Knowledge representation--depends on which kind. The kind that involves logic and resolution turns out to be just another problem that you have to represent as graph search.

On the low, algorithmic level that's often true, but a lot of the difficulty imo is at higher levels of the stack. We're very good at writing SAT-solvers, for example, but directly writing problems as SAT is not a particularly good knowledge representation, especially if you want to build systems that are flexible and can interact with humans.

So a lot of the interesting work (imo) is on non-graph knowledge representations, like answer-set programming, situation calculus, etc. They often ground out in some variety of graph search to do the inference, but that's just the solver algorithm, not where the research that interests me is at. It's like saying that graph search grounds out in x86 asm twiddling bits in a computer, so all AI is just bits in a computer, which is also true but misses the point.

Though it does remind me that there was a comment from someone in the 60s or 70s amounting to, "all AI boils down to heuristic search".