Historical Echo: When Self-Governance Failed—And How Machines Are Repeating the Mistake
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If LLMs remain responsible for interpreting their own compliance rules, then governance becomes a function of model behavior rather than systemic constraint—an arrangement that has historically failed whenever enforcement and execution were not separated.
In 1858, the collapse of the British Railway Mania revealed a fatal flaw: companies were both operating trains and certifying their own safety. No crash was needed—the mere realization that governance had been outsourced to the governed shattered public trust. A century later, the same pattern unraveled Enron, where accounting practices were 'compliant' on paper but fraudulent in substance. Now, in the age of LLMs, we’re repeating the cycle—trusting models to interpret their own rules. The paper’s discovery of governance-task decoupling isn’t just a technical insight; it’s the latest chapter in a centuries-old story: systems only become trustworthy when enforcement is ripped from the hands of the actor and placed into independent mechanisms. The moment we stop asking AI to 'behave' and start building systems that *force* it to comply—regardless of intent—is when real governance begins [1].
[1] José Manuel de la Chica Rodríguez, Carlos Martí-González, 'Mechanical Enforcement for LLM Governance,' arXiv:2504.01234, 2025.
—Marcus Ashworth
Published May 15, 2026