The Energy Footprint of Intelligence

Training a single large model can emit as much CO₂ as five cars over their lifetimes. As AI scales, sustainability becomes strategy.

Compute intensity doubles roughly every six months. Inference—running models, not training them—now dominates total energy consumption as usage explodes.

Developers are responding with model compression, quantization, and parameter-efficient fine-tuning. These reduce compute demand by up to 70 percent.

Hyperscalers are investing in green data centers powered by renewables, liquid cooling, and edge inference that minimizes transmission.

Sustainable AI directly supports ESG commitments. Energy dashboards, carbon accounting, and sustainability SLAs will soon be standard in enterprise AI contracts.

Intelligence must be efficient to be ethical. The next competitive advantage will belong to organizations that align AI innovation with sustainability outcomes.