On October 3, 2026, The Economic Times relayed a Bloomberg interview in which US Treasury Secretary Scott Bessent criticized tech leaders’ apocalyptic AI warnings as alarmist and called on AI labs to take responsibility for managing risks. Bessent backed President Trump’s preference for self-regulation and a US–China notification channel over new federal AI laws.
This article aggregates reporting from 1 news source. The TL;DR is AI-generated from original reporting. Race to AGI's analysis provides editorial context on implications for AGI development.
Bessent’s comments crystallize one plausible equilibrium for US AI policy in the short term: loud public concern about extreme risks, but a governing class that still prefers industry led solutions to binding rules. When the Treasury Secretary waves away doomsday rhetoric while insisting that labs must “own” their risks, the message to frontier companies is clear. Keep pushing the technology, talk about safety and help design voluntary standards, but do not expect Washington to slam on the brakes.
For the race to AGI, that stance tilts the competitive field toward US labs that can move fast while projecting an image of responsible self regulation. It also nudges firms to treat international coordination as a safety valve. The proposed US–China AI incident notification channel acknowledges that uncontrolled agents and cyber misuse are shared risks, but it stops well short of joint caps on compute or model size. That is a very different world from the ones imagined by some pause advocates.
Strategically, investors and boards should read this as a green light for continued heavy capital allocation into frontier models, with political cover so long as headline incidents do not spiral. At the same time, Bessent’s framing that labs, not regulators, must manage existential risk pushes more responsibility onto internal safety, red teaming and deployment governance. If those fall short, the political pendulum could swing hard the other way after a serious failure, making this a high variance policy environment.