The Switch Is the Bottleneck: Why AI Infrastructure Has a Network Problem

AI systems are experiencing a critical bottleneck: while compute chips have become extremely powerful, the network switches that connect and coordinate data flow between them cannot keep pace, leaving expensive chips idle more than half the time during training. As AI workloads demand higher bandwidth speeds, switches become the limiting factor in overall system performance, and simply upgrading cables and connection speeds does not solve this underlying problem. The industry developed network components independently rather than designing them together as an integrated system, resulting in wasted capacity and underutilized computing power. Fixing this requires redesigning the network architecture starting from actual AI workload patterns and moving toward more sophisticated switching technologies that can handle the dynamic, asymmetric traffic patterns of AI systems.