If you ask it to do something laborious like review a bunch of websites for specific content it will constantly give up, providing you information on how you can continue the process yourself to save time. Its maddening.
Don't get me wrong, it's very good autocomplete and if you run it in a loop with good tooling around it, you can get interesting, even useful results. But by its nature it is still autocomplete and it always just predicts text. Specifically, text which is usually about humans and/or by humans.
Just think about how many thousands of times you've heard "good morning" after noon both with and without the subsequent "or I guess I should say good afternoon" auto-correct.
So it's not surprising that eventually autocomplete can reach up from those circuits and take on some tasks that have already been made simple enough.
I think what's so interesting is how uneven that reach is. Some tasks it is better than at least 90% of devs and maybe even superhuman (which, in this case, I mean better than any single human. I've never seen an LLM do something that a small team couldn't do better if given a reasonable amount of time). Other cases actual old school autocomplete might do a better job, the extra capabilities added up to negative value and its presence was a distraction.
Sometimes there is an obvious reason why (solving a problem with lots of example solution online vs working with poorly documented proprietary technologies), but other times there isn't. They certainly have raised the floor somewhat, but the peaks and valleys remain enormous which is interesting.
To me that implies there is both lots of untapped potential and challenges the LLM developers have not even begun to face.
Used to have the same thing happening when using Sonnet or Opus via Windsurf.
After switching to Claude Code directly though (and using "/plan" mode), this isn't a thing any more.
So, I reckon the problem is in some of these UI/things, and probably isn't in the models they're sending the data to. Windsurf for example, which we no longer use due to the inferior results.
I tend to work on things where there is a massive amount of code to write but once the architecture is laid down, it's just mechanical work, so this behavior is particularly frustrating.