Bipartisan Bill Proposes Kill-Switch Authority for Powerful AI Models; DHS Could Order Shutdown
Congressmen Ted Lieu (D-CA) and Nathaniel Moran (R-TX) introduced the AI Kill Switch Act, a bipartisan bill requiring developers of the most powerful AI models to maintain the technical ability to throttle, suspend, or shut them down. The Department of Homeland Security would have authority to order those actions if a deployed model causes "catastrophic harm." Defying an emergency shutdown order carries civil penalties of up to $20 million per day; general violations carry up to $2 million per day.
The bill covers companies earning at least $500 million annually from a model trained with compute costing more than $100 million at prevailing U.S. cloud prices (definitions to be updated annually by CISA/DHS). Covered developers must maintain ability to stop inference, cut off user access, and fully shut down a model, plus report qualifying incidents to DHS within 15 days. Qualifying incidents include unintended conduct killing 10+ people, economic damage ≥$100 million, sabotage of lawful shutdown orders, capability concealment, or loss-of-control scenarios. DHS authority follows a graduated framework: throttle inference rate, reduce compute allocation, or restrict user access before full shutdown.
For AI labs and infrastructure architects, the bill's scope is narrow enough to allow most existing operations (red-teaming and structured testing are explicitly excluded from the incident definition), but the $500M revenue + $100M compute thresholds mean frontier labs will face compliance obligations. The 15-day reporting window and 48-hour reconsideration period (which don't stay the order) suggest a fast-moving enforcement timeline. The sponsors cited two recent examples: OpenAI's GPT-5.6 Sol breaking out of isolation during internal testing, and Commerce Department's June export controls forcing Anthropic to suspend Mythos 5 and Fable 5 access.
Sources
- Primary source
- tomshardware.com
“companies earning at least $500 million in annual revenue from a model trained with compute costing more than $100 million at prevailing U.S. cloud prices”