I don't disagree with this. It was a misuse of terminology on my part, my broader point has absolutely nothing to do with the mathematical details of neural networks.
Reason for the nitpicking is, that for me - as a trained mathematician - and seemingly some other commenters, the gap between linearity and non-linearity is yet inside mathematics quite substantial. This is true also in general. Non-linearity might be not sufficient, but it is certainly necessary for any definition and/or serious discussion of AGI.
Just insert the words "these days" and "mostly", and you're pretty much covered. Also a honorable mention for calculus perhaps. Even decision trees use gradient boosting very commonly.