Another intuition is simply that anytime your causal relationships in the training data are sequential you are having a lower probability of getting the correct token at a certain position because you have less of the causal information leading up to that position than you would have with AR and thus during training you almost always have a worse model with near certainty (think of the words in a function of source code, even if some of the functions are unsorted and thus a tree at the high level). Imagine you somehow already have N tokens in a sequence: is it easier to next predict token N+1 or N+15? I do like the performance tradeoff for some usecases though and I hope we see more models soon. For image tokens my argument does not hold because causality is not as clear as for text, math, code, or timeseries.