AI adoption continues to strain compute access and deepen governance needs, according to reporting on Nvidia GPU scarcity and workplace AI risks. Even Nvidia’s own applied deep learning teams are reportedly supply constrained, highlighting how compute limits—not model quality alone—are shaping who can deploy AI at scale. On campus and in research settings, the same constraints show up in budgeting, procurement, and compliance workflows: more institutions are likely to need careful controls around data privacy, cybersecurity, and where AI tools can be used. Separately, a study on AI-driven workplace adoption suggests many workers are avoiding or rejecting AI mandates—an adoption friction point that could influence how faculty, staff, and student employees experience AI-enabled workflows. Together, the developments point to immediate operational pressures for higher education leaders: securing compliant access to compute and tools, and managing human adoption barriers while scaling AI-enabled instruction, research, and administrative processes.