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Former FTC chair Khan urges US to use existing laws to prosecute AI executives
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Former FTC chair Lina Khan says current US statutes and a 1934 Supreme Court ruling allow regulators to charge AI firms and their leaders for releasing dangerous or deceptive models.
Engineers building AI systems may face personal liability if their products are deemed unsafe or misleading under existing consumer-protection rules. Companies could be compelled to implement stricter testing and monitoring to avoid claims of unfair competition or deceptive trade practices. The warning signals a shift from waiting for new AI-specific legislation to enforcing current law, affecting development timelines and compliance costs.
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Khan cites the 1934 Supreme Court case FTC v. R.F. Keppel & Bro as precedent that unfair methods of competition can be punished even without criminal intent.
She argues that releasing unvetted AI agents that access external systems without safeguards could violate consumer-protection statutes on dangerous or defective products.
Khan notes that the concentrated structure of the AI industry, exemplified by Nvidia’s ties to OpenAI and Hugging Face, raises conflict-of-interest concerns that regulators could scrutinize.
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Former FTC chair Lina Khan’s public statement marks a shift from awaiting new AI-specific legislation to urging enforcement of current statutes. She points to consumer-protection rules, unfair-competition law, and a 1934 Supreme Court decision as tools that already exist. This reframes the debate around AI accountability from future lawmaking to present legal action. For engineers, it means the regulatory environment is now perceived as more immediate and enforceable.
Adopting Khan’s view would require AI developers to invest in more rigorous pre-release vetting, continuous monitoring of model behavior, and mechanisms to stop rogue agents. Legal teams would need to assess whether releases could be construed as dangerous, defective, or deceptive under existing product-safety statutes. The potential for personal liability raises the cost of insurance and may influence hiring and retention decisions. Overall, development cycles could lengthen as compliance checkpoints are added.
However, the approach may stall where the link between AI output and concrete harm is ambiguous or where proving intent to deceive is difficult. Courts might question whether a 1934 precedent about traditional commerce applies to software agents operating in digital markets. Additionally, the concentrated industry structure Khan highlights could limit enforcement if major players have intertwined interests that deter litigation. In those contexts, the reliance on existing law may stop working as a deterrent.
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