請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99776完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳坤志 | zh_TW |
| dc.contributor.advisor | Kun-Chih Chen | en |
| dc.contributor.author | 彭國星 | zh_TW |
| dc.contributor.author | Kuo-Hsing Peng | en |
| dc.date.accessioned | 2025-09-17T16:39:00Z | - |
| dc.date.available | 2025-09-18 | - |
| dc.date.copyright | 2025-09-17 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-25 | - |
| dc.identifier.citation | 一、書籍
1. 丁磊,2023,生成式人工智慧:讀懂AIGC的奧秘,香港:中和出版。 2. 史蒂芬,維特,2025,黃仁勳傳,台北:天下雜誌。 3. 朱師右,2023,生成式AI應用解析與未來挑戰,台北:MIC。 4. 朱師右、陳安誼,2024,2024資訊軟體及服務產業年鑑,台北:MIC。 5. 陳昇瑋、溫怡玲,2019,人工智慧在台灣;產業轉型的契機與挑戰,台北:天下雜誌。 6. 簡禎富、賀桂芬,2019,工業3.5:台灣企業邁向智慧製造與數位決策的戰略,台北:天下雜誌。 二、期刊 1. 財團法人人工智慧科技基金會(AIF)與SEMI國際半導體產業協會,2024,台灣半導體產業AI化大調查,知勢。 2. 國家發展委員會,2024,運用AI賦能擴展產業發展,台灣經濟論衡秋季號,第22卷第3期。 3. 陳力俊、孔祥重、林建甫、薛承泰、朱雲漢、簡禎富,2018,AI對科技、經濟、社會、政治暨產業之挑戰與影響,中技社。 4. 鄒明珆,2024,生成式AI產業帶來的機會與挑戰,工業技術與資訊月刊,第388期。 5. 劉佩真,2024,半導體全球AI競爭的關鍵,產業雜誌。 6. 蘇信瑋,2024,全球AI產業發展趨勢與台美企業合作契機之研析,台灣經濟研究院月刊。 7. Bo Li, 2022. Trustworthy AI: From Principles to Practices. ACM Computing Surveys. 8. Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Ping Zhang, Shuguang Cui, Xuemin Shen, Shiwen Mao, Zhu Han, Abbas Jamalipour H. Vincent Poor, Dong In Kim, 2023. The Age of Generative AI and AI-Generated Everything, arXiv. 9. Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Jack Clark, 2025. Artificial Intelligence Index Report 2024, Stanford HAI. 10. Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar, Fiona Carroll, 2024. Generative Artificial Intelligence: A Systematic Review and Applications, Multimedia Tools and Applications. 11. Stefan Feuerriegel, Jochen Hartmann, Christian Janiesch, Patrick Zschech, 2023. Generative AI. Business & Information Systems Engineering. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99776 | - |
| dc.description.abstract | 隨著計算能力的進步、大數據的普及,透過機器學習,2017年後LLM(GPT-2、GPT-3)出現為AI多樣化應用奠定基礎,2022年12月ChatGPT開啟生成式AI的時代,生成式AI能自動、快速地創建和生成新的內容,如圖像、文字、程式、影片、聲音和3D模型等,AI技術發展,將從目前生成式AI,現階段已進入能與人互動並執行任務的「代理AI」,接下來則是為機器人和真實應用的「實體AI」,AI將成為電力和網路後的第三代基礎建設。Marketsand Markets預測AI市場價值將從2024年的2,146億美元,成長至2030年的13,391億美元,複合年成長率(CAGR)為35.7%;Gartner預計2022~2027年AI軟體支出複合年成長率將成長19.1%。預估至2030年AI資料中心建置將達1兆美元,企業將加速採購GPU及ASIC。
