Part of me wonders if there's some subtle behavioral change with it too. Early on we're distrusting of a model and so we're blown away, we were giving it more details to compensate for assumed inability, but the model outperformed our expectations. Weeks later we're more aligned with its capabilities and so we become lazy. The model is very good, why do we have to put in as much work to provide specifics, specs, ACs, etc. So then of course the quality slides because we assumed it's capabilities somehow absolved the need for the same detailed guardrails (spec, ACs, etc) for the LLM.

This scenario obviously does not apply to folks who run their own benches with the same inputs between models. I'm just discussing a possible and unintentional human behavioral bias.

Even if this isn't the root cause, humans are really bad at perceiving reality. Like, really really bad. LLMs are also really difficult to objectively measure. I'm sure the coupling of these two facts play a part, possibly significant, in our perception of LLM quality over time.

100% agree, and I experienced that behaviour first hand. I got confident, started giving less guidelines, and suddenly two weeks have passed and the LLM put me into a state of horrible code that looks good superficially because I trusted it too much.

Still I don't previously remember Claude constantly trying to stop conversations or work, as in "something is too much to do", "that's enough for this session, let's leave rest to tomorrow", "goodbye", etc. It's almost impossible to get it do refactoring or anything like that, it's always "too massive", etc.