A working interface can now appear before the team has agreed on the rules it represents. That is a useful opportunity for exploration. It can also make an unresolved idea feel more finished than it is.
Our view is that AI-assisted development should increase the attention paid to product judgment. Generating an implementation does not decide what the customer should be able to do, which system owns the result, or what must happen when the normal path fails.
Separate a convincing demonstration from an accepted capability
Imagine generating a request-approval interface. It has a list, detail pages, and approve and reject buttons. The demonstration feels complete until someone asks whether the requester can approve their own item, what happens after an approval is withdrawn, or how two simultaneous decisions are handled.
Those questions are part of the product definition. A generated screen can help expose them, but it should not silently answer them through whatever behaviour happened to be produced.
Use prototypes to make assumptions visible. Label the unresolved behaviour, invite the right people to challenge it, and turn the decisions into acceptance criteria before treating the work as ready for release.
Review the behaviour that matters
The amount of generated code is a poor proxy for progress toward a dependable product. A useful review asks whether the implementation follows the agreed rules and fits the existing system.
For the approval example, examine who can act, which record changes, what history remains, and what the requester sees afterward. Test failure and conflict cases with the same care as the obvious success path.
This review needs context. Someone must understand the surrounding architecture, operating process, and consequence of a mistake. That responsibility does not disappear because a tool drafted the change.
Spend the saved effort deliberately
When a tool reduces effort on a particular implementation task, the team has choices. It can explore another option, improve the recovery path, strengthen verification, or deliver the accepted capability sooner. Decide which use serves the product rather than automatically expanding the feature list.
Avoid promising a universal productivity multiplier. The useful question for a specific engagement is what the team can demonstrate: which work was completed, how it was reviewed, and whether the resulting capability meets the agreed need.
An expert team can make effective use of modern tools when the people involved remain close to the decisions. The value to the customer is the responsibility they take for the result, supported by evidence the customer can inspect.
AI changes how implementation work can be approached. It does not remove the need to decide what deserves implementation. That is why we lead with product engineering and partnership, rather than a catalogue of tools or a count of generated lines.