> AlphaZero neatly side steps this by generating it's own training data by playing itself. If how to do this was "obvious", it would have been done a long time ago.

Learning by self-play is nearly as old as AI itself. TD Gammon, one of the very first algorithms to reach superhuman levels in a nontrivial game, learned by self play. The basic ingredients for AlphaGo Zero, Monte-Carlo tree search and use of a convnet to evaluate board positions, were known already.

The major contribution of the AlphaGo Zero and AlphaZero was IMO the realisation that MCTS acts as a "policy improvement operatior", and that reinforcement learning becomes far more stable when it's used in conjunction with MCTS.

It's a major contribution and could represent a big shift in the field. But we won't be able to judge how big of a contribution until the research is more reproducable.