I disagree. You can find the optimal NN and it still might not be very good. For example, imagine feeding all the pixels of an image into a big NN. No matter how good the optimization, it will do way worse than one which exploits the structure of images. Like convolutional NNs, which have massive regularity and repeat the same pattern many times across the image (an edge detector on one part of an image is the same at another part.)

If you can find the optimal NN, that basically lets you solve circuit minimization, an NP-hard task. This will allow you to find the best computationally-tractable hypothesis for any problem, which is similar to Solomonoff induction for practical purposes. It will certainly be a huge improvement over current NN approaches, and it may indeed lead to AGI. Unfortunately, it's probably impossible.

It's really not. Typical reinforcement learning is much more primitive than AIXI. AIXI, as best I understand it, actually simulates every hypothesis forward and picks the series of actions that lead to the best expected reward.

I was only trying to say that if you're finding the best NN, then simulating them is easy. I agree that this is not the full AIXI. I guess I misunderstood you - I thought you were trying to say that the reason NN doesn't give us AGI is because they are hard to simulate.