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 responsible, iterative approach to AI regulation begins not with new frameworks, but by auditing existing laws. Domain experts should update current rules for professions like medicine or finance to ensure they explicitly cover actions performed by or with AI, addressing immediate gaps without stifling future innovation.

India is taking a measured, "no rush" approach to AI governance. The strategy is to first leverage and adapt existing legal frameworks—like the IT Act for deepfakes and data protection laws for privacy—rather than creating new, potentially innovation-stifling AI-specific legislation.

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.

The UK's strategy of criminalizing specific harmful AI outcomes, like non-consensual deepfakes, is more effective than the EU AI Act's approach of regulating model size and development processes. Focusing on harmful outcomes is a more direct way to mitigate societal damage.

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.

A16z advocates for a "gap analysis" approach to AI regulation. Instead of assuming a legal vacuum exists, lawmakers should first examine how existing, technology-neutral laws—like consumer protection or civil rights statutes—already apply to AI harms. New legislation should only target clearly identified gaps.

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.

There is a temptation to create a flurry of AI-specific laws, but most harms from AI (like deepfakes or voice clones) already fall under existing legal categories. Torts like defamation and crimes like fraud provide strong existing remedies.