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Vans attributed the data center protests to power shortages, stating that electricity in the United States should be cheap enough not to require metering

U.S. Vice President JD Vance stated at the All-In Summit that U.S. AI data centers are facing increasing opposition, largely because the power supply has not kept up. Residents see data centers being built continuously while their electricity bills are rising, which naturally leads to backlash. He believes the solution is not to build fewer data centers but to increase power generation capacity accordingly.Vance mentioned that the U.S. has not built enough power facilities in the past generation, and in the future, it should pursue a state where "electricity is so cheap that it doesn't need to be metered." He also compared this to China, arguing that the slow growth of power generation in the U.S. in recent years has already affected the development of new industries like AI. "So cheap that it doesn't need to be metered" is an old slogan from the history of U.S. nuclear energy. In 1954, Lewis Strauss, the chairman of the U.S. Atomic Energy Commission, used it to describe the era of cheap electricity brought by atomic energy.The IEA estimates that data centers will contribute to about half of the new electricity demand in the U.S. by 2025; the U.S. Congress is also advancing legislation this week to try to prevent the costs of expanding data centers from being passed on to ordinary households.

first_img Citigroup: AI continuous learning will extend the storage supply shortage until 2031

Citigroup analysts pointed out that leading memory chip manufacturers are expected to benefit from the structural changes in the development of artificial intelligence. The agency anticipates that continuous learning will drive a significant increase in memory demand, leading to a supply shortage in the market that will continue until 2031. Continuous learning strengthens models by training on new tasks and knowledge, which will create a sustained demand for model updates and access to historical data, driving the storage usage of products such as HBM, server DDR5, and eSSD.Citigroup expects HBM bit demand to grow by 62% year-on-year to 75.2 billion gigabits in 2027, and by 69% year-on-year to 127 billion gigabits in 2028. Global DRAM demand is expected to grow by 30% and 35% year-on-year in 2027 and 2028, respectively, while supply is expected to grow by only 19% and 22% during the same period, resulting in supply-demand ratios of -8.7% and -9.7%. In terms of NAND, demand is expected to grow by 29% and 33% year-on-year in 2027 and 2028, respectively, exceeding supply growth of 21% and 25%, with supply-demand ratios of -6.1% and -5.5%.Citigroup's preferred storage targets include Samsung Electronics, SK Hynix, Micron, Sandisk, and Kioxia, corresponding to the logic of storage shortages brought about by continuous learning, demand for HBM and server DDR5, DRAM supply shortages, and tightening supply of high-density eSSD and NAND.

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.
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