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題名 生成式人工智慧 (GenAI) 的發展趨勢對台灣高科技產業之影響
The Impact of Generative Artificial Intelligence (GenAI) Development Trends on Taiwan’s High-Tech Industry
作者 陳玄政
Chen, Hsuan Cheng
貢獻者 謝明華
陳玄政
Chen, Hsuan Cheng
關鍵詞 生成式人工智慧
台灣高科技產業
AI硬體設計
技術創新
數位轉型
GenAI
日期 2025
上傳時間 4-Aug-2025 13:02:33 (UTC+8)
摘要 本研究深入探討生成式人工智慧 (GenAI) 技術對台灣高科技產業的深遠影響，並分析台灣在全球GenAI生態系中的角色與策略定位。隨著GenAI技術的迅猛發展，台灣的高科技產業，尤其是在半導體、電子製造及AI硬體設計領域，正處於技術與市場的轉型期。生成式人工智慧在推動全球產業創新的同時，也引領台灣企業在高效能運算、AI晶片及先進製程領域的持續競爭優勢。 在半導體產業方面，台灣的企業，特別是台積電，憑藉其在先進製程技術上的卓越表現，成為全球AI晶片需求增長的核心支柱。台積電在3奈米及更先進製程領域的技術突破，進一步加強了其在高效能運算與AI伺服器晶片市場中的主導地位。與此同時，華碩與台達電等企業也積極將GenAI技術應用於產品開發、智慧製造及數位化轉型中，藉此提升自身在全球市場中的競爭力與影響力。 在全球競爭環境日益激烈的背景下，台灣需進一步加強對技術創新的投入，推動跨領域合作與整合，並提升數位基礎設施建設以支援未來GenAI技術的商業化應用。隨著資料隱私與安全問題日益嚴峻，台灣企業須建立健全的資料治理機制，確保技術應用的合規性與透明度，以維護企業的市場信任及競爭優勢。 綜上所述，台灣高科技產業必須把握生成式AI技術的發展脈絡，強化自主創新能力，並透過與國際企業的協同合作，進一步拓展在全球GenAI生態系中的影響力
參考文獻 1. 英文文獻 Adobe. (2025). Adobe Firefly: The next evolution of creative AI is here. Adobe Blog. https://blog.adobe.com/en/publish/2025/04/24/adobe-firefly-next-evolution-creative-ai-is-here Ali, O., Ally, M., & Dwivedi, Y. (2020a). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102–199. Aliaga, V., & Miyagusuku Ríos, V. (2023). Generative Artificial Intelligence Era: a view from the Global South. https://doi.org/10.61249/pi.vi134.98 Appier Inc. (2023). Appier integrates GenAI into its products to optimize key marketing applications for businesses. https://www.appier.com/en/press-media/appier-integrates-genai-into-its-products ASML. (2024). This film wasn’t filmed: ASML harnesses generative Artificial Intelligence to make its latest brand film. Retrieved May 13, 2025, from https://www.asml.com/en/news/press-releases/2024/asml-brand-film Bhati, R., Karamouzis, F., Sallam, R., & Clendaniel, S. (2024). AI and generative AI case study snapshots. Gartner, Inc. Bharti, I., Chauhan, K., & Aggarwal, P. (2024). Generative AI. Advances in Linguistics and Communication Studies, 1–36. https://doi.org/10.4018/979-8-3693-9246-1.ch001 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Chen, M. Y. (2024). Toyota is deploying AI agents to harness the collective wisdom of engineers and innovate faster. Microsoft Source Asia. https://news.microsoft.com/source/asia/features/toyota-is-deploying-ai-agents-to-harness-the-collective-wisdom-of-engineers-and-innovate-faster/ Delta Electronics, Inc. (2024). Delta’s diverse smart green solutions widely acclaimed at Automation Taipei 2024. https://www.deltaww.com/en-US/news/39202 Deutsche Telekom. (2023). Deutsche Telekom uses artificial intelligence for fiber-optic roll-out. Deutsche Telekom AG. https://www.telekom.com/en/media/media-information/archive/dt-uses-artificial-intelligence-for-fiber-optic-roll-out-544552 3 Deutsche Telekom. (2024a). Telekom Security: Tricks and pitfalls of artificial intelligence. Deutsche Telekom