In 1950, the very first paper ever written on computer chess, by Shannon, gave the algorithm that would play perfect chess given unlimited computing power. In reality, computing power was limited, so computers did not play superhuman chess until 47 years after that.
However, it remains true that if you don't know how to play chess using unlimited computing power, you definitely can't play chess using limited computing power. In 1830, Edgar Allen Poe, who was also an amateur magician, carefully argued that no automaton could ever play chess, since at each turn there are many possible moves, but machines can only make deterministic motions. Between Poe and Shannon there was a genuine increase in the understanding of computer chess, represented by the intermediate work of Turing, Church, and others.
Similarly, in modern AI and especially in value alignment theory, there's a sharp divide between problems we know how to solve using unlimited computing power, and problems which are confusing enough that we can't even state the simple program that would solve them given a larger-than-the-universe computer. It is an alarming aspect of the current state of affairs that we know how to build a non-value-aligned hostile AI using unbounded computing power but not how to build a nice AI using unlimited computing power. The unbounded analysis program in value alignment theory centers on crossing this gap.