Of course, they’re different types of things. You give hard equations with lots of x and y to a chatbot, or ask it about a method you don’t understand, so it can explain it to you.

AI would have to find a revolutionary new way of solving the equation to make it faster than the hardware.

This doesn’t sound impossible. It reminded me of how AlphaGo, and how AlphaGo Zero became “the world’s top player” of Go by letting it train itself by trial-and-error instead of by watching human players using existing Go strategies:

During the games, AlphaGo played several inventive winning moves. In game two, it played Move 37 — a move that had a 1 in 10,000 chance of being used.

Source: AlphaGo | Google DeepMind

AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play. A neural network is trained to identify the best moves and the winning percentages of these moves. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration.

Source: AlphaGo | Wikipedia

Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is “often expensive, unreliable or simply unavailable.” Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was “no longer constrained by the limits of human knowledge”.

Source: AlphaGo Zero | Wikipedia

Following this way of thinking, why let a human figure out how to solve equations most efficiently if the machine can find some way of calculating/computing that we had never even been able to think of?

Note, I’m investigating this with curiosity, and I’m no expert in the field.