AI Workloads: New Rules of Fiber Infrastructure

AI compute workloads are no longer confined to hyperscale campuses. As training clusters expand and inference moves closer to users, operators across regional, utility, and broadband networks are beginning to feel the infrastructure impact.

AI is driving unprecedented traffic growth, accelerating the shift from 400G to 800G and beyond, and introducing new low-latency requirements that challenge traditional metro and DCI design assumptions.

This session will explore how AI-driven compute is reshaping practical network architecture decisions, from fiber topology and spectrum utilization to power adjacency and automation. As data centers seek proximity to generation sources and rural markets become strategic interconnect points, fiber and power planning can no longer operate independently. Operators must rethink ring vs. mesh designs, regeneration strategy and fiber utilization to support multi-campus GPU fabrics and scalable DCI deployments. Equally critical is operational readiness. AI customers expect infrastructure to deploy in weeks, not months. Zero-touch provisioning, automated span equalization, integrated monitoring, and resilient optical architectures are becoming baseline requirements – not differentiators.

Attendees will leave with a clear understanding of how AI workloads are influencing fiber density planning, metro architecture, and operational tooling, along with practical considerations for building networks that can support the next wave of compute infrastructure reliably, efficiently, and at scale.