Incorporating Quantum Computing Into Genesis Mission Could Help Address the AI Energy Crunch COMMENTARY As demand for computing power surges with increased adoption of AI, incorporating energy-efficient solutions is becoming a national priority. Artificial intelligence is increasingly essential across public and private sectors, serving as an important tool to help modernize systems, strengthen security and tackle complex challenges. From calls to greater use of AI across the government to the Genesis Mission, a program focused on next-generation technologies, including AI and advanced computing infrastructure, the rapid pace of AI adoption is placing a strain on the infrastructure required to support it. The United States is facing a growing compute and energy crunch driven by the massive processing power needed to train and run advanced AI systems. This challenge is substantial and immediate. The Electric Power Research Institute’s projection that AI data centers alone could consume up to 9% of U.S. electricity generation by 2030 raises concerns about potential energy shortages and higher electricity costs for consumers. At the same time, the electric grid faces a fundamental timing mismatch: demand from AI is rising now, while new infrastructure can take a decade or more to permit, approve and build. To meet this challenge, companies are considering building their own power plants, while others are exploring ambitious long-term concepts for next-generation AI infrastructure, such as orbital data centers. While intriguing, another option should also be considered. Quantum computing, specifically annealing quantum computing, offers a more immediate pathway for energy efficient computing by helping solve certain computationally complex problems more efficiently and potentially reducing the amount of classical compute required across AI workflows. Optimizing AI workflows could also play a meaningful role in improving AI’s energy efficiency, easing infrastructure strain and enabling more sustainable scaling of advanced AI systems. In fact, a breakthrough