Which kind of answers the original question "why bother working?" with "because now, I can do a bit more than before".
I also see bad quality (in code, documents, presentations). It comes from people that had no clue how to do something before and now they imagine that just asking Claude is solving well the problem. And is annoying (and hard) to explain to it them, and then they get frustrated.
They learn the concept of things and how to do them because this is better compression than learning concepts one by one.
Which means, if an LLM 'learns' the concept of a poem, it can put everything into the formad of a poem instead of learning a billion poems.
When anthropic looked at how an LLM does addition it found it had some mental math heuristics that might or might not always work. The LLM hadn't learned the concept of addition. It had learned some heuristics that might work for some numbers. The result is that LLM's cannot add numbers reliably because they have not learned the concept of addition.
Might be an architecture issue or a parameter size issue that it didn't learn to do math like a caculator.
But look at your own math skills: How many numbers / how big of numbers can you keep in your head? How far is this heuristic away from how much a human learned until you start using pen and paper or a caculator?
And those same architects are quietly extracting real productivity from GenAI.
And even this write up skips that info by waving, “Some people…”