Special Breaking Analysis: Nvidia’s AI networking moat is real – but the lock-in debate continues
In a special editorial discussion hosted by Dave Vellante and Bob Laliberte, Nvidia Corp. networking chief Gilad Shainer explains why agentic inference turns the network into part of the computer. We believe Nvidia is materially ahead of the field, but in this Special Breaking Analysis we evaluate Nvidia’s claims of openness, which must be analyzed at the system level – not merely at the Ethernet protocol layer.
Editor’s note: Performance figures attributed to Nvidia below come from company-supplied materials. They should be treated as vendor claims unless independently validated against a disclosed workload, topology, software stack and competitive baseline.
Artificial intelligence infrastructure has reached an important architectural milestone.
In the first phase of generative AI, the industry focused primarily on training ever-larger models. The network was critical, especially as flash storage shifted the I/O bottleneck and pushed it to networking. But the network’s role was still generally described as connecting accelerators and moving data. In the emerging era of real-time inference and agentic AI, that concept is no longer valid.
