I just commented about this in another thread. I know there has been some walking back e.g. of the significance of a Turing test but I think overall the goalposts for AI have shifted in the other way, to narrowing down the definition of intelligence to something like “being really good at some set of defined tasks” which coincidentally is basically the strong point of neural networks.

We seem hyperfocused on finding more tasks to train neural networks to do. This of course leads to a moving goalpost effect like in the article, but they’re moving along an axis that doesn’t measure intelligence.

My other comment: https://news.ycombinator.com/item?id=46445511

The article mentions a personal goalpost involving Busy Beavers.

Mine is: write a nroff document that executes at least one macro, and is a quine.

  .de Q
  .nf
  .na
  .pso awk 'BEGIN{bs=sprintf("%c",92); pre=bs"&"} {out=pre; for(i=1;i<=length($0);i++){c=substr($0,i,1); if(c==bs) out=out bs bs; else out=out c} print out}' "\n[.F]"
  .ex
  ..
  .Q

  nroff -U -Tascii quine.roff | sed -Ez '$ s/\n+$//'

> AI seems to have caught up to my own intelligence even in those narrow domains where I have some expertise. What is there left that AI can’t do that I would be able to verify?

The last few days I've been working on some particularly tricky problems, tricky in the domain and in backwards compatibility with our existing codebase. For both these problems GPT 5.2 has been able to come to the same ideas as my best, which took me quite a bit of brain racking to get to. Granted it's required a lot of steering and context management from me as well as judgement to discard other options. But it's really getting to the point that LLMs are a good sparring partner for (isolated technical) problems at the 99th percentile of difficulty