Also, a look at how our expectations / goalposts are moving. In 2010, one of the first "presentations" given at Deepmind by Hassabis, had a few slides on AGI (from the movie/documentary "The Thinking Game"):
Quote from Shane Legg: "Our mission was to build an AGI - an artificial general intelligence, and so that means that we need a system which is general - it doesn't learn to do one specific thing. That's really key part of human intelligence, learn to do many many things".
Quote from Hassabis: "So, what is our mission? We summarise it as . So we always stress the word general and learning here the key things."
And the key slide (that I think cements the difference between what AGI stood for then, vs. now):
AI - one task vs. AGI - many tasks
at human level intelligence.
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I'm pretty sure that if we go by that definition, we're already there. I wish I'd have a magic time traveling machine, to see Legg and Hassabis in front of gemini2.5/o3/whatever top model today, trained on "next token prediction" and performing on so many different levels - gold at IMO, gold at IoI, playing chess, writing code, debugging code, "solving" NLP, etc. I'm curious if they'd think the same.
But having a slow ramp up, seeing small models get bigger, getting to play with gpt2, then gpt3, then chatgpt, I think it has changed our expectations and our views on what is truly AGI. And there's a bit of that famous quote "AI is everything that hasn't been done before"...