Data-Center

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. ...