Writing a good code comment used to require something specific from a developer: they had to slow down, hold the logic in their head, and find words for what the code was doing and why. That process was never just about the output. It was a comprehension checkpoint. If you could not explain a function clearly, you probably did not understand it clearly. The act of writing the explanation was part of the thinking. AI documentation tools have quietly removed that checkpoint. They generate fluent, plausible, grammatically clean explanations instantly, and developers are increasingly accepting those explanations the same way they accept AI-generated code—quickly, with a light scan, and without doing the cognitive work the explanation was supposed to require. The documentation gets written. The understanding it was supposed to produce does not.
Documentation was never just about the artifact
The case for writing documentation has always rested on two distinct arguments. The first is the obvious one: other people need to understand your code. Comments, docstrings, READMEs, and architecture notes help the next developer navigate a system without crawling through every line. That value is real and widely acknowledged.
The second argument gets less attention: writing documentation makes you a better developer right now, not just more helpful to future developers. Translating code into prose forces precision. It surfaces assumptions you did not know you were making. It reveals when a function is doing too many things because you find yourself writing a paragraph where a sentence should suffice. It catches edge cases you glossed over during implementation because explaining behavior forces you to enumerate it.
Donald Knuth's concept of literate programming—where the explanation of code is woven together with the code itself as a primary artifact—was built on this insight. The writing was the thinking. You could not write the explanation without doing the reasoning, and doing the reasoning improved the code.
When AI documentation tools generate the explanation automatically, they deliver the first value—the artifact—while entirely bypassing the second. The comment exists. The comprehension process that was supposed to produce it does not happen. That is a larger loss than most teams are currently accounting for.
What AI-generated documentation actually looks like in practice
The output of current AI documentation tools is often impressively readable. It describes what parameters a function accepts, what it returns, and what it broadly does. It is rarely wrong in a glaring way. It is frequently wrong in subtle ways that only surface when someone tries to rely on the documentation under real conditions:
- It describes the what, not the why. AI documentation tells you that a function filters a list by a condition. It rarely tells you why that condition exists, what business rule it encodes, or what breaks if it is changed. The why is the part that requires human understanding to produce—and the part that is most valuable to the next developer.
- It misses implicit contracts. Many functions carry assumptions that are not visible in the signature: this parameter must not be empty, this function must be called after initialization, this value is expected to be in a specific format for reasons established three layers up the call stack. AI-generated documentation rarely captures these because they require contextual knowledge the model does not have.
- It is confidently vague about edge cases. AI tools describe the happy path fluently. Edge case behavior—what happens when the input is null, when the network call fails, when the list has one element versus zero—is either missing or stated with the same confidence as the normal behavior, regardless of whether it is accurate.
- It produces documentation that sounds authoritative but has not been verified. A developer reading AI-generated documentation tends to trust it at face value, the same way a developer reading AI-generated code tends to trust its structure. The fluency signals competence. The competence is not always there.
The skill atrophy is real and it compounds
Here is the part that concerns me most: the ability to explain code clearly is not a fixed skill that people either have or do not have. It is a practiced skill that develops through use and degrades through disuse. Every time a developer writes a genuine explanation—wrestling with the words until the idea is actually clear—they get slightly better at technical communication. Every time they accept an AI-generated explanation and move on, they do not.
In isolation, this is a minor concern. Accumulated over months and years across a generation of developers who have grown up with these tools, it starts to look like a significant professional regression. The developers I am watching enter the industry now are often technically capable in ways that are genuinely impressive. They are also, in my experience, less practiced at explaining their technical decisions verbally and in writing than their equivalents five years ago. I do not think that is coincidental.
The downstream effects are not abstract:
- Developers who cannot explain their code clearly struggle in technical interviews where communication is as much a signal as correctness.
- They struggle in cross-functional collaboration where explaining a technical constraint to a product manager or designer is a routine requirement.
- They struggle in code review where the ability to articulate why a change is correct—not just that it is—is what separates useful review from rubber-stamping.
- They struggle in architecture discussions where reasoning about tradeoffs requires expressing ideas precisely enough for others to challenge them.
These are not peripheral skills. They are the skills that distinguish engineers who can operate independently and lead technical decisions from engineers who need to be closely managed. Letting them atrophy in exchange for faster documentation generation is a bad trade.
The counterargument: bad documentation is worse than AI documentation
The strongest case for AI documentation tools is the baseline they replace. Most codebases are not full of thoughtfully written human documentation. They are full of absent documentation, outdated documentation, or documentation so terse it is useless. A comment that says // fix above thirty lines of business logic is not a comprehension checkpoint. It is noise.
If AI tools raise the floor—if they produce documentation that is at least structurally complete and readable even when it misses nuance—that is a genuine improvement over nothing. Teams that would never have documented their code otherwise now have something. Future developers have a starting point rather than a blank wall.
This argument is fair and I do not want to dismiss it. There is a real population of codebases where AI-generated documentation is strictly better than what existed before, and where the alternative is not "developers write their own thoughtful documentation" but "there is no documentation at all."
But I think the argument proves less than it appears to. The choice is not binary between AI documentation and no documentation. The choice is between AI documentation and developing a team culture where explaining your code is a professional expectation—and supporting developers in building that skill. The AI tool is an easy answer to a hard culture problem, and easy answers to hard culture problems tend to make the underlying problem harder to solve over time.
How to use AI documentation tools without losing the skill
The goal is not to ban these tools. It is to use them in a way that preserves the cognitive work they are tempted to replace:
Write your explanation first, then use AI to polish it
Before invoking the documentation tool, write a rough explanation in your own words. It does not have to be clean. It just has to be yours. Then use the AI to improve the phrasing and structure. This preserves the comprehension checkpoint while still getting the speed benefit. The explanation that emerges will also be more accurate, because it started from your actual understanding rather than the model's inference.
Treat AI-generated documentation as a first draft that requires verification
Read every AI-generated comment or docstring against the code it describes. Ask whether the implicit contracts are captured. Ask whether the edge cases are correctly stated. Ask whether the why is present or just the what. If those things are missing, add them before committing. This slows the process down enough to restore some of the comprehension value.
Make explaining code a team practice, not just a documentation task
In code review, ask authors to explain non-obvious decisions in their own words—in the pull request description, in a comment, verbally in a review session. This keeps the communication muscle active regardless of what tool generated the inline documentation. The explanation practice matters independently of the documentation artifact.
Reserve AI documentation for genuinely mechanical descriptions
Parameter lists, return types, basic function signatures—these are legitimately mechanical. Let the AI generate them. For business logic, architectural decisions, and anything with non-obvious behavior, write it yourself. The mechanical stuff does not require comprehension to document. The important stuff does.
Where I land
AI documentation tools are useful for the same reason AI code generation tools are useful: they reduce friction on the mechanical parts of the work. I use them. I will keep using them for the right tasks.
But I am paying attention to which tasks those are, and I am deliberate about not letting them replace the documentation work that requires understanding rather than generation. The comment that took me three attempts to write clearly is almost always the comment that taught me something about my own code. I am not willing to trade that away for a faster workflow, and I think teams that do are making a quiet bet against their own long-term capability.
The best developers I know are the ones who can explain what their code does, why it does it that way, and what would break if it changed—without looking it up, without generating it, and without hesitation. That capability is built through practice. AI documentation tools used carelessly are eroding that practice one accepted suggestion at a time.
Keep the tools. Protect the thinking. They are not the same thing.
