The Impact of Artificial Intelligence on Mathematical Prestige: Epistemic Burden, Professional Identity, and Status Threat Under Automated Discovery

Automated mathematical discovery can increase the supply of results while changing the distribution of verification labour, research autonomy, and academic recognition. This theoretical working paper separates epistemic burden, professional identity, status threat, and governance concerns rather than treating opposition as evidence of jealousy. A small additive model distinguishes discovery-label-invariant formal validity from workload and utility. Eight Lean-checked propositions establish conditional workload relations, the possibility of overload, and the non-identification of latent motives from aggregate opposition. Public primary sources provide context but no representative psychological evidence. Deterministic synthetic examples illustrate the assumptions, and a prospective factorial study is proposed to test responses to source labels, verification packages, and credit rules. No participants were recruited and no empirical effects were estimated. The framework’s central contribution is an auditable separation of deductive consequences, documented positions, and hypotheses requiring evidence.