X：Rohan Paul

As models become more capable, the risks associated with developing and testing them internally also grow. We temporarily paused reinforcement learning (RL) training on our latest models intended for deployment for two weeks while we hardened and red-teamed our research environments and expanded monitoring coverage. Our largest planned frontier RL run remains on hold while smaller-scale training and evaluations validate these safeguards and establish more evidence of alignment. https://t.co/ecbMMmVoox

China’s open-weight AI strategy could turn model adoption into long-term dependence on Chinese technical standards. FT published a piece. “The next shock will not arrive in a container ship.” US restrictions created stronger pressure for technological self-reliance, from Huawei's 2019 Entity List designation to broader controls on advanced computing chips beginning in 2022. China is therefore competing through distribution as much as raw model capability, especially where countries want lower costs, local hosting or less dependence on US providers.

"For the next few years, America is likely to win (in AI). then it will be a function of who controls the AI chip fabrication. If more of them are controlled by China, then China will win. And 100% of advanced AI chips are made in Taiwan." - Elon Musk https://t.co/WMvf2zrShq