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.
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.