What happens when AI workloads begin to overwhelm the network infrastructure originally designed for human browsing and SaaS consumption?

In this episode of IT Infrastructure as a Conversation, I’m joined by Jamie Pugh from Globalgig to discuss why enterprise connectivity is rapidly becoming one of the biggest blind spots in the AI era. While much of the industry conversation focuses on GPUs, models, and data centers, Jamie explains why the network itself is now under growing pressure from entirely new traffic patterns driven by AI systems communicating with other AI systems.

We explore how enterprise infrastructure was largely built around human behavior, employees accessing applications, downloading files, and consuming cloud services. AI changes that model completely. Today, agents are constantly interacting with tools, inference engines are querying massive data stores, and cloud environments are exchanging huge volumes of east-west traffic across regions in real time. Jamie explains why many SD-WAN architectures and broadband-heavy deployments were never designed for these sustained, burst-heavy workloads.

The conversation also examines the growing importance of cloud on-ramps and why many organizations discover bottlenecks only after deploying AI-enabled services into production. Jamie shares how asymmetric broadband connections, fragmented carrier relationships, and static connectivity models can quietly introduce latency, resilience, and observability problems that directly impact AI performance and user experience.

One of the most interesting parts of the discussion centers on how dependent modern workflows are becoming on AI tools. Jamie talks candidly about using platforms like Claude, Perplexity, and ChatGPT throughout his working day and why losing connectivity now feels less like a temporary inconvenience and more like losing access to an essential member of the team. That shift in expectation is forcing infrastructure leaders to rethink resilience, automation, and real-time observability across hybrid and multi-cloud environments.

We also discuss programmable networks, predictive routing, network-as-a-service fabrics, and the growing move toward centralized control planes that can dynamically adapt to changing AI traffic patterns. Jamie explains why enterprises need to stop thinking purely about north-south traffic and start preparing for a future dominated by east-west communication between clouds, data centers, agents, and inference platforms.

There is also a valuable conversation around governance, security, and data sovereignty as organizations increasingly bring AI inference closer to private infrastructure rather than relying entirely on public models. Jamie argues that networking, security, and AI strategy teams can no longer operate in silos if businesses want to scale AI safely and effectively.

If your organization is building toward an AI-first future, this conversation offers a timely look at the infrastructure challenges many enterprises are only beginning to recognize.