#startups

Seems about right. Interesting metrics on startups too:

• Foundation Model Labs: Revenue must grow faster than Compute Costs.
• Enterprise AI Platforms: High Gross Retention because of high AI Feature Adoption.
• Application Layer: Net Revenue Retention (NRR) > 120% and CAC Payback < 12 months.
• Inference API Players: High Revenue per GPU-Hour (pricing power).
• Energy/Infrastructure: Structural Energy Cost Advantage and high utilization.

Energy infrastructure, unlike GPUs that become obsolete in five years, compounds in value over decades.

Consider the math: A single large AI training cluster can require 100+ megawatts of continuous power — equivalent to a small city. The United States currently generates about 1,200 gigawatts of electricity total. If AI compute grows at projected rates, it could demand 5-10% of the nation’s entire power generation within a decade.

And unlike fiber optic cable or GPU clusters, power infrastructure can’t be deployed quickly. Nuclear plants take 10-15 years to build. Major transmission lines face decades of regulatory approval. Even large solar farms require 3-5 years from planning to operation.

The companies prepping themselves to survive scarcity aren’t just stockpiling compute—they’re building root systems deep enough to tap multiple resources: energy contracts locked in for decades, gross retention rates above 120%, margin expansion even as they scale, and infrastructure that can flex between training and inference as market dynamics shift.