AI infrastructure has hit physical limits: data centers now struggle with power consumption and cooling demands that traditional facilities cannot support.

Scaling AI models is no longer constrained by compute or algorithms, but by real-world infrastructure challenges like energy availability and thermal management

Data center operators must fundamentally redesign facilities to handle the extreme power density and heat output of modern AI training and inference workloads

Physics and energy constraints are becoming the primary bottleneck for AI advancement, forcing companies to rethink hardware placement, cooling strategies, and power distribution architectures