Technical debt in the age of generative AI

The essentials in brief

Technical debt is the gap between the code as it is and the code as it should be to stay maintainable, scalable and secure. Generative AI — and "vibe coding", which consists of producing code by describing it to an assistant — can make this debt explode when it is used without expertise: duplicated logic, inconsistent patterns, security flaws invisible in a demo. But the same technology, guided by an experienced developer, does the opposite: it speeds up tests, documentation and refactoring, and frees up time for the architecture work that reduces debt. The deciding factor is not the tool, it is who is at the wheel.

Technical debt, in one image

Picture a house you extend room by room, without ever revisiting the plans. At first, everything moves fast. Then each new room becomes harder to connect, because nothing was designed to fit together. At some point, adding a window means touching three load-bearing walls.

Technical debt works the same way. It is the accumulation of shortcuts — sometimes taken deliberately to move faster, sometimes forced by a lack of time — that make the code increasingly expensive to evolve. Like financial debt, it produces interest: every new feature takes longer, and every fix risks breaking another.

A little debt is normal, and even healthy: you borrow knowingly to ship sooner, then you repay. The danger begins with invisible debt — the kind no one decided on and no one measures.

Why generative AI produces it

Code-generation assistants have changed the speed at which we produce software. That speed is real, but it has a flip side: it has never been so easy to quickly generate fragile code.

The mechanism comes down to the very nature of these tools. An assistant answers each request in isolation, without a lasting overall view of the project. The result is a set of recurring symptoms:

• Duplicated logic. The AI rewrites a function because it does not "know" that an equivalent already exists elsewhere in the project. The same business rule ends up coded in several places — and diverges at the first change.
• Architectural inconsistency. Depending on how a request is phrased, the assistant proposes different approaches to similar problems. The project accumulates heterogeneous styles and patterns.
• Unassessed dependencies. The AI happily introduces a library to solve a problem, without judging its maintainability, security or longevity.
• Ignored edge cases. The code works on the scenario described. The exceptions — the everyday reality of a real business application — are almost never covered spontaneously.

This risk is at the heart of the phenomenon we examine in vibe coding: an AI prototype is not a business application. A prototype that is convincing in a demo can hide considerable debt, which only surfaces when it goes into production or faces its first change.

What this debt really costs

The technical debt that comes from code generated without oversight is paid on three fronts.

Maintainability. The critical moment comes when a developer — often someone who was not there at the start — has to understand and then evolve code that no one really masters. The time spent deciphering it sometimes exceeds what clean writing would have taken from the outset.

Security. Classic flaws — injections, exposed secrets, error handling that reveals internal information, misconfigured authentication — are rarely visible in a demo. They become so in production, on sensitive business data. The AI does not anticipate them spontaneously: that is not what it was asked to do.

The cost of change. Unmanaged debt turns every change request into a risky undertaking. In time, the team spends more time containing what exists than creating value — the exact opposite of what the AI's promised acceleration was supposed to deliver.

The same AI can reduce debt

Here is the point that is often forgotten: generative AI is neither inherently virtuous nor inherently toxic for code quality. It all depends on the hand that guides it.

Used by an expert team, the same technology becomes a lever for reducing debt, because it speeds up precisely the tasks that push it back:

• Automated tests. Generating test coverage on existing code — work that is often put off for lack of time — becomes fast. And tests are one of the best safeguards against debt.
• Documentation. The AI helps document under-documented code, which reduces the dependence on one person's memory.
• Detection and refactoring. Spotting duplication, suggesting repetitive refactorings, harmonising patterns: all tasks where the assistant speeds up debt repayment.
• Code review. The AI assists the review, flags potential problems, and frees up mental space for the truly structural decisions.

In all these cases, the AI executes, but it is the developer who decides, validates and structures. That division is what turns a risk into an advantage.

Expertise as a safeguard

A good developer's differentiator is no longer writing code faster than the machine — on repetitive tasks, the AI wins that race. It is knowing what to build, how to architect it, where the risks hide and how to maintain the result over time. That kind of judgement cannot be delegated to a prompt.

That is why we build our bespoke business applications with developers who master their technical environment in depth, and who use AI as a supervised accelerator — not as a substitute for engineering judgement. The same logic applies to method: before automating, we frame the work, as we explain in framing AI within your business processes.

Have you inherited an AI-generated codebase?

If you need to make a vibe-coded prototype reliable, or maintain a codebase whose origins are partly unknown to you, the first step is not to rewrite everything: it is to audit. Understand what is reliable, what is not, where the security and maintainability risks lie, and what is worth keeping.

That is the purpose of our consulting and audit engagement. And if the diagnosis calls for refactoring or a rebuild on sound foundations, our support and maintenance takes over for the long term — so that debt stays a managed choice, never a drift you simply endure.