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題名 AI晶片設計產業之動態競爭分析 —— 以Nvidia和AMD為例
An Analysis of Competitive Dynamics in the AI Chip Design Industry: Case Studies of Nvidia and AMD作者 姜蘊玲
Chiang, Yun-Ling貢獻者 楊宗翰
Yang, Tsung-Han
姜蘊玲
Chiang, Yun-Ling關鍵詞 AI晶片產業
GPU
Nvidia
AMD
動態競爭
AMC分析
外部環境
AI Chip Industry
GPU
Nvidia
AMD
Competitive Dynamics
AMC Model
External Environment日期 2025 上傳時間 1-Sep-2025 16:05:30 (UTC+8) 摘要 隨著人工智慧AI(Artificial Intelligence)與高效能運算(High-Performance Computing)應用的崛起，AI晶片產業已成為全球半導體平台競爭的核心。自2000年代以來，GPU(Graphics Processing Unit)從圖形處理器逐步轉型為AI運算的基礎設施，推動AI模型規模與算力成長，帶動產業進入新競爭時代。Nvidia憑藉「軟硬體協同」加「生態鎖定」的競爭策略，使Nvidia在AI訓練市場市占率長期維持在80%以上，成為產業技術標準與生態規則的制定者。 AMD(Advanced Micro Devices, Inc.)則推動「開放」加「異質」策略使AMD在推 論市場、邊緣運算與部分資料中心場景取得突破，市占率逐步提升，成為AI產業格局中具威脅性的挑戰者。 本研究採用動態競爭理論之市場共同性與資源相似性指標與AMC（察覺-Awareness、動機-Motivation、能力-Capability）分析架構，針對Nvidia與AMD於AI晶片產業的競爭行動與回應進行個案剖析。本研究主要結論如下： 結論一：AI晶片設計產業中，具有市場領先優勢的先進廠商相較於挑戰者，其在產品佈局上為更完整，技術迭代上具有較深遠且連貫的升級，且投入更多資源在高附加價值的高階產品市場，挑戰者多僅以片段式技術升級與回應，並投注資源在中低階產品市場。 結論二：AI晶片設計產業中，挑戰者需善用生態系統、供應鏈協同或技術路線調整，並結合策略模仿與差異化，並針對產業技術轉換或外部環境變動的關鍵時機，尋求切入與突破的機會，才能有效降低進入障礙，提升市場地位。 結論三：AI晶片設計產業中，具有市場及技術優勢的領導者廠商可透過態壁壘強化、技術迭代加速與供應鏈韌性反制挑戰者發起的攻擊，提升競爭障礙，維持領導者地位。 本研究理論貢獻在於驗證動態競爭理論與AMC模型於AI晶片產業的適用性。未來可進一步探討AI晶片產業地緣政治、產業政策與新興應用之競爭影響。
Driven by the growth of Artificial Intelligence (AI) and High-Performance Computing (HPC), AI chips have become central to global semiconductor competition. Since the 2000s, GPUs have transitioned from graphics accelerators to essential AI computing infrastructure, enabling larger AI models and greater computational power. Nvidia, through a “hardware-software co-optimization” and “ecosystem lock-in” strategy, has sustained over 80% market share in AI training and set prevailing industry standards. In contrast, Advanced Micro Devices (AMD) leverages an “open” and “heterogeneous” approach to achieve gains in inference, edge computing, and select data center markets, positioning itself as a significant competitor in the AI chip sector. This study adopts the dynamic competition theory’s indicators of market commonality and resource similarity, along with the AMC (Awareness–Motivation–Capability) analysis framework, to conduct a case analysis of the dynamic competitive actions and responses between Nvidia and AMD within the AI chip industry. The fianl conclusions of this study are as follows: 1. In the AI chip design industry, advanced firms with market leadership have more comprehensive product portfolios, deeper and more consistent technical iterations, and invest more resources in the high value-added, high-end product market. Challengers, on the other hand, mostly respond with fragmented technological upgrades and allocate resources to the mid- and low-end product markets. 2. In the AI chip design industry, challengers must fully leverage ecosystems, supply chain collaboration, or adjust their technology paths. By combining strategic imitation and differentiation, and seizing critical opportunities stemming from technological shifts or changes in the external environment, challengers can effectively lower entry barriers and enhance their market positions. 3. In the AI chip design industry, leading firms with market and technological advantages can fortify entry barriers, accelerate technical iteration, and reinforce supply chain resilience to counteract challengers’ initiatives, thereby raising competitors’ hurdles and maintaining their leadership positions. The theoretical contribution of this study lies in validating the applicability of dynamic competition theory and the AMC model to the AI chip industry. Future research may further explore the impact of geopolitics, industrial policy, and emerging applications on competition within the AI chip sector.