How the AI boom is redefining the future of network infrastructure

As AI adoption accelerates across industries, the underlying digital infrastructure is being pushed to evolve rapidly. No longer just about speed, networks must now support vast, real-time, intelligent workloads. Here are five ways this transformation is unfolding.

Unprecedented infrastructure demand

AI is driving massive growth in data center interconnection, with workloads requiring exponentially more bandwidth and lower latency. The shift from traditional compute to AI-first infrastructure is happening at a record pace.

Different needs for training vs. inference

Training models demands tightly linked, high-density compute environments, while inference is increasingly distributed, requiring responsive, low-latency networks closer to end users.

Geographic distribution

Due to power constraints and latency needs, AI compute is spreading across multiple locations. Coordinating these systems requires robust interconnection strategies and deterministic performance across distances.

Focus on the edge

Inference is moving to the network edge, enabling real-time responses and seamless user experiences. Proximity to users is becoming a necessity rather than a luxury.

Optics and automation

To meet growing performance requirements, optical technologies are advancing rapidly. But with complexity rising, automation is essential to manage and scale dynamic, multi-layered AI infrastructure.

AI is driving not just innovation, but a full-scale reengineering of how networks are built and operated. Organizations that embrace this shift now will be better positioned to lead in an AI-centric future.

Learn more in this recorded webinar with Nokia and 650 group.