Opacity in A.I. infrastructure and narrow measurement tools are widening the gap between corporate climate commitments and environmental reality. Courtesy Microsoft

As A.I. adoption accelerates, companies are making sustainability claims they cannot reliably substantiate, using metrics that fail to adequately capture the true environmental costs of A.I. systems. This creates a new risk: even well-intentioned companies committed to sustainability may unintentionally greenwash their activities, undermining public trust, investor confidence and corporate credibility.

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A.I.’s environmental impacts are significant. They include rising energy demand, increased carbon emissions, water consumption, local environmental degradation and unequal distribution of environmental benefits and harms. Despite growing attention to these issues, the scale of A.I.’s environmental footprint remains difficult to quantify. Much of the A.I. infrastructure is owned by private entities that disclose little about energy use, while technological developments mean existing impact measurement approaches struggle to keep pace. The combination of opacity and rapid change has created a widening gap between what organizations adopting A.I. report about their environmental impact, and what it costs the environment. 

The environmental costs of A.I.

A.I.’s environmental impacts span the entire technology stack, from the extraction of raw materials for hardware, through model training........

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