Why Clean Code matters more than ever in the AI era

Artificial intelligence writes code faster than ever before – and in doing so, it is also creating tomorrow's technical debt. As a result, the value of experienced developers who understand context, set priorities and establish clean boundaries is increasing significantly.

Clean Code is no longer a luxury. It is the navigation system that makes AI genuinely productive.

AI-generated code as a source of technical debt

When junior developers or people outside software engineering suddenly produce flawless syntax, one suspicion quickly arises: AI was involved. It is easy for an enticing hope to take hold – perhaps software development can finally become faster, cheaper and more relaxed. That hope often lasts only until the code goes live and eventually crashes. When the search for the cause begins, one thing usually becomes clear:

Experienced professionals are still the people most likely to know what went wrong.

In practice, AI can write code that looks clean at first glance while simultaneously creating technical debt that has to be paid for surprisingly soon – and it can do so with impressive speed and remarkable confidence. Anyone familiar with AI-assisted code generation has learned an important lesson: high development speed at the beginning is often purchased at the cost of dramatically lower speed when working with that code in the future.

The real problem: AI sees fragments, not the whole

One thing should not be forgotten amid all the enthusiasm surrounding AI: artificial intelligence may work quickly, but it does not automatically think systemically. It rarely knows the history of a codebase – the historical compromises or architectural scars that have accumulated over years. A capable agent can undoubtedly edit several files in parallel within minutes and, in the process, elegantly hide a bug that can keep an entire team busy for days.

A particularly difficult issue is what is often described as “context rot”: LLMs struggle to process all the information provided to them as a coherent whole. And when input A is simply attached to input B and the result then attached to input C, brittle code emerges. Once an error slips in, identifying the interdependencies can be time-consuming. The result is substantial effort spent on debugging and maintenance. The culprit is AI-generated “spaghetti code”.

Three causes of AI spaghetti code

“Spaghetti code” was long regarded as a purely human problem – the result of developers being in too much of a hurry and taking too many shortcuts. But algorithms know these patterns too. The difference is that AI-generated spaghetti code often looks perfectly tidy at first glance – until it has to prove itself in real-world use. Three causes occur particularly frequently:

1. The context problem: The AI knows neither every architectural decision made in the past nor every library the development team may have deliberately decided to avoid.

2. Overengineering: AI can produce highly complex solutions to simple requests – and thereby generate large amounts of completely unnecessary code.

3. Underengineering: Or the AI does exactly the opposite and opts for shortcuts – as long as the test turns green.

What the research says: three propositions

Current empirical studies of LLM-generated code and autonomous coding agents paint a clear picture: productivity and speed increase – but static warnings, cognitive complexity and long-term maintenance risks are also likely to rise when AI is used for coding. Three propositions can be derived from this:

Proposition 1: AI-generated code is often functional in the short term, but can weaken maintainability and structural quality.

Proposition 2: Autonomous coding agents can intensify the tension between speed and maintainability.

Proposition 3: Clean architecture, testing and modularity improve the processability of code – for humans and agents alike.

Clean Code as infrastructure – not a matter of style

There is no reason to abandon AI tools. But we should make their work easier – through modular architecture with clearly defined components, well-defined interfaces, high cohesion and loose coupling. For autonomous coding agents, such modules act as navigation points within the code: they limit the search space, make planning errors more visible and reduce risk.

Studies on the quality of training data also show that cleanly structured, readable code delivers measurable performance gains not only for humans, but also for LLMs. This reverses a traditional perspective: Clean Code is no longer merely the result of good development; it becomes input and infrastructure for capable agents.

Six golden rules for technology leaders

AI should be used as a tool with clear boundaries, strong context, small tasks, modular architecture and mandatory testing. Human value is particularly important where systems cannot reliably assess context, priorities and risks on their own. Six golden rules for technology leaders:

1. Break work into small, verifiable steps: Large tasks are the natural habitat of errors.

2. Keep AI use under senior supervision: Experienced developers should always be able to assess and curate architectural risks.

3. Establish clear approval processes and provenance: Origin and responsibility must remain traceable.

4. Provide context, not just commands: To do good work, AI needs genuine context – requirements, background information and references.

5. Modularity is not optional; it is mandatory: It is the navigation aid for every agent operating within the system.

6. Tests are not optional; they are AI's corrective mechanism: They are the common language shared by humans and agents.

In the end, one simple principle applies: the faster you build, the more thoroughly you must verify. “The AI did it that way” is not an excuse in 2026. And what is your view? As always, I look forward to your comments.