My understanding of it is also pretty surface level, but I was not under the impression that it develops "expertise" in a particular subject matter, at least not in a way that's easy to harness. From what I've read, it develops expertise at the token level.

Because the natural continuation of this is to say like, "Ok I want to load the bird detection expert and the navigation expert but leave the medieval European history expert behind", and my understanding is that this is not really how it works. At least at the moment.

You are right that at training the main goal is balancing between the sections to avoid certain paths becoming the only path. In the end the inference will be routed token by token to a mixture of say 3 or 4 sections of the model. The combination can change at each turn. It’s really a statistical optimization. As for many things in neural networks, the original intuition coming from anthropomorphism once implemented becomes something very non human!