參考文獻 Advanced Micro Devices, Inc. (2007). 2006 Annual Report on Form 10-K. Advanced Micro Devices, Inc. (2021). AMD and Cray Win Frontier Supercomputer DOE Deal. Moor Insights & Strategy. Advanced Micro Devices, Inc. (2024). AMD Reports Fourth Quarter and Full Year 2023 Financial Results. AMD Investor Relations. AnandTech. (2004). Price Guides April 2004: Video Cards and Memory. https://www.anandtech.com/show/1300 AMD. (2020). AMD CDNA architecture white paper. Advanced Micro Devices, Inc. https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-4-architecture-whitepaper.pdf AMD. (2024). AMD to Acquire Xilinx, Creating the Industry’s High Performance Computing Leader. AMD Corporate News. https://ir.amd.com/news-events/press-releases/detail/977/amd-to-acquire-xilinx-creating-the-industrys-high-performance-computing-leader AMD. (2025). AMD ROCm™ software. AMD. https://www.amd.com/en/products/software/rocm.html Barney, J. B. (1986). Strategic factor markets: Expectations, luck, and business strategy. Management Science, 32(10), 1231-1241. Barney, J. B., & Hoskisson, R. E. (1990). Strategic groups: Untested assertions and research proposals. Managerial and Decision Economics, 11(2), 187-198. Business Insider. (2024). AWS AI Chips Gain an Edge With Wider Regional Availability. https://www.businessinsider.com/aws-ai-chips-wider-regional-availability-microsoft-google-2024-9 Chen, M.-J. (1996). Competitor analysis and interfirm rivalry: Toward a theoretical integration. Academy of Management Review, 21(1), 100-134. Chen, M.-J., & Miller, D. (2012). Competitive dynamics: Themes, trends, and a. prospective research platform. Academy of Management Annals, 6(1), 135-210. Chen, M.-J., & Miller, D. (2015). Reconceptualizing competitive dynamics: A multidimensional framework. Strategic Management Journal, 36(5), 758–775. Chen, M.-J., Michel, J. G., & Lin, W. (2021). Worlds apart? Connecting competitive. dynamics and the resource-based view of the firm. Journal of Management, 47(7), 1781–1805. DIGITIMES. (2025). Global AI server market and industry trends, 2025. DIGITIMES Research. https://www.digitimes.com/reports/item.asp?id=20250108RS400 Ferrier, W. J. (2001). Navigating the competitive landscape: The drivers and. consequences of competitive aggressiveness. Academy of Management Journal, 44(4), 858–877. Forbes. (2016). AMD CEO Lisa Su and the art of a turnaround. Forbes Technology. https://www.forbes.com/sites/patrickmoorhead/2016/11/01/amd-ceo-lisa-su-and-the-art-of-a-turnaround/ Forbes. (2022). Together, AMD, Pensando, And Xilinx Advance Their Roadmaps. Forbes Technology. https://www.forbes.com/sites/tiriasresearch/2022/07/20/together-amd-pensando-and-xilinx-advance-their-roadmaps/ GameDev Magazine. (2004). The Rise of GeForce: Developer Ecosystem and Game Optimization. GameDev Magazine, 12(4), 32-39. Global Information, Inc. (2023). Artificial Intelligence (AI) Chip Market - Growth, Trends, and Forecasts (2023–2029). GII. https://www.gii.tw/report/moi1406240-artificial-intelligence-ai-chip-market-growth.html Harvard Business School Digital Initiative. (2021). NVIDIA’s winning platform strategy with CUDA. https://d3.harvard.edu/platform-digit/submission/nvidias-winning-platform-strategy-with-cuda/ Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2019). Strategic management: Concepts and cases: Competitiveness and globalization (12th ed.). Cengage Learning. Hsu, M.