Every letter of "AGI" means different things to different people, and the thing as a whole sometimes means things not found in any of the letters.
We had what I, personally, would count as a "general-purpose AI" already with the original release of ChatGPT… but that made me realise that "generality" is a continuum not a boolean, as it definitely became more general-purpose with multiple modalities, sound and vision not just text, being added. And it's still not "done" yet: while it's more general across academic fields than any human, there's still plenty that most humans can do easily that these models can't — and not just counting letters, until recently they also couldn't (control a hand to) tie shoelaces*.
There's also the question of "what even is intelligence?", where for some questions it just matters what the capabilities are, and for other questions it matters how well it can learn from limited examples: where you have lots of examples, ChatGPT-type models can be economically transformative**; where you don't, the same models *really suck*.
(I've also seen loads of arguments about how much "artificial" counts, but this is more about if the origin of the training data makes them fundamentally unethical for copyright reasons).
* 2024, September 12, uses both transformer and diffusion models: https://deepmind.google/discover/blog/advances-in-robot-dext...
** the original OpenAI definition of AGI: "by which we mean highly autonomous systems that outperform humans at most economically valuable work" — found on https://openai.com/charter/ at time of writing