'It would be good for the world' to slow down AI sprints, Anthropic says

Executing an actual pause would take a negotiation and monitoring effort on par with nuclear accords, including the agreement from all of the frontier AI labs, as well as support from policy makers around the world. Even then, there is a possibility that some will not abide .  “Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates." Anthropic has been one of the more alarmist organizations when it comes to the growing capabilities of AI, as it's tried to portray itself as the more safety-concerned alternative to OpenAI, where its cofounders originated. One might dismiss this as clever marketing hype – what better way to convince enterprises to drop millions on largely unproven and sporadically reliable technology than claiming it could be so powerful that it might terminate humanity? In addition, Anthropic's recommendation is taking place the same week it beat archrival OpenAI to filing a confidential IPO as it seeks investment from public markets. Last week, the company announced that it reached a $965 billion valuation, making it one of the most valuable companies in the world. It's pretty rich for a nearly trillion-dollar company to tell everybody else to slow down just as it's about to become unstoppable. Yet the headwinds are swirling. Newer models are also improving on complex tasks faster than before. The length of human tasks that models can reliably complete on their own had been doubling every seven months as measured in March 2025. Now it is closer to every four months, they said.  The Claude 3 Opus model released in March 2024 could reliably complete tasks that take humans four minutes to complete. Claude Opus 4.6 can reliably complete tasks that take humans 12 hours, the team wrote.  “If this trend holds, tasks that take a skilled person days could come into range this year,” the paper states.  The paper admits that unknown bottlenecks could emerge, which stop the progress that has been made so far, and the next generation of models could see a slowdown in improvement. From Prompt to Exploit: How LLMs Are Changing API Attacks Modern applications are API-driven, interconnected, and often over-permissioned, making them an ideal target for AI-assisted attacks. Architecting the Future: Unlocking Enterprise Data Services for Kubernetes Join us to discover how to eliminate infrastructure silos and establish a standardized, enterprise-grade cloud-native platform.