Why Stochastic-AI Regulation Is a Fallacy.. And Yet, Planetarily Discussed

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The global debate over artificial intelligence regulation rests on a premise that has neverbeen examined: that the object of regulation—stochastic large language models—is agovernable entity. This paper argues that it is not, and that the entire planetary discussion is therefore a fallacy in the precise sense: not a mistake of degree, but a category error of kind. The argument proceeds in five parts. First, it distinguishes five kinds of standard that the word "standard" conceals, only one of which—functional safety—certifies behavior, and shows that this one is theoretically impossible for sampled systems. Second, it demonstrates that the impossibility is architectural, not incremental: stochasticity is not a defect to be engineered away but the defining property of the paradigm, and hallucination is a mathematical consequence of calibration. Third, it shows that the four existing functionalsafety standards—ISO 26262, IEC 61508, DO-178C, IEC 62443—already exclude stochastic systems from the safety path, and that ISO/IEC TR 5469:2024 exists specificallyto describe this gap without closing it. Fourth, it explains why the debate continues anyway: incentive capture, institutional vacancy, and the performative function of governance certification. Fifth, it states what follows. The fallacy is not that regulation fails. The fallacy is that the object of regulation cannot be governed, and that a deterministic alternative—already specified, already open, already deployable—has been excluded from the conversation by the same forces that sustain the debate. The paper concludes that the correct response is not better regulation of the ungovernable, but adoption of the standard that governs the layer where governance is technically possible. Keywords: AI regulation, stochastic systems, functional safety, certification impossibility,regulatory capture, category error, deterministic architecture