Ai-Infrastructure

A bilingual research brief for The Mid-2026 Semiconductor Correction: AI Infrastructure, Model Mis-Specification, and Memory as a Bottleneck Asset — SSRN Working Paper No. 7013899, Huang, Po-Sung (Sinclair), 2026. What the paper studies. The mid-2026 semiconductor correction generated two competing narratives: a “bubble-burst” reading grounded in legacy DRAM cycle models, and an “infrastructure repricing” reading grounded in bottleneck economics and AI capex fundamentals. The paper uses the divergence in analyst and retail commentary around Micron Technology as a case study in regime mis-specification — the application of a prior-cycle statistical model to a structurally transformed market environment. ...

A Taiwan supply-chain note on sovereign AI, physical AI, power, quantum — and valuation stress Sinclair 2026–06–29 本文以中文寫作，從台灣供應鏈、市場情緒與個人現場觀察出發，觀察 AI 需求、資本支出、實體部署與估值壓力之間的拉扯。 Written in Chinese, from a Taiwan supply-chain perspective. AI 故事還在，但價格已經在喘。 這幾天再寫 AI 供應鏈，其實有點尷尬。台股剛經歷劇烈回檔，市場還在消化外資賣超、台指期偏空部位與 AI 估值壓力。6 月 29 日，指數反彈，盤中重新站上 45,000 點，最後卻收在 44,999.90，差一點沒有站回整數關卡；同日外資現貨仍賣超 60.11 億元，投信買超 137.54 億元。期交所資料則顯示，台股期貨（TX）外資未平倉淨額為 -76,627 口，其中多方 7,494 口、空方 84,121 口。Ref A1Ref A2 Taiwan market and futures snapshot 資料截點：本文使用 2026–06–29 盤後與期交所公開資料。這不是即時交易建議，而是用當天市場結構作為壓力測試的觀察入口。 這個收盤數字很有意思。 它不像恐慌延續，也不像壓力解除。它更像市場正在問一個中間問題：AI 與半導體的長期故事還在，但今天的價格，還能不能承受這個故事？ 我不想把這篇寫成「AI 回檔就是機會」。 也不想寫成「AI 泡沫要破了」。 我真正想問的是另一件事：當市場從 AI euphoria 進入 valuation stress，哪些需求還能被現場驗證？哪些只是價格上漲時，大家願意相信的敘事？ ...

From “What’s the next AI stock?” to “Where’s the next bottleneck, who gets paid, and what would prove us wrong?” Sinclair Huang This essay helps answer four practical questions: Which AI themes are real constraints rather than just attractive stories? Who can capture gross margin when a constraint binds? How long might the bottleneck last before capacity, substitution, or efficiency relieves it? What evidence would prove the thesis wrong? The main argument: large AI demand explains why the sector is hot; bottlenecks explain who gets paid; spillovers explain who absorbs hidden costs; monetised usage determines whether the buildout is sustainable. ...

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. ...

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. ...

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. ...