Essentially, every task that AGI can do is the same as the traveling salesman problem - you have some system with dynamics where you can make decisions and to get to the right outcome, you are trying to find an optimal path. The best it can do at this moment is to create parallel simulations (i.e AlphaZero/MuZero MCTS - the tree represents the dynamics and evolution of the game board and it effectively does a simulation of the game prior to making a move). To do this with something as complex as the world, including all the humans within it, most likely means that you have to have more compute than reality allows - otherwise you are taking a hit in accuracy, which given chaotic effects creates problems where you can be way off on the predictions.

On the flip side if P=NP, that means that instead of dedicating compute to running branching simulations, An AGI can dedicate compute to just solving directly the actions it needs to do for any given outcome. This is a shortcut to reality, which means that reality in itself is compressible.