Big Long Complex May 2026

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.”

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. BIG LONG COMPLEX

Example: In 2022, a major AI company certified that its recommendation algorithm was “fair” under a state law, using a proprietary metric. An independent audit later found that the metric ignored exactly the kinds of disparate impact the law was designed to prevent. The company was legally compliant and dangerously unfair. If a country imposes strict AI safety rules, frontier development will move elsewhere. This is not speculation—it is history. When the US tightened biotech regulations in the 1970s, research moved to the UK. When the EU enforced strict data localization, cloud providers opened data centers in Ireland. Today, if the US bans training runs above a certain FLOP threshold, a Chinese or Middle Eastern state-funded lab will simply ignore it. The risk does not disappear; it relocates to jurisdictions with weaker institutions, less transparency, and potentially fewer scruples. Within months, ChatGPT, the poster child of generative