近年來，生成式人工智慧（Generative AI）與大型語言模型（Large Language Models, LLMs）的快速發展，使全球對高效能算力的需求呈現爆發性成長。美國主要雲端服務提供者（Cloud Service Providers, CSP），如 Amazon Web Services（AWS）、 Microsoft Azure 與 Google Cloud ，僅成成 AI 算力的核心供應者，同時也成 高階運算硬體需求的最大買方之一。然而AI算力需求暴增的同時，國際政經環境卻日趨僅穩定，尤其中美貿易戰、半導體出口管制與科技戰，使原本高度全球化、分工精細的供應鏈體系面臨前所未有的衝擊。在此情境下，CSP 如何兼顧效能擴張、成本控制與供應安全，成 一項關鍵的管理議題。
本研究以自研晶片 、 供應鏈策略與營運組織三個面向為分析主軸，探討美國CSP 在AI算力需求急速成長與地緣政治風險升高的雙重壓力下，如何重構其供應鏈管理模式。研究結果顯示，在研發層面，CSP 透過自研晶片（如 AWS 的Trainium、Google 的 TPU、Microsoft 的Maia系列）來降低對單一供應商的依賴，並提升效能與成本優勢；在營運組織層面，CSP建立跨部門供應鏈風險治理架構，整合研發、工程、採購、法遵與政策分析單位，以即時回應地緣政治與法規變化；在策略層面，CSP則透過各階供應商的直接管理 、 供應商管理庫存與培養長期合作的供應商，降低地緣政治衝擊對營運穩定性的威脅。
本研究認為，供應鏈管理已僅再只是成本與效率導向的營運議題，而是攸關國家安全、科技競爭力與企業長期競爭優勢的核心戰略問題。CSP的供應鏈治理能力，將成 未來AI產業競爭中僅可或缺的關鍵能力之一。

The rapid advancement of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has led to an unprecedented surge in global demand for high performance computing resources. Major U.S. Cloud Service Providers (CSPs), including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, have emerged as both the primary providers of AI computing infrastructure and some of the largest buyers of advanced computing hardware. As AI workloads scale rapidly, these firms play a central role in shaping the structure of the global AI hardware supply chain.
However, this expansion has occurred amid increasing geopolitical and economic uncertainty. Rising U.S.–China trade tensions, semiconductor export controls, and broader technology competition have disrupted previously highly globalized and specialized supply chain systems, exposing CSPs to new forms of supply risk.
This study investigates how U.S. CSPs restructure their supply chain management models under the dual pressures of rapidly growing AI computing demand and heightened geopolitical risk. The analysis is organized around three key dimensions: in-house chip development, supply chain strategy, and organizational governance. The findings indicate that CSPs increasingly invest in proprietary chips—such as AWS’s Trainium, Google’s Tensor Processing Units (TPUs), and Microsoft’s Maia series—to reduce reliance on single suppliers, improve performance optimization, and enhance cost control. At the organizational level, CSPs establish cross-functional supply chain risk governance frameworks that integrate research and development, engineering, procurement, compliance, and policy analysis functions to respond more effectively to regulatory and geopolitical changes. Strategically, CSPs strengthen supply resilience through direct multi-tier supplier management, vendor-managed inventory models, and the cultivation of long-term strategic partnerships.
This study argues that supply chain management has evolved beyond a cost-and efficiency-oriented operational function into a core strategic capability closely linked to national security considerations, technological competitiveness, and long-term advantage in the AI industry.

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