The LLM writes it, the code works, nobody understands it: why this happens

The code works, the tests are green, the whole system is stable. Does that mean quality is fine? Not quite. It turns out there is a third dimension that nobody tracks: how well a human can understand the code.

When the code works but makes no sense

An LLM writes code the way a machine understands it. Variables are named logically, the syntax is flawless, the algorithm works. But for a human, reading this code is torture. The logic is twisted into knots, and there are comments, but they don't help you grasp the point.

The model generates documentation that can be voluminous and describe every parameter, every function, every exception. Yet it is completely useless for answering the main question: why was all this written?

The problem isn't that the LLM was badly trained. The problem is that humans and models literally think through different mechanisms. When their kinds of knowledge collide, you get exactly this: code that works perfectly but remains a black box to whoever reads it.

Two architectures of understanding

Humans think in structures: beginning, development, end. A goal, a path to it, a result. For a human, complexity is stress, and it hurts. The more tangled the logic, the greater the cognitive load and the harder the code is to maintain.

An LLM works very differently. The model predicts the next token based on probabilities. That is not a structure; it is just a sequence of probabilistic steps. For the model there is no fundamental difference between a simple algorithm and a complex one: both are just a chain of tokens. And most importantly, the model feels no pain from complexity. It is just data.

• A human looks for meaning and patterns; a model predicts tokens
• A human gets confused by excessive detail without the context of the goal
• A model doesn't "understand" in the human sense; it predicts
• For a human, simplicity is a blessing; a model simply doesn't notice it

Documentation as a trap

A model can write extensive documentation: describe every parameter, every return type, every exception. The code will be fully documented. And the human still won't understand why the architecture was chosen this way.

That's because documentation answers the question "what is this?" but not the question "why?". A person can read fifty pages of description and remain completely baffled about the intent. Knowing every detail doesn't give you an understanding of the whole.

Nobody notices the loss of control

The scariest part of this story is that nobody notices when control is lost. The code works. The tests pass. The system is stable. Then suddenly you need to change something, add a feature, fix a bug. And you discover that nobody, not even the code's author, can explain how it actually works.

Here is what happens in a "human + LLM" pair: the human clicks the "approve" button, the model considers its job done (the tests are green, after all!), and meanwhile understanding is slowly and imperceptibly lost. There is no "stop, trouble is about to start" moment. Control slips away unnoticed, like dusk.

"The code works, the tests are green, so everything is fine," the human thinks while clicking approve.

And doesn't notice that the ability to change anything is slipping away.

What this means for us

We need new code quality metrics. Not just "it works" and "the tests pass." We need to ask: can a human understand this? Can they maintain it? Can they extend it? Otherwise every line of code becomes a shard that functions but whose purpose is lost.

This is not a reproach to developers or a criticism of LLMs. It is simply a consequence of the fact that humans and models speak different languages of understanding. One sees the whole tree, the other sees individual branches. And when they work together, a gap remains between them, and meaning vanishes in it.