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.
Competing AI strategies for the US and China
English Summary
The US and China are pursuing divergent AI strategies: the US focuses on maintaining a lead through massive capital expenditure and frontier model performance, while China is adapting to U.S. export controls by prioritizing efficiency, adoption, and physical integration. Key evidence shows China compensating for limited compute resources by heavily utilizing techniques like Mixture-of-Experts and quantization, coupled with an open-source model strategy that is gaining global developer popularity. This shift implies that the AI competition is evolving from a pure compute race to a multi-front battle focused on cost-effective deployment, open-source ecosystem building, and leveraging existing industrial supply chains for embodied AI.
中文摘要
美中兩國正推動分歧的 AI 戰略：美國著重於透過大規模資本支出和前沿模型性能來維持領先地位；而中國則是在適應美國出口管制的前提下，將重點放在效率、應用普及和實體整合。關鍵證據顯示，中國正透過大量利用專家混合模型（Mixture-of-Experts）和量化等技術，來彌補計算資源的限制，同時結合開放原始碼模型策略，使其在全球開發者群體中日益受歡迎。這一轉變暗示，AI 競爭正從純粹的算力競賽，演變為一場多面向的戰鬥，重點聚焦於具成本效益的部署、開放原始碼生態系統的建立，以及利用現有的工業供應鏈來發展具身 AI。
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