Former $MSFT employee on why $MSFT leads the hyperscaler capacity race and what that means for the competition ( $GOOGL, $META, $AMZN ): - The expert sees no slowdown in data center demand, with construction at an all-time high and available capacity at an all-time low, driven entirely by the need for AI training and inference. He doesn't expect the capacity crunch to ease anytime soon, with a large wave of new supply anticipated around 2028 based on current build-out timelines across the U.S. and globally, and some further relief potentially coming by 2030. However, the expert stops short of calling it a full resolution. - The expert ranks $MSFT as the clear leader on current capacity, with $GOOGL in second place, moving quickly on both deployment and the commercialization of its TPUs for external use. $META comes in third, though the expert notes some uncertainty around how much capacity $META actually has available right now. $AMZN sits in fourth place, which the expert attributes to overindexing on the Inferentia line, which slowed capacity buildout in the short term, though the expert does not see that gap as permanent. - The expert sees behind-the-meter microgrids as a practical and economically compelling solution for data center power, pointing to natural gas-rich areas where facilities can generate power at $0.03 to $0.07 per kilowatt hour compared to $0.17 to $0.21 from the grid, a cost difference significant enough to justify building out the infrastructure entirely. - He believes the near-term mix will likely be gas turbines alongside battery energy storage systems tied to solar, but the expert sees SMRs as the ultimate destination, with companies making meaningful progress and permitting activity accelerating. - The expert puts the cost of a standard non-GPU data center at around $10 to $12 million per megawatt all in, rising to roughly $20 to $22 million per megawatt for an AI-focused build, with the difference driven by higher power density requirements, different cooling strategies, and more specialized equipment. According to the expert, regional land costs add another layer on top pushing prices significantly higher regardless of workload type.

Leopold has some explaining to do

HTML is the new markdown. I've stopped writing markdown files for almost everything and switched to using Claude Code to generate HTML for me. This is why.

I think CSPs are the next big disruption target. Distributed compute in a world of AI seems like an obvious outcome. We’re just waiting for its “Hello world” moment so we know it is possible.