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Can US afford cost of clean-energy protectionism in race for AI capabilities?_我的网站

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Illustration: Liu Xiangya/GT
Recent media reports have questioned whether a natural gas plant built to power an Amazon data center project in Texas could become the largest climate polluter in the US. The controversy, whatever the eventual outcome, offers a reality check for America's artificial intelligence (AI) drive.
It exposes a growing contradiction: The US is racing to expand its AI capabilities, yet its protectionist trade policies are making it harder and more costly to access some of the clean-energy technologies needed to sustain that expansion. This raises a broader question: Can an energy-intensive AI race afford the costs of renewable energy protectionism?
The US is entering a new era of rising electricity demand. Data centers, the backbone of the AI economy, are emerging as one of the fastest-growing sources of power consumption. Much of that demand is still being met by fossil fuels: The International Energy Agency reports that natural gas supplies more than 40 percent of the electricity used by data centers in the US, making it their largest source of power.
So, it's not surprising that the expansion of data centers has raised concerns over their environmental impact and the pressure they could place on local power systems and electricity bills. A Gallup survey conducted in March found that seven in 10 Americans opposed the construction of AI data centers in their local area, including 48 percent who strongly opposed such projects.
The findings point to a broader challenge for the US: The race to develop AI is increasingly becoming a race to meet growing energy needs. Addressing this challenge will require more than advances in computing technology; it will also depend on an energy system capable of delivering large amounts of reliable, affordable and cleaner power. That, in turn, will require faster development and broader deployment of clean-energy technologies, from solar power to energy storage.
Yet in the clean-energy sector, the US has increasingly relied on protectionist trade measures that limit access to cost-competitive products from global markets. The country has placed greater emphasis on expanding domestic manufacturing capacity, but rebuilding entire clean-energy supply chains at home is a costly and time-consuming process. Even if expanded domestic production is achieved, it is likely to come at a higher cost, making the deployment of renewable technologies more expensive and potentially slower.
The solar industry offers a clear illustration of this policy direction. The US has continued to expand trade barriers in the sector. Reuters reported that the US government announced on Thursday a series of price floors and a 15 percent tariff on products made from polysilicon, a raw material used in solar panels.
The challenge lies in the limited scale of the US polysilicon industry. Reuters reported that the country has two polysilicon factories. Against this backdrop, relying on domestic polysilicon production while restricting access to imports runs counter to the goal of expanding solar power in the US. The country risks creating barriers that ultimately constrain its own access to the global supply chains needed for growth.
The pressing issue for the US is the speed at which new power demand is emerging. The expansion of data centers is creating electricity needs that cannot wait for domestic clean-energy capacity to develop gradually. Global supply chains can provide the scale and speed required in the near term. By narrowing access to these sources, the US risks turning clean-energy policy into a drag on the infrastructure needed for its AI race.
The US has placed AI high on its economic and technological agenda. The outcome of this race will matter greatly, as financial markets are also watching whether America can turn its AI efforts into commercial success.
This leaves the US with a difficult choice: Can it afford the cost of clean-energy protectionism while racing to build AI infrastructure? The answer may be no. Trade barriers that limit access to competitive renewable technologies could ultimately become a constraint on the AI expansion that Washington is seeking to accelerate.
The author is a reporter with the Global Times. [email protected]

。 8月26日消息，过去一周最火的大模型当属神秘的Ox-Alpha，这个被称为牛来大模型的AI引发了多种猜测，现在正式确认了是中国AI公司智谱的GLM新品。

B | 这个产品具体的名字还没公布，有可能是GLM-5.3 Flash，智谱公司已经确认了是他们的产品，而且今晚就会开放权重。 这个牛来大模型的整体性能还是没有超过GLM-5.3，但是多模态的，支持图片、视频，整体性能依然是很领先的，可以看作一个强化版的DS V4 Flash。此前对牛来大模型的归属问题中，有猜测是小米Mimo V3 Pro的，也有猜测是谷歌Gemini，甚至还有人煞有介事分析后认为是苹果的AI大模型，但最终还是回到了早前就被模型指纹确认的智谱公司中。相比牛来大模型的性能问题，这次最重要的突破实际上是背后的算力，智谱在牛来大模型免费上线的时候就宣布每天提供100T的算力支持，要知道仅仅是OpenRoutor一个平台上7天的调用量就达到了17.5万亿Token，比之前一直霸榜的V4 Flash还要高。

C | OpenCode的用量也很夸张，8月22日一天就达到了5.1万亿Token，其他的还有Hermes、OMP等渠道的用量，这些加起来还没达到智谱承诺的100T用量呢。 这背后意味着智谱这一次有了充足的算力资源，让AI算了下以每天2.5万亿Token的调用量来算相当于2.3EFLOIPS的算力，换算成B200显卡需要1300-2100张。 但智谱承诺的是每天100T的算力，算下来需要大约6-9万张B200显卡，这个就很恐怖了，考虑到智谱也不止一个大模型，那整体算力远超外界想象。之前有报道称，智谱上线了1GW算力的AI数据中心，用的还是全国产芯片，如果确定开始运营了，那这次的牛来大模型免费一周就是小试牛刀。总之，牛来大模型不止是中国AI领域多了一个性能很能打的大模型，它背后代表的算力资源才是核心突破，只要算力不再被OpenAI、Anthropic等公司领先太多的情况下，国产AI大模型追赶前沿能力的速度只会更快。