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Stranded capacity in AI data centers — PhysaFlow research report

PhysaFlow

Stranded Capacity: The Hidden Constraint Behind AI Infrastructure

August 20, 2026 | Report

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Stranded Capacity Index

5. References & Key Literature

  • Uptime Institute. (2024). Annual Data Center Industry Survey. Uptime Institute Intelligence.
  • Uptime Institute. (2023). Data Center Power and Cooling: Managing the Demand Frontier.
  • IEEE. (2022). IEEE 3000 Series: Power Systems Design Standards for Industrial and Commercial Facilities.
  • ASHRAE. (2021). Thermal Guidelines for Data Processing Environments (TC 9.9).
  • NVIDIA Corporation. (2023). NVIDIA Architecture and Data Center Design Reference Architectures.
  • EPRI. (2023). Powering Intelligence: Data Center Load Growth and Grid Implications.
  • Jouppi, N. P., et al. (2017). In-Datacenter Performance Analysis of a Tensor Processing Unit (ISCA). IEEE.
  • Patterson, D., et al. (2021). Carbon and Energy Footprint of the Current Phase of Deep Learning Research and Development. arXiv:2105.01226.
  • Chowdhury, M., et al. (2023). Optimus: Hardware-Accelerated GPU Co-Scheduling. arXiv.
  • PhysaFlow. (2026). PhysaFlow Stranded Capacity Index (PSCI): Internal Field Measurements (whitepaper).

Reference values, benchmarks, and loss ranges quoted in this report are illustrative aggregations unless a primary source is explicitly cited.