I don’t think people fully appreciate the amount of demand that inference will create for AI infrastructure. Training trillion parameter models will pale in comparison to volumes of trillions of concurrent tokens performing 24/7/365 across every business, organization, and consumer activity. I’m so bullish on this buildout. And of course $NVDA does well, but names in chips, servers, energy, networking, and agentic platforms will all rise with it. 💪🏻 Mark it 😎🚀

Highlights from AMZN call: -AI Infrastructure Capacity Shortfall: AWS has "more demand than we have capacity" with power being the single biggest constraint; expects supply issues to persist for "several quarters" despite $31.4 billion quarterly CapEx in "chips, data centers and power" -Multi-Year Infrastructure Buildout: Acknowledges building sufficient AI capacity will take "several quarters" but expects improvement each quarter; views AI as "biggest technology transformation in our lifetime" requiring sustained heavy investment -Inference Economics Driving Strategy: Expects 80-90% of AI costs will shift from training to inference at scale; positions custom silicon as critical advantage since customers will anchor for better price performance like they did with CPUs -Massive Revenue Opportunity Constrained: AWS at $123 billion annualized run rate but "could be doing more revenue and helping customers more" if capacity existed; generative AI already a "triple-digit year-over-year percentage multibillion-dollar business"