Hyper-scaling from the cloud to the tower: platform enclosure and GenAI capture in higher education

The recent adoption of artificial intelligence (AI) platform services in higher education is notable not just for its speed of implementation but for how it is reshaping academic activities on campuses that are often slow to change. This article examines how cloud computing platforms are impacting knowledge production practices and scholarly infrastructure in US research universities through an analysis of academic cloud storage policy updates, AI feature rollouts, and institutional responses to AI service adoption. Drawing on critical university studies and critical informatics perspectives, we argue that cloud providers’ functional sovereignty over academic data infrastructure is reshaping how universities produce, store, and share knowledge with platforms. Through critical case studies of major research universities’ responses to Google Workspace’s storage policy changes and Microsoft’s Copilot expanding AI integration in campus enterprise software offerings, we demonstrate how cloud providers leverage scaling strategies, network effects, and institutional dependencies to extend their control over knowledge production and higher education. Our analysis shows how cloud infrastructure reinforces the neoliberal university’s reliance on corporate computing systems, now subject to forces of hyper-scaling. As universities increasingly adopt AI tools for data-driven research and teaching methods, their dependence on commercial cloud services and big tech platforms deepens, strengthening firms’ ability to exercise power through control of information infrastructure.