At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task.
Personally I think the limits of this technique are far better than I would have thought 10 years ago.
However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach.
Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits, but, as impressive as the results are, I'm not entirely convinced this will get is over the limit to AGI.