The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility

Current AI development suffers from a structural flaw called the 'Inversion Error,' where models are built with a massive symbolic layer but lack a foundational enactive layer for physical, causal grounding. This concept, based on Jerome Bruner's stages of human cognitive development, argues that the symbolic peak of AI is unstable without an enactive base. This architectural gap is presented as the root cause of issues like hallucination and a lack of true understanding, which cannot be solved by simply scaling up models. The author links this flaw to AI safety challenges like corrigibility, reframing it as a 'reversibility problem' and advocating for 'State-Space Reversibility' to ensure human oversight.