Although it's not better than existing solutions, it's a cool example of how good results can be achieved in a relatively automatic way - by contrast, the evaluation functions of the best chess engines have been carefully engineered and fine-tuned over many years, at least sometimes with assistance from people who are themselves master-level chess players. On the other hand this neural network approach took a relatively short time and could have been applied by someone with little chess skill.

But how much of its performance comes from the neural network learning some non-trivial evaluation function and how much comes from brute-forcing the game tree on a modern computer?

If the neural network was replaced by a trivial heuristic, say, material balance, how would the engine perform?