The idea is that such end-to-end techniques can be used to tackle problems for which we don't have background knowledge. For example, (according to DeepMind's latest paper on MuZero) real-world robotics tasks etc. Atari games are a good use-case for this because they dont' have very clearly defined rules like board games have, so a model basically has to first learn to play the game before it can learn to play it well.

You can perhaps design a game that is difficult to solve by AI (though I wouldn't bet on it). The question with AI is usually not what it can't do but what it _can_ do. If a certain system fails at your task but performs well in a range of other tasks that are considered useful and challenging, then it's worth consideration. Especially so if there is no system that can perform well on that one task.