Over the last several years, the discussion around AI infrastructure centered predominantly on training clusters. The focus was on larger models, sizable GPU estates, denser scale-out fabrics, and the enormous synchronization demands created by collective communication across thousands of accelerators.
Fiber planning reflected those priorities with longer optical runs across hall and campus, high-volume east-west traffic, and ultra-dense interconnect environments. That model remains important. However, deployment patterns in 2026 increasingly point in a different direction.
One of the most significant developments in AI over the last year is that inference has overtaken training as the dominant operational workload. Simply, more compute effort is now being spent using models than building them. In many ways, this represents AI maturing from a research-centric discipline into an operational one.
Inference introduces a very different set of infrastructure behaviors. In recent months, I recognized that the infrastructure conversation had not yet fully caught up with the operational reality emerging inside AI environments.
Much of the public discussion still revolved around accelerator counts, power consumption, and hyperscale training clusters. Far less attention focused on the practical consequences for optical infrastructure, pathway allocation, topology planning, and physical network architecture.
