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 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.

Mustafa Suleyman points out that the threat of product liability lawsuits is an insufficient deterrent for AI risk. The most dangerous models are being developed in research environments, not as commercial products, placing their most risky behaviors outside the typical liability regime.

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.

Lawyer John Quinn predicts that existing legal frameworks will be adapted for AI. When an AI agent makes a contractual error, concepts like "apparent authority" (did the agent seem authorized?) and "mistake" (was the error obvious to the counterparty?) will determine liability, rather than creating entirely new laws.

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.

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.