[nl]

the current state-of-the-art is combining a bunch of layers together in a pseudo random fashion

Please don't say things like this. Neural network is nothing like a random process - people understand very well when to use what kind of layer, and when to add more layers.

It's generally pretty well accepted that more layers = better because it gives you better ability to deal with multi-dimensional relationships between features.

There are two reasons not to add layers:

1) If you are overfitting. In visual tasks this is fairly rare, and there are other better ways to combat this.

2) It's harder to train. Until ResNet came out, the idea of training 100+ layers was considered unapproachable. Even now, more layers make a much harder training task.