In my admittedly very cursory skim of the paper, I gave it the benefit of the doubt on this point, because this seemed convincing:
"for the purpose of prediction, we are, of course, not so much interested in the probabilities issued by the measure functions, as in the conditional probabilities that give the corresponding predictors’ outputs."
Do you disagree?
(Also, my original summary is wrong at least in that I should have said limit computable where I said semi computable!)
I guess the question is whether we want universality over all limit-computable predictors, or only those derived from lower-semicomputable semimeasures. Since the latter class contains its own universal element and also every computable predictor, maybe it's okay? But yeah, I can see how you can have a different takeaway. Sorry for being a bit harsh in the previous comment.
The key is section 4.3.3, which draws a distinction between measures and predictors. The Solomonoff lower-semicomputable semimeasure is universal for all lower-semicomputable semimeasures, not just for some narrower class. But if we forget the idea of lower-semicomputable semimeasures and treat Solomonoff as a member of a wider class - limit-computable predictors - it turns out to be not universal for that class. In fact, moving from measures to predictors is so lossy that no predictor can be universal for its own class, by a simple diagonal argument. To me that means Solomonoff's approach with measures is more nuanced and informative than the approach with predictors.