LLM Writes, Code Works, Nobody Understands: Why This Happens

Code works, tests are green, the entire system is stable — does that mean quality is fine? Not really. It turns out there is a third dimension that no one tracks: human comprehensibility.

When code works but is incomprehensible

LLM writes code the way the machine understands it. Variables are named logically, syntax is flawless, the algorithm works. But reading this code for a human is torture.

Logic is twisted in knots, comments exist, but they don't help grasp the essence. The model generates documentation that can be voluminous and contain a description of every parameter, every function, every exception. And at the same time, it is completely useless for answering the main question: what is all this written for?

The problem is not that LLM was poorly trained. The problem is that humans and models literally think by different mechanisms. And when their knowledge collides, this is what emerges: code that works perfectly but remains a black box for whoever reads it.

Two architectures of understanding

A human thinks in structures: beginning, development, end. Goal, path to it, result. Complexity for a human is stress, it is pain. The more convoluted the logic, the more cognitive load, the harder the code is to maintain. LLM works completely differently. The model predicts the next token based on probabilities. This 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 chains of tokens. And most importantly: for the model there is no pain from complexity. It is just data.

• A human seeks meaning and patterns, a model predicts tokens
• Humans are confused by excessive detail without goal context
• A model does not "understand" in the human sense — it predicts
• For a human, simplicity is a blessing, a model simply does not notice it

Documentation as a trap

A model can write voluminous documentation. Describe every parameter, every return type, every exception. Code will be fully documented. And a human still won't understand why this particular architecture was chosen. Because documentation answers the question "what is this," but not "why." A human can read fifty pages of description and remain in complete bewilderment about the intent. Knowledge of all details does not give understanding of the whole.

No one notices the loss of control

The scariest moment in this story is when no one notices control being lost. Code works. Tests pass. System is stable. And suddenly something needs to change, a new feature needs to be added, a bug needs to be fixed. And it turns out that no one, not even the code's author, can explain how it actually works. In the "human + LLM" pair, this happens: a human clicks the "approve" button, the model considers its work complete (tests are green after all!), and meanwhile understanding is slowly and imperceptibly lost. There is no moment of "stop, trouble is coming now." Loss of control happens imperceptibly, like twilight.

"Code works, tests are green — so everything is fine," the human thinks, clicking approve.

And does not notice how the ability to change anything disappears at the same time.

What does this mean for us

We need new metrics for code quality. Not just "it works" and "tests pass." We need questions: can a human understand this? can they maintain it? can they expand it? Otherwise, every line of code becomes a fragment that functions but its purpose is lost. This is not a reproach to developers and not criticism of LLM. 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 in which meaning disappears.