It's certainly not the first NN chess program. You may remember one of OP author's Giraffe NN (https://arxiv.org/abs/1509.01549) which was essentially 'AlphaGo for chess'. But like the original AG, it struggles to learn and Lai had a lot less computation as a student than he does now at DM. What they're doing is applying AlphaGo Zero expert iteration with some simplifications and TPUs. And that pwns previous work like Giraffe the way AlphaGo Zero pwns AlphaGo. Quantity becomes a quality all its own.

Look at Figure 2, and remember that DM has access to a lot of hardware. At short thinking times, AlphaZero is weaker than Stockfish. This is equivalent to longer thinking times with weaker hardware, and it is likely that the former applications of NNs to chess had hardware that was a 1000-fold slower than what DM has access to. This means that even if the approach was identical to DMs, they would not have seen a better performance of NNs than the classical alpha beta approach.

In essence MCTS + NN is just another way of tree search just like AlphaBeta or its brute force cousin Minimax.

AlphaZero just tries to be smarter about which branches to evaluate so it can go deeper.

But I would love to see AlphaZero (trained) run side by side with stockfish on an iPhone hardware and defeat it. That would be a more apples to apples comparison.

they are a huge company (Google) with access to top top top talent (experts) and infinite hardware resources. I don't know why it would be surprising if they acheived performance that hadn't been acheived before