The article argues that the Federal Reserve's recent rate hike decision, while justifiable on inflation grounds, highlights a critical lack of an underlying, transparent policy framework. The core problem is that the Fed's decisions appear discretionary, leading to market uncertainty because the committee's judgment, rather than clear data, dictates policy shifts. The author proposes that the Fed adopt a formal monetary policy rule—an algebraic formula linking the target rate to indicators like inflation and unemployment—to replace subjective guidance. Implementing such a rule would provide market predictability, enhance transparency, and shield the Fed from political attacks by making deviations from the standard easily quantifiable.
Are the US and China really in an AI “race”?
English Summary
Brookings argues that framing US-China AI development as a simple 'race' oversimplifies the complex strategic reality. The U.S. strategy is heavily focused on achieving Artificial General Intelligence (AGI) and Superintelligence, driving massive compute spending within advanced labs. Conversely, China’s approach emphasizes practical application, aiming to integrate AI into every sector of its economy—from manufacturing to healthcare—rather than focusing solely on theoretical benchmarks. Policymakers must recognize that true power lies not in achieving the highest benchmark score, but in the widespread diffusion and real-world usability of the technology.
中文摘要
布魯金斯學會（Brookings）指出，將美中人工智慧發展描繪為單純的「競賽」，過度簡化了複雜的戰略現實。美國的策略高度聚焦於實現通用人工智慧（AGI）和超級智慧，推動先進實驗室進行大規模的運算資源投入。相對地，中國的方法則強調實際應用，旨在將AI整合到經濟的各個領域——從製造業到醫療保健——而非僅專注於理論基準測試。政策制定者必須認識到，真正的實力並非來自取得最高的基準分數，而是源自技術的廣泛普及和真實世界的可用性。
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