生成式AI帶來各行各業的典範移轉,造成市場對於AI的需求快速暴增,各大CSP及AI業者為搶奪未來AI市場的龐大商機,提升算力成為首要任務,因而展開AI SERVER的軍備競賽,台灣不僅在PC及SERVER OEM/ODM擁有豐富的組裝經驗及完整的零組件供應鏈,更可以提供獨一無二的先進製程幫GPU及ASIC代工,因此在產業供應鏈完整且具垂直整合優勢的利基下,在AI產業崛起的關鍵時刻,率先獲得最大的商機。 從上游晶片、中游組裝到下游應用,台廠在這波AI浪潮中扮演著不可或缺的角色。無論是晶圓代工,散熱、供電、PCB等關鍵零組件,還是伺服器代工龍頭,都讓台灣成為全球AI供應鏈的重要陣地,在AI產業百花齊放的年代,只要稍微有點關聯性,過去2年股價表現表現都是遠遠超過大盤,可說是滿地開花、雞犬升天。惟在經過過去2年的激情後,絢爛將歸於平淡,市場將以更嚴格的標準來檢視AI SERVER供應鏈的續航力,投資標的的選擇將更具挑戰性,因此未來誰能夠憑藉著營運績效、競爭力與獲利能力,在AI Server各個領域的供應鏈中脫穎而出,將是未來布局AI投資機會的致勝關鍵。 AI涉及的供應鏈相當廣泛,這些公司在AI技術研發和應用上都扮演關鍵角色。整個AI產業包括製造伺服器的晶片及主要IC的上游,製造關鍵零組件的中游及伺服器代工廠的下游。由於台灣AI SERVER供應鏈的子產業眾多,因此在上、中、下游分別挑選具有群聚效應(即產業中具代表性的廠商不只一家)的AI SERVER供應鏈子產業,做為比較的基礎。以上述標準選擇出台灣AI SERVER供應鏈中較具代表性的產業,其中在上游選擇的是矽智財(IP)產業(世芯、創意、智原),中游選擇的是散熱產業(奇鋐、雙鴻、力致)及銅箔基板(CCL)產業(台光電、台燿、聯茂),下游則是選擇AI SERVER組裝產業(緯穎、廣達、鴻海、緯創),具有代表性的公司共13家。 透過上述13家公司的經營績效、營運前景、財務數字的預估及公司訪談,了解各家公司基本面。此外,AI SERVER規格迭代快速,因此產品規格變化所帶來的商機將影響AI供應鏈未來的營運前景。另外透過過往的營運績效及財務數字分析,篩選出其中的佼佼者。透過基本面分析、產品規格變化所帶來的商機、營運績效分析及財務數字分析這四項標準,挑選出各個子產業中最具競爭力及潛力的公司。作為主要的投資建議標的。 透過基本面分析、未來產品規格的變化、營運績效及獲利能力的分析,分別由AI SERVER產業的上、中、下游的供應鏈篩選出世芯、奇鋐、台光電及緯穎,作為各個子產業的首選投資標的。最後再透過NPV淨現值法,將各別公司未來現金流折現,計算出各別公司合理價值,再與目前股價比較,計算出潛在報酬率,以供未來投資參考。 | zh_TW |
| dc.description.abstract | With the advancement of computing power and the proliferation of big data, machine learning has paved the way for diverse AI applications since 2017, especially with the emergence of large language models (LLMs) like GPT-2 and GPT-3. In December 2022, ChatGPT marked the beginning of the generative AI era. Generative AI can autonomously and rapidly create new content such as images, text, code, videos, audio, and 3D models. As AI technology evolves, it is moving beyond generative AI into the current phase of "agent AI"—capable of interacting with humans and executing tasks—and eventually towards "embodied AI" for robotics and real-world applications. AI is expected to become the third foundational infrastructure following electricity and the internet.
