An arXiv preprint released in July 2026 describes artificial intelligence systems that can generate research-level mathematics and warns that U.S. policy is weakening the pipeline of human mathematicians. The paper, authored by Jun‑Yong Park, references a May 2026 AI‑generated proof that disproved a long‑standing conjecture by Paul Erdős concerning the planar unit distance problem. It also notes recent cuts to federal funding for mathematical research and training programs. The essay argues that the ability to verify, interpret, and challenge mathematical reasoning functions as critical infrastructure, comparable to semiconductor manufacturing. It contends that this capacity should be treated as a strategic asset. To address concerns, the paper proposes that AI systems engaged in consequential reasoning disclose their key claims in formal, machine‑checkable formats, making their reasoning auditable. The proposal raises questions about how future AI development will be regulated and whether existing educational pipelines can be restored to support mathematical expertise.