黃柏松 Po-Sung (Sinclair) Huang#
資深產業經營者｜企業成長與轉型顧問｜獨立研究者#
從市場機會與企業策略出發，連結產品與技術方向、資本配置、營運能力及組織執行。
黃柏松（Po-Sung / Sinclair Huang）是一位資深產業經營者，擁有逾三十年於上市公司的高階經營經驗，產業橫跨電子、工業材料、化工與生命科學。
其職涯歷任多家上市公司之財務長、執行副總、總經理與事業群財務經管主管，曾任職於鴻海科技集團（Foxconn）、元太科技（E Ink）、國際中橡（CSRC）等企業，任職期間歷經企業從成長、規模化到轉型的不同階段。這些職務的共同點，是必須同時面對市場位置、產品與技術方向、資本配置、營運能力與組織執行——策略能否成立，取決於這幾件事能否接得起來。
其顧問服務聚焦於企業成長、轉型與重大決策：從市場機會與企業策略出發，連結產品與技術方向、資本配置、營運能力及組織執行。財務分析、流程改善、資料分析與 AI 是用來驗證判斷、解除瓶頸的工具，而不是顧問工作的起點或邊界。決策權與執行責任留在企業管理團隊。
在顧問工作之外，他以獨立研究者身分持續研究 AI、半導體、先進材料與資本配置。這些是目前研究與顧問判斷所關注的領域；研究支撐判斷，而不取代它。
策略視角#
企業經營與跨功能判斷
歷任上市公司之財務、策略、營運與組織管理等高階職務，責任範圍涵蓋市場與客戶、產品與技術方向、資本配置、營運能力與組織執行。具備在證據尚不完整時界定問題並作出決策的實務判斷。
成長、轉型與重大決策
顧問服務聚焦於跨部門、無法由單一功能獨立解決的問題：獲利成長、競爭位置、重大投資與融資、產能與布局，以及執行所需的組織能力。顧問提供獨立判斷與決策架構，決策權與執行責任留在企業管理團隊。
AI、資本與產業轉型
目前的研究關注 AI 與產業變遷如何重新分配價值、改變競爭格局。在顧問工作中，AI 是協助企業處理經營問題、驗證判斷與改善決策的工具，而不是顧問工作的起點或邊界；技術建置由適當的內部團隊或技術夥伴負責。
了解顧問服務 → ｜ 查看研究發表 →
English Summary#
Senior Industry Executive | Growth & Transformation Advisor | Independent Researcher
Connecting market opportunity and business strategy with product and technology direction, capital allocation, operating capability and organisational execution.
Po-Sung (Sinclair) Huang is a senior industry executive with more than 30 years of senior management experience in publicly listed companies, across electronics, industrial materials, chemicals and life sciences. He has served as CFO, Executive Vice President and General Manager, as well as in group-level finance and business-management leadership roles, through periods of growth, scaling and transformation.
His advisory focus is growth, transformation and major business decisions: where a business should compete, which products and capabilities deserve investment, how capital should be committed, and whether the organisation can execute. Financial analysis, process improvement, data analysis and AI are tools used to test judgement and clear bottlenecks — not the starting point or the boundary of the work. Decision rights and execution responsibility stay with the company’s management team.
His independent research focuses on AI, semiconductors, advanced materials and capital allocation. This research informs, rather than defines, his advisory perspective.
Advisory services → | Full profile →
Selected Work#
View All Publications → | ORCID: 0009-0007-8173-5672
COMPUTEX 2026 and Taiwan’s shift from supply chain to co-design and deployment marketplace AI Infrastructure Notes｜Article 4
Sinclair Huang
Field Note v4 — updated with GTC Taipei and statistical-science reference materials, using public and user-provided sources available as of June 6, 2026.
I am writing this at a strange distance from Taiwan.
While friends in Taipei are celebrating a market that seems to have escaped ordinary scale, I am abroad, watching the same numbers with admiration, unease, and a growing sense that another AI market commentary would not be enough.
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Product exposure, process-control exposure, and the physical bottlenecks behind the AI capex wave AI Infrastructure Notes | Part 3
Sinclair Huang
A reader recently left a comment on my ABF substrate piece that stayed with me.
