Everyone is talking about the GPU shortage. GPUs are expensive, scarce, power-hungry, and central to AI infrastructure. But the real issue is much deeper.

In this episode of The Deep Edge Podcast, Ray Mota breaks down why AI infrastructure is not fundamentally a GPU problem — it is a network economics problem. As AI workloads scale, the hidden bottleneck is not just compute capacity, but the ability of the network to move data efficiently, keep GPUs utilized, reduce latency, control power consumption, and avoid massive infrastructure waste

Ray explains why enterprises, service providers, and hyperscalers must rethink AI infrastructure through the lens of network design, utilization economics, east-west traffic, data movement, automation, and total cost of ownership.

The key message: buying more GPUs will not solve the problem if the network cannot support the economics of AI at scale.