It's not that AIXI thinks that "the universe will start ignoring AIXI" -- the Solomonoff induction part starts by giving weight to an infinite set of models in which AIXI's actions have no effect whatsoever. It's that AIXI is learning that there's this agent running around the universe doing stuff and the universe is responding to it. The identification part happens because the program specifies that the set of bits in the simulated agent's input registers is the predicted observation stream.

Hypotheses in which AIXI's actions already have no effect on the environment are useless for action guidance; all actions have the same utility.

Because hypotheses of smaller K-complexity have failed to predict the observation stream.

Well yes, I know that is how Solomonoff induction works. But the (useless for action guidance) hypothesis you just suggested is ridiculously high K-complexity, and the hypothesis I suggested has even higher K-complexity. Even worse: these are actually families of hypotheses, parameterized by the the AIXI approximation algorithm being used (and in the case of the hypothesis I suggested, also the time-step on which the switch occurs), and as the number of observations increases, the required accuracy of the AIXI approximation, and thus its K-complexity, also increases. I'm skeptical that this sort of thing could ever end up as a leading hypothesis.