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Decision Guide · AI Development Series
A decision guide. When AI-assisted development makes sense, when it does not, and the questions to ask before you commit either way.
If yes, you need access control built by a developer who understands multi-tenancy. AI tools consistently miss this. It is the most common serious failure in AI-generated applications.
AI-generated databases are not designed for scale. Queries that work at ten records fail at ten thousand. Concurrency handling that works for one user breaks for ten simultaneous users.
If the answer is nobody, you are accepting generated output on trust. The output may be correct for the demo scenario. It may have critical gaps that only appear in production. Reviewing and understanding the code is the difference between AI-assisted development and vibe coding.
AI-generated codebases have no consistent structure because the tool that generated them has no design philosophy. Maintaining and extending them is progressively harder. If this software has a future beyond the initial build, the maintainability question matters.
A prototype that fails is an inconvenience. A production application that leaks user data or produces incorrect financial records is a serious problem. The appropriate level of engineering rigour should match the consequence of failure.
This is the core question. AI makes good developers faster. It does not make thinking unnecessary. The architecture, the data model, the security design, and the business logic must all be decided by a human who understands the problem. If you are using AI to skip that step, the tool is deciding your system's structure for you. It will not be a good architect.
If you want the benefits of AI-assisted development without the risks of vibe-coded software, the approach is straightforward. It is not about avoiding AI. It is about keeping the right person in charge.
This is how we work. AI accelerates the implementation. Humans decide everything that matters. The result is software that is faster to build and reliable enough to run a business on.
For a single-user prototype with no security implications, possibly. For anything that handles multiple users' data, scales under real load, needs to be maintained, or carries a serious consequence if it fails: no. The demo is not the same as the production environment, and AI tools optimise for the demo.
AI makes good developers faster, and we pass that saving on. The build cost can genuinely come down. What you must not cut is the thinking: the architecture, the security model, the data design. Skip that and you are not saving money; you are deferring it until it costs more to fix.
Describe the problem to a developer, not to a tool. The developer will ask questions the tool cannot: what happens when two users do this simultaneously, what does this data mean, what are the edge cases, what does success look like in a year? That conversation produces the specification. The specification is what the AI then helps to implement.
We use AI ourselves. We also clean up what it produces when nobody was paying attention. We can advise on both.
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