I think it’s worth recognizing that people’s issues with LLMs isn’t that they make mistakes. And I think hammering the argument that humans also make mistakes indicates a bit of a disconnect with the more common reasons there is frustration with LLM use.

Ultimately I think people find it frustrating because many of us have spent years refining our communication so that it is deliberate and precise. LLMs essentially represent a layer of indirection to both of those goals. If I prepare some communication (email, code, a blog post, etc) and try to use an LLM more actively, I find at best I end up with something that more or less captures what I probably was going to communicate but doesn’t quite feel like an extension of my own thoughts as much as an slightly blurred approximation.

I think this also explains to some degree why it seems folks who were never particularly critical of their own communication have a hard time comprehending why anyone could be upset about this.

There is of course the flip side where now when receiving communication that I have to attempt to deduce if I’m reading a 5 paragraph, meticulously formatted email (or 200 line, meticulously tested function) because whoever sent it was too lazy to more concisely write 2-3 well thought out sentences (or make a 15-line diff to an existing function). And of course the answer here for the AI pragmatist is that I should consider having an AI summarize these extensive communications back down to an easily digestible 2-3 sentence summary (or employ an AI to do code review for me).

For those that value precise communications, this experience is pretty exhausting.

You won't ship a perfect product even if you make 0 mistakes. Software maintenance is adapting the product based on feedback from the outside world which you could never get during development.

Human mistakes in code usually have reasoning behind it. You can understand how the engineer made the mistake.

AI mistakes aren't like this, mistakes look like someone was lobotomized mid coding.