An AI-generated prototype can make an idea tangible quickly. Its generation cost tells you very little about the cost of operating a dependable product. Those are different stages with different responsibilities.
State what the prototype proves
A prototype may show that a user can understand a flow or that a technical approach is plausible. It may use sample data, simplified permissions and a narrow happy path. Document those limitations beside the demonstration.
Do not infer production readiness from the fact that the screen works once. A real product must behave when data is missing, a service is unavailable or two people act at the same time.
Inventory the remaining work
Authentication, access control, data handling, error recovery, observability and support can all require work beyond the initial interface. The exact list depends on the product and its consequences.
Include maintenance ownership. Someone needs to understand the implementation well enough to diagnose a problem and make a safe change after the original generation session has ended.
Use the prototype to reduce uncertainty
Put it in front of the intended user with a defined task. Observe where the flow helps and where it fails. If the core problem does not matter, learning that before building production infrastructure is valuable.
Budget the next stage separately
Estimate the work required to turn the demonstrated concept into a bounded pilot. Use real integrations and realistic failure cases at that stage. Expand exposure as evidence improves.
The transcript in the source library described an extremely cheap MVP. This article does not reproduce that cost as an expectation. The useful lesson is that a cheap experiment can improve a decision, provided its price is not confused with the full cost of the business it might become.