Semiconductors

The AI Capex Money Map v0.2 — America Spends. Who Actually Keeps the Margin?#### From $650B to HBM, CoWoS and power — mapping who gets paid, who keeps margin, and when the bottlenecks move.Most people are asking whether AI is a bubble. It is an important question, but not a very operational one. A better one is: Of all the AI capex US hyperscalers are spending, whose revenue, whose margin, and whose moat does it actually end up in? ...

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? ...

## COMPUTEX 2026 and Taiwan’s shift from supply chain to co-design and deployment marketplaceAI 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. ...

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. ...

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. ...

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. ...

Customer prepayments, HBM margin structure, capital expenditure intensity — the numbers say more than the narratives do.Series: AI Compute Supply Chain | Part 3 of 5 By Po-Sung(Sinclair) Huang The previous article built a framework. This one tests it with documents. Before writing this piece, I set aside the analyst summaries and went to the source: TSMC’s 2024 20-F filed with the SEC on April 17, 2025; Micron’s most recent 10-K and two 10-Q filings; and SK Hynix earnings call transcripts for the past three quarters. ...

From TSMC to SK Hynix to Ajinomoto — who holds pricing power, and who is just riding the narrative? Series: AI Compute Supply Chain | Part 2 of 5 Author: Po-Sung(Sinclair) Huang The first article in this series explained what CoWoS, HBM, and ABF are. This one answers the harder question underneath: who controls them? Knowing that these three layers matter is the entry ticket. The real analytical edge comes from understanding who in each layer has the power to say no — and what happens to the whole chain when they do. ...