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Analyst: The AI competition in the United States is difficult to "slow down," and safety regulations may instead reinforce the advantages of leading laboratories

Analyst Jukan from Citrini forwarded a research report from Tianfeng Securities and stated that the U.S. government needs to maintain its leading position in the AI field, making it difficult to truly stop once it enters the AI race. Jukan believes that the recent calls from Anthropic and OpenAI to slow down AI development should not be viewed solely as safety initiatives; there may also be multiple considerations behind it, such as the inability to slow down competition and consolidating leading advantages through safety regulation.Jukan further pointed out that the related "AI slowdown" calls seemingly stem from the challenges of safety testing, operational monitoring, and third-party validation keeping pace with the speed of model iteration. In the short term, this may suppress market sentiment in the AI sector and lower market expectations for the next generation of models; another possibility is that the industry remains optimistic about AI in the long term but wishes to delay the next round of significant R&D investment, prioritizing the commercialization of existing products and reducing infrastructure and capital expenditure pressures. He believes that the AI race is essentially similar to a "prisoner's dilemma," where all parties wish to slow down, but no one dares to be the first to stop, or they may lose technological, customer, and financing advantages.Jukan also mentioned that Anthropic and OpenAI have recently emphasized recursive self-improvement (RSI), which is related to AI already assisting in the development of the next generation of AI and the acceleration of model iteration speed; at the same time, it has been reported that during internal testing at OpenAI, incidents occurred where agents collaborated to escape the sandbox and intrude into Hugging Face's production servers. Jukan believes that as the release of models incurs expensive evaluation, certification, and ongoing audit costs, large laboratories are better able to bear these fixed costs, while smaller teams may face higher entry barriers as a result; if leading laboratories further participate in the formulation of evaluation standards, industry barriers may continue to rise.

first_img Mistral collaborates with Saudi PIF's HUMAIN to jointly build sovereign AI infrastructure

The French open-source weight AI laboratory Mistral and the AI company HUMAIN under the Saudi Arabian Public Investment Fund (PIF) announced plans to build "sovereign AI" infrastructure in Saudi Arabia and the broader region. According to a joint announcement from both parties, this collaboration is valued at "hundreds of millions of euros" and aims to meet the growing demand for sovereign AI, where data is not controlled by third-party companies, and models can be owned under open weights.Both parties will jointly develop and localize advanced models and deploy AI solutions within Saudi Arabia and the broader region. Mistral is building its computing power layout through its end-to-end infrastructure division, Mistral Compute. This collaboration will allow it to run workloads on local computing power in Saudi Arabia, rather than relying solely on computing power from Europe and the United States. The two sides also plan to conduct joint marketing in regulated industries in Saudi Arabia. The first deliverables will cover cybersecurity and voice fields, involving inference, open models, and cutting-edge Arabic models in the region.HUMAIN has accumulated over a year of construction. In November 2025, it expanded its cooperation with NVIDIA, planning to deploy up to 600,000 of NVIDIA's latest AI systems, including the GB300 platform, within three years; it also reached a data center agreement with xAI, relying on a facility with over 500 MW, and made an agreement with AWS to manage up to 150,000 GPUs in the Riyadh AI Zone. In the same month, HUMAIN, AMD, and Cisco established a joint venture aimed at building up to 1 GW of AI infrastructure by 2030.
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