Which is uncomputable, and an approximation would presumably benefit from increased computing power.

I gave the simplest possible counter example to your objection. I never proposed that we actually use pure Solomonoff induction.

EDIT: I realize you said something different. You implied that an approximation of Solomonoff induction would benefit from more computing power, and so would act as an agent to obtain it. This is totally incorrect. Solomonoff induction can be approximated in various ways by bounding the run time of the programs, or using simpler models instead of computer programs, etc. None of these create any agentness. They still just do prediction. I'm not sure you understand the distinction between agents and predictive non-agents, and this is very important for FAI work.

Like that's simple? How exactly do you make an AI that isn't an agent? With no goals, why does it do anything?

The entire field of machine learning is about building practical approximations of Solomonoff inductions. Algorithms which can predict things and which are not agents. Agents are just special cases of prediction algorithms, where they take the action that has the highest predicted reward.

But why are plans that take less computing power to come up with more likely to be safe?

Because they are plans that less powerful intelligences could have come up with. We don't worry about humans taking over the world, because they aren't intelligent enough. The danger of superintelligence is because it could be far more powerful than us. This is a limit on that power.

Besides, if it calculates that searching for simple solutions is likely not going to meet the 90% criteria, it can forgoe that and jump straight to complicated ones.

That's a feature. We don't know how much computing power is necessary. We just want it to minimize it.