Table of Contents
Quick Answer
- AI-dedicated data center power (2027): 228 TWh (IEA)
- Share of global electricity: 0.8%
- US AI data center power: 118 TWh (Lawrence Berkeley Lab)
- GPT-5 training emissions (est): 18,400 tCO2e (Stanford HAI)
- Hyperscaler nuclear PPAs signed (2024–27): 24 GW
Key AI Energy Statistics
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.
Market Data
| 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 |
Top Hyperscaler Nuclear Deals
| 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 |
Training Emissions Estimates
| 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 |
Sources
- IEA — Electricity 2027 Report
- Lawrence Berkeley National Lab — US DC Energy 2027
- Stanford HAI — AI Index 2027 (Compute chapter)
- SemiAnalysis — AI Power Consumption 2027
- BloombergNEF — Nuclear + AI 2027
- UNEP — Data Center Water 2027
- Uptime Institute — Global DC Survey 2027
- Microsoft / Amazon / Google / Meta — Sustainability Reports 2027
- Goldman Sachs — AI Power Demand 2027
- Epoch AI — Frontier Model Compute Tracker 2027
Conclusion
AI's energy footprint in 2027 is substantial and growing. More at misar.blog.