AG. https://www.telekom.com/en/media/media-information/archive/telekom-security-tricks-and-pitfalls-of-artificial-intelligence-1082030 4 Deutsche Telekom. (2024b). Deutsche Telekom details development goals for the next three years: AI and global scale as growth drivers. Deutsche Telekom AG. https://m.c114.com.cn/w5366-1280418.html Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. Gu, F. (2024). The prospect exploration of artificial intelligence technology and its application. https://doi.org/10.62051/sn7t0x23 Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239. Hsu, Y.-C., & Chang, R. C.-H. (2020). Intelligent Chips and Technologies for AIoT Era. Asian Solid-State Circuits Conference, 1–4. https://doi.org/10.1109/A-SSCC48613.2020.9336122 IMD. (2024). World competitiveness ranking 2024. International Institute for Management Development. https://www.imd.org/wcc/world-competitiveness-ranking/ Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Li, X., et al. (2021). Recent advances in multimodal deep learning. arXiv:2105.11087. Liu, Y., Xu, G., Zhong, L., & Xiao, Y. (2022). Multi-Modal Transport Logic Architecture Analysis Based on Autonomous Transportation System. CICTP 2022. https://doi.org/10.1061/9780784484265.043 Mohammed, M. Y., & Skibniewski, M. J. (2023). The Role of Generative AI in Managing Industry Projects: Transforming Industry 4.0 Into Industry 5.0 Driven Economy. Priloženie k Žurnalu Predprinimatelʹskoe Pravo. https://doi.org/10.2478/law-2023-0006 NVIDIA. (2024). TSMC and Synopsys bring breakthrough NVIDIA computational lithography platform to production [Press release]. NVIDIA Newsroom. https://nvidianews.nvidia.com/news/tsmc-synopsys-nvidia-culitho Pang, G. (2022). The AI chip race. IEEE Intelligent Systems, 37, 111–112. https://doi.org/10.1109/MIS.2022.3165668 Patil, D. (2025). Multimodal Artificial Intelligence In Industry: Integrating Text, Image, And Audio For Enhanced Applications Across Sectors. https://doi.org/10.2139/ssrn.5057428 Raghuweanshi, P. (2024). Revolutionizing semiconductor design and manufacturing with ai. Journal of Knowledge Learning and Science Technology, 3(3), 272–277. https://doi.org/10.60087/jklst.vol3.n3.p.272-277 Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: A systematic review and applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-20016-1 Singh, N., Chaudhary, V., Singh, N., Soni, N., & Kapoor, A. (2024). Transforming Business with Generative AI: Research, Innovation, Market Deployment and Future Shifts in Business Models. https://doi.org/10.48550/arxiv.2411.14437 Tivari, G. D., Khara, S., Dave, J., & Patel, V. (2024). Enhancing Reality: Exploring the Potential of Generative Artificial Intelligence. Indian Scientific Journal Of Research In Engineering And Management, 08(07), 1–13. https://doi.org/10.55041/ijsrem36378 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Wang, N., Li, S., Li, C., & Wang, L. (2024). Current Status and Emerging Trends of Generative Artificial Intelligence Technology: A Bibliometric Analysis. Journal of Internet Technology, 25(3), 477–485. https://doi.org/10.53106/160792642024052503013 Wen, Z., Zhang, X., Liu, C., & Gong, S. (2024). Innovative Application of Multimodal Large Model in Image-text Fusion Understanding. 