The rapid expansion of artificial intelligence is expected to significantly increase energy consumption and carbon emissions in the United States unless changes are made to how these systems are developed and deployed. Training and running large AI models — especially those used for generative tasks like text, image, and video creation — require massive computational resources, which in turn demand substantial electrical power. With much of today’s power still coming from fossil fuels, the environmental impact of AI could be sizeable, adding pressure to already strained decarbonization efforts.
One core driver of this trend is the sheer scale of modern AI models. As companies pursue ever-larger neural networks to improve performance, the amount of computing needed grows exponentially. Data centers that house the specialized hardware for AI operations can consume as much energy as small cities, and even small gains in model accuracy can come at a high energy cost. Without innovation in efficiency, this pattern risks locking in substantial emissions as AI use expands across industries.
However, experts argue that the environmental footprint of AI is not predetermined. Advances in hardware design, such as more efficient chips and cooling technologies, can reduce the energy required per computation. Likewise, shifting data centers toward renewable energy sources — wind, solar, and hydroelectric power — can help decouple AI growth from carbon emissions. Some organizations are already prioritizing sustainable infrastructure to ensure that expanding AI capacity aligns with climate goals rather than working against them.
The article also highlights that software-level optimizations and smarter resource allocation can make a difference. Techniques such as model sparsity, better algorithms, and workload scheduling can cut unnecessary energy use while preserving performance. By combining improvements at the hardware, software, and policy levels, it is possible to support both AI innovation and environmental sustainability — charting a path where AI’s benefits do not come at the expense of the planet.