-Y., & Chen, M.-J. (2006). Competitor analysis and inter-firm rivalry by integrating market commonality and resource similarity. [Working Paper]. Darden Graduate School of Business, University of Virginia. IBM. (2024. What’s the difference between AI accelerators and GPUs? IBM Think. https://www.ibm.com/think/topics/ai-accelerator-vs-gpu Jon Peddie Research. (2001).Add-in Board Market Report. Jon Peddie Research. Jon Peddie Research. (2005, October 12). Workstation Market Revitalized in 2005, States Jon Peddie Research Workstation Report. https://www.jonpeddie.com/news/workstation-market-revitalized-in-2005-states-jon-peddie-research-workstati/ Jon Peddie Research. (2022). Nvidia's GeForce 256: The First Fully Integrated GPU. Electronic Design. https://www.electronicdesign.com/technologies/embedded/article/21178111/jon-peddie-research-nvidias-geforce-256-the-first-fully-integrated-gpu Jon Peddie Research. (2023). The history of the GPU: From inception to AI https://www.jonpeddie.com/news/the-history-of-the-gpu-from-inception-to-ai-3/ Leopold, G. (2019). AWS upgrades its GPU-backed AI inference platform. AIWire. https://www.aiwire.net/2019/03/19/aws-upgrades-its-gpu-backed-ai-inference-platform/ Mahoney, J. T., & Pandian, J. R. (1992). The resource‐based view within the. conversation of strategic management. Strategic Management Journal, 13(5), 363-380. Miller, D., & Chen, M.-J. (1996). The simplicity of competitive repertoires: An empirical. analysis. Strategic Management Journal, 17(6), 419–439. Micron Technology, Inc. (2025, June 12). Micron HBM Designed into Leading AMD AI Platform. https://investors.micron.com/news-releases/news-release-details/micron-hbm-designed-leading-amd-ai-platform McGee, J., & Thomas, H. (1986). Strategic groups: Theory, research and taxonomy. Strategic Management Journal, 7(2), 141-160. McKinsey & Company. (2025). The cost of compute: A $7 trillion race to scale data centers. McKinsey Insights. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers NVIDIA. (2002). NVIDIA GeForce FX GPUs and Microsoft DirectX 9.0 API [White paper]. NVIDIA Corporation. https://www.pny.com/file%20library/company/support/product%20brochures/geforce%20graphics/user%20guides%20and%20tutorials/microsoftdirectx_v1.pdf NVIDIA. (2017). NVIDIA TESLA V100 GPU Architecture. [White paper]. NVIDIA. https://images.nvidia.com/content/volta-architecture/pdf/volta-architecture-whitepaper.pdf Nvidia. (2020). What is a GPU? https://www.nvidia.com/en-us/drivers/what-is-gpu/ NVIDIA. (2023). Volta Tensor Core GPU Achieves New AI Performance Milestones. NVIDIA Developer Blog. https://developer.nvidia.com/blog/tensor-core-ai-performance-milestones/ NVIDIA. (2023). Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines. NVIDIA Developer Blog. https://developer.nvidia.com/blog/structured-sparsity-in-the-nvidia-ampere-architecture-and-applications-in-search-engines/ NVIDIA. (2023). Accelerating Inference with Sparsity Using the NVIDIA Ampere Architecture and TensorRT. NVIDIA Developer Blog. https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt/ NVIDIA. (2025). NVIDIA Grace CPU and Arm Architecture. NVIDIA. https://www.nvidia.com/en-us/data-center/grace-cpu/ NVIDIA. (2025). NVIDIA unveils NVLink Fusion for industry to build semi-custom AI infrastructure partner ecosystem. NVIDIA Newsroom. https://nvidianews.nvidia.com/news/nvidia-nvlink-fusion-semi-custom-ai-infrastructure-partner-ecosystem Porter, M. E. (1979). How competitive forces shape strategy. Harvard Business Review, 57, 137-145. Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard Business. Review, 86(1), 25-40. Raina, R., Madhavan, A., & Ng, A. Y. (2009). Large-scale deep unsupervised learning. using graphics processors. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 873–880). ACM. https://dl.acm.org/doi/10.1145/1553374.1553486 ResearchAndMarkets.com. (2023). Artificial Intelligence (AI) Chip Market Size, Share & Trends Analysis Report By Chip Type, By Processing Type, By Technology, By Application, By End-use, By Region, And Segment Forecasts, 2023 - 2029. Research and Markets. https://www.researchandmarkets.com/reports/ai-chip-market Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper Perennial. Shimpi, A. L. (2006). AMD & ATI: The acquisition from all points of view. AnandTech. https://www.anandtech.com/show/2044 Smith, K. G., Ferrier, W. J., & Ndofor, H. (2001). Competitive dynamics research: Critique and future directions. In M. A. Hitt, R. E. Freeman, & J. S. Harrison (Eds.), The Blackwell handbook of strategic management (pp. 308–334). Blackwell. Smith, R., & Bonshor, G. (2025). AMD advancing AI 2025. AnandTech. TSMC. (2004). TSMC Technology Report 2004. Taiwan Semiconductor Manufacturing Company. https://investor.tsmc.com/sites/ir/annual-report/2004/2004_Business_Overview_E.pdf DigiTimes. (2025). 美光HBM4已出貨主要客戶2025市佔目標穩健達陣 [Micron HBM4 shipment to major customers with strong 2025 market share goal]. https://www.digitimes.com.tw/tech/dt/n/shwnws.asp?id=0000724530_PHI6O7LO21ZYPU1WF4L2S 孫瑞隆（2021）。平台領先者及後進者之競爭策略與覆蓋策略研究[未出版之碩士論文]。國立台灣大學國際企業研究所 江秉修（2022）。行動通訊產業IC設計廠的動態競爭—以高通及聯發科為例[未出版之碩士論文]。國立政治大學科技與智慧財產研究所。 張詠晴（2024）。從專利分析探討企業發明人流動與組織間知識創造之關係 ─以Intel與AMD為例[未出版之碩士論文]。國立政治大學科技與智慧財產研究所。 描述 碩士
國立政治大學
科技管理與智慧財產研究所
111364116資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111364116 資料類型 thesis dc.contributor.advisor 楊宗翰 zh_TW dc.contributor.advisor Yang, Tsung-Han en_US dc.contributor.author (Authors) 姜蘊玲 zh_TW dc.contributor.author (Authors) Chiang, Yun-Ling en_US dc.creator (作者) 姜蘊玲 zh_TW dc.creator (作者) Chiang, Yun-Ling en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-Sep-2025 16:05:30 (UTC+8) - dc.date.available 1-Sep-2025 16:05:30 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2025 16:05:30 (UTC+8) - dc.identifier (Other Identifiers) G0111364116 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159244 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 科技管理與智慧財產研究所 zh_TW dc.description (描述) 111364116 zh_TW dc.description.abstract (摘要) 隨著人工智慧AI(Artificial Intelligence)與高效能運算(High-Performance Computing)應用的崛起，AI晶片產業已成為全球半導體平台競爭的核心。自2000年代以來，GPU(Graphics Processing Unit)從圖形處理器逐步轉型為AI運算的基礎設施，推動AI模型規模與算力成長，帶動產業進入新競爭時代。Nvidia憑藉「軟硬體協同」加「生態鎖定」的競爭策略，使Nvidia在AI訓練市場市占率長期維持在80%以上，成為產業技術標準與生態規則的制定者。 AMD(Advanced Micro Devices, Inc.)則推動「開放」加「異質」策略使AMD在推 論市場、邊緣運算與部分資料中心場景取得突破，市占率逐步提升，成為AI產業格局中具威脅性的挑戰者。 本研究採用動態競爭理論之市場共同性與資源相似性指標與AMC（察覺-Awareness、動機-Motivation、能力-Capability）分析架構，針對Nvidia與AMD於AI晶片產業的競爭行動與回應進行個案剖析。本研究主要結論如下： 結論一：AI晶片設計產業中，具有市場領先優勢的先進廠商相較於挑戰者，其在產品佈局上為更完整，技術迭代上具有較深遠且連貫的升級，且投入更多資源在高附加價值的高階產品市場，挑戰者多僅以片段式技術升級與回應，並投注資源在中低階產品市場。 結論二：AI晶片設計產業中，挑戰者需善用生態系統、供應鏈協同或技術路線調整，並結合策略模仿與差異化，並針對產業技術轉換或外部環境變動的關鍵時機，尋求切入與突破的機會，才能有效降低進入障礙，提升市場地位。 結論三：AI晶片設計產業中，具有市場及技術優勢的領導者廠商可透過態壁壘強化、技術迭代加速與供應鏈韌性反制挑戰者發起的攻擊，提升競爭障礙，維持領導者地位。 本研究理論貢獻在於驗證動態競爭理論與AMC模型於AI晶片產業的適用性。未來可進一步探討AI晶片產業地緣政治、產業政策與新興應用之競爭影響。 