Where AI Autonomy Should Stop: Designing the Stop Button

Deciding Where AI Autonomy Should End

This piece argues that the hardest design decisions in AI products sit at the point where a model's interpretation turns into real action. It opens with a striking example: the same prompt, run at zero temperature, still produced varied outputs because of how the underlying infrastructure handled computation. From there, the author contrasts classic user flows, where designers defined every state in advance, with AI-driven flows where the system decides how to approach a task. The key idea is that non-determinism is not the problem in itself. The problem is when probabilistic outputs trigger bookings, record changes or document transfers without anyone deciding which steps are allowed to vary.

For brand builders, product designers and founders, this is a useful reframing of AI work. The piece separates interpretation from execution, and it makes the case that many features are better served by a form or a confirmation button than by a prompt. It also looks at a healthcare automation company that uses generative AI to build workflows while running production on logged, repeatable logic. Anyone shipping AI features, or briefing teams on them, should read it to think about user control, error recovery and trust before the agent is switched on.