I am not saying it is not. My condition is in what if neural network can solve a problem without any domain knowledge. I think it's a huge win even if we don't understand anything about the solution. Be it well solved problems like approximate minimum distance in a graph to practically unsolvable like automatically proving unproved theorems in mathematics.
I take it this way. How many problems can you solve using knowing mostly exact cover. Not very many. But, if we can build ML systems that can solve mostly any problem(we can't today) even without giving any insight, I would say it will be one of the things whose impact will surpass anything. Note, I am not getting into AGI, just saying a system that can solve objective problems. And while sudoku is not a very good example, but I think it shows we can do many cool things from neural networks. Neural networks are the most sure bet for such a system.
You'd be surprised how far exact cover can get you. The trick isn't in knowing exact cover, per se. But in seeing how to map problems to different problems.
That is, the "exact cover" nature of Sudoku is not immediately obvious to everyone. At least, it wasn't obvious to me. Seeing how quickly you can map it to that and then get a solution was a lot of fun and ridiculously educational.
A neural network isn't simpler in any sense of the word. You might as well throw a simulated annealer at the problem.