AI: throwaway prototype or real product?
AI can generate a site or a tool in a few hours. The real question is no longer “is it possible”, but “should you build it, and how to make it last”.
An AI-generated prototype dazzles in a demo and collapses in production: no tests, opaque dependencies, nobody to maintain it. It all depends on the use. A throwaway to validate an idea has nothing like the requirements of a product real users rely on daily.
The possible paths
Throwaway prototype
To validate an idea or convince: AI is enough, and you accept it'll end up in the bin.
To frame
The idea holds, but real users are coming: rework the architecture before going further.
Maintainable product
Sensitive data, scaling, evolution: it gets built properly, AI or not.
What actually tips the decision
AI codes fast; it doesn't decide
Generating isn't designing. Choosing what to build — and what not to — stays human.
An AI prototype's real cost is maintenance
Code nobody understands costs more to take over than to rewrite. Ask who will maintain it in six months.
Frame before you generate
A few upfront decisions — data, scope, code ownership — save you from rebuilding three times.
Useful AI ≠ AI everywhere
An AI feature is only justified if it solves a real problem for THIS need. Otherwise it's an expensive gimmick.
The content is enough to understand. A call or audit applies it to your real case before you spend — the tools qualify, the advice decides.
Test it yourself, free
Get a decision on your case
Tech decision call
An AI project or a prototype to weigh? We decide what to build on a call.
Tech Impact Roadmap
From prototype to product? A roadmap to frame data, scope and architecture.
Free audit
An idea to challenge? I take a look and point you the right way.