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.

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.

Seemingly reasonable terms like 'no autonomous lethal weapons' are impossible for a private company to enforce. They require moral and legal judgments about warfare—like defining a civilian or collateral damage—that are the exclusive and complex domain of a sovereign government, not a tech vendor.

Federal and state governments are massive customers of technology. Instead of relying solely on legislation, they can use their procurement power to enforce AI safety and ethical standards. By setting strict purchasing requirements, they can compel companies to build more responsible products.

Technical research is vital for governance because it provides concrete artifacts for policymakers. Demonstrations and evaluations showing dangerous AI behaviors make abstract risks tangible, giving policymakers a clear target for regulation, aligning with advice from figures like Jake Sullivan.

The US nuclear weapons industry operates as a hybrid: the government owns the IP and facilities, but private contractors like Honeywell and Boeing operate them and build delivery systems. This established public-private partnership model could be applied to manage the risks of powerful, privately-developed AI.

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 popular idea of a government 'sign-off' before an AI model's release is based on a false premise. Risk isn't a one-time event at launch; it's continuous, existing during model development, internal use, and post-release updates. Effective oversight must reflect this ongoing reality.