How to think about AI products beyond demos.

Most AI products start strong. The first demo lands. People nod. Somewhere in the next month, the team realizes the demo and the product are different objects — and the product is the harder one.

What demos optimize for

Demos optimize for the best case. One clean input, one impressive output, an audience that wants to be impressed. They prove a capability exists. They don’t prove anything about behavior under stress: messy inputs, ambiguous goals, partial context, repeat use.

What products have to handle

Products live in the long tail. A user shows up tired, distracted, with a half-formed question. The system has to do something useful — or, more importantly, do something honest about what it can’t do. That’s the line between “wow” and “trust.”

The bridge

Three habits, in order:

• Define the loss states first. What does “wrong” look like for this feature? Surface that before you write a prompt.
• Treat eval as a product surface. If you can’t measure it, you can’t change it without anxiety.
• Design the disagreements. What does the system do when the user disagrees with it? That’s where most products break trust.

None of this is glamorous. It’s the unsexy work between “it works once” and “it works on a Tuesday.”

The real test

A useful test I keep coming back to: would the user choose to keep this feature on after the novelty wore off? If the honest answer is no, the demo was the product, and the product was the demo. That’s a fine experiment — but it isn’t a business.