Okay, at this point, I'm convinced no one in here has even a bare minimum understanding of machine learning. This isn't a pedantic prescriptivism thing:

1. "Machine learning" is a major branch of AI. That's just what it is. Literally every paper and every book ever published on the subject will tell you that. Go to the Wikipedia page right now: "Machine learning (ML) is a field of study in artificial intelligence". The other type of AI of course means that the machine can't learn and thus a human has to explicitly program everything; for example, video game AI usually doesn't learn. Being uninformed is fine; being wrong is fine. There's calling out pedantry ("reee you called this non-Hemiptera insect a bug") and then there's rendering your words immune to criticism under a flimsy excuse that language has changed to be exactly what you want it to be.

2. Transformers, used in things like GPTs, are a type of machine learning. So even if you say that "AI is just generative AI like LLMs", then, uh... Those are still machine learning. The 'P' in GPT literally stands for "pretrained", indicating it's already done the learning part of machine learning. OP's statement literally self-contradicts.

3. Meanwhile, deep learning (DNNs, CNNs, RNNs, transformers, etc.) is a branch of machine learning (likewise with every paper, every book, Wikipedia ("Deep learning is a subset of machine learning that focuses on [...]"), etc.) wherein the model identifies its own features instead of the human needing to supply them. Notably, the kind of vision detection the original commenter is talking about is deep learning like a transformer model is. So "AI when they mean machine learning" by their own standard that we need to be specific should be "AI when they mean deep learning".

The reason "AI" is used all the time to refer to things like LLMs etc. is because generative AI is a type of AI. Just like "cars" are used all the time to refer to "sedans". To be productive about this: for anyone who wants to delve (heh) further into it, Goodfellow et al. have a great 2016 textbook on deep learning*. In a bit of extremely unfortunate timing, transformer models were described in a 2017 paper, so they aren't included (generative AI still is), but it gives you the framework you need to understand transformers (GPTs, BERTs). After Goodfellow et al., just reading Google's original 2017 paper gives you sufficient context for transformer models.

*Goodfellow et al.'s first five chapters cover traditional ML models so you're not 100% lost, and Sci-Kit Learn in Python can help you use these traditional ML techniques to see what they're like.

Edit: TL;DR: You can't just weasel your way into a position where "AI is all the bad stuff and machine learning is all the good stuff" under the guise of linguistic relativism.