By 2030, generative AI will require 38 Google-class data centers—each consuming 1 gigawatt of power. This isn’t a projection buried in an appendix; it’s the binding constraint that determines how fast AI can actually scale.
The 2030 Projection framework quantifies what “AI scaling” actually requires in physical terms:
The Numbers:
347 TWh: Annual AI energy consumption (Schneider Electric estimate)
4.5%: Share of global electricity generation (IMF projection)
$1.5T: Global AI infrastructure spending through 2030
Why Physical Constraints Matter:
Software scales infinitely. Hardware does not. You cannot train larger models or serve more users without physical data centers connected to physical power plants. Building a 1 GW data center takes 3-5 years from planning to operation. Grid upgrades take longer.
This creates a strategic chokepoint. Companies that secured land, power agreements, and construction capacity early have advantages that softwareinnovation cannot overcome. The “AI race” is increasingly a real estate and energy race.
Investment Implications:
Data center REITs benefit from structural demand growth
Utilities near major tech hubs face capacity constraints
Nuclear and renewable investments accelerate
Geographic concentration creates single points of failure
AI’s future is being determined not just in research labs, but in construction sites and utility planning meetings.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
