There are straightforward proofs of this fact, I think. For example, observe that Solomonoff induction does only a constant worse than multiplicative weights over the space of computable predictors. Thinking about it this way seems more productive, unless I am missing something important.
The tricky part for me was proving the equivalence of two views of the Solomonoff prior: as a probability distribution over programs that print bitstrings and never make mistakes, and as a mixture of all "lower semi-computable" probability distributions over finite prefixes of bitstrings that are allowed to make mistakes. Once you derive the second view from the first, the conclusion is indeed trivial.
Shane Legg's text Solomonoff Induction has helped me a lot over the last few days. I was trying to iron out the kinks in my understanding of Eliezer's argument in this old thread:
Eliezer's statement is correct (more precisely, a computable human cannot beat Solomonoff in accumulated log scores by more than a constant, even if the universe is uncomputable and loves the human), but understanding his purported proof is tricky. Legg's text doesn't give any direct answer to the question at hand, but all the technical details in there, like the difference between "measures" and "semimeasures", are really damn important if you want to work out the answer for yourself. I know mathematics has many areas where an "intuitive understanding" kinda sorta suffices. This is not one of those areas.