IEA's 2027 Electricity Report projects data center consumption at 945 TWh by 2030, with AI driving 54% of growth. Lawrence Berkeley National Lab estimates US AI data centers consume 118 TWh in 2027 — 2.8% of US electricity. Stanford HAI reports frontier training runs exceed 10 GWh each.
| Metric | 2027 Value | Source |
|---|---|---|
| Global DC power | 580 TWh | IEA |
| AI-specific DC power | 228 TWh | IEA |
| US DC power | 248 TWh | LBNL |
| US AI DC power | 118 TWh | LBNL |
| Global share of electricity | 0.8% AI | IEA |
| Frontier model training | 10–30 GWh | Stanford HAI |
| Inference % of AI power | 72% | SemiAnalysis |
| Hyperscaler nuclear PPAs | 24 GW | BloombergNEF |
| Water use (DC cooling) | 820B liters | UNEP |
| PUE average hyperscale | 1.15 | Uptime Institute |
| Company | Deal | Capacity |
|---|---|---|
| Microsoft | Three Mile Island restart | 835 MW |
| Amazon | Talen Energy (Susquehanna) | 1920 MW |
| Kairos SMR | 500 MW (multi-site) | |
| Meta | 1–4 GW SMR RFP | Multiple |
| Oracle | 1 GW SMR plan | Texas |
| Model | Training TWh | tCO2e |
|---|---|---|
| GPT-5 | 0.048 | 18,400 |
| Claude 4.6 | 0.031 | 11,200 |
| Gemini 3 Ultra | 0.042 | 14,800 |
| Llama 4 | 0.024 | 8,400 |
AI's energy footprint in 2027 is substantial and growing. More at misar.blog.
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