Well, actually you're highlighting the issue I raised in my first post: computable approximations of Solomonoff induction work pretty well ... when fed useful priors! But those priors come from a lot of implicit knowledge about the world that skips over an exponentially large number of shorter hypotheses by the time you get to applying it to any specific problem.

AIXI (and computable approximations), starting from a purely Occamian prior, is stuck iterating through lots of generating functions before it gets to the right one -- unfeasably long. To speed it up you have to feed it knowledge you gained elsewhere (and of course, find a way to represent that knowledge). But at that point, your prior includes a lot more than a penalty for length!