Higher education capacity planning increasingly reflects the infrastructure demands of AI research and teaching. At the University level, campus executives are confronting the need for high-performance computing that can support large-scale model training, complex research workloads, and modern analytics. Reporting tied these needs to real examples of AI tools failing when institutional computing resources are insufficient. Meanwhile, broader industry coverage continued to signal tightening economics and rising risk around AI capex, emphasizing that data centers, chips, and grid constraints are part of the same operational story. Campuses are now faced with long lead times and cost pressures when scaling compute—shaping who can run advanced coursework, labs, and research. As students and employers increasingly expect AI competencies, computing capacity is becoming a prerequisite for program differentiation, not just a technical back-office requirement.