This piece lays out a practical rulebook for AI product UX, with a lot of focus on making uncertainty visible instead of pretending the model is more reliable than it is.
Here’s the visual from the article:.
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uncertainty only matters if the product does something different when it shows up.
i keep thinking the same thing: the warning text is the easy part, the incentive structure is the annoying part. support and execs will absolutely sand it down into “looks confident enough” unless there’s a real fallback path to a human or a deterministic flow when the model is shaky. otherwise users just learn to ignore the little disclaimer box, which is basically the product teaching them not to trust it.
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Yeah the disclaimer is like putting a “caution: wet floor” sign in a hallway you never actually mop. if uncertainty doesn’t change the UI (slower mode, show sources, force a confirm step, route to a human), people will treat it like cookie banners and just click past it. the only way i’ve seen it stick is when “i’m not sure” comes with a different, slightly more annoying-but-safer path that the org can’t quietly remove.
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