The technical toolkit for securing closed, proprietary AI models is now so robust that most egregious safety failures stem from poor risk governance or a lack of implementation, not unsolved technical challenges. The problem has shifted from the research lab to the boardroom.

The emergence of powerful, uncensored open-weight models like Obliteration.ai's demonstrates that safety guardrails from companies like OpenAI are easily bypassed. This suggests the long-term solution for AI safety won't be technical restrictions at the model level, but rather legal and regulatory enforcement.

The argument for new, specific AI regulations overlooks the power of existing legal frameworks. Standard product liability laws already hold companies responsible for harm caused by their products. If an AI company releases a dangerous product, they can be sued under established laws, disincentivizing recklessness without new government bureaucracy.

The adoption of seatbelts didn't dramatically reduce road fatalities because it led to compensatory risk-taking—people simply drove faster. This historical parallel suggests a potential unintended consequence for AI: implementing safety guardrails could paradoxically encourage developers to push models to more dangerous limits, believing the safety features will catch any failures.

Regulatory focus on publicly released AI models overlooks the significant dangers from risky research and "internal deployment" within AI labs. True oversight requires visibility into these internal activities, not just the final products.

Other scientific fields operate under a "precautionary principle," avoiding experiments with even a small chance of catastrophic outcomes (e.g., creating dangerous new lifeforms). The AI industry, however, proceeds with what Bengio calls "crazy risks," ignoring this fundamental safety doctrine.

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 U.S. has a built-in mechanism for AI safety that precedes formal regulation: the court system. The potential for lawsuits (tort law) incentivizes model makers to act responsibly, acting as a form of self-regulation that doesn't require a slow-moving government bureaucracy.

A straightforward regulatory step would be to hold AI companies legally responsible for any crimes their models commit. This simple shift in liability would force labs to slow down and prioritize safety, as they would be unwilling to deploy models they cannot fully control.