The usual picture of machine learning is that a neural network learns a function.
I think that picture is now actively misleading.
The most important neural networks learn how to construct a function from context.
Universal function approximators haven't really taken the shape I expected. But whatever circuits work is happening in context really does feel like a function factory that operates sans gradients