The main problem is that we still lack good generative models and good ways of interrogating them. GANs are unstable and difficult to apply to time series, VAEs suffer from posterior collapse, WaveNet/PixelRNN grow with the input size and overemphasize the details, RNNs are hard to train because we lack good training algorithms. Generally, small errors tend to compound in step-wise predictions because NNs do not generalize very well and gradients tend to vanish and shatter. If you just regard computation time to roll out the future, modeling domains in which the rules are simple enough to be hand-coded and evaluated quickly (such as Go and Chess) probably makes MCTS a million times more suitable compared to domains in which you need a complex model.