In her AI Speaker Series presentation at Sutter Hill Ventures, Google Distinguished Engineer Nandita Dukkipati explained how AI/ML workloads have completely broken traditional networking. Here's my notes from her talk:

AI broke our networking assumptions. Traditional networking expected some latency variance and occasional failures. AI workloads demand perfection: high bandwidth, ultra-low jitter (tens of microseconds), and near-flawless reliability. One slow node kills the entire training job.

Why AI is different: These workloads use bulk synchronous parallel computing. Everyone waits at a barrier until every node completes its step. The slowest worker determines overall speed. No "good enough" when 99 of 100 nodes finish fast.

Real example: Gemini traffic shows hundreds of milliseconds at line rate, but average utilization is 5x below peak. Synchronized bursts with no statistical multiplexing benefits.…