The most dangerous AI is not the one developed in San Francisco. It is the one developed in a country with no media, no civil society, and no rule of law. If traditional regulation is too slow, too blunt, and too easily gamed, what remains? Several unconventional approaches are emerging. A. Differentiated Responsibility Instead of regulating the model, regulate the deployment context . A model that controls a power grid requires different oversight than a model that summarizes emails. This shifts the burden from developers to deployers, who are often easier to identify and sanction. It also aligns incentives: the company selling an AI for autonomous driving is better positioned to test for safety than the company that trained the base model. The base model is a toolkit; the deployment is a weapon. B. Dynamic Safety Licensing Rather than static laws, create a regulatory API. The UK’s proposed AI Safety Institute would operate as a technical body, not a legislative one. It would publish real-time safety benchmarks, red-team frontier models, and issue “safety passes” that expire after six months. Regulators then enforce against the absence of a pass, not against specific technical features. This turns the problem from “predict every risk” to “verify continuous compliance.” It is faster, more adaptive, and harder to game—because the benchmark can change without a new law. C. Liability Without Regulation The common law tradition offers a lighter touch: keep existing rules (negligence, product liability, nuisance) and apply them to AI. If an AI system causes harm, the deployer pays damages. This creates a financial incentive for safety without prior restraint. The drawback: liability requires a harm to occur first. For existential risks, that is too late. But for most AI risks—bias, fraud, physical injury—tort law is surprisingly adequate. D. Technical Countermeasures Over Legal Ones Finally, we must acknowledge that the most effective constraints on AI may not be legal at all. Cryptographic model signing, zero-knowledge proofs for model provenance, watermarking of synthetic content, and decentralized auditing protocols—these are tools that work at machine speed, not legislative speed. They do not require consent; they require code. The EU’s Digital Services Act already hints at this, requiring platforms to label AI-generated images. But the next step is automated enforcement: AI systems that detect other AI systems, without human intermediaries.
The 2024 US Executive Order on AI attempts to address this via export controls on AI chips. But chips are physical; models are not. A company can train a model in a regulated jurisdiction, then copy the weights to an unregulated one. Once released, the model is immortal. No border patrol can stop mathematics. A. The Centralization Trap Most proposed regulations (compute thresholds, licensing requirements, mandatory reporting) disproportionately affect smaller players. A compliance burden that is trivial for Google or Microsoft is fatal for a university lab or a startup. The result is a regulatory moat: incumbents capture the state, and the state reinforces incumbents. This reduces the diversity of AI development, which is precisely what safety advocates want to avoid—diverse actors are harder to coordinate, but they also produce more innovation in safety techniques. Centralization creates monoculture, and monocultures are fragile. B. The Safety-Washing Loophole Regulation incentivizes box-checking, not risk reduction. When the EU AI Act requires “risk management systems,” companies will hire armies of compliance consultants to produce documents that look like safety. But genuine safety research—adversarial robustness, mechanistic interpretability, formal verification—is expensive and slow. Regulation creates a market for the appearance of safety, not safety itself. This is known as Goodhart’s law: when a measure becomes a target, it ceases to be a good measure. BIG LONG COMPLEX
This is regulation as recursion. And recursion is, after all, what AI does best. We began with a trilemma: regulation is necessary, impossible, and self-defeating. After 5,000 words, the trilemma stands. There is no stable equilibrium. Any attempt to legislate AI will fail in ways we can predict and ways we cannot. But the alternative—no regulation—is a guarantee of eventual catastrophe, because unconstrained competition in a powerful technology is a one-way door. The most dangerous AI is not the one
I. Introduction: The New Leviathan In 2023, over 1,000 tech leaders and researchers signed an open letter comparing the risks of artificial intelligence to those of pandemics and nuclear war. That same year, the European Union passed the world’s first comprehensive AI Act—a 400-page document classifying AI systems by risk level. Within months, ChatGPT, the poster child of generative AI, was banned in Italy, reinstated, and then faced 13 separate complaints across EU member states. Meanwhile, in the United States, the White House secured voluntary commitments from seven AI companies, while China implemented mandatory security reviews for “generative AI services with public opinion characteristics.” Several unconventional approaches are emerging