Probably not. It's not clear how to scale other methods to make use of so much compute. They top out sooner, with fewer parameters and less compute.

One way to look at why deep learning is having the impact it does is that unlike other ML methods, it's actually capable of making use of so much compute. It gives us modular ways to add more and more parameters and still fit them effectively.