AI coding tools are now part of everyday engineering, including ours. They write boilerplate, suggest tests and explain unfamiliar code in seconds. The question we hear from clients is no longer whether to use them, but what happens to a codebase built this way once the excitement fades.
Writing code was never the hard part
Most of the cost of software comes after the first version: changing it safely, fixing it under pressure, onboarding new people to it. Generated code can make that harder if nobody fully understands it, if it duplicates logic that already exists, or if it follows a different pattern every time it is asked.
How we keep AI-written code maintainable
- People own every change. An engineer reviews and can explain anything that is merged, whoever wrote it.
- Conventions first. Clear patterns and examples in the repository, so tools generate code that fits.
- Tests as the contract. Generated code is accepted when it passes tests written for the behaviour, not because it looks right.
- Architecture decisions stay human. Tools help implement a design; they do not choose it.
AI makes good engineering teams faster. It does not make engineering judgment optional.
Six months later, the codebases that age well are the ones where AI sped up the work and people kept control of the design. That is the standard we hold our own teams to.