373–377. https://doi.org/10.1109/eiecs63941.2024.10800581 World Economic Forum. (2020). The Global Competitiveness Report Special Edition 2020: How Countries are Performing on the Road to Recovery. https://www.weforum.org/reports/the-global-competitiveness-report-2020 2. 中文文獻 王薪棉 (2023)。台灣電子產業鏈概況。台北外匯市場發展基金會委託研究計畫。 行政院科技會報辦公室 (2021)。智慧國家方案（2021－2025年）（核定本）。行政院。https://digi.nstc.gov.tw/File/E8BE929F910C30CA 行政院 (2023)。晶創臺灣方案-奠基臺灣未來10年科技國力。行政院全球資訊網。https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/6dd41826-ed84-4b92-9f51-e6ebeb8621f8 洪志銘 (2019)。中小企業創新能力觀察。中華經濟研究院。 許萬寶 (2025)。破解生成式AI整合難題：華碩AI Hub的創新解方。ASUSTeK Computer Inc. 官方部落格。https://press.asus.com/tw/blog/overcoming-gen-ai-integration-challenges-with-the-asus-ai-hub-zh/ 劉佩真 (2024)。全球半導體產業競逐制高點。產業雜誌，第650期。台灣經濟研究院。
描述 碩士
國立政治大學
經營管理碩士學程(EMBA)
112932072
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112932072
資料類型 thesis
dc.contributor.advisor 謝明華zh_TW
dc.contributor.author (Authors) 陳玄政zh_TW
dc.contributor.author (Authors) Chen, Hsuan Chengen_US
dc.creator (作者) 陳玄政zh_TW
dc.creator (作者) Chen, Hsuan Chengen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 13:02:33 (UTC+8)-
dc.date.available 4-Aug-2025 13:02:33 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 13:02:33 (UTC+8)-
dc.identifier (Other Identifiers) G0112932072en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158334-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經營管理碩士學程(EMBA)zh_TW
dc.description (描述) 112932072zh_TW
dc.description.abstract (摘要) 本研究深入探討生成式人工智慧 (GenAI) 技術對台灣高科技產業的深遠影響，並分析台灣在全球GenAI生態系中的角色與策略定位。隨著GenAI技術的迅猛發展，台灣的高科技產業，尤其是在半導體、電子製造及AI硬體設計領域，正處於技術與市場的轉型期。生成式人工智慧在推動全球產業創新的同時，也引領台灣企業在高效能運算、AI晶片及先進製程領域的持續競爭優勢。 在半導體產業方面，台灣的企業，特別是台積電，憑藉其在先進製程技術上的卓越表現，成為全球AI晶片需求增長的核心支柱。台積電在3奈米及更先進製程領域的技術突破，進一步加強了其在高效能運算與AI伺服器晶片市場中的主導地位。與此同時，華碩與台達電等企業也積極將GenAI技術應用於產品開發、智慧製造及數位化轉型中，藉此提升自身在全球市場中的競爭力與影響力。 在全球競爭環境日益激烈的背景下，台灣需進一步加強對技術創新的投入，推動跨領域合作與整合，並提升數位基礎設施建設以支援未來GenAI技術的商業化應用。隨著資料隱私與安全問題日益嚴峻，台灣企業須建立健全的資料治理機制，確保技術應用的合規性與透明度，以維護企業的市場信任及競爭優勢。 綜上所述，台灣高科技產業必須把握生成式AI技術的發展脈絡，強化自主創新能力，並透過與國際企業的協同合作，進一步拓展在全球GenAI生態系中的影響力zh_TW
dc.description.tableofcontents 第一章 緒論 6 第一節 研究背景與動機 6 一、 全球GenAI技術的發展趨勢與應用 6 二、 台灣高科技產業面臨的機遇與挑戰 7 第二節 研究目的與問題 9 一、 探討GenAI技術對台灣高科技產業的影響 9 第三節 研究範圍與研究方法 10 第二章 文獻回顧 13 第一節 生成式人工智慧 (GENAI) 概述 13 第二節 台灣高科技產業現況與發展特性 14 一、 台灣高科技產業的產業結構與創新能力分析 15 二、 台灣高科技產業之國際競爭力與挑戰 17 第三節 GENAI對全球高科技產業的影響 19 一、 GenAI帶來的科技創新與產業變革 19 二、 全球企業如何應用GenAI技術 20 第三章 GENAI發展趨勢分析 27 第一節 GENAI技術的演進路徑與創新模式 27 一、 大型語言模型及多模態模型發展趨勢 27 二、 多模態模型與技術創新 28 第二節 GENAI商業應用趨勢 29 一、 GenAI在各產業的應用與發展 29 二、 產業鏈垂直整合與跨領域合作趨勢 32 第四章 GENAI對台灣高科技產業之具體影響 35 第一節 半導體與硬體產業 35 一、 GenAI晶片需求增加對台灣晶圓代工及IC設計業之機會 35 