AI-Created Content
Beyond the Algorithm: The Environmental Ledger of Generative AI
Generative AI's rapid adoption carries a heavy environmental burden: training and serving large models demand enormous electricity and water, often in already-stressed regions. This analysis breaks down the energy and water costs of training and inference, highlights how distillation and compact models can cut those costs substantially, and argues that standardized disclosure of AI's compute footprint is needed to drive accountability and sustainable growth.
