The Realization–Experience Gap: Multiple Realizability, Intelligence, and the Conditions for Attributing Consciousness to Artificial Systems

Conversational AI systems now reason, interpret literature, and critique manuscripts with a fluency that leads some observers, Richard Dawkins prominently among them, to infer that they are conscious. This paper argues that the inference is invalid as it stands. Artificial systems show that many intelligent capacities are multiply realizable; they do not show that phenomenal consciousness is. The Realization–Experience Gap separates three levels (functional performance, causal realization, and phenomenal occurrence) and shows that realizing intelligence in humans, realizing consciousness in humans, and realizing intelligence in machines do not jointly entail machine consciousness without a bridge premise. The paper specifies what such a bridge requires. A Property-Specificity Requirement blocks the transfer of multiple realizability from one property to another merely because they co-occur. A Cross-Substrate Invariance Principle licenses attribution only to the extent that conditions constitutive of experience are shown to be preserved at a justified level of description. An Experiential Specificity Requirement and a Discriminative Relevance Principle separate indicators bearing on experience from indicators of cognition alone, and a Counterfactual Preservation Test replaces output matching with intervention-sensitive comparison. These are organized into a three-stage validation programme (biological identification, causal discrimination, cross-substrate validation) and a four-grade scale of epistemic warrant. The framework is tested against organizational invariance, biological naturalism, zombie arguments, circularity, and underdetermination. It defends disciplined epistemic openness: artificial consciousness is neither excluded in principle nor established by intelligent performance. The contribution is epistemological; it does not claim to solve the hard problem.