zh_TW dc.description.abstract (摘要) Driven by the growth of Artificial Intelligence (AI) and High-Performance Computing (HPC), AI chips have become central to global semiconductor competition. Since the 2000s, GPUs have transitioned from graphics accelerators to essential AI computing infrastructure, enabling larger AI models and greater computational power. Nvidia, through a “hardware-software co-optimization” and “ecosystem lock-in” strategy, has sustained over 80% market share in AI training and set prevailing industry standards. In contrast, Advanced Micro Devices (AMD) leverages an “open” and “heterogeneous” approach to achieve gains in inference, edge computing, and select data center markets, positioning itself as a significant competitor in the AI chip sector. This study adopts the dynamic competition theory’s indicators of market commonality and resource similarity, along with the AMC (Awareness–Motivation–Capability) analysis framework, to conduct a case analysis of the dynamic competitive actions and responses between Nvidia and AMD within the AI chip industry. The fianl conclusions of this study are as follows: 1. In the AI chip design industry, advanced firms with market leadership have more comprehensive product portfolios, deeper and more consistent technical iterations, and invest more resources in the high value-added, high-end product market. Challengers, on the other hand, mostly respond with fragmented technological upgrades and allocate resources to the mid- and low-end product markets. 2. In the AI chip design industry, challengers must fully leverage ecosystems, supply chain collaboration, or adjust their technology paths. By combining strategic imitation and differentiation, and seizing critical opportunities stemming from technological shifts or changes in the external environment, challengers can effectively lower entry barriers and enhance their market positions. 3. In the AI chip design industry, leading firms with market and technological advantages can fortify entry barriers, accelerate technical iteration, and reinforce supply chain resilience to counteract challengers’ initiatives, thereby raising competitors’ hurdles and maintaining their leadership positions. The theoretical contribution of this study lies in validating the applicability of dynamic competition theory and the AMC model to the AI chip industry. Future research may further explore the impact of geopolitics, industrial policy, and emerging applications on competition within the AI chip sector. en_US dc.description.tableofcontents 第一章 緒論 7 第一節 研究背景與動機 7 第二節 研究目的與研究問題 8 第二章 文獻回顧 10 第一節 AI晶片產業發展與技術演進 10 第二節 動態競爭理論 19 第三章 研究方法 32 第一節 研究架構 32 第二節 資料搜集 34 第三節 研究變數與分析構面 37 第四章 個案分析：NVIDIA與AMD競爭歷程 40 第一節 個案公司簡介與發展歷程 40 第二節 競爭考量分析 52 第三節 雙方競爭與AMC分析 71 第五章 研究發現與討論 99 第六章 結論 108 第一節 研究結果 108 第二節 研究貢獻 113 第三節 研究限制 115 參考文獻 116 zh_TW dc.format.extent 3551356 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111364116 en_US dc.subject (關鍵詞) AI晶片產業 zh_TW dc.subject (關鍵詞) GPU zh_TW dc.subject (關鍵詞) Nvidia zh_TW dc.subject (關鍵詞) AMD zh_TW dc.subject (關鍵詞) 動態競爭 zh_TW dc.subject (關鍵詞) AMC分析 zh_TW dc.subject (關鍵詞) 外部環境 zh_TW dc.subject (關鍵詞) AI Chip Industry en_US dc.subject (關鍵詞) GPU en_US dc.subject (關鍵詞) Nvidia en_US dc.subject (關鍵詞) AMD en_US dc.subject (關鍵詞) Competitive Dynamics en_US dc.subject (關鍵詞) AMC Model en_US dc.subject (關鍵詞) External Environment en_US dc.title (題名) AI晶片設計產業之動態競爭分析 —— 以Nvidia和AMD為例 zh_TW dc.title (題名) An Analysis of Competitive Dynamics in the AI Chip Design Industry: Case Studies of Nvidia and AMD en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Advanced Micro Devices, Inc. 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