Measuring is fundamental to understanding and managing AI's environmental impact. It clarifies how and where AI consumes resources, enabling sustainable practices.
Granular data provides actionable insights to optimize AI training and inference, reduce compute waste, and foster energy-efficient algorithms and hardware. This yields not only environmental but also cost benefits.
Proactive measurement is vital for navigating evolving regulations, preparing for mandatory carbon accounting, and building trust through transparent environmental reporting.