Location-Robust Cost-Preserving Blended Pricing for Multi-Campus AI Data Centers

Abstract

Large-scale AI data center portfolios procure identical SKUs across geographically heterogeneous campuses, yet finance and operations require a single system-level 'world price' per SKU for budgeting and planning. A common practice is deployment-weighted blending of campus prices, which preserves total cost but can trigger Simpson-type aggregation failures: heterogeneous location mixes can reverse SKU rankings and distort decision signals. I formalize cost-preserving blended pricing under location heterogeneity and propose two practical operators that reconcile accounting identity with ranking robustness and production implementability. A two-way fixed-effects operator separates global SKU effects from campus effects and restores exact cost preservation via scalar normalization, providing interpretable decomposition and smoothing under mild missingness. A convex common-weight operator computes a single set of campus weights under accounting constraints to enforce a location-robust benchmark and prevent dominance reversals; I also provide feasibility diagnostics and a slack-based fallback for extreme mix conditions. Simulations and an AI data center OPEX illustration show substantial reductions in ranking violations relative to naive blending while maintaining cost accuracy, with scalable distributed implementation.