Ep 19: AI Networking at Scale: Why Traditional Data Center Models Break Down

In this episode, Scott Raynovich, Founder & Principal Analyst at Futuriom, sits down with Chid Perumal, Co-Founder & CTO of Aviz Networks, to unpack what really changes when networks are built for AI.

As AI workloads scale across thousands of GPUs, the network is no longer a passive transport layer, it becomes part of execution. Small, transient issues can snowball into GPU idle time, job retries, and massive performance loss.

Key topics covered in this conversation:

1) Why AI networking is fundamentally different from traditional data center networking

2) How microsecond-level congestion and synchronization impact AI workloads

3) The limits of human-driven network operations at AI scale

4) Why automation, contextual alerts, and proactive telemetry are critical

5) Managing multi-vendor, heterogeneous AI fabrics without operational lock-in

6) How to shift from device-centric troubleshooting to path- and workload-aware operations

7) What normalized, evidence-based root cause analysis looks like in AI environments

8) What to expect from AI networking in 2026 and beyond This discussion is a must-watch for network architects, infrastructure leaders, and executives navigating AI-driven data centers and large-scale GPU fabrics.

Watch now to learn how AI is reshaping network operations and what it takes to run AI fabrics reliably at scale.

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