“Composition in red, yellow, blue and black” (1921) | Piet Mondrian
Status check on open source consensus titles evolving around distributed ledger technologies for financing, planning, design, operation & maintenance of the #WiseCampus. print(“Wise Campus”).
The rapid growth of AI training and inference drives hyperbolic demand for data center capacity, creating a classic boom-bust dilemma on large university campuses.
Overbuild: Universities rushing to construct hyperscale or AI-focused data centers risk massive stranded capital if AI progress slows, federal funding dries up, or cheaper cloud alternatives dominate. These facilities require enormous power (often 100–500+ MW), water cooling, and land—resources that could lock campuses into 20–30-year commitments while diverting funds from core academic missions. Critics fear a repeat of the 1990s fiber-optic glut, leaving half-empty “ghost” buildings.
Underbuild: Failing to invest sufficiently risks losing top AI faculty and students to better-equipped peers (Stanford, MIT, CMU), forfeiting federal grants (e.g., NSF, CHIPS Act), and diminishing national competitiveness. In a winner-take-all AI race, campuses without GPU clusters and high-performance networking quickly fall behind in recruiting and research output.
Universities are thus caught between fear of wasteful mega-projects and fear of irrelevance.
