隨著生成式人工智慧的快速發展，NVIDIA 已從傳統 GPU 供應商轉型為全球 AI 商業生態系的核心主導者。然而，現有文獻對企業如何於生態系演化中調整定位與策略，仍缺乏縱向實證研究。故本研究以 NVIDIA 為個案，探討其生態系演化歷程與關鍵策略機制。本研究採用縱向個案研究法，以Rong et al. (2015) 提出之商業生態系統生命週期理論（BELC）為主要架構，輔以Jacobides et al. (2018) 之生態系結構理論，針對 NVIDIA 在 2017 年至 2025 年間之供應商與客戶數據。
研究發現如下：第一，NVIDIA 生態系演化可分為三階段。收斂期（2017–2018）透過 CUDA 平台建立 AI 運算標準，完成由零組件供應商轉向規則制定者；鞏固期（2019–2023）藉由 A100/H100 晶片與雲端客戶建立深度合作，並透過與台積電及 SK Hynix 的共同專業化掌控關鍵供應資源，形成雙向生態系壁壘；更新期（2024–2025）面對核心客戶開始投入自研晶片所帶來的邊際替代風險，則透過與下游硬體夥伴合作啟動生態系再造，並持續深化與關鍵供應商的合作規模。
第二，本研究提出「客戶採購屬性由銷貨成本（COGS）轉向資本支出（CAPEX）」可作為生態系進入鞏固期的重要指標，顯示客戶已將 NVIDIA 產品視為長期基礎設施，形成高度路徑依賴與鎖定效果。
第三，NVIDIA 透過供應鏈共同專業化與軟硬體整合，建立高轉換成本與瓶頸資源控制能力，進一步強化其生態系主導地位。

With the rapid advancement of generative artificial intelligence (AI), NVIDIA has successfully transformed from a traditional Graphics Processing Unit (GPU) supplier into a central orchestrator of the global AI business ecosystem. However, existing literature still lacks longitudinal empirical studies on how firms adjust their positioning and strategies throughout ecosystem evolution. Therefore, this study adopts NVIDIA as a case to explore the evolutionary process of its ecosystem and the strategic mechanisms behind it.
This research employs a longitudinal case study approach based primarily on the Business Ecosystem Life Cycle (BELC) framework proposed by Rong et al. (2015), supplemented by the ecosystem structure theory of Jacobides et al. (2018). The study systematically analyzes NVIDIA’s supplier and customer data from 2017 to 2025.
The findings are as follows. First, NVIDIA’s ecosystem evolution can be divided into three major stages. During the Converging Stage (2017–2018), NVIDIA established AI computing standards through the CUDA platform and transformed itself from a component supplier into a rule setter. During the Consolidating Stage (2019–2023), NVIDIA strengthened its ecosystem dominance through the A100/H100 chip architecture, deep collaborations with cloud service providers, and co-specialization partnerships with TSMC and SK Hynix, thereby forming both supply-side and demand-side ecosystem barriers. During the Renewing Stage (2024–2025), NVIDIA further expanded into sovereign AI and enterprise private cloud markets through system integrators, successfully renewing ecosystem growth momentum.
Second, this study proposes that the shift in customer procurement attributes from Cost of Goods Sold (COGS) to Capital Expenditure (CAPEX) can serve as an important indicator of ecosystem consolidation. This transition reflects how customers increasingly regard NVIDIA’s products as long-term infrastructure assets, thereby creating strong path dependence and lock-in effects.
Third, NVIDIA established high switching costs and control over bottleneck resources through supply chain co-specialization and hardware-software integration, further strengthening its dominant position within the ecosystem.

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