AI, China, and Why Geography Is Becoming the Real Infrastructure Advantage

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