latency targets are now product promises not infra metrics

Interactive AI workflows are making latency an explicit user contract; long or volatile responses now feel like broken behavior rather than technical variance (Google web vitals).

see also: latency is becoming cultural not technical · queue aware batching improves gpu utilization stability

expectation reset

Users compare assistants to real-time interfaces, so perceived delay now influences trust as much as output quality.

operating consequences

• Product teams set latency SLOs by workflow criticality.
• Routing and cache policy are now UX levers.
• Tail latency failures drive churn in recurring tasks.

my take

Latency discipline is now part of product truthfulness.

linkage

• [[latency is becoming cultural not technical]]
• [[queue aware batching improves gpu utilization stability]]
• [[prompt cache invalidation strategies reduce tail latency]]

ending questions

which user journey should define the primary latency budget for an ai product?