[D] Should the praise of "Data-centric" AI be taken with a grain of salt?

I hear co-workers, professors, entrepreneurs talk about just how important it is to switch from model-centric AI to a data-centric approach. Personally I don't see a problem focusing on attaining better data but I have been seeing this simplification that AI performance = model + data which seems massively misleading. A better generalization would be AI performance = model * data or even AI performance = model * log(len(data)).

Looking at the rapidly changing problem of image synthesis, it is clear that data alone could not produce the same results seen even two years ago.

On the other hand, it can seem unproductive for the field as a whole to try to create another classification model that achieves a .02% increased performance on ImageNet... again and again...

And this is maybe why we hear more talk in industry about focusing on the data instead of models: Industry generally faces simpler tasks with negligible improvement as of recently (regression, classification). Therefore it's more easy and valuable to increase and improve the training data.

And yes, most machine learning models NEED a large training set to approximate a complex distribution. We have already known this since forever.

I think the term has just recently become a pet peeve for me as a novel "catch all" solution to any problem. What are your opinions on the matter?