The Constraint: AI Demands More Power Than Grids Can Deliver
- Single H100 GPU: ~700W under load
- Training cluster (10,000 GPUs): ~7 MW
- Large data center: 100-500 MW
- Meta’s 2026 needs: Multiple GW
Grid Reality Check
- US grid adds ~20 GW/year capacity
- AI companies collectively need 50+ GW by 2030
- Traditional grid expansion can’t keep up
Meta’s Response: Secure Your Own Power
Nuclear Power Commitment: 6.6 GW
- Largest corporate nuclear commitment ever
- Equivalent to ~6 nuclear reactors
- Powers ~5 million homes worth of compute
- 24/7 baseload (unlike solar/wind)
- Carbon-free alignment with climate goals
Solving the constraint others are still hitting
Competitor Comparison
| Company | Nuclear Strategy | Capacity |
|---|---|---|
| META | 6.6 GW nuclear secured | ✓ |
| MSFT | Three Mile Island restart | ~1 GW |
| GOOG | SMR deals (Kairos) | ~500 MW |
| AMZN | Nuclear exploration | TBD |
Why Nuclear? The Technical Fit
- 24/7 Baseload: AI inference never stops. Nuclear runs continuously.
- Carbon Free: Regulatory + ESG pressure. Nuclear = zero emissions.
- Energy Density: Small footprint, massive output. Unlike solar farms requiring huge land.
- Price Stability: Fuel costs are tiny %. Predictable for decades.
The chip shortage was last decade’s constraint. The energy shortage is this decade’s. Meta is solving it first.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.
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