This portfolio presents the main research and writing themes that define my current work across AI, semiconductors, industrial strategy, advanced materials, and capital markets.
Core Research Themes
1. AI, Infrastructure, and Capital Allocation
This theme examines how AI is reshaping value distribution, industrial structure, and investment logic. Rather than focusing on model engineering itself, the emphasis is on how compute, energy, infrastructure, and capital interact in the real economy.
Selected work
- Essays on AI infrastructure and value migration
- Research on capital allocation in the AI era
- Ongoing framework development on energy, compute, and industrial strategy
2. Semiconductor Strategy and Industrial Bottlenecks
This theme focuses on semiconductors as the physical foundation of the AI economy, including supply-chain constraints, packaging, capacity, and competitive positioning.
Selected work
- Writing on semiconductor supply chains and bottlenecks
- Research on valuation, positioning, and industrial competitiveness
- Ongoing analysis of packaging, foundry dynamics, and capacity cycles
3. Advanced Materials and Industrial Transformation
This theme explores how materials capability, process economics, and manufacturing know-how shape long-term industrial competitiveness.
Selected work
- Carbon black and industrial materials knowledge-building
- Process-focused writing on manufacturing and capability development
- Ongoing work related to CB Academy and technical knowledge transfer
4. AI-Enabled Biological Systems Modeling
This theme explores conceptual and computational applications of AI in biological research, including non-clinical work related to structure, modeling, and scientific interpretation.
Selected work
- Essays on AlphaFold 3 and scientific discovery
- Conceptual work on AI in life-science research
- Non-clinical and non-medical computational perspectives
Research Approach
My work is grounded in real-world executive experience and focuses on how technology, industrial structure, and capital interact over time. It combines industrial observation, strategic reasoning, and long-cycle thinking rather than purely technical or purely academic abstraction.
Ongoing Development
Ongoing areas of development include new essays, working papers, teaching materials, and long-form research projects across the themes above.
研究版圖
本頁整理我目前研究與寫作的主要主題，涵蓋 AI、半導體、產業策略、先進材料與資本市場等相互連動的領域。
核心研究主題
1. AI、基礎設施與資本配置
此主題探討 AI 如何改變價值分配、產業結構與投資邏輯。重點不在模型工程本身，而在於理解算力、能源、基礎設施與資本如何在真實世界中交互作用。
代表內容
- AI 基礎設施與價值遷移相關文章
- AI 時代資本配置的研究與評論
- 能源、算力與產業策略之分析框架建構
2. 半導體策略與產業瓶頸
此主題聚焦半導體作為 AI 經濟之物理基礎，關注供應鏈限制、先進封裝、產能配置與競爭定位。
代表內容
- 半導體供應鏈與瓶頸分析文章
- 估值、競爭定位與產業競爭力研究
- 先進封裝、晶圓代工與產能週期之持續分析
3. 先進材料與產業轉型
此主題探討材料能力、製程經濟與製造 know-how 如何影響長期產業競爭力。
代表內容
- 碳黑與工業材料之知識建構
- 製程、能力培養與工業轉型相關寫作
- 與 CB Academy 及技術知識傳承相關之持續內容發展
4. AI 賦能的生物系統建模
此主題以計算與概念層次探討 AI 在生物研究中的應用，包括結構、建模與科學解釋等非臨床方向。
代表內容
- AlphaFold 3 與科學發現相關文章
- AI 在生命科學研究中的概念性分析
- 非臨床、非醫療之計算研究視角
研究方法
我的研究建立於真實世界的高階經營經驗，重點在於理解技術、產業結構與資本如何隨時間互動，並重塑競爭格局。其方法結合產業觀察、策略推理與長週期思維，而非僅停留於純技術或純學術式的抽象分析。
持續發展中
目前持續發展中的內容包括新文章、工作論文、教學教材與長篇研究計畫，並將圍繞上述主題持續擴充。