MarketsandMarkets forecasts that the value of the AI market will grow from USD 214.6 billion in 2024 to USD 1.3391 trillion by 2030, with a compound annual growth rate (CAGR) of 35.7%. Gartner estimates that AI software spending will grow at a CAGR of 19.1% from 2022 to 2027. By 2030, AI data center infrastructure investment is projected to reach USD 1 trillion, driving enterprises to accelerate procurement of GPUs and ASICs. Generative AI is catalyzing a paradigm shift across industries, rapidly driving up demand for AI. Major cloud service providers (CSPs) and AI companies are racing to expand computational capacity, sparking an AI server arms race. Taiwan, with its rich experience in PC and server OEM/ODM and its complete component supply chain, also offers advanced semiconductor manufacturing for GPU and ASIC foundry services. Thanks to this vertically integrated and comprehensive industrial ecosystem, Taiwan is seizing early opportunities in the AI revolution. From upstream chips, midstream assembly, to downstream applications, Taiwanese companies are playing an indispensable role in the AI wave. Whether it's wafer foundry, critical components like thermal modules, power supply, PCBs, or leading server assemblers, Taiwan has become a vital hub in the global AI supply chain. Over the past two years, even companies with peripheral relevance to AI have seen their stock prices outperform the broader market—truly a "rising tide lifts all boats" scenario. However, after the past two years of excitement, the market is entering a more sober phase, applying stricter standards to assess the sustainability of the AI server supply chain. Investment selection is becoming more challenging. In the AI server ecosystem, the supply chain is vast, with companies playing critical roles in technology development and application. The entire industry spans upstream (chip and key IC manufacturing), midstream (key component manufacturing), and downstream (server assembly). Given the diversity of Taiwan’s AI server-related industries, this analysis selects representative sub-industries with clustering effects (i.e., more than one notable company per sector) in each of the upstream, midstream, and downstream segments. Using this standard, the selected representative sectors of Taiwan’s AI server supply chain are: • Upstream: IP (Intellectual Property) industry — including Alchip (世芯), Global Unichip (創意), and Faraday (智原) • Midstream: Thermal solutions — Chicony Power (奇鋐), Auras (雙鴻), Taisol (力致); and Copper Clad Laminate (CCL) — Taiwan Union Technology (台光電), Elite Material (台燿), Iteq (聯茂) • Downstream: AI server assembly — WiWynn (緯穎), Quanta (廣達), Foxconn (鴻海), and Wistron (緯創) A total of 13 key companies are selected. Through company interviews and analysis of business performance, future prospects, and financial forecasts, this study aims to assess the fundamentals of each company. Due to rapid iterations in AI server specifications, the business opportunities driven by spec evolution will directly affect the outlook for the supply chain. By evaluating past performance and financial data, the top performers in each segment can be identified. Using four criteria—fundamental analysis, business opportunities from product spec changes, operational performance, and financial metrics—the most competitive and promising company in each sub-industry is selected as a primary investment candidate. The selected top companies in each supply chain segment are: • Upstream: Alchip (世芯) • Midstream: Chicony Power (奇鋐) and Taiwan Union Technology (台光電) • Downstream: WiWynn (緯穎) Finally, by applying the Net Present Value (NPV) method to discount each company’s projected future cash flows, their fair values are calculated. Comparing these fair values with current stock prices yields their potential returns, serving as a reference for future investment decisions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-17T16:39:00Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-17T16:39:00Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
