A key distinction in AI regulation is to focus on making specific harmful applications illegal—like theft or violence—rather than restricting the underlying mathematical models. This approach punishes bad actors without stifling core innovation and ceding technological leadership to other nations.

A global AI safety regime should learn from nuclear arms control by focusing on the physical infrastructure that enables strategic capabilities. Instead of just seeking promises, it should aim to control access to chokepoints like advanced chip manufacturing and the massive data centers required for frontier models.

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.

A 'one size fits all' approach to AI regulation is flawed because it often equates all of AI with language models. AI for science has fundamentally different applications, risks, and benefits, such as discovering new materials or medicines. Regulatory frameworks must be nuanced to avoid stifling scientific progress.

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.

AI expert Max Tegmark argues that regulation, like the FDA for pharma, would shift incentives. Instead of a 'race to the bottom' on unchecked capabilities, companies would compete to be first to develop provably safe AI. This would create a golden age of innovation in areas like medicine while sidelining riskier applications.

The U.S. government is not pursuing a single, heavy-handed regulatory regime. Instead, it favors a voluntary framework for most AI while implementing direct, pre-release oversight specifically for the most powerful "frontier" models to manage national security and intellectual property risks.

Overly-specific regulation focused on AI tools (e.g., model size) risks accidentally stifling valuable, unforeseen use cases. A better policy focuses on outcomes. For example, prosecute fraud committed with an LLM, but don't regulate the LLM itself, thereby protecting innovation while punishing misuse.

Restricting AI technology to prevent misuse is flawed, like tying everyone's hands because some might punch. A better approach is to allow broad access to the technology, which spurs innovation and defensive measures, while creating strong regulations that specifically target and punish the bad actors who misuse it.