AI's energy footprint is impossible to ignore. Training a single large model can consume as much electricity as dozens of households use in a year. As AI scales, so does its energy demand—and the industry's responsibility to meet it sustainably.
This hub covers the energy side of AI infrastructure: from renewable power procurement to efficiency innovations that reduce consumption at its source.
Key Areas
- Power Sources — Solar, wind, hydro, and nuclear options for AI data centers: availability, economics, and reliability trade-offs
- Small Modular Reactors — The nuclear renaissance and what it means for data center power: SMR timelines and feasibility
- Carbon Accounting — Scope 1, 2, and 3 emissions: understanding and reducing AI infrastructure's carbon footprint
- Efficiency Innovation — PUE improvements, workload optimization, and hardware advances that reduce energy per computation
- Renewable Procurement — PPAs, RECs, and strategies for matching AI workloads to clean energy availability
Sustainable AI isn't optional—it's a business requirement. Our energy coverage helps you navigate the path to AI infrastructure that's both powerful and responsible.