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.
The Greenspan Lesson for the AI Race
English Summary
The article argues that maintaining U.S. technological leadership in the AI race requires massive, sustained capital investment, which demands careful monetary policy support. Drawing on Alan Greenspan's historical success during the 1990s tech boom, the analysis warns that current macroeconomic institutions risk misreading a profound productivity paradigm shift caused by AI. Because AI-driven growth is not fully captured by traditional statistics, policymakers must avoid raising interest rates or slowing investment based on backward-looking data. Failing to do so could weaken U.S. long-term technological competitiveness against rivals like China.
中文摘要
本文論述，若美國欲在人工智慧（AI）競賽中維持技術領先地位，必須進行大規模且持續的資本投入，這進而要求謹慎的貨幣政策支持。分析援引了艾倫·格林斯潘（Alan Greenspan）於1990年代科技繁榮時期的歷史成功經驗，警告當前的宏觀經濟體系有誤判由AI所導致的深刻生產力範式轉移的風險。由於AI驅動的成長難以完全納入傳統統計數據的捕捉範圍，政策制定者必須避免僅基於過往回顧性數據來提高利率或減緩投資。若未能做到這一點，可能會削弱美國與中國等競爭對手之間的長期技術競爭力。
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