While everyone obsesses over GPU shortages and model training costs, a quieter crisis is unfolding in data centers worldwide. Organizations are discovering that their most expensive hardware—those coveted GPUs—sits idle not because of compute limitations, but because of networking bottlenecks that can take weeks to resolve.

The irony is stark: companies spend millions on bleeding-edge AI hardware, then watch it gather dust while network engineers manually configure hundreds of switches to provision a single new tenant. This isn’t just inefficiency—it’s a fundamental mismatch between the speed of AI innovation and the pace of traditional infrastructure.

Read my full article on LinkedIn.