The massive energy consumption of AI has made tech giants the most powerful force advocating for new power sources. Their commercial pressure is finally overcoming decades of regulatory inertia around nuclear energy, driving rapid development and deployment of new reactor technologies to meet their insatiable demand.

The 'FDA for AI' analogy is flawed because the FDA's rigid, one-drug-one-disease model is ill-suited for a general-purpose technology. This structure struggles with modern personalized medicine, and a similar top-down regime for AI could embed faulty assumptions, stifling innovation and adaptability for a rapidly evolving field.

The belief that AI development is unstoppable ignores history. Global treaties successfully limited nuclear proliferation, phased out ozone-depleting CFCs, and banned blinding lasers. These precedents prove that coordinated international action can steer powerful technologies away from the worst outcomes.

Regulating technology based on anticipating *potential* future harms, rather than known ones, is a dangerous path. This 'precautionary principle,' common in Europe, stifles breakthrough innovation. If applied historically, it would have blocked transformative technologies like the automobile or even nuclear power, which has a better safety record than oil.

Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

The true exponential acceleration towards AGI is currently limited by a human bottleneck: our speed at prompting AI and, more importantly, our capacity to manually validate its work. The hockey stick growth will only begin when AI can reliably validate its own output, closing the productivity loop.

Most of the world's energy capacity build-out over the next decade was planned using old models, completely omitting the exponential power demands of AI. This creates a looming, unpriced-in bottleneck for AI infrastructure development that will require significant new investment and planning.

An anonymous CEO of a leading AI company told Stuart Russell that a massive disaster is the *best* possible outcome. They believe it is the only event shocking enough to force governments to finally implement meaningful safety regulations, which they currently refuse to do despite private warnings.