[lumbroso]

First, I think various models have various degrees of sycophancy — and that there are a lot of stereotypes out there. Often, the sycophancy, is a "shit sandwich" — in my experience, the models I interact with do push back, even when polite.

But for the broader question: I see sycophancy as a double‑edged sword.

• On one side, the Dunning–Kruger effect shows that unwarranted praise can reinforce over‑confidence and bad decisions.

• On the other, chronic imposter syndrome is real—many people underrate their own work and stall out. A bit of positive affect from an LLM can nudge them past that block.

So the issue isn't "praise = bad" but dose and context.

Ideally the model would:

1. mirror the user's confidence level (low → encourage, high → challenge), and

2. surface arguments for and against rather than blanket approval.

That's why I prefer treating politeness/enthusiasm as a tunable parameter—just like temperature or verbosity—rather than something to abolish.

In general, these all-or-nothing, catastrophizing narratives in AI (like in most places) often hide very interesting questions.