Sure, but you can do Monte Carlo with a double-dummy solver.
The point is that, especially for games perceived as being lower-status like MTG and other board games, I'm more inclined to believe the answer is closer to "nobody is willing to pour in the resources to seriously try" as opposed to "we definitively cannot with current science and technology."
Monte Carlo with a double-dummy solver is fundamentally insufficient for good single-dummy play because it is incapable of understanding information. It won't take discovery plays (lines aimed at discovering more information about the opponents' hands before choosing a line of play) and will systemically overvalue positions which are good double dummy but require a guess. It doesn't understand falsecarding (because double dummy, it doesn't matter).
I agree that many games could make progress if people were actually inclined to try.
I think it will get a bit better in the coming decade thanks to continued hardware improvements & powerful LLM coding agents making it more feasible for amateurs to tackle these things at home. Personally I've been working on a game AI project for the last month at home based around published techniques for a similar game, using my 5090 for training and Opus for implementation and orchestrating tasks and so on. It's going quite well and it looks like I'm on track for a SOTA, superhuman AI at the end. Doing this ten years ago would have been incomparably harder.
I think computer bridge will take off soon. LLMs have allowed double-dummy solvers to move from ~0.1sec to microseconds (if you allow 99.9% accuracy) and parsing human bidding system descriptions must either be possible or v close.
The point is that, especially for games perceived as being lower-status like MTG and other board games, I'm more inclined to believe the answer is closer to "nobody is willing to pour in the resources to seriously try" as opposed to "we definitively cannot with current science and technology."