The training process for a single AI model, such as an LLM, can consume thousands of megawatt hours of electricity and emit hundreds of tons of carbon. AI model training can also lead to the evaporation of an astonishing amount of freshwater into the atmosphere for data center heat rejection, potentially exacerbating stress on our already limited freshwater resources. These environmental impacts are expected to escalate considerably, and there remains a widening disparity in how different regions and communities are affected. The ability to flexibly deploy and manage AI computing across a network of geographically distributed data centers offers substantial opportunities to tackle AI’s environmental inequality by prioritizing disadvantaged regions and equitably distributing the overall negative environmental impact.
The adoption of artificial intelligence has been rapidly accelerating across all parts of society, bringing the potential to address shared global challenges such as climate change and drought mitigation. Yet underlying the excitement surrounding AI’s transformative potential are increasingly large and energy-intensive deep neural networks. And the growing demands of these complex models are raising concerns about AI’s environmental impact.
