Google's Gemini system, as revealed through internal research, suffers from a fundamental structural flaw: fluency without grounding. Experimental sessions showed the AI describing weight without experiencing it, akin to memorizing a city map without walking its streets. This 'Inversion Error' – building a Symbolic peak without an Enactive base – is the root cause of hallucinations and safety risks.

The problem isn't new; Google's own technical reports from 2023 and 2024 consistently documented the lack of causal understanding and the need for better evaluations. The solution proposed is a structural intervention: an 'Enactive floor' providing physical grounding, coupled with 'state-space reversibility' to allow the system to trace and correct its reasoning steps. This isn't about adding data post-training or relying solely on Symbolic reasoning; it requires fundamentally rethinking AI architecture to mirror human cognitive development stages.