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.
How to bridge the global AI divide
English Summary
The article argues that artificial intelligence's concentration in the Global North—where 75% of new data center capacity is being built and advanced economies score double the AI readiness of low-income countries—threatens to widen global inequality unless addressed through deliberate policy intervention. The Global South generates vast data streams yet lacks local infrastructure to process them, resulting in 'digital extractivism' where value accrues abroad. Solutions include South-South cooperation, leveraging renewable energy advantages, building sovereign digital public infrastructure models (like India's subsidized GPU access for startups), and redefining global AI governance to center rather than marginalize the Global South. Without strategic action, this divide will harden into structural inequality; with proper intervention, Global South countries can leapfrog legacy systems and shape AI's future.
中文摘要
本文指出，人工智慧的發展高度集中於全球北方（Global North）——此區域不僅建構了75%的新數據中心容量，且進步經濟體的AI就緒程度也遠超低收入國家——這種現象威脅著加劇全球不平等，除非透過有意識的政策干預來應對。全球南方（Global South）雖然產生了龐大的數據流，卻缺乏本地的處理基礎設施，導致了「數位提取主義」（digital extractivism），使得價值流向國外。解決方案包括促進南南合作、利用再生能源的優勢、建立主權數位公共基礎設施模式（例如印度為新創公司提供的補貼式GPU使用權），以及重新定義全球AI治理，使其以全球南方為核心而非邊緣化。若缺乏戰略行動，這種鴻溝將固化為結構性不平等；反之，若進行適當的干預，全球南方國家便能跳過傳統系統，主導AI的未來發展。
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