The role of fiber in AI scaling
As AI systems scale, limits in networking are increasingly holding back performance, leaving costly GPUs underused and reducing the payoff from large infrastructure investments.
Manish Rawat, a semiconductor analyst at TechInsights, pointed out that optical fiber is now emerging as the next structural constraint on AI scaling with potentially long-term implications.
“Fiber is the silent dependency that scales non-linearly with AI growth,” Rawat said. “AI workloads generate massive east-west traffic, requiring tight synchronization across thousands of GPUs, which sharply increases intra-data-center and inter-campus optical demand.”
But the so-called networking wall is not a single bottleneck, according to Sanchit Vir Gogia, chief analyst at Greyhound Research.
“It’s an overlapping set of constraints that surface when AI workloads hit scale, spanning fiber availability, switching density, optical transceiver limits, and architectural inefficiencies,” Gogia said.
The combined stress of AI scale and concurrent government broadband rollouts has snapped the historical assumption that fiber is abundant and cheap, Gogia added.
