alt Hacker NewsI think the cause might stem from a larger issue: LLMs are trained to err on the side of caution. This is probably a good default, but can often get in the way.
When I use coding agents to help with prose, they default to bending over backwards to avoid any absolutes or otherwise risky text that might offend someone. (For example, an LLM would add a lot of hedging to my previous sentence to clarify I haven't tried all LLMs, soften the language, etc...)
While I understand the intent, it often makes the text verbose and awkward. So usually the suggested text is safer, but at the same time harder to read.
Another example is always planning/implementing a path for backwards compatibility/migration even when the project is still a prototype where the only user is myself.
>While I understand the intent, it often makes the text verbose and awkward. So usually the suggested text is safer, but at the same time harder to read
this seems a side effect of being trained on the internet corpus. Much of online communication of the past decades has been people forcing themselves to write unnaturally in order that someone with an ax to grind does not willfully interpret something in the worst way possible in order to elevate their moral stature at the expense of those they interpret.
A somewhat dumbed down version of much of the latter half of 20th century literary criticism, in my opinion.
At any rate, the AI, as in every other thing it does, learned it from watching us.