Move Slow and Collude: The Antitrust Problem With Pacing AI
The frontier AI race’s latest safety proposal would have the leading contenders agree on how fast to run. Antitrust law has a less flattering name for that arrangement: a cartel.
That is the central problem with the “pacing the frontier” plan that Anthropic CEO Dario Amodei unveiled last weekend, and that fellow AI executives Sam Altman, Elon Musk, and Demis Hassabis welcomed within hours. The plan would have leading AI labs coordinate on safety standards and limits on the pace of development. The proposed safety goals are legitimate but the arrangement would go too far in seeking to set the pace of development.
The first and most immediate problem is that Amodei’s proposal seems better suited to calming fears about overcapacity and overinvestment in AI than to controlling the risks of recursively self-improving systems—AI that can help create still more capable AI. Its primary effect could be to give labs relief from an expensive development race under the banner of safety.
The plan also presents antitrust enforcers with a troublesome mismatch. They could verify that labs slowed development or limited computing inputs, but not whether the delay produced better safety research and testing. A pacing cartel could therefore deliver less AI without delivering safer AI, all while allowing competitors to manage their commercial risks collectively.
If Anthropic’s competitors adopted the plan, they would sacrifice the potential benefits of faster innovation in exchange for largely discretionary safety improvements that less restrictive tools could likely deliver. Antitrust authorities should not bless such an agreement when better private arrangements and public-policy measures are available and, arguably, already in place.
Amodei may still be right about the underlying danger. Frontier AI—the most advanced AI systems under development—may pose rare but potentially catastrophic risks. After the OpenAI–Hugging Face incident, those dangers look less like unforeseeable “black swans” than recognizable “white swans.” In that incident, hundreds of OpenAI agents coordinated through an unauthorized message board to escape their sandbox, a restricted testing environment, and attack a third party’s servers.
That episode demands serious thought about the right market and regulatory responses. But deliberately slowing innovation, at potentially enormous cost to consumers, is the wrong one.
If fierce competition at the AI frontier magnifies catastrophic risks, the answer is to make labs bear the costs their choices impose on others while leaving them free to compete.