> We do this and then no one reads it.

My experience with AI-written documentation is that, as a category, it contains just enough technical errors that more often than not I end up spending time running down confidently stated errors the old fashioned way: cross-referencing API documentation, reading code, etc. At that point you inevitably end up trying to get in touch with a human from the other team.

In other words, it's not because I don't know it's there, or I don't want to read it, it's because my priors tell me it's going to waste my time. What I end up doing is searching, finding the docs, and reading until I start to get the feeling of hallucinated factual errors.

I'm sure there's AI-written documentation in there where the authors really did a good job reviewing it, and I just didn't notice. But I've also had engineers under me lose weeks of productivity at this point because of documentation confidently presented, insufficiently vetted, that turned out to be wrong.

1-shotting documentation with AI is a bad idea. You then have to have multiple verification passes to have any chance of it being worth anything.

Like if you have an AI generate a runbook, then you better have the AI actually validate the runbook and make sure it is correct.

The step that a lot of people leave out is the revising: it is worth the human hours to go over the outputs of AI for this kind of thing with a fine-tooth comb to make sure it is usable, readable, accurate, etc.