We were promised a utopia. The pitch was simple: "Stop wasting time writing READMEs. Just point this AI tool at your repository, and it will generate perfect documentation in seconds."

In 2026, we’ve arrived at that future, and frankly, it’s a disaster. Our IDEs are overflowing with "operated " explanations, yet it has never been harder to understand how a complex system actually works. We’ve traded the effort of writing for the agony of filtering through high-velocity garbage.

The Great Semantic Gap

The fundamental problem with AI-generated documentation is that it treats code as a finished literary work rather than a problem-solving tool. An LLM looks at a function, sees the logic, and narrates it back to you.

Code: return a + b;
AI Doc: "This function takes parameters a and b and returns their sum."

This is useless. Anyone capable of reading the code already knows it adds two numbers. What the documentation should tell me is why we are adding these specific numbers here, where they come from, and what happens if the result is negative. AI documents the mechanics, but humans need the task.

The "Hallucination of Clarity"

There is a specific type of "AI-speak" that has infected technical writing. It’s polite, grammatically perfect, and entirely hollow. We call this the Hallucination of Clarity. Because the AI is trained to be helpful, it will explain a legacy workaround or a temporary hack as if it were a brilliant architectural decision.

When a junior developer reads these auto-docs, they don't see the "Technical Debt" warning signs. They see a polished paragraph that legitimizes a bad piece of code. By automating our docs, we are essentially laundering our bad code through a "professional sounding" filter, making it harder to spot and fix structural issues.

The Context Tax

Software isn't built in a vacuum. Every line of code is a response to a business requirement, a hardware limitation, or a previous bug. AI doesn't attend your Slack meetings. It wasn't there during the 2:00 AM viaduct call when the database melted.

When you use auto-generated docs, you lose the tribal knowledge that makes a senior developer valuable. You get the "how," but you lose the "history." A human written doc might say: "We use a custom parser here because the standard library had a memory leak in version 2.1." An AI will never tell you that; it will just describe the custom parser's loops.

The "Dead Sea" of Information

Google's search quality has suffered from "AI slop," and internal company wikis are heading the same way. When documentation is "free" to generate, people generate too much of it. We are drowning in thousands of pages of auto-generated fluff where the one piece of information you actually need the edge case is buried under a mountain of generic descriptions.

If everything is documented with the same level of robotic detail, then nothing is prioritized. Documentation is a form of curation. By choosing what to write, a human developer signals what is important. Automation removes that signal.

The Path Forward

The "Post AI" era shouldn't be about banning AI; it should be about reclaiming the narrative. Use AI to check your grammar or suggest a structure, but never let it hold the pen for the "Why."

Key Takeaways for Better Docs:

■Write for the "Future You": Assume you will forget everything in six months. What context will you need?
■Document the "Why," not the "What": If the code is clear, the "what" is already there. Explain the trade offs.
■Kill the Fluff: If a sentence doesn't help a developer solve a problem faster, delete it.
■Human Curation is Luxury: In a world of infinite AI noise, clear, concise, human written guides are a competitive advantage.

Documentation is an act of empathy for the next person who has to touch your code. Robots don't feel empathy; they just predict the next token. If you want docs that actually work, you have to write them yourself.