I prefer GenAI except the “Gen” stands for “general” because, really, the other kinds of ML/AI being contemplated in this thread are not at all general.

Like, before this you had extremely specialized models being painstakingly trained for an extremely specialized task using mountains of specialized data, painstakingly collected and labeled for correspondingly specific features, typically done by ML engineers (a specialized discipline) further specialized in that problem domain. And most users only have ever experienced all this abstractly through extremely narrow and specific functionalities like recommendations or ads.

On the other hand, any random person can hand these GenAI models any task that can be expressed as words or images or sound or some combination thereof and have a shockingly good chance of getting useful results. Yes they sometimes contain mistakes, but if you simply point them out, the models can often fix them!

I don’t know how much more general things can get than that.

It does get a bit uncomfortably close to “AGI”, which is a vague, heavily contentious term. But this sidesteps all that by not calling it AGI. The conventional “Generative AI” interpretation of GenAI thus becomes a convenient decoy for “General AI” ;-)