I agree and like the analogy. And it's lossy to become useful as an AI in the first place. The "Learning" process has two effects on a machine learning model: In the beginning, it memorizes the facts it is trained on. But at some critical point, when it has no more capacity to memorize more facts, it starts to generalize. (This is why it's harder to train large models - large training datasets are needed.) And generalization is where AI models become very useful: For coding, writing poems, or any other task where memorization is not sufficient.