The next AI fight is over the wiring, not the chips

The story of the AI build-out has been told as a contest over accelerators: who ships the fastest processor, who books the most capacity. But Broadcom’s October launch of Thor Ultra, a networking part engineered to lash together enormous clusters of chips, is a reminder that the more durable advantage tends to sit in the connective tissue. Modern AI training is less a single supercomputer than a warehouse of processors pretending to be one, and that illusion is held together by the network.

Nvidia’s dominance has never rested on silicon alone. It rests on the proprietary fabric — the high-speed links and switching that let tens of thousands of chips move data as if they shared a single memory. Buy the accelerators and you are quietly buying the plumbing too, along with the switching costs that come with it.

The chip earns the headlines; the network decides who is locked in.

The counter-move is standardization. A coalition of Nvidia’s rivals is pushing open, Ethernet-based interconnects as the backbone for AI clusters, betting that customers spending tens of billions on infrastructure will eventually resent depending on one vendor for both the brains and the nervous system. Whether that bet pays off will not be settled by benchmarks. It will be settled by which architecture the hyperscalers standardize on over the next two years — a decision made in procurement meetings, far from the product launches, that will shape the economics of AI for the rest of the decade.