It is random, at least while it is learning. It would have most likely tried 5 layers, or even 50.

But the point is to simplify it enough while still working the way it should. And when maximizing the efficiency, you generally get only a handful of efficient ways your problem can be solved.

In practice it's very systematic for small networks. You perform a search over a range of values until you find what works. We know the optimisation gets harder the deeper a network is so you probably won't go over 3 hidden layers on tabular data (although if you really care about performance on tabular data you would use something that wasn't a neural network).

But yes, fundamentally, it's arbitrary. For each dataset a different architecture might work better, and no one has a good strategy for picking it.