Vibe coding an app: what it's genuinely good at, and where it breaks

Vibe coding is real and it works — right up until it doesn't. The difference between the two outcomes is almost never the tool.

A founder can now describe an app in a paragraph and watch working software appear. That is not hype — it genuinely happens, and it has changed what a person with an idea and no engineering background can accomplish.

It has also created a new and expensive failure mode, and the founders hitting it are usually surprised, because the demo went so well.

What vibe coding is actually good at

Give the tools credit where it is due. AI-assisted building is legitimately excellent at:

• Prototypes. Getting a clickable, testable version of an idea in front of real people in hours instead of weeks.
• Internal tools. Software with a handful of users who can tolerate rough edges and report bugs directly to you.
• Simple, self-contained apps. A focused utility with a clear core loop, minimal data, and few integrations.
• Getting unstuck. Producing a first version of something you can react to, which is often easier than starting from a blank page.

For validation work especially, this is a gift. The cheapest way to learn whether people want your idea used to be a landing page and a clickable mockup. Now it can be something closer to the actual thing.

Where it breaks

The trouble starts as complexity grows — and it rarely announces itself.

Complex state. Anything where multiple things change at once, users interact with each other, or data has to stay consistent across sessions and devices. AI tools produce code that looks right and works in the demo, then behaves unpredictably when two things happen at the same time.

Integrations. Connecting to payments, calendars, CRMs, or any third-party system means handling their quirks, their failures, and their changes. AI will generate a plausible integration. Whether it handles a declined card, a rate limit, or a timeout correctly is a different question.

Security and data handling. This is the one that should genuinely worry you. An AI tool will happily produce an app that stores data insecurely, exposes credentials, or leaves an authentication gap — and nothing about the output will look wrong to someone who cannot read the code.

Edge cases. No network. Empty lists. Invalid input. A payment that fails halfway. AI builds the happy path beautifully because that is what you described. It fills the rest with guesses.

Maintenance. A vibe-coded app you do not understand is difficult to change safely. The first version is fast; the tenth change is where people discover they have built something nobody can maintain, including the AI that wrote it.

The real problem isn't the code

Here is what actually kills most vibe-coded apps, and it has nothing to do with code quality.

An AI coding engine implements exactly what you specify. Every place you were vague, it makes a decision on your behalf — instantly, confidently, and without telling you. Ask for "a simple booking app" and it will decide what a booking is, what happens when two people book the same slot, whether bookings can be cancelled, what a user sees when something goes wrong, and a hundred other things you never said out loud.

Some of those decisions will match what you meant. Many will not. And you will not discover which is which until real users hit them.

This is the same failure that has always plagued app projects, just accelerated. Vague requirements have always produced the wrong product; AI has simply removed the friction that used to force a conversation. A human developer handed a half-formed idea asks questions. An AI does not. It builds.

That is the trade. You get speed, and you lose the friction that used to catch your gaps.

What actually determines the outcome

The founders getting good results from AI builds are not using better tools. They are giving better instructions.

Specifically, they know:

• Who the app is for, tightly enough to make design decisions.
• What the core loop is — the sequence a user repeats to get value — so the build stays focused instead of sprawling.
• What "done" means for each feature, in terms someone could actually test.
• What happens when things go wrong — the empty states, the failures, the edge cases.
• What is explicitly out of scope, so the AI does not helpfully invent features you did not ask for.

That is a specification. It is the same document a good agency would want before quoting your build, and it does the same job here: it removes the guesswork. The difference is that with AI, the guesswork does not surface as questions in a kickoff meeting. It surfaces as software.

A reasonable way to use it

Vibe coding earns its place in a plan that looks something like this:

1. Validate the idea first. Talk to real users about the problem before building anything. AI makes it cheap to build the wrong thing quickly, which is not actually progress.
2. Write the spec. Core loop, user stories, acceptance criteria, edge cases, scope boundary. This is the step people skip and the one that decides the outcome.
3. Vibe-code the prototype. Put it in front of people. Watch where they get stuck. This is where the speed genuinely pays.
4. Decide honestly what to do next. If the app is simple and low-risk, the AI build may be enough. If it handles payments, personal data, or real scale, get an experienced engineer to review it before it goes anywhere near real users.

The last point matters more than founders want it to. "It works" and "it is safe to run a business on" are different standards, and the gap between them is invisible from the outside.

The honest summary

AI has made building cheaper. It has not made deciding what to build any easier — and that was always the expensive part.

If anything, preparation is worth more now than it was three years ago. When building was slow and expensive, a vague idea got caught early, because someone had to quote it. Now a vague idea sails straight through to working software that solves the wrong problem, and you find out after launch.

The tool is not the constraint anymore. The clarity is.

For the method, see what a build-ready PRD needs and how to turn an app idea into a PRD. And before any of it, validate the idea.