AI workloads are going distributed

AI is shifting from centralized model training to distributed inference and agentic systems. Networks must respond quickly to AI applications, not just passively transport data.

Low latency everywhere is required

AI applications need low-latency connectivity among clouds, datacenters, edge infrastructure, enterprises, and distributed GPU clusters.

New infrastructure classes are emerging

The shift creates demand for ultra-high-capacity optical transport, AI interconnection fabrics, edge inference connectivity, and sovereign AI deployment models.

Data location drives everything

Enterprises build AI infrastructure around their data, focusing on location and sovereignty. They view proprietary data and AI systems as their crown jewels.

“Many enterprises regard their proprietary data and AI systems as their "crown jewels," and they are eager to lock down ownership, security, and control”

Geopolitics demands digital sovereignty

Geopolitical pressures are increasing the need for digital sovereignty. Enterprises want to lock down ownership, security, and control over their own data.

Networks are the bottleneck now

Enterprise architects frequently cite network infrastructure as a bottleneck to connecting AI systems. Operators can solve this with dynamic, responsive bandwidth and compute.

Hybrid multicloud is the answer

Distributed AI platforms need networks connecting multiple clouds and enterprise infrastructure with software-driven management, APIs, and global colocation points.

Hybrid cloud delivers five key benefits

Futuriom's study of over 100 hybrid cloud cases found key benefits: operational efficiency, security, cost optimization, faster app development, and resiliency.

Three roles for operators

Operators must choose where to participate in the AI value chain: as AI enablers through connectivity, AI service providers hosting inference, or AI-powered operators.