Artificial intelligence is playing a growing role in advancing the clean energy transition, despite concerns about the high energy demands of AI data centers. While AI infrastructure can strain power grids and increase energy consumption, its applications in energy efficiency, grid management, renewable integration, and materials science offer powerful tools to accelerate the shift to sustainable energy. AI is already helping reduce energy use and emissions across key sectors. In buildings, smart systems use AI to optimize heating, cooling, and lighting based on occupancy and weather patterns. In transportation, AI improves route planning and energy management in electric vehicles, while also enabling smarter traffic control to reduce congestion and fuel use. In industrial processes, AI-driven automation leads to more efficient operations and lower waste. On the power grid, AI is critical for managing the increasing complexity of modern energy systems. As solar and wind power grow, so does the challenge of balancing variable supply with fluctuating demand. AI algorithms can predict energy needs, manage the flow of electricity in real time, and ensure grid stability at millisecond speeds. They also help integrate distributed energy resources, such as rooftop solar and home batteries, by enabling dynamic control of supply and demand. One of the most promising uses of AI is in predictive maintenance. By continuously monitoring equipment performance, AI can detect early signs of failure, allowing for timely repairs before outages occur. This not only prevents blackouts but also extends the life of critical infrastructure, reduces maintenance costs, and improves safety. AI is also transforming long-term energy planning. Grid operators must decide where and when to build new power plants, transmission lines, and storage facilities—decisions that can take over a decade. AI models can analyze vast amounts of data, including weather patterns, energy demand trends, and climate risks, to forecast future needs with greater accuracy. This helps planners make better-informed decisions, especially in the face of extreme weather events and the growing uncertainty of a changing climate. In materials science, AI is dramatically speeding up the discovery of new materials essential for clean energy. Researchers at MIT and elsewhere are using AI to simulate atomic-level behavior, identify promising compositions, and guide lab experiments in real time. AI-powered systems can suggest the next best experiment based on past results and scientific literature, allowing for faster iteration and reduced trial and error. This approach has the potential to cut the time to develop new materials—from decades to just a few years—enabling breakthroughs in battery technology, solar cells, nuclear reactors, and hydrogen production. MIT’s Energy Initiative (MITEI) is at the forefront of this work, supporting research that uses AI to model fusion plasma, interpret regulatory and climate data, and design more efficient energy systems. MITEI is also developing AI-driven robots for maintenance tasks, reducing the need for human workers in dangerous environments, and exploring ways to make data centers more energy-efficient through better chip design, algorithms, and building layouts. In May 2025, MITEI hosted a symposium titled “AI and energy: Peril and promise,” bringing together experts from academia, industry, and government to discuss AI’s dual role in both increasing energy demand and enabling a sustainable future. The event highlighted the need for collaboration across disciplines and sectors to ensure that AI is used responsibly and effectively in the energy transition. As William H. Green, director of MITEI, noted, the challenge of meeting data center energy needs while harnessing AI’s potential for clean energy is now a top research priority. With the right approach, AI can be a key enabler of a reliable, resilient, and sustainable energy system.