Describe what you want to an AI tool, watch a working version appear the same afternoon, no developer, no invoice, no waiting. It is a genuinely appealing way to get a first version of something built, and it is also, right now, the subject of a very active argument inside Kenya's own developer community about whether it actually produces anything worth keeping.
Both sides have a point. Here is a straight way to work out which one applies to your specific project.
The Case for Doing It Yourself
For a genuinely simple, well-defined, internal tool, a booking form, a basic inventory count, a one-off way to log something your team currently tracks on paper, building it yourself with an AI tool can be entirely reasonable. It costs little beyond your own time, it proves in a day whether the idea is even worth pursuing, and if it turns out nobody actually uses it, you have lost an afternoon rather than a developer's fee. For low-stakes, low-traffic, single-purpose tools, this is often the right first move, not a shortcut you should feel guilty about.
The Case the Developer Community Keeps Making
Kenya's own developer community has been unusually vocal about the other side of this, and it is worth taking seriously rather than dismissing as gatekeeping. The concern is not that non-developers are building things. It is that these tools are optimised to produce something that looks finished in a demo, not something that has actually been checked against every real condition it will eventually meet. A tool that works perfectly when you are the only person testing it can behave very differently once real customers, real payments, or real data are involved, and that gap does not show up until it is already a problem.
The Actual Test: What Is It Actually For
The honest dividing line is not how simple the tool looks, it is what happens if it gets something wrong. An internal attendance log or a staff scheduling sheet getting something wrong is an annoyance. A tool that touches customer payments, personal data, or has to match an external requirement exactly, a bank's integration rules, a payment provider's callback format, a specific compliance rule, is a different category entirely, because those require getting the details precisely right, not just producing something that runs. If your project falls in the second group, this is exactly where AI-generated tools tend to fall over, quietly, in ways that only surface once something real is riding on them.
The Middle Path Most People Skip
You do not have to choose between building it yourself forever and hiring a developer from day one. The genuinely sensible path for a lot of founders is to build the rough first version yourself, cheaply, to prove the idea deserves further investment, and then bring in someone to properly build and harden the version that will actually carry real customers and real money. That second step is precisely what a proper code cleanup is for: taking what you already proved works and making it something that will not quietly break once it matters.
If you have already built your first version this way and are starting to wonder whether it is ready for real use, our list of signs your AI-built app needs a professional look is the honest next check, before you find out the hard way.