二、 產業鏈重組與供應鏈變化分析 35 第二節 軟體與資訊服務產業 37 一、 軟體產業的GenAI導入應用與服務創新模式 37 二、 資訊安全與資料治理新挑戰 38 第三節 電子製造與自動化產業 39 一、 GenAI驅動智慧製造與工廠自動化的轉型 39 二、 產業競爭態勢的變化與人才需求調整 42 第五章 台灣高科技產業面臨之挑戰與因應策略 44 第一節 GENAI人才培育與技術能力之建構 44 第二節 台灣產業於全球GENAI生態鏈之定位與策略 45 第三節 產官學研合作模式強化與建議 46 第六章 研究發現與討論 50 第一節 GENAI對台灣高科技產業之短期與長期影響分析 50 第二節 台灣高科技產業把握GENAI趨勢之關鍵因素 52 第三節 相關產業的最佳實務與成功案例分享 54 第七章 結論與建議 56 第一節 研究結論：GENAI發展趨勢為台灣高科技產業帶來之機遇與挑戰 56 第二節 產業政策與企業發展建議 56 第三節 未來研究方向建議 57 參考文獻 59zh_TW
dc.format.extent 1693058 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112932072en_US
dc.subject (關鍵詞) 生成式人工智慧zh_TW
dc.subject (關鍵詞) 台灣高科技產業zh_TW
dc.subject (關鍵詞) AI硬體設計zh_TW
dc.subject (關鍵詞) 技術創新zh_TW
dc.subject (關鍵詞) 數位轉型zh_TW
dc.subject (關鍵詞) GenAIen_US
dc.title (題名) 生成式人工智慧 (GenAI) 的發展趨勢對台灣高科技產業之影響zh_TW
dc.title (題名) The Impact of Generative Artificial Intelligence (GenAI) Development Trends on Taiwan’s High-Tech Industryen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. 英文文獻 Adobe. (2025). Adobe Firefly: The next evolution of creative AI is here. Adobe Blog. https://blog.adobe.com/en/publish/2025/04/24/adobe-firefly-next-evolution-creative-ai-is-here Ali, O., Ally, M., & Dwivedi, Y. (2020a). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102–199. Aliaga, V., & Miyagusuku Ríos, V. (2023). Generative Artificial Intelligence Era: a view from the Global South. https://doi.org/10.61249/pi.vi134.98 Appier Inc. (2023). Appier integrates GenAI into its products to optimize key marketing applications for businesses. https://www.appier.com/en/press-media/appier-integrates-genai-into-its-products ASML. (2024). This film wasn’t filmed: ASML harnesses generative Artificial Intelligence to make its latest brand film. Retrieved May 13, 2025, from https://www.asml.com/en/news/press-releases/2024/asml-brand-film Bhati, R., Karamouzis, F., Sallam, R., & Clendaniel, S. (2024). AI and generative AI case study snapshots. Gartner, Inc. Bharti, I., Chauhan, K., & Aggarwal, P. (2024). Generative AI. Advances in Linguistics and Communication Studies, 1–36. https://doi.org/10.4018/979-8-3693-9246-1.ch001 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Chen, M. Y. (2024). Toyota is deploying AI agents to harness the collective wisdom of engineers and innovate faster. Microsoft Source Asia. https://news.microsoft.com/source/asia/features/toyota-is-deploying-ai-agents-to-harness-the-collective-wisdom-of-engineers-and-innovate-faster/ Delta Electronics, Inc. (2024). Delta’s diverse smart green solutions widely acclaimed at Automation Taipei 2024. https://www.deltaww.com/en-US/news/39202 Deutsche Telekom. (2023). Deutsche Telekom uses artificial intelligence for fiber-optic roll-out. Deutsche Telekom AG. https://www.telekom.com/en/media/media-information/archive/dt-uses-artificial-intelligence-for-fiber-optic-roll-out-544552 3 Deutsche Telekom. (2024a). Telekom Security: Tricks and pitfalls of artificial intelligence. Deutsche Telekom