One thing that even relatively senior ML people often fail to grasp is that deep learning models are curves fitted to a data distribution. You cannot expect them to solve tasks outside of their training distribution (which is the sort of thing that you need intelligence for). "Emergent learning" is an incorrect label -- if a model demonstrates performance on task A that it wasn't trained on, that simply means that there is significant overlap between A and all the data that you did train on. Competence doesn't magically emerge out of nowhere.