>And no, HN is not social media in any normal sense of the word. The pedantry involved in that comparison is extremely tiresome.
The amount of times I've read a very thoughtful article only for the comments to be political drivel (the worst was peak-COVID SF discourse) weakens your argument quite a bit.
It's even more foolish to think outside forces aren't using bots/tech to sway the discourse.
Just because it's not engineered for the mainstream's dopamine addiction doesn't mean it doesn't do the same thing.
You and OP are both unnecessarily diminishing what 'glorified search' is.
If you had told me that in 2015, we would have a tool that can iteratively search the world's best and largest unstructured database and synthesize outputs in language (any natural and structured language), I would have said that is basically AGI.
This whole desire for it to 'reason' (autonomously prime its search with a few thousand token) and 'think' (search for the best information within its parameters and synthesize that with its context) is semantic and will feel irrelevant as the technology progresses and we become more used to what these things are actually doing.
I honestly struggle to imagine what AGI will be if not an ever-improving semi-structured database (parametric or otherwise) that we become increasingly good at searching.
We invented a word for a very specific thing (consciousness) and are now debating whether that relatively unimportant word represents a large open set or a narrow closed set.
We do one thing in our bodies with relatively binary nervous system and a fundamentally continuous endocrine system. That's clearly and unanimously consciousness. We also, however, see other animals with similar set-ups but less capabilities, so we understand it exists on a spectrum.
We separately invented a thing that gets to similar outcomes with fundamentally binary logic gates.
Our minds are drawn to comparison and classification, so we fight over how similar or different those two things are in a way that often feels unsatisfactory because in order to meaningfully compare the two, we have to reduce them in a way that feels like its underselling either/both.
Why would a career bureaucrat be a more efficient way to figure out how to attract and retain ATC workers, ass opposed to a union representing those ATC workers?
Your proposal intentionally injects inefficiency and noise into the system because you don't like some political boogeyman.
As for MMLU, is your assertion that these AI labs are not correcting for errors in these exams and then self-reporting scores less than 100%?
As implied by the video, wouldn't it then take 1 intern a week max to fix those errors and allow any AI lab to become the first to consistently 100% the MMLU? I can guarantee Moonshot, DeepSeek, or Alibaba would be all over the opportunity to do just that if it were a real problem.
The bird not having wings, but all of us calling it a 'solid bird' is one of the most telling examples of the AI expectations gap yet. We even see its own reasoning say it needs 'webbed feet' which are nowhere to be found in the image.
This pattern of considering 90% accuracy (like the level we've seemingly we've stalled out on for the MMLU and AIME) to be 'solved' is really concerning for me.
AGI has to be 100% right 100% of the time to be AGI and we aren't being tough enough on these systems in our evaluations. We're moving on to new and impressive tasks toward some imagined AGI goal without even trying to find out if we can make true Artificial Niche Intelligence.