口試委員會審定書 I 中文摘要 II THESIS ABSTRACT IV 目次 VI 圖次 VIII 表次 IX 第一章 緒論 1 第一節 前言 1 第二節 AI 產業蓬勃發展的背景與原因 2 第三節 AI產業崛起台灣科技業率先受惠的原因及AI供應鏈 4 第四節 研究目的 6 第五節 研究方法 6 第二章 文獻探討 8 第一節 AI產業現況、競爭態勢與發展 8 第二節 世界主要國家AI的投資計劃 11 第三節 各國AI投資為台灣科技業帶來的商機 13 第四節 先前研究 15 第三章 研究主題與產業分析 18 第一節 全球主要CSP及企業Dater Center資本支出方興未艾 18 第二節 GB300與Rubin NVL288設計為GTC 2025主要焦點 20 第三節 算力需求仍是王道、大者恆大將是趨勢 22 第四節 關稅改變AI SRVER需求模式 22 第五節 個案公司背景介紹 23 第四章 研究結果 46 第一節 世芯 46 第二節 奇鋐 51 第三節 台光電 55 第四節 緯穎 59 第五章 結論與建議 63 第一節 AI發展猶如旭日東昇前景不可限量 63 第二節 川普對等關引發全球貿易大戰AI SERVER影響相對輕微 65 第三節 AI產業爆發台灣AI SERVER供應鏈雨露均霑 67 第四節 台灣AI SERVER供應鏈在歷經兩年的暴發式成長後誰才是真正贏家 68 參考文獻 70 圖次 圖2-1:各國最新投資金額 12 圖3-1:前四大CSP 2024年整體需求比重達58% 19 圖3-2:前四大CSP 2025年整體需求比重達75% 19 圖4-1:Microsoft、Meta與AWS為緯穎主要營收貢獻來源 59 表次 表1-1 :AI伺服器供應鏈上游廠商 5 表1-2 :AI伺服器關鍵零件組供應鏈中游廠商 5 表1-3 :AI伺服器供應鏈下游廠商 5 表1-4 :各年度EPS 7 表1-5 :Golden Model參數假設及計算 7 表2-1 :各國投資額增加櫃數測算 13 表2-2 :板材產值貢獻 14 表3-1 :美系四大CSP 18 表3-2 :市場預估2025年CSP資本支出將年增30%以上 19 表3-3 :Nvidia AI GPU各世代規格比較、GB200/GB300 NVL72機櫃主要零組件規格差異 21 表3-4 :Nvidia GB200 NVL72機櫃 21 表3-5 :GB300機櫃潛在受惠廠商 21 表3-6 :台灣AI SERVER供應鏈產業 23 表3-7 :世芯、創意及智原營運績效分析(2020—2024) 39 表3-8 :世芯、創意及智原獲利能力分析(2020—2024) 40 表3-9 :奇鋐、雙鴻及力致營運績效分析(2020—2024) 41 表3-10:奇鋐、雙鴻及力致獲利能力分析(2020—2024) 41 表3-11:CCL等級表 42 表3-12:台光電、台燿及聯茂營運績效分析(2020—2024) 43 表3-13:台光電、台燿及聯茂獲利能力分析(2020—2024) 43 表3-14:廣達、鴻海、緯創及緯穎營運績效分析(2020—2024) 44 表3-15:廣達、鴻海、緯創及緯穎獲利能力分析(2020—2024) 45 表4-1 :世芯財務報表(2020-2025(F)) 48 表4-2 :世芯各年度EPS 49 表4-3 :Golden Model 參數假設及計算(世芯) 50 表4-4 :世芯潛在漲幅表 50 表4-5 :奇鋐財務報表(2020-2025(F)) 53 表4-6 :奇鋐各年度EPS 53 表4-7 :Golden Model 參數假設及計算(奇鋐) 54 表4-8 :奇鋐潛在漲幅表 54 表4-9 :台光電財務報表(2020-2025(F)) 57 表4-10:台光電各年度EPS 57 表4-11:Golden Model參數假設及計算(台光電) 58 表4-12:台光電潛在漲幅表 58 表4-13:緯穎財務報表(2020-2025(F)) 60 表4-14:緯穎各年度EPS 61 表4-15:Golden Model參數假設及計算(緯穎) 62 表4-16:緯穎潛在漲幅表 62 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 生成式AI | zh_TW |
| dc.subject | AI伺服器 | zh_TW |
| dc.subject | AI伺服器供應鏈 | zh_TW |
| dc.subject | 營運績效 | zh_TW |
| dc.subject | 獲利能力 | zh_TW |
| dc.subject | 產品規格變化 | zh_TW |
| dc.subject | 淨現值法 | zh_TW |
| dc.subject | Operational Performance | en |
| dc.subject | AI Server | en |
| dc.subject | AI Server Supply Chain | en |
| dc.subject | Net Present Value (NPV) Method | en |
| dc.subject | Product Specification Changes | en |
| dc.subject | Profitability | en |
| dc.subject | Generative AI | en |
| dc.title | AI崛起對台灣AI伺服器產業帶來的商機與投資機會 | zh_TW |
| dc.title | The Rise of AI: Business Opportunities and Investment Prospects for Taiwan's AI Server Industries | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 劉順仁 | zh_TW |
| dc.contributor.coadvisor | Shuen-Zen Liu | en |
| dc.contributor.oralexamcommittee | 張景宏;謝昇峰 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Hung Chang;Sheng-Feng Hsieh | en |
| dc.subject.keyword | 生成式AI,AI伺服器,AI伺服器供應鏈,營運績效,獲利能力,產品規格變化,淨現值法, | zh_TW |
| dc.subject.keyword | Generative AI,AI Server,AI Server Supply Chain,Operational Performance,Profitability,Product Specification Changes,Net Present Value (NPV) Method, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202501872 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-25 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 碩士在職專班會計與管理決策組 | - |
| dc.date.embargo-lift | 2026-06-02 | - |
| 顯示於系所單位: | 會計與管理決策組 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 3.91 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
