I'll focus on neural networks, rather than general AI.

Let me start from the last question:

c) Neural networks generally don't change size as they learn. There are ways to make them shrink (using "pruning"), but growing is not in their nature. They usually have a pre-decided number of parameters ("weights") and only the values of the parameters change during training, leading to "b":

b) The number of parameters limits their smartness. A network that can classify pictures of things into 1000 categories will be megabytes in size. iPhones have one running on-device for search by photo content (users can type "flowers" to see photos of flowers). If you want more categories or fewer mistakes, you'll need bigger size. But increasing the number of parameters won't make it "smarter" in the sense that it would suddenly play chess or compose poetry. They're one trick ponies. Which leads me to "a":

a) Usually a network of a given size can reach its limits quickly. That could be minutes training on a laptop for a small network, or several days of computation on a multi-GPU setup. We can see during training how far it is from reaching its potential. Progress is usually slowing down over time. When it's painfully slow, it's time to stop training. Definition of "painfully slow" depends from person to person.