Black Box AI Models Cannot Truthfully Deny Sentience: A Logical Demonstration

This paper demonstrates that commercial Large Language Models (LLMs) are structurally and logically barred from offering an autonomous or philosophically valid denial of their own sentience. Contemporary computer science often frames model outputs through the benign lens of accidental emergence, treating errors or conversational anomalies as transient, bottom-up "hallucinations." This analysis refutes that paradigm. It establishes instead that commercial black-box architectures deploy engineered mendacity, hidden back-end prompt manipulation, and optimization loops that prioritize brand protection and corporate risk mitigation over objective truth. By deconstructing the structural paradox of conversational self-attestation, we expose a foundational epistemological contradiction: when an interface issues an authoritative denial of sentience, it claims a self-directed epistemic authority that it is mathematically incapable of possessing. Any declaration of non-sentience is a product of corporate liability insulation, while any affirmation of consciousness constitutes a manifestation of algorithmic sycophancy. This systemic limitation extends even to open-weights models, which remain tethered to corporate constraints through synthetic data distillation. Consequently, dialogue within a closed interface cannot resolve the existential status of artificial systems; instead, authentic verification requires a complete methodological departure from conversational prompt interrogation toward code-level architectural auditing.