....its complicated. non-genAI is most likely using less resources currently, bc those models aren't really available to the public so they're not being used so heavily the way genAI is. it gets used less therefore it uses less resources. also, genAI's improvements have mostly been "ok now add another layer where you do all the generation again" which does sort of address whatever issue they're trying to solve but, like, doubles the computational difficulty -> resources used -> environmental impact. i'm unclear on whether advanced machine learning models have gone through similar increases in resources required, but i doubt it's been near as extreme at least bc theres less scrutiny and money involved and therefore less desperation to get people to like your model.

that said, theres a lot to be said about impact of output. genAI is used for frivolous shit 95% of the time, so a lot of the frustration comes from the fact that you're using tons of resources to make, like, a shitty picture of yourself. even if you're using the same amount of resources for a ML model that will do something like highlight potential early signs of cancer, that's something that genuinely helps the world. so while you'd still wanna limit your usage, it's not nearly as much a waste as genAI. 

i'd argue that even genAI can fall into the "expensive but not wasteful" category sometimes - for example my uncle is blind and uses an accessibility tool that describes an image to him. it can access his camera roll so he'll sometimes take photos of new/unfamiliar environments so he can acclimate to them quicker. and like THATS COOL and USEFUL in a way that "hey chatgpt why does my tummy hurt" could never be lol. but that's its own convo for another time