Vibe coding promised a faster way to build software: describe what you want, let AI generate the code, make a few tweaks, and ship.

For prototypes and experiments, it can feel magical. Features that once took days appear in hours. People with limited engineering experience can turn ideas into working demos. Even experienced developers can avoid repetitive boilerplate and explore solutions more quickly.

But many development teams are now discovering the other side of that speed.

The prototype gets promoted to production. The original creator moves on. Then the engineering team inherits thousands of lines of plausible-looking code, with inconsistent architecture, missing tests, duplicated logic, security gaps, and edge cases nobody considered.

The code was fast to create. It is painfully slow to own.

The problem is not that AI-generated code never works

Most of it does work, at least under the conditions used to generate and test it.

That is precisely what makes the problem difficult. Obviously broken code is easy to reject. Vibe-coded software often looks polished and behaves correctly in a demo while hiding structural problems that only emerge later: authentication checks implemented inconsistently, database queries that fail under real load, dependencies added without review, error handling that covers only the happy path, business rules duplicated across files, and tests that confirm the implementation rather than the requirement.

Each issue may be manageable in isolation. Together, they create a system that becomes harder and riskier to change.

Speed is being measured at the wrong point

Vibe coding optimises for the moment code first appears. Engineering teams have to optimise for the entire life of the software.

A feature is not finished when it works on a laptop. It must be reviewed, tested, secured, deployed, monitored, maintained, documented, and eventually modified by someone who did not create it.

AI can dramatically reduce the cost of typing code, but typing was rarely the most expensive part of software development. The real cost lies in understanding requirements, making sound architectural decisions, handling failure, and maintaining the system over time.

If a tool saves four hours during implementation but creates several days of debugging and refactoring later, the organisation has not moved faster. It has simply moved the work downstream, from the person generating the feature to the team responsible for keeping it alive.

Developers become the cleanup crew

This creates an increasingly frustrating dynamic inside teams. Someone generates an application or feature quickly and presents it as nearly complete. Professional developers are then asked to tidy it up or make it production-ready.

But production readiness is not a final layer of polish. It is the result of decisions made throughout development: data modelling, permission boundaries, failure modes, observability, testing strategy, deployment design, and maintainability.

When those decisions are missing, developers are not merely fixing a few bugs. They are reverse-engineering intent, identifying hidden assumptions, and often rebuilding significant parts of the system.

Worse, the cleanup work can look slow compared with the original generation. It took an afternoon to produce the feature, so why should it take two weeks to make it safe? The team that uncovers the complexity can appear less productive than the tool that concealed it.

Technical debt is now generated at machine speed

Technical debt is not new. Teams have always taken shortcuts. What has changed is the rate at which those shortcuts can be produced.

A developer might previously have written one questionable module in a day. An AI assistant can now generate an entire questionable application in the same time, complete with enough polish to make its weaknesses difficult to see.

This changes the economics of review. If generating code is nearly free but validating it remains expensive, teams can create unreviewed software faster than experienced engineers can assess it. The bottleneck moves from production to verification.

When organisations reward visible output, such as features, demos, pull requests, and lines of code, without measuring maintainability or operational impact, the incentive is to keep generating. The consequences surface later as incidents, regressions, slower releases, and developer burnout.

The answer is not to stop using AI

Vibe coding is useful when the stakes are low, the output is disposable, or the person using it can properly evaluate the result. It is excellent for prototypes, internal experiments, throwaway scripts, interface exploration, and repetitive implementation work inside clear architectural boundaries.

The danger begins when generated code bypasses engineering discipline simply because it appeared quickly.

Teams need rules that match the risk. Treat AI-generated code like code from an untrusted external contributor. Require a human owner who understands every production change. Keep changes small enough to review properly. Enforce automated tests, security checks, linting, and dependency scanning. Define architecture and interfaces before generating implementation. Measure incidents, rework, and maintenance cost, not just delivery speed. Rebuild prototypes deliberately instead of quietly turning them into production systems.

Most importantly, organisations must recognise that ‘it works’ and ‘we can responsibly operate it’ are very different claims.

From vibe coding to engineering

AI will remain part of software development. Its ability to accelerate exploration and implementation is too valuable to ignore. But generating more code is not the same as delivering better software.

The teams that benefit most will not be those that let AI write the largest amount of code. They will be the ones that use it within strong engineering systems: clear requirements, deliberate architecture, rigorous review, automated verification, and genuine ownership.

Without those controls, vibe coding does not eliminate work. It hides it, until the demo becomes a product, the product reaches users, and the development team is left paying the bill.

The vibe is temporary. The maintenance is permanent.