AG. https://www.telekom.com/en/media/media-information/archive/telekom-security-tricks-and-pitfalls-of-artificial-intelligence-1082030 4 Deutsche Telekom. (2024b). Deutsche Telekom details development goals for the next three years: AI and global scale as growth drivers. Deutsche Telekom AG. https://m.c114.com.cn/w5366-1280418.html Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. Gu, F. (2024). The prospect exploration of artificial intelligence technology and its application. https://doi.org/10.62051/sn7t0x23 Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239. Hsu, Y.-C., & Chang, R. C.-H. (2020). Intelligent Chips and Technologies for AIoT Era. Asian Solid-State Circuits Conference, 1–4. https://doi.org/10.1109/A-SSCC48613.2020.9336122 IMD. (2024). World competitiveness ranking 2024. International Institute for Management Development. https://www.imd.org/wcc/world-competitiveness-ranking/ Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Li, X., et al. (2021). Recent advances in multimodal deep learning. arXiv:2105.11087. Liu, Y., Xu, G., Zhong, L., & Xiao, Y. (2022). Multi-Modal Transport Logic Architecture Analysis Based on Autonomous Transportation System. CICTP 2022. https://doi.org/10.1061/9780784484265.043 Mohammed, M. Y., & Skibniewski, M. J. (2023). The Role of Generative AI in Managing Industry Projects: Transforming Industry 4.0 Into Industry 5.0 Driven Economy. Priloženie k Žurnalu Predprinimatelʹskoe Pravo. https://doi.org/10.2478/law-2023-0006 NVIDIA. (2024). TSMC and Synopsys bring breakthrough NVIDIA computational lithography platform to production [Press release]. NVIDIA Newsroom. https://nvidianews.nvidia.com/news/tsmc-synopsys-nvidia-culitho Pang, G. (2022). The AI chip race. IEEE Intelligent Systems, 37, 111–112. https://doi.org/10.1109/MIS.2022.3165668 Patil, D. (2025). Multimodal Artificial Intelligence In Industry: Integrating Text, Image, And Audio For Enhanced Applications Across Sectors. https://doi.org/10.2139/ssrn.5057428 Raghuweanshi, P. (2024). Revolutionizing semiconductor design and manufacturing with ai. Journal of Knowledge Learning and Science Technology, 3(3), 272–277. https://doi.org/10.60087/jklst.vol3.n3.p.272-277 Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: A systematic review and applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-20016-1 Singh, N., Chaudhary, V., Singh, N., Soni, N., & Kapoor, A. (2024). Transforming Business with Generative AI: Research, Innovation, Market Deployment and Future Shifts in Business Models. https://doi.org/10.48550/arxiv.2411.14437 Tivari, G. D., Khara, S., Dave, J., & Patel, V. (2024). Enhancing Reality: Exploring the Potential of Generative Artificial Intelligence. Indian Scientific Journal Of Research In Engineering And Management, 08(07), 1–13. https://doi.org/10.55041/ijsrem36378 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. 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