The foundations of ML aren't changing. The models change, the data pipelines become more sophisticated, but the core skills are still important. Imagine you're trying to predict a binary event. Do you want to predict whether a given instance will be a 0/1 or do you want to predict the probability of each instance being a 1? Why? What do all those evaluation metrics mean? Even if you're using a super advanced AutoML platform backed by LLMs or whatever, you still need to be able to understand the base concepts to build ML apps in the real world.