The article argues that recent executive overreach and politicization have severely undermined established norms of U.S. civil-military relations. To restore professional standards, the incoming Congress must actively exercise its oversight powers, moving beyond mere criticism to demanding transparency in defense policy and personnel decisions. Key reforms include enforcing meritocratic promotion processes, requiring documented justification for senior officer removals, and preventing the partisan use of the military. These actions are crucial for bolstering public trust and ensuring the military remains a nonpartisan instrument of civilian control.
Spending it faster: What a 10,000-agent proof means for AI’s borrowed expertise
English Summary
The article argues that while generative AI has made scientific result production a scalable industrial process, the true bottleneck is shifting from generating results to human absorption and verification. Evidence from large-scale AI proofs demonstrates that the volume of machine-generated science far outstrips the capacity of human experts to evaluate it, highlighting a critical mismatch. Policy must therefore pivot from merely funding AI compute to strengthening human capital, institutional review processes, and the apprenticeship pipeline necessary to train qualified evaluators, lest the scientific community be overwhelmed by unverified, high-volume output.
中文摘要
本文論述，儘管生成式AI已使科學成果的產出成為一個可規模化的工業流程，但真正的瓶頸正從「生成成果」轉移到「人類的吸收與驗證」環節。大型AI驗證的證據顯示，機器生成科學的數量遠超人類專家評估的能力，這突顯了一個關鍵的結構性不匹配。因此，政策制定必須從單純資助AI運算能力，轉向強化人力資本、制度性審查流程，以及建立必要的學徒制管道以訓練合格的評估人員。否則，科學界恐將被海量且未經驗證的產出所淹沒。
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