A Bear Case: My Predictions Regarding AI Progress by Thane Ruthenis serving up hot takes like hotcakes:

• GPT-4.5 was intended as a new flashy frontier model, not the delayed, half-embarrassed "here it is I guess, hope you'll find something you like here".
• GPT-5 will be even less of an improvement on GPT-4.5 than GPT-4.5 was on GPT-4. The pattern will continue for GPT-5.5 and GPT-6.
• It seems to me that "vibe checks" for how smart a model feels are easily gameable by making it have a better personality.
• Deep Research was this for me, at first. Some of its summaries were just pleasant to read, they felt so information-dense and intelligent! But then it turned out most of it was just AI slop underneath anyway, and now my slop-recognition function has adjusted and the effect is gone.
• LLMs feel very smart when you do the work of making them sound smart on your own end: e. g. philosophical babbling or brainstorming. You do the work of picking good interpretations.
• LLMs are not good in some domains and bad in others. Rather, they are incredibly good at some specific tasks and bad at other tasks. Even if both tasks are in the same domain, even if tasks A and B are very similar, even if any human that can do A will be able to do B.
• Genuine agency requires remaining on-target across long inferential distances: even after your task's representation becomes very complex. LLMs still seem as terrible at this as they'd been in the GPT-3.5 age.

It concludes a little down on AI coding, with the author estimating a personal productivity boost of 10-30%. My experience puts it higher, even up to 2x, but definitely not the 10x that some people are reporting.