Unconscious cognitive filling: why AI emergence is a property of human–AI systems, not models alone

Large language models exhibit emergent abilities — chain-of-thought reasoning, in-context learning, and multi-step planning — that appear abruptly as model scale increases. The prevailing explanation attributes these transitions solely to scale and training data volume. Here, we argue that a systematic variable has been overlooked: during routine interaction, the human brain unconsciously performs semantic completion, intentional-state projection, and error correction, providing the cognitive scaffolding across which AI competence develops. We term this mechanism unconscious cognitive filling (UCF) and situate it within the distributed-cognition and extended-mind traditions, on which cognitive processes are spread across coupled human–artefact systems rather than contained in either part alone. We characterize three UCF mechanisms, show how their accumulation drives a positive feedback loop capable of inducing critical transitions in AI competence, and propose falsifiable experiments to test the UCF hypothesis. UCF reframes emergence as a property of coupled human–AI systems rather than as an attribute of isolated models, with direct implications for alignment, responsibility attribution, and interactive AI design.