Not exactly unsupported. There are a bunch of papers showing a correlation between patterns of brain activation and weights activations inside an AI model when they are given the same task or stimuli. And this correlation increases with AI model accuracy. Doesn't that suggest that AI models are partly mimicking the same human reasoning pathways?

This is an open and active question, but there really isn't any literature that's strongly trusted to show that there is a truly deep relationship between how current machine learning models operate (at the mathematical level) and how brains work. The suggestion is there, no doubt, but it's unclear where that suggestion will ultimately lead. There certainly has been quite a deal of health cross-fertilization between neuroscience and computer science.

It seems save to say that ML has been moving further and further away from what neuroscience suggests, and towards efficient execution on fast GPUs and TPUs. That's in part b/c neuroscience is currently not equipped to explain how human high-level intelligence works.

Agreed! I think one useful direction to take this debate is to admit that in order for you or me to interact with the tool we need to have a mental model of the tool (so we have some idea of how to interact with it).

So which mental model is superior when evaluated under the criterion of "ability to interact productively with the tool"? (a) It's just statistics / stringing together phrases from the internet or (b) it's more like a human brain with vastly superior general knowledge?

Personally, I would put money on (b) resulting in better interactions. This is probably something we could study empirically.

But ultimately I don't really care that much about the philosophical issues, my mental model of it (a more nuanced version of b) has enabled me to use it productively almost every day to do things that I couldn't do before.