Jensen Huang made a comment recently about Nvidia's supply limits that completely changed how I'm looking at the AI supply chain. Everyone knows Nvidia makes the best GPUs, but a GPU is basically a paperweight if you can't get high-bandwidth memory, cool down the rack, or pull enough gigawatts off the local grid. $NVDA growth isn't constrained by orders—it's constrained by the physical bottlenecks holding up the deployment of AI factories. Here are 5 bottleneck suppliers sitting directly in that supply chain path that I've been researching: $MU (HBM3e & HBM4) — High-Bandwidth Memory is still one of the tightest bottlenecks in advanced AI chips. Without dense memory stacks right next to the logic die, compute stalls. $VRT (Liquid Cooling & Power Management) — As racks push past 100kW per cabinet, air cooling physically fails. Liquid cooling isn't an upgrade anymore; it's a structural requirement. $ETN / $SBGSY (Grid & Power Infrastructure) — Data center capex is running into a wall of power availability. Power transformers, switchgear, and microgrids are where deployments are getting stuck. $TSM (CoWoS Advanced Packaging) — The bottleneck often isn't silicon wafer production—it's Chip-on-Wafer-on-Substrate packaging capacity. $CRDO / $AVGO (Custom ASIC & High-Speed Interconnects) — Moving massive datasets between thousands of GPUs requires specialized retimers, switches, and custom silicon. I'm not jumping headfirst into all of these blindly, but I'm watching the infrastructure bottlenecks much more closely than the headline chipmakers right now. If the whole world is building AI factories, the companies supplying the power, cooling, and memory infrastructure are sitting in a very unique position. Would you rather own the primary GPU designer here, or build a position in the bottleneck infrastructure around it?
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David Monroe@d0p3y71 · 31m
I sorta am in on the ground floor , I have CIBR in my tax account. So far it’s been good to me
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