The pursuit of Artificial General Intelligence (AGI) is largely predicated on the “Scaling Hypothesis”, the observation that model performance predictably improves with more scale, compute & parameter count. This paradigm presupposes a substrate independent, functionalist view of cognition, where sufficiently large syntactic processing is expected to eventually yield semantic understanding & generality. My paper challenges this assumption by analyzing the emergence of a persistent tension between the appearance of fluency & reality of robustness as these models scale. I use failures such as the reversal curse, where models fail to generalize the symmetry in identity relations (A=B -> B=A), and plateauing performance on novel reasoning challenges (ARC-AGI) to argue that these limitations represent a fundamental architectural barrier in achieving general intelligence: the lack of ontic grounding.

Synthesizing Stevan Harnad’s "Symbol Grounding Problem" with Evan Thompson's intrinsic normativity framework in autopoietic systems, I argue that true generality requires "Sense-Making", a process distinct from "Information Processing", whereby an agent’s internal states are causally coupled to its environment via thermodynamic or survival stakes. Current LLMs, lacking this intrinsic normativity, remain epistemic instruments rather than ontic agents. By defining this "Ontic Gap," I offer a framework for distinguishing between the simulation of reasoning and true understanding, with urgent implications for AI safety and governance.