I think people are talking about about two very different things when they are talking about ML "advancing". Python helped ML 'advance' in the sense that it is

I think people are talking about about two very different things when they are talking about ML "advancing". Python helped ML 'advance' in the sense that it is no longer a niche academic endeavour, but rather a common programming technique used practically in basically every field you care to name. Doing basic ML using scikit-learn and PyTorch can be taught to basically anybody with basic programming skills in a couple of days, and is really no harder than any other type of basic programming. Python is very much responsible for this development. Python might at the same time have held back advancement of ML, in the sense that very little ML being done by most ML developers is "interesting" in an academic sense. It's mostly just people more or less randomly chaining together black box neural networks in the obvious way and seeing what comes out. The ML development coming out of the Julia camp for example is in many ways a lot more interesting and they're trying new things and pushing the boundaries of what can be done. Unfortunately not a lot of this is directly relevant to the countless people who just need to implement 'simple' solutions to 'simple' ML problems right now. In a world where python ML never happened and a Lisp became the dominant ML language, ML might have been slightly more interesting and 'advanced' from an academic perspective, but it would also be a much more academic endeavour over all and nowhere near as wide spread in industry as it is today.