Edit: I wrote my comment a bit too early before finishing the whole article. I'll leave my comment below, but it's actually not very closely related to the topic at hand or the author's paper.

I agree with the gist of the article (which IMO is basically that universal computation is universal regardless of how you perform it), but there are two big issues that prevent this observation from helping us in a practical sense:

1. Not all models are equally efficient. We already have many methods to perform universal search (e.g., Levin's, Hutter's, and Schmidhuber's versions), but they are painfully slow despite being optimal in a narrow sense that doesn't extrapolate well to real world performance.

2. Solomonoff induction is only optimal for infinite data (i.e., it can be used to create a predictor that asymptotically dominates any other algorithmic predictor). As far as I can tell, the problem remains totally unsolved for finite data, due to the additive constant that results from the question: which universal model of computation should be applied to finite data? You can easily construct a Turing machine that is universal and perfectly reproduces the training data, yet nevertheless dramatically fails to generalize. No one has made a strong case for any specific natural prior over universal Turing machines (and if you try to define some measure to quantify the "size" of a Turing machine you realize this method starts to fail once the number of transition tables becomes large enough to start exhibiting redundancy).