We extend the Operational Coherence Bound (OCB) and quantum OperationalCoherence Bound (qOCB) frameworks to artificial intelligence, formalizing the Neural Operational Coherence Bound (NOCB). We prove that neural networks are finiteobservers with bounded operational entropy capacity Amax = 4NLd log(e). Applying this constraint, we derive: (1) Hallucination is a formal OCB violation withan architectural lower bound; (2) Catastrophic forgetting rates are determined bythe training contraction coefficient κtrain = ηµ; (3) Softmax attention is the uniquePetz-optimal distinguishability mechanism for the qOCB metric, with head countdetermined by sparsity-corrected metric tessellation; (4) Chinchilla scaling law exponents α ≈ 0.34 and β ≈ 0.28 arise from the qOCB geometry of the loss landscapeand intrinsic language dimension. All results are grounded in the foundational OCBframework (1) and the qOCB extension (2), and provide testable predictions formodel architecture and training dynamics.