Computer scientists are working on multiple approaches to reduce the energy demands of advanced AI systems. Across recent coverage, researchers focus on both software and hardware: new algorithms and more efficient computing methods are being developed to reduce how much energy is required to train and run AI models. Hardware improvements are also part of the effort, including changes that can lower power use for large-scale computation. In parallel, the articles describe operational and infrastructure steps. One recurring theme is the strategic siting of data centers—choosing locations that can better support efficient power sourcing and cooling. Another is increasing the share of “green” electricity used to power AI workloads. Together, these efforts aim to address concerns that large AI deployments can drive higher electricity consumption, while enabling continued progress in AI capabilities. The emphasis is on practical reductions in power demand at every stage, from model development to everyday operation and the energy mix that supplies data center electricity.