The exponential growth of artificial intelligence (AI), electrified transport, and renewable generation is accelerating a structural shift in how societies produce, deliver, and consume electricity. We argue that the next frontier is not incremental efficiency but Energy Intelligence (EI): the embedding of predictive analytics, adaptive control, and material-aware design directly into power-conversion hardware. In this view, power electronics functions as the cognitive layer that links digital intelligence to the physical flow of energy. Wide-bandgap (WBG) semiconductors—gallium nitride (GaN) and silicon carbide (SiC)—provide the material foundation for higher switching frequencies, superior power density, and real-time controllability, enabling compact and efficient converters for data-centers, EV charging, and grid-interactive resources. We formalize an EI reference architecture (predictive, adaptive, material-efficient, data-driven), review the convergence of AI methods with converter design and operation, and outline a GaN/SiC-enabled data-center power path as an illustrative case. Finally, we examine sustainability and sovereignty, highlighting exposure to critical materials (Ga, Si, In, rare earths) and proposing a roadmap that integrates technology, policy, and education. By reframing power electronics as an intelligent, learning infrastructure, this work sets an agenda for systems that are not only efficient but also self-optimizing, explainable, and resilient.
Nikolay Hinov (2025) studied this question.