AI Workloads Are Rewriting Infrastructure Requirements
Our Analysis
Traditional data centers were designed for cloud workloads: relatively uniform compute, 5-15 kW per rack, air cooling, and flexible location. AI workloads—particularly training and large-scale inference—break every assumption.
Modern AI racks draw 40-100+ kW, requiring liquid cooling infrastructure that most facilities lack. Power density per square foot has increased 10x in five years. And critically, AI inference demands low latency to end users, meaning facilities must be located near population centers—not in remote locations where power is cheap but connectivity is limited.
This creates a bifurcated market: AI-ready facilities command significant premiums and face years-long waitlists, while traditional cloud facilities see commoditization. For developers, the message is clear: build for AI from day one, or risk obsolescence before construction is complete.
Key Takeaways
- 1AI racks draw 40-100+ kW vs. 5-15 kW for traditional cloud—a 5-10x increase
- 2Liquid cooling is now mandatory for AI workloads; air cooling is insufficient
- 3AI inference requires proximity to users—remote 'power cheap' locations don't work
- 4AI-ready facilities command 30-50% premiums and have multi-year waitlists
- 5Traditional cloud facilities are commoditizing; new builds must be AI-first
Why This Matters for Infrastructure Investors
This is the generational shift in infrastructure investing. Every new facility we develop is designed for AI from the ground up: liquid cooling infrastructure, 50+ kW/rack power density, dual-feed power for uptime requirements, and locations that balance power availability with network connectivity. The facilities being built today to 'legacy' cloud specifications will struggle to attract tenants within 3-5 years. AI-ready infrastructure is the new baseline.
AI is driving a 10x increase in power density
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