Nvidia's demonstration at its developer conference carried a message that the venture capital community has been circling for some time but that the company now stated with directness: the performance that enterprise users experience from AI systems is determined less by the raw capability of the underlying model than by the quality of the infrastructure surrounding it. Latency, throughput, memory management, and the efficiency of the orchestration layer that connects models to enterprise data and tools — these variables, Nvidia argued with technical specificity, now account for performance differentials that exceed the differentials between the leading models themselves.

The implication for the market is significant. If the harness matters more than the model, then the companies building inference infrastructure, serving layers, and enterprise integration tooling are capturing value that model providers cannot easily appropriate. It is a version of the argument Sequoia made in its State of AI report — that foundation models have become infrastructure — expressed in engineering rather than investment terms.