His point was simple: CoWoS and HBM get most of the attention, but substrate materials often sit below the level where many equity models even begin.
I think that observation captures a broader problem in AI infrastructure analysis.
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Why CPO is not an optics story — it is a process-integration story. AI Infrastructure Notes | Part 2
Sinclair Huang
Everyone says AI needs more bandwidth.
That part is true.
As AI clusters scale from thousands of accelerators to tens of thousands, and then toward million-GPU-scale systems, the network stops being a background layer. It becomes part of the compute fabric itself. Copper reaches its distance, power, and signal-integrity limits. Optical interconnect moves closer to the switch ASIC. Co-packaged optics becomes the logical next step.
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** In a world of infinite content and scalable intelligence, the scarcest resource may no longer be intelligence itself — but the ability to truly see another human being, and to be seen in return.**
By Sinclair Huang
I walked into a bookstore — and realised something uncomfortable A few days ago, I walked into a bookstore.
Surrounded by shelves of books — many of them thoughtful, deeply human, and difficult to summarise — I had a quiet realisation:
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Everyone Is Betting on AI — But Almost No One Asks: Where Are You Standing?The AI boom is real. The harder question is where value, risk, and leverage accumulate inside the stack. Position matters more than opinion.
Over the past few weeks, I have been thinking about AI through a different question:
Why can the same AI cycle feel so powerful in markets, yet so uncertain in daily life?
My own view is more optimistic than pessimistic.
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Work, ownership, and the new architecture of economic security
By Po-Sung(Sinclair) Huang
For decades, people believed that working hard was enough.
In the AI era, that assumption is quietly breaking.
Not because work disappears, but because ownership matters more than ever.
In the AI era, relying on labour income alone is no longer a neutral choice. It is an active form of risk.
That sentence may sound harsh. But it captures a structural shift that many people can already feel, even if they do not yet have the language for it. They feel it in markets. In career anxiety. In the fear of being left behind by a technology wave they did not ask for, do not fully control, and may not directly benefit from.
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From dBase and enterprise systems to the internet revolution and generative AI, I have come to see AI not as a sudden break, but as the latest step in a long slope of automation now reaching human cognitive work itself.*
By Po-Sung(Sinclair) Huang
I did not decide to write this essay because AI suddenly became fashionable.
It came out of two images that collided in my mind.
One was a group of younger people trying to imagine new ventures built around AI. The other was the story of a father facing a rare disease so obscure it seemed to leave almost no path forward, and yet continuing, late into the night, to search for a way through with the help of computation, search, and structured reasoning.
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From compute bottlenecks to industrial consequences — where value may actually concentrate through 2030
Series: AI Compute Supply Chain | Part 5 of 5
Author: Po-Sung (Sinclair) Huang For the past four articles in this series, I have written about CoWoS, HBM, ABF substrates, SEC filings, and the fault lines that could eventually crack today’s moats.
On the surface, that may look like a semiconductor series.
It is not.
What these articles really reveal is something larger: AI is no longer just a software story, and no longer just a model race. It is becoming an industrial system — one that depends on power, cooling, capital expenditure, advanced packaging, memory bandwidth, substrate materials, qualification cycles, and the physical discipline of manufacturing scale.
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TurboQuant, HBM demand reversal, geopolitics, and glass substrates — not a doomsday scenario, but a disciplined analysis.
Series: AI Compute Supply Chain | Part 4 of 5
Author: Sinclair Huang
Four pressure vectors. Four clocks are already ticking. The question is not whether these moats will last forever — it’s whether you know which one breaks first.
I developed a habit during my years in the electronics industry that I haven’t been able to shake in research and writing: every time I become confident about something, I force myself to find the strongest argument against it.
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A new study in Nature Machine Intelligence suggests AI may do more than summarise knowledge or accelerate discovery. It may begin to shape which scientific questions are noticed first.
When AI Starts Predicting the Next Scientific QuestionWe have grown used to thinking about AI as an answer machine. It summarises papers, organises data, generates hypotheses, and accelerates analysis. In that familiar picture, AI helps scientists move faster toward results that humans still define.
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