The cost composition of a new data center has inverted. Historically a 50/50 split between construction and IT gear, it's now approximately one-third physical building and two-thirds expensive equipment like advanced chips and servers, changing project economics.

The widely discussed GPU supply crunch is only half the problem. There's a severe shortage of suppliers who can operate data centers with the high reliability and SLAs required for mission-critical inference. Out of many providers, only a handful meet the "gold tier" for operational excellence.

In AI infrastructure, the capital cost of GPUs (~80%) dwarfs operational costs. Therefore, getting a multi-billion dollar cluster online a few months earlier generates far more value than optimizing for TCO, justifying seemingly wasteful spending on stopgaps like mobile chillers to bypass construction delays.

The high cost of GPUs means any inefficiency during model training is extremely expensive. This economic reality justifies building specialized, AI-focused infrastructure with features like advanced observability and optimized storage to maximize GPU utilization and prevent costly delays from failures or slowdowns.

The staggering $100B+ guarantee from Nvidia is strictly for the "PowerShell" – the land, power, and physical data center building. This financing is completely separate from the even larger capital required to purchase the Nvidia GPUs that will fill it. This reveals a two-tiered financing challenge in AI infrastructure, requiring distinct capital stacks for the physical shell and the computational hardware within.

Accessing next-generation GPUs at scale is no longer a simple purchase. The market now demands three-to-five-year commitments with a significant portion (20-30%) of the total contract value paid upfront. This makes a company's cost of capital a critical competitive factor in acquiring compute capacity.

The primary constraint on building new AI data centers isn't acquiring land or power, but securing "powered shells"—fully energized buildings with cooling and components. Supply chains for transformers and a severe shortage of accredited electricians are the true limiting factors.

While powerful, Google's TPUs were designed solely for its own data centers. This creates significant adoption friction for external customers, as the hardware is non-standard—from wider racks that may not fit through doors to a verticalized liquid cooling supply chain—demanding extensive facility redesigns.

Public announcements for massive new data centers may be "pollyannish." The reality is constrained by long lead times for critical hardware components like power generators (24 months) and transformers. This supply chain friction could significantly delay or derail ambitious AI infrastructure projects, regardless of stated demand.