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I never claimed that everyone wants to claim AI for themselves.

Artificial intelligence is ofcourse a scientific field on it's own right, even before machine learning was a thing. I'm just saying that AI scientists have used concepts from statistics to create an approach to AI called machine learning. I'm not saying that ML is a subset of statistics, mind you, but the statistical underpinnings of it definitely are. ML is not _just_ statistics too.

Moreover, why would statisticians be pissed about the efficacy of a model?

Firstly, many problems/questions that I work on are not concerned with prediction.

Secondly, even if I did, I would love to use DNNs. It's just that I never have a use for it considering I'm only looking at tabular data. Why bother with DNNs when, say, a random forest will do?