To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently general to encompass diverse paradigms of intelligent behavior, including reinforcement learning, generative models, classification, analogical reasoning, and goal-directed decision-making. We propose a general criterion based on sample fidelity: intelligence is the ability, given sample(s) from a category, to generate sample(s) from the same category. We formalise this intuition as \(\varepsilon \) -category intelligence: it is \(\varepsilon \) -intelligent with respect to a category if no chosen admissible distinguisher can separate generated from original samples beyond tolerance \(\varepsilon \) . We present the formal framework, outline empirical protocols, and discuss implications for evaluation, safety, and generalization. By defining intelligence based on the principle of generative fidelity to a category, our definition provides a single yardstick for comparing biological, artificial, and hybrid systems, and invites further theoretical refinement and empirical validation.