity implications, including over-recruitment with thin mentoring, selectively misleading letters, and opaque pipelines, and outline light-touch governance (process visibility, AI-use disclosure, and limited viva/replication checks) that preserves efficiency while reducing unethical supervision and screening practices.