Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of "Certainty-Scope" in AI

The recently published "certainty - scope" conjecture offers a compelling insight into the inherent trade - off present within artificial intelligence (AI) systems. As general research, this investigation remains vital as a philosophical undertaking and a potential guide for directing AI investments, design, and deployment, especially in safety - critical and mission - critical domains where risk levels are substantially elevated. W hile maintaining intellectual coherence, its formalization ultimately consolidates this insight into a suspended epistemic truth, which resists operational implementation within practical systems. This paper argues that the conjecture's objective to furnish insights for engineering de sign and regulatory decision - making is limited by two fundamental factors: first, its dependence on incomputable constructs and its failure to capture the generality factors of AI, rendering it practically unimplementable and unverifiable; second, its foundational ontological assumption of AI systems as self - contained epistemic entities, distancing it from the complex and dynamic socio - technical environments where knowledge is co - constructed. We conclude that this dual breakdown -- an epistemic closure deficit and an embeddedness bypass -- hinders the conjecture's transition to a practical and actionable framework suitable for informing and guiding AI deployments . In response, we point towards a possible framing of the epistemic challenge, emphasizing the inherent epistemic burdens of AI within complex human - centric domains. Keywords: artificial intelligence (AI), AI governance, algorithmic information theory (AIT), certainty - scope trade - off, complex systems, computability & operationalization, epistemic entanglement, epistemic certainty, hybrid AI systems, information theory, Kolmogorov complexity, risk - based assurance, safety - critical AI, socio - technical systems, verification and validation (V&V).