Anthropic's Fable shutdown spotlights open-source AI as China gains ground amid export controls

Anthropic's Fable shutdown is a big moment for open-source AI

Chinese open-source AI names, including Zhipu and MiniMax, surged as investors bet the fight could boost demand for open models. Anthropic's suspension of its top AI models late last week drove home a hard truth for the companies that were counting on them: access can be cut off at any time. It's been a big theme this week, and one that Wall Street will be watching closely as Anthropic and OpenAI gear up for potentially massive IPOs in the coming months. Microsoft CEO Satya Nadella is warning of the risks, even as his company is the principal investor in OpenAI, and backed Anthropic last year to the tune of billions of dollars. He wrote Monday in a post on X that companies need to "build agentic systems that improve over time, while still retaining control over their IP." "