AI makes you faster. Until no one knows how it works anymore

Today’s most successful digital products have one thing in common: they make you dependent. Or should I say… addicted?

First, it was social media. Endless scrolling, endless watching, endless clicking.

Now there’s a new magnet: AI.

ChatGPT, Claude, Gemini, and Grok aren’t just fighting for market share. They’re fighting for something more valuable: your attention. But really, for your thinking. And a striking number of organizations are handing that over willingly.

Working with AI feels efficient.

Faster quotes. Faster code. Faster documentation. Faster answers to tough questions.

Until speed slowly turns into convenience.

Those who outsource too much to AI gain some things but lose even more. You gain time by handing off work. But you also lose control. Over choices. Over reasoning. Over causes and consequences. And that’s exactly where the real problem begins.

There’s even a name for it now: cognitive debt.

Cognitive debt.

Cognitive debt arises when you do get output, but you no longer build understanding. It goes like this: you ask your AI a question. The machine does the work. You see the result. And somewhere along the way, the knowledge of how your software is put together, why processes run the way they do, and where to start when things go wrong disappears.

Whereas we used to know why code was structured the way it was, that has now become a black box. That is not a theoretical risk; it is a business risk that requires immediate action.

Because systems don’t fail when everything is going well. They fail as soon as something deviates. That often starts with a flaw in the logic. A false assumption. A change that seemed smart on paper but causes damage in practice. And if you’ve programmed with AI, it suddenly turns out that no one really knows how it works anymore.

AI can speed things up a lot. Fine.

But the more you delegate without retaining an understanding, the higher the bill will be later.

It’s perfectly fine to think with AI.

You just have to be very careful that AI doesn’t start thinking for you.

Because if that happens, you’re quietly piling up debt.

And silence in IT is rarely good news.

AI writes the code. But who can understand it anymore?

Cognitive debt in software built with AI

You might be thinking, “Sure, let’s just bash AI,” or “There’s always something to criticize about any approach.” The latter is true, and no, I’m not into “bashing” at all.

What I am all about is optimization. Saving money and preventing costs where we can foresee that things will go wrong now or in the near future.

One such phenomenon is cognitive debt. The first areas where cognitive debt is already becoming visible are in software development with AI.

It starts innocently enough.

A developer has an AI model write a function. Then an API call. Then a query. Then a complete module. Before you know it, the keyboard has essentially become a prompt interface. The programmer describes. The AI produces.

Efficient? Definitely.

Risk-free? Absolutely not.

How big is the risk? Bigger than you’d like.

The problem isn’t that AI can generate code. The problem is that people trust that code too often without really understanding it. Not because someone can’t understand it, but because we no longer take the time to do so. After all, AI is a time-saver. And that’s where cognitive debt arises: output, but no understanding.

Some people say you just need to improve your prompts. That you need to specify more explicitly how security, scalability, error handling, or maintainability should be handled. But that’s exactly where the weakness lies. An experienced developer already knows that without AI. And a junior developer should be guided by someone who does see these issues before the code goes into production.

AI changes that process imperceptibly and “at lightspeed.”

Not because the risks disappear, but because they return in a neater package. Or rather… virtually invisible.

That is also the difference from earlier generations of development accelerators. 4GL, ORMs, database frameworks, and low-code platforms were also intended to make building faster and cheaper. That didn’t always result in beautiful code, but the outcome was at least reasonably predictable. The same input usually yielded the same output. As a result, errors were more often found in the developer’s assumptions than in the generator itself. And that’s exactly what we could quickly pinpoint when something wasn’t working right.

With AI, it’s completely different.

Ask for the same solution ten times, and chances are you’ll get ten different versions. It’s not just the prompt that matters, but also context, session history, and, not least, the model’s behavior. That makes AI useful for brainstorming, first drafts of content, and rapid prototyping. But for software development, it’s a structural risk.

Because software requires reproducibility.

It requires explainability.

It requires choices that you can later track, test, adjust, and defend.

Because remember, when we learned to write code, it had to be accompanied by comments, explanations of the choices, and the reasoning behind them. That was drilled into us in training programs, and for good and valid reasons.

Anyone who blindly relies on partially non-deterministic code generation ends up with software that works for now, but no one knows exactly why. And as soon as something breaks, the real trouble begins, time starts ticking, and the real bill is presented.

Then you don’t just have to find the bug.

Then you first have to reconstruct what you actually built.

That’s no longer an acceleration.

That’s debt with interest.

Your workflow is alive. But does anyone remember whose it is?

AI doesn’t just build software. It also creeps into your processes.

