I think chess is less compelling because, in a sense, it is a "solved problem" - superhuman AI chess players already exist.
And chess, while it does have more complex base rules, has a much lower combinatorial complexity than Go.
And chess, while it does have more complex base rules, has a much lower combinatorial complexity than Go.
The problem with the current chess bots is that they play badly, badly. They choose a terrible random mistake to make every few moves, while some of their other moves are brilliant. They cannot accurately mimic beginner or intermediate level players.
I wouldn't bet on it though. SMP is notoriously hard to work with alpha-beta search and there are a lot of clever tricks (which is probably still not perfect). Maybe with ASICs, you could make it stronger, but then it wouldn't be as fair a comparison.
I'm talking about something similar to the described in the paper, 100% self-learned solution without using human heuristics, based on NNs. That could bring a totally new ideas into chess.
But shogi is much more obscure outside of Japan than go or chess, so it gets less interest, especially in the large-board variants.
But maybe not sexy enough, or we just don't hear about it as much.
Giraffe attempted this (with more standard tree search than MCTS and with only a value function rather than a combined policy/value network), but only reached IM level -- certainly impressive, but nowhere close to Stockfish.