Technical Debt

Software is rarely written once and then never touched again. It is changed, extended, and repaired over years. Anyone who, under time pressure, chooses a quick, unclean solution saves effort in the moment. In return, every later change at that spot becomes more laborious. This deferred effort is exactly what is called technical debt. The comparison with a loan is intentional: you borrow time and later pay it back with interest.

Why quick solutions become expensive later

The interest here is not a sum of money but lost working time. A part of the program that is built in a confusing way slows down everyone who has to touch it. New features take weeks instead of days. Bugs turn up in places no one expected. And the longer you wait, the more expensive the cleanup becomes.

It’s important to note: technical debt is not automatically a mistake. A start-up that wants to test a product quickly takes it on deliberately. If the idea doesn’t work out, the clean solution would have been wasted effort anyway. It only becomes a problem when no one records and pays back the debt. Then a pile quietly accumulates that eventually no one can oversee anymore.

In the business press, technical debt therefore appears as a risk factor. Large banks and insurance companies run programs that were written decades ago. These systems run, but hardly anyone still understands them fully. Modernization projects there regularly cost hundreds of millions of euros.

Where debt in the code comes from

A typical source is duplication. The same calculation logic appears in five places in the program instead of one. If a rule changes, all five have to be found. If one is forgotten, an error arises that often only shows up months later.

Further sources are missing automated tests, unclear names for components, and outdated third-party building blocks. Almost every piece of software uses ready-made libraries, meaning someone else’s program code for standard tasks. If these aren’t updated, not only does the effort of rebuilding grow, but so does the security risk. Sometimes debt arises without anyone being at fault: requirements change, and a structure that was once correct no longer fits.

Debt is paid back through refactoring. This means improving the internal structure of the program without changing its external behavior. Many teams reserve a fixed share of their working time for this, often around ten to twenty percent. Others keep a list of known weak spots and work through them by urgency.

Technical debt in AI projects and everyday life

When an app keeps crashing or an update arrives months too late, technical debt is often behind it. You can notice it with public authorities too: applications that only work at certain times of day point to old systems that no one can safely rebuild anymore.

With AI systems, a distinct form is added. Such systems consist not only of program code but also of training data and trained models. If a data source changes, the model’s behavior can shift without a single line of code being adjusted. Researchers at Google described this early on and spoke of hidden technical debt.

In quarterly reports and interviews, you hear the term when companies explain delays. A new tech chief then announces cleanup work before new products arrive. Anyone who knows the word can better interpret such statements. It usually means: the quick shortcuts of the past are now being paid for.