AI Engineer World's Fair 2026 — unofficial import demo

Voice is our favorite way to command AI assistants and robots — and it is error-prone. The industry's reflex is to chase accuracy, but accuracy is only one knob: we can control system behavior in other ways to increase user satisfaction.

This talk shifts the lens from accuracy to user outcomes. Give the AI agent more than one move: besides acting, let it stop, reject, confirm, clarify, or disambiguate. The question stops being "how often are we right?" and becomes "what does each outcome cost the user?" Bad outcomes are not equally bad to users — so price them relatively, then have the AI system minimize that user cost. Call it OUCH: Outcome User Cost Heuristic; we optimize system behavior to minimize the OUCH. Same accuracy, lower user cost, greater user adoption.

We will walk through practical AI assistant examples illustrating this approach, then show how the same framework extends across AI environments — smart speakers, TVs, glasses, embodied AI, robots, wearables, and vehicles — by repricing outcomes and swapping the confirmation UI.

Why this matters now: the cost of voice-command errors is escalating as we move into AI assistants and embodied AI, where wrong actions can be more expensive and dangerous. Mainstream voice adoption will not come from chasing accuracy alone; we need systems to price in the cost of being wrong.