  Foundation models (FMs) currently dominate news headlines. They employ
advanced deep learning architectures to extract structural information
autonomously from vast datasets through self-supervision. The resulting rich
representations of complex systems and dynamics can be applied to many
downstream applications. Therefore, FMs can find uses in electric power grids,
challenged by the energy transition and climate change. In this paper, we call
for the development of, and state why we believe in, the potential of FMs for
electric grids. We highlight their strengths and weaknesses amidst the
challenges of a changing grid. We argue that an FM learning from diverse grid
data and topologies could unlock transformative capabilities, pioneering a new
approach in leveraging AI to redefine how we manage complexity and uncertainty
in the electric grid. Finally, we discuss a power grid FM concept, namely
GridFM, based on graph neural networks and show how different downstream tasks
benefit.
