NFL works with algorithms operating on finite problems. With algorithms operating on unbounded problems, you can benefit from Blum's speedup theorem: for every algorithm and every computable measure of performance, there's a second algorithm performing better than the first on almost all inputs.

I suspect here's happening something similar: AIXI is finitely bias-able, and there are environments that can exploit that to arbitrarily constrain the agent's behaviour. If the analogy holds, there's then a class of environments for which AIXI, however finitely biased, is still optimally intelligent.