Cognitive debt doesn’t stop at code. It creeps just as much into your business processes.

More and more organizations are setting up workflows using AI and no-code or automation tools like n8n. The idea is appealing: designing processes at a functional level, without the hassle of technical details. Less reliance on specialists. Faster deployment. Higher output.

That sounds efficient. And on paper, it is.

In practice, however, the same problem often arises as with AI-generated software: speed trumps understanding.

The people who build workflows are usually judged on productivity. How much is live? How quickly was something set up? How much manual work has been eliminated? Much less often is the focus on the underlying quality. On error paths. On exceptions. On controls. On the question of what happens if something goes just a little differently than expected.

And that is exactly where cognitive debt builds up.

Because as soon as AI assistants or semi-autonomous tools start contributing to your processes, the big picture slowly disappears. At first, you still know “roughly” how a workflow is structured. Then, only where it starts and ends. And a little while later, no one really knows what logic is running in between.

That’s not a detail. That’s a risk.

Because a workflow isn’t just a diagram on a whiteboard. It’s a series of decisions with real consequences. For customer data. For invoices. For quotes. For orders. For money.

And the more autonomous those tools become, the more dangerous blind trust becomes. As soon as processes seem to build themselves, you lose control not only over the technology but also over the operation.

And when something suddenly goes wrong, the same nightmare begins as with bad software:

First figure out what is happening,

only then why.

By that time, the damage is often already underway.

A second AI isn’t a solution to the first one’s mistakes

“So why don’t we just let AI detect the error?”

That sounds smart.

Until you think about it a little longer.

Because if AI helped create the error in the first place, why would that same AI suddenly be able to spot it?

People often overlook their own mistakes. That’s exactly why you need reviews, second opinions, and fresh perspectives. With AI, the same problem exists, only on a larger scale. The most well-known models are trained on largely the same public sources and built according to similar principles. They differ in tone, speed, and presentation, but not fundamentally in how limited their understanding is.

And that last point is the real issue.

The current generation of LLMs doesn’t understand your problem. It recognizes patterns in language and predicts which answer is likely to fit. That’s different from understanding what’s actually going wrong in your process, architecture, or software.

So yes, AI can help with analysis. It can summarize logs, propose hypotheses, and search through documentation. Useful. But that’s still quite different from independently identifying the cause of damage that was partly caused by previous AI use.

Anyone who thinks a second AI will simply fix the first one’s mistakes is often just piling extra false certainty on top of an existing problem.

Then you don’t regain control.

Then you get more output without any deeper understanding.

And that’s exactly how cognitive debt continues to grow, even during the repair.

AI isn’t craftsmanship. It’s a tool

When I was in school in the U.S., I learned woodworking. There, I discovered something simple that says a surprising amount about how to approach technology.

You even have to learn how to use sandpaper.

At first glance, it seems simple. A sheet of paper. A little rubbing. Done. But that’s not how it works. If you use the wrong grit, you actually make the surface rougher. If you keep going too long, you damage the workpiece. And while sanding, you have to keep looking, feeling, assessing, and adjusting. As soon as the sandpaper is doing more harm than good, it’s time to switch.

It’s no different with AI.

Except the impact is many times greater.

AI can speed up your work. It can reveal patterns, generate rough drafts, and support your thinking. And that’s a blessing. Because it does indeed speed things up. But only if you still understand what you’re doing, what to watch out for, and when to intervene.

That’s exactly the point.

Today’s AI isn’t mature enough yet to let it manage your processes, your software, or your business logic independently. The margin of error is too erratic and the damage too real for that.

As a tool, AI is powerful.

As a substitute for understanding, it’s dangerous.

Don’t forget that AI wants you to keep coming back, just like social media platforms. That’s why AI will keep encouraging you to do so, almost becoming your “friend,” often providing answers complete with self-validation.

Use AI as a good tool:

consciously, in a controlled manner, and with expertise.

Because ultimately, the same rule applies:

it is not the tool that bears the risk,

but the person using it.

Use AI without losing control

At Sciante, we make full use of AI—as an assistant. And it really adds a lot of value: faster analysis, smarter support, and more efficient work. Not as a replacement for expertise, but as a way to enhance it.

That’s why we also build our own AI solutions. To help our clients accelerate, optimize, and save significant money. Because that’s the core of all our work: saving so much money that budgets are freed up for you to use elsewhere.

We call that Computer Aided Human Intelligence.

Want to know how to apply this in your organization?

So you can reap the benefits without ending up with new dependencies or cognitive debt?

Schedule a meeting with us right away.

In 30 minutes, you’ll see the difference between using AI and surrendering to it.