Instead of trying to anticipate every potential harm, AI regulation should mandate open, internationally consistent audit trails, similar to financial transaction logs. This shifts the focus from pre-approval to post-hoc accountability, allowing regulators and the public to address harms as they emerge.

In China, mayors and governors are promoted based on their ability to meet national priorities. As AI safety becomes a central government goal, these local leaders are now incentivized to create experimental zones and novel regulatory approaches, driving bottom-up policy innovation that can later be adopted nationally.

Top Chinese officials use the metaphor "if the braking system isn't under control, you can't really step on the accelerator with confidence." This reflects a core belief that robust safety measures enable, rather than hinder, the aggressive development and deployment of powerful AI systems, viewing the two as synergistic.

The emphasis on long-term, unprovable risks like AI superintelligence is a strategic diversion. It shifts regulatory and safety efforts away from addressing tangible, immediate problems like model inaccuracy and security vulnerabilities, effectively resulting in a lack of meaningful oversight today.

When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.

Instead of trying to legally define and ban 'superintelligence,' a more practical approach is to prohibit specific, catastrophic outcomes like overthrowing the government. This shifts the burden of proof to AI developers, forcing them to demonstrate their systems cannot cause these predefined harms, sidestepping definitional debates.

Demis Hassabis argues that market forces will drive AI safety. As enterprises adopt AI agents, their demand for reliability and safety guardrails will commercially penalize 'cowboy operations' that cannot guarantee responsible behavior. This will naturally favor more thoughtful and rigorous AI labs.

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 approach to AI safety isn't new; it mirrors historical solutions for managing technological risk. Just as Benjamin Franklin's 18th-century fire insurance company created building codes and inspections to reduce fires, a modern AI insurance market can drive the creation and adoption of safety standards and audits for AI agents.