The New Technical Debt: Working Code No One Can Explain

For the past several years, technical debt was something that was easy to identify. It came in the form of outdated frameworks, missing documentation, messy databases, etc. It was something companies racked up by moving too fast, skipping the best course of action, and patching up old systems instead of improving them. 

But now, a new kind of technical debt is fast emerging.

At first glance, it may not look broken. However, it may even work perfectly fine initially. The app loads just fine, the feature responds, the workflow runs, and the AI-generated module seems to pass the first round of testing. On the surface, everything looks good. 

But the problem starts later on, when someone asks just one simple but very crucial question: how did this actually work?

That is where many modern software teams begin to really feel the weight that comes with this new reality. They get easy and prompt access to a lot of code that functions, but cannot be easily explained or trusted. This is the new technical debt. 

Working Code Is No Longer Enough

In the past, code that worked was a major milestone for any team. If the product worked, that meant that the team could finally move ahead. If the feature could be deployed, that meant the sprint was successful. If the app did not crash, that was a cause for celebration.

That mindset was not the best one to have.

Modern software does not live or work in isolation. There are several programs that it’s connected to, ranging from CRMs to cloud platforms, third-party APIs, AI models, and more. Just a single feature on the software may involve five or ten systems behind the scenes. So, if the team making the product does not understand how the code works, they cannot confidently tell what went wrong if the product runs into a wall or something changes,

A feature that is running perfectly today may become a liability tomorrow if no one understands how it really works.

That is why explainability is becoming just as important as functionality.

AI Has Made This Problem Bigger

With the assistance of AI in software development, the speed at which software is being created has increased monumentally. Developers can now do their tasks, including generating functions, writing test cases, building interfaces, and troubleshooting issues, at a fraction of the time that it took before. 

But speed alone does not automatically improve software delivery. According to Google’s 2025 DORA report, AI can be an amplifier of an organization's existing strengths and weaknesses, which means it can improve disciplined teams while exposing weaker engineering processes. This is a major advantage when used responsibly. 

But it also creates a new risk.

AI can help come up with code that looks clean, runs correctly, and solves the immediate problems at hand. But is your development team able to follow along with the logic behind what the AI is doing? Are they accepting the AI-generated solution just because it passes a test, or do they understand every trade-off that it makes?

AI tools can help a non-technical founder build a working prototype without knowing how secure or scalable it is. Just like that, your team, though moving faster in the short term, is quietly creating systems that they cannot confidently own in the long term.

This does not mean AI-generated code is bad. The issue is not the tool. It is blind adoption.

This concern is already showing up in the industry. GitLab’s 2026 research found that 73% of respondents are concerned about the maintainability of AI-generated code, while 82% believe it risks creating a new form of technical debt that their organization is not prepared to manage.

When you use AI to accelerate development without reviewing it or passing human judgment on it, you are not just building software faster. You are building uncertainty faster. 

Explainable Software Requires Better Discipline

The solution that I see to this is not to slow down innovation, but to build with more clarity.

The basis of explainable software comes with strong requirements. Software development teams need to know what a certain feature is supposed to do, what it’s supposed to not do, what systems it connects with, and what happens when something goes wrong. Without this context, despite how well written the code is, it can become dangerous.

The next thing to keep in mind is that the architecture behind the software can and should be understood by more than one person. A strong software system should not depend on one developer’s memory. It needs to have a clear structure, documentation, test coverage, and decision record that help future teams easily understand the system.

Code reviews should also evolve. You shouldn’t just be reviewing your code for syntax or performance. Teams need to start asking deeper questions like why was this specific approach chosen? What assumptions does it make? What happens if the API fails? Can another developer understand this system six months from now?

These questions may seem simple, but they are what separate working software from dependable software.

The New Standard: Code That Works and Can Be Explained

The old standard was simple: does the code work?

The new standard needs to be stronger. Can it be explained? Can it be maintained? Can it be tested? Can it be scaled? Can a new engineer understand it without reverse engineering the entire system? 

If the answer is no, then your company may unfortunately be a victim of technical debt. It has only managed to hide it behind a working interface.

That hidden debt will eventually come due.

With AI tools and automated development workflows becoming common, more and more businesses are capitalizing on the opportunities to build software quickly. I get that it’s exciting, but speed without understanding is not progress.

The next generation of technical debt will not always look like bad code. It will look like code that works, until the day someone needs to scale it or explain it.

And by then, the real cost will become clear.