Most AI teams treat compute as a commodity. It's not.

Consider two teams provisioning 8,192 GPUs for a large training run. Same model, same dataset, same budget. Team A lands on a facility purpose-built for AI with sufficient power density, carefully engineered liquid cooling, a high-performance network fabric, and field engineers who have tuned similar runs before. Team B lands on a conventional facility with lower redundancy, air cooling, commodity networking, and a support team that handles tickets.