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.

In the absence of federal legislation, product liability lawsuits are becoming a de facto regulatory mechanism. The legal strategy used against Big Tobacco—arguing companies knowingly sold harmful products—is now being applied to social media companies, creating a precedent for holding AI developers liable.

The narrative that AI is becoming sentient and uncontrollable absolves creators of responsibility. A better model is to hold leaders like Sam Altman personally accountable, much like arresting fraternity presidents for noise violations. This creates powerful incentives to build in safeguards.

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.

Creating a new regulatory framework for AI is premature because nearly all feared harms—from malpractice to non-consensual imagery—are already illegal under existing laws. The initial focus should be on applying these established laws to AI-assisted actions, not inventing a new regime from scratch.

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.

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.