MEAGHAN TOBIN2026年7月28日在上海举行的世界人工智能大会期间，参观者来到月之暗面展台，该展台展示了Kimi K3模型。 Hector Retamal/Agence France-Presse — Getty ImagesChina’s leading technology companies have been steadily releasing artificial intelligence models that perform nearly as well as the best systems in the world. But they are all facing the same problem: overcoming the punishing economics behind the technology.中国领先的科技公司一直在稳步推出性能几乎可与全球顶尖系统媲美的人工智能模型。但它们都面临着同一个问题：如何克服这项技术背后严酷的经济难题。Chinese A.I. companies have tried different approaches to bring in enough money to sustain the enormous expense of building A.I. systems.中国的人工智能公司尝试了不同的方法来获取足够的资金，支撑构建人工智能系统的巨额开支。Start-ups like DeepSeek and Moonshot AI have raised billions from investors. Alibaba, China’s A.I. heavyweight, has started charging users for access to its best models. ByteDance, the parent company of TikTok and an A.I. powerhouse itself, launched a tiered pricing system, hoping to get people to pay more for using its most advanced models.深度求索和月之暗面等初创公司已经从投资者那里筹集了数十亿美元。中国的人工智能巨头阿里巴巴已开始向使用其最先进模型的用户收费。TikTok的母公司、本身也是人工智能巨头的字节跳动推出了分级定价系统，希望让人们为使用其最先进的模型支付更多费用。To build state-of-the-art A.I. models, A.I. companies constantly need to buy powerful computer chips — enough to build the models, more to test improvements and still more to ensure they can perform for users all over the world. The companies also need to build or rent space in data centers that house all this computing power.为了构建最先进的人工智能模型，人工智能公司需要不断购买强大的计算机芯片——一部分用来构建模型，更多的用来测试改进，还有更多是用来确保它们能为全球用户提供服务。这些公司还需要建设或租赁数据中心的空间来容纳所有这些计算能力。Chinese A.I. companies are not the only ones confronting these tough economics. Their giant rivals in Silicon Valley, from OpenAI and Anthropic to Google, are also investing more in A.I. than they are earning from it.面临这些严峻经济挑战的不仅仅是中国人工智能公司。从OpenAI、Anthropic到谷歌，硅谷的强大竞争对手在人工智能领域的投资也远远超过了收益。For Chinese companies, the challenge is compounded by a central paradox of the country’s approach to A.I. development. Most of China’s leading A.I. systems are open source or open weight. That has accelerated their development — the entire industry gains when every company shares its work in public.对于中国公司来说，这一挑战因中国人工智能发展方式中的一个核心悖论而加剧。中国大多数领先的人工智能系统都是开源或开放权重的。这加速了它们的发展——每家公司都公开分享其成果时，整个行业都会从中受益。Anthropic正在大力投资人工智能领域，其投入规模与中国领先的人工智能企业的大手笔投入不相上下。China’s open-source approach has spawned a crowded field of innovative and intensely competitive start-ups all offering systems at low cost.中国的开源方法催生了一个竞争激烈的创新型初创企业拥挤领域，它们都以低成本提供系统。So attempts to increase revenue by charging more for access to certain models can scare away customers. Price-conscious Chinese consumers — businesses and individuals alike — are quick to hop across platforms in search of inexpensive A.I. tools.因此，试图通过提高某些模型的使用费来增加收入的做法可能会吓跑客户。无论是企业还是个人，注重价格的中国消费者会迅速在各个平台之间跳转，寻找更便宜的人工智能工具。While China’s A.I. companies are searching for sustainable business models, spending is high and revenue low, said Richard Lin, a vice president at the Silicon Valley company Datastrato.硅谷公司Datastrato的副总裁理查德·林（音）表示，尽管中国的人工智能公司正在寻找可持续的商业模式，但目前的状况是支出高昂，而收入微薄。“In two or three years, we will still be trying to figure out how large models can earn money,” he said.“在两三年内，我们可能还在努力摸索大模型该如何赚钱，”他说。Offering low prices has helped the Chinese firms gain users, including in Silicon Valley, where many companies depend on the more affordable systems. Yet Chinese companies have struggled to translate huge numbers of global users into profits.提供低价帮助中国公司赢得了用户，包括在硅谷，那里有许多公司依赖于这些更实惠的系统。然而，中国公司一直难以将庞大的全球用户群体转化为利润。Last month, the Chinese start-up Z.ai released a model, GLM-5.2, that it said was nearly as powerful as Anthropic’s best. Many software developers and start-ups in Silicon Valley quickly started using it, in part because it was cheaper than the American systems.上个月，中国初创公司智谱AI发布了一款名为GLM-5.2的模型，称其功能几乎与Anthropic最好的模型一样强大。硅谷的许多软件开发者和初创公司迅速开始使用它，部分原因是它比美国的系统更便宜。Z.ai’s models improved and became more popular, and the company’s revenue more than doubled last year. But Z.ai lost nearly $700 million.智谱AI的模型不断改进并变得更加受欢迎，该公司去年的收入增加了一倍多，但亏损了近7亿美元。中国人工智能初创公司智谱AI凭借其低成本模型吸引了众多开发者，但去年仍亏损近7亿美元。