For years, infrastructure strategy assumed the internet behaved as a largely uniform system. Deploy in one region, scale vertically, and serve globally. Latency differences were treated as performance details, not architectural constraints. AI workloads change that assumption. Unlike traditional web traffic, AI inference is sensitive not only to average latency but to latency variance. Stability matters more than peak throughput. Public network measurements consistently show that cross-border routing between mainland China and Europe or North America introduces higher round-trip times and significantly greater variability than intra-regional traffic. That variability does not simply slow systems down — it changes how distributed workloads behave. For static web applications, this mostly affects user experience. For distributed inference systems, it affects cost structure and scaling behavior. Consider a simplified scenario: if a baseline retry rate in an inference pipeline rises from 1% to 3% due to unstable routing, the difference may look minor. At scale, it is not. With 10 million daily inference calls, that shift creates 200,000 additional backend executions per day. Even assuming only 50 milliseconds of additional compute per execution, that translates into more than 80 extra CPU-hours per month — generated not by growth in demand, but by network variance. This is where the idea of “universal infrastructure” begins to break down. Adding compute does not eliminate routing instability. More CPU does not remove jitter. More memory does not prevent retransmissions. The constraint shifts from hardware capacity to architectural adaptability. Infrastructure providers respond to this in different ways. Hyperscalers such as AWS, Azure, and Google Cloud mitigate fragmentation primarily through geographic segmentation, including dedicated mainland China regions operating under separate networking and regulatory environments. Edge and CDN-oriented providers optimize proxim