隨著人工智慧（Artificial Intelligence, AI）在全球快速發展，其應用已廣泛滲透至醫療、金融、交通運輸與製造等領域，成為推動產業轉型的重要動力。在這樣的背景下，如何分析與理解企業在 AI 產業中的市場佈局與策略，已成為學術研究與實務界的重要課題。傳統的 4P 行銷模式偏重於企業端對產品、價格、通路與促銷的規劃，但在消費者自主性日益提升與市場競爭加劇的情況下，僅以供給端角度觀察已不足以完整解釋顧客的購買行為與決策邏輯。為彌補此不足，本研究採用巫立宇與邱志聖所提出的「4C 顧客成本架構」作為分析工具，從顧客角度切入，檢視輝達（NVIDIA）如何在 AI 市場中透過降低或提高不同類型的顧客成本，來形成自身的競爭優勢並鞏固市場地位。
研究發現，NVIDIA 在 買者外顯單位效益成本（C1） 上，透過高效能 GPU 與整合平台，降低了買者的長期總體成本，即便產品價格高昂，仍因效能與心理效益而具吸引力。在 資訊搜尋成本（C2） 上，CUDA 與雲端合作使顧客能快速接觸並驗證技術，減少學習與資訊不對稱。在 道德危機成本（C3） 的處理上，品牌信譽與第三方驗證有效降低了顧客對產品能否達標的疑慮。至於 專屬陷入成本（C4），公司則以相容性設計降低初期門檻，再透過專屬知識、系統依賴與品牌認同，逐步提高轉換障礙。
綜合而言，本研究指出 NVIDIA 的成功，不僅來自於產品性能與技術創新，更在於其能有效管理顧客的顯性與隱性成本，從而建立完整的市場佈局。最後，本文建議 NVIDIA 未來在產品策略上應持續提升效能與能效比，強化資訊透明度與產業合作，並在專屬資產建構與顧客黏著度上進一步深化，以確保在快速演變的 AI 產業中持續維持領導地位。

With the rapid global development of artificial intelligence (AI), its applications have penetrated widely into fields such as healthcare, finance, transportation, and manufacturing, becoming a key driver of industrial transformation. Against this backdrop, analyzing and understanding companies' market layout and strategies in the AI industry has become a crucial topic in both academic research and practice. The traditional 4P marketing model focuses on companies' planning of products, pricing, distribution channels, and promotions. However, with increasing consumer autonomy and intensifying market competition, a supply-side perspective alone is insufficient to fully explain customer purchasing behavior and decision-making. To address this shortcoming, this study employs the "4C Customer Cost Framework" proposed by Wu Lei-Yu and Chiou Jyh-Shen as an analytical tool. From a customer perspective, it examines how NVIDIA creates competitive advantages and strengthens its market position in the AI market by reducing or increasing costs for different types of customers.
The study found that NVIDIA's high-performance GPUs and integrated platform reduce buyers' long-term total costs in terms of customer apparent unit cost (C1). Despite its high price, the performance and psychological benefits of its products remain attractive. Regarding information search costs (C2), CUDA and cloud collaboration enable customers to quickly access and verify technology, reducing learning and information asymmetry. Regarding ethical hazard costs (C3), brand reputation and third-party verification effectively reduce customer concerns about product compliance. Regarding proprietary entrapment costs (C4), the company lowers initial barriers to entry through compatible design, then gradually increases switching barriers through proprietary knowledge, system dependency, and brand recognition.
In summary, this study indicates that NVIDIA's success stems not only from product performance and technological innovation but also from its ability to effectively manage customers' explicit and implicit costs, thereby establishing a comprehensive market presence. Finally, this article recommends that NVIDIA's future product strategy should continue to improve performance and energy efficiency, strengthen information transparency and industry collaboration, and further deepen proprietary asset building and customer retention to ensure continued leadership in the rapidly evolving AI industry.

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