When it went public in Hong Kong, Z.ai said it planned to use most of the money to improve its models, largely by acquiring more chips. A few weeks after the stock listing, Z.ai said it was looking for partners to share computing power resources and apologized after users complained about slow service.在香港上市时，智谱AI表示计划将大部分资金用于改进其模型，主要是通过购买更多芯片。股票上市几周后，智谱AI表示正在寻找合作伙伴来共享算力资源，并就用户投诉服务速度慢致歉。“Open models are a powerful distribution strategy, but they are not a complete business model,” said Wei Sun, a principal A.I. analyst at Counterpoint Research in Beijing. “An I.P.O. can finance the next training cycle, but it cannot by itself create sustainable economics.”“开放模型是一种强大的分发策略，但它们不是一个完整的商业模式，”总部位于北京的Counterpoint Research首席人工智能分析师孙伟（音）说。“首次公开募股可以为下一个训练周期提供资金，但它本身无法创造可持续的经济效益。”This month, Moonshot released a model called Kimi K3, which the company said performed better than models from OpenAI and Anthropic did on some tasks. But within two days the company was forced to announce that it needed to stop accepting new users because it couldn’t get enough computer chips to serve them.本月，月之暗面发布了一款名为Kimi K3的模型，该公司表示它在某些任务上的表现优于OpenAI和Anthropic的模型。但不到两天，该公司就被迫宣布需要停止接受新用户，因为它无法获得足够的计算机芯片来为他们提供服务。China’s A.I. industry has faced years of U.S. trade restrictions that confine its ability to buy the world’s most powerful chips. To get around these limitations, many Chinese companies rent remote access to data centers outside China stocked with advanced chips.中国的人工智能行业多年来一直面临美国的贸易限制，这些限制束缚了其购买世界上最强大芯片的能力。为了绕过这些限制，许多中国公司租用位于中国境外、配备先进芯片的数据中心的远程访问权限。But Chinese A.I. start-ups have far less money to buy computing power than their deep-pocketed American rivals.但与资金雄厚的美国竞争对手相比，中国人工智能初创公司用来购买算力的资金要少得多。Last month, DeepSeek held one of China’s most anticipated funding rounds, raising money from investors including the internet giant Tencent, the battery maker CATL and the country’s state investment fund for artificial intelligence. DeepSeek raised $7.5 billion. In May, Moonshot raised $2 billion.上个月，深度求索进行了中国最受期待的融资轮之一，从互联网巨头腾讯、电池制造商宁德时代以及中国国家人工智能投资基金等投资者那里筹集了资金。深度求索筹集了75亿美元。今年5月，月之暗面筹集了20亿美元。深度求索和月之暗面等初创公司已筹集数十亿资金，而阿里巴巴最先进的人工智能模型已经收费。By comparison, Anthropic raised $65 billion in May alone.相比之下，Anthropic仅在5月就筹集了650亿美元。Chinese tech firms have far less capital, but U.S. tech executives and investors worry that China’s open-source push threatens to upend the economics of A.I. production.中国科技公司的资本要少得多，但美国科技高管和投资者担心，中国的开源热潮可能会颠覆人工智能生产的经济模式。Chinese A.I. start-ups say years of export controls have pushed them to use chips as efficiently as possible, potentially challenging the idea that has motivated much of global A.I. investment: that making cutting-edge A.I. systems will always require increasing investment in more chips and data centers.中国的人工智能初创公司表示，多年的出口管制迫使它们尽可能高效地使用芯片，这可能会挑战推动全球大量人工智能投资的理念：即制造尖端人工智能系统将始终需要不断增加对更多芯片和数据中心的投资。Now, a debate is raging in the United States that could make it even harder for Chinese A.I. companies to earn money.现在，美国正围绕这一问题展开激烈辩论，这可能会让中国人工智能公司更难盈利。Leading Silicon Valley companies including Anthropic and OpenAI have claimed that Chinese firms improperly harvested data from their A.I. systems to accelerate the development of the Chinese models. Some American tech companies and investors want Washington to limit access to Chinese open-source models, saying they could be a threat in the wrong hands.包括Anthropic和OpenAI在内的硅谷领先公司声称，中国公司从其人工智能系统中不当采集数据，以加速中国模型的开发。一些美国科技公司和投资者希望华盛顿限制对中国开源模型的访问，称如果它们落入别有用心的人手中，可能会成为威胁。Kevin Xu, the founder of Interconnected Capital, a hedge fund that invests in A.I. technologies, said, “Many Silicon Valley start-ups rely on open-weight models to both customize their product and also not have to pay OpenAI and Anthropic their high prices.”投资人工智能技术的对冲基金Interconnected Capital的创始人凯文·许（音）表示：“许多硅谷初创公司依赖开放权重模型来定制其产品，同时又不必向OpenAI和Anthropic支付高昂的价格。”These American tech companies have been an important source of revenue for many Chinese A.I. firms. If they are cut out of it, they risk losing that.这些美国科技公司一直是中国许多人工智能公司的重要收入来源。如果它们被排斥在外，它们将面临失去这一收入的风险。Xinyun Wu自台湾台北对本文有研究贡献。Meaghan Tobin是时报科技记者，常驻台北，报道亚洲地区的商业和科技新闻，重点关注中国。翻译：纽约时报中文网点击查看本文英文版。获取更多RSS：https://feedx.net https://feedx.site