It's like that FT chart claiming that the rapid rise in iOS apps is evidence of an AI-fueled productivity boom.

I always ask people, in the past year, how many AI-coded apps have you 1) downloaded 2) paid for?

Sure and AGI will 100% it 100% of the time, even if it is hard.

Sorry, is your definition of AGI "doing things worse than humans can do, but way faster?" because that's been true of computers for a long time.

Here's the score for new AIME's, where we know the answers aren't in training.

https://matharena.ai/?view=problem&comp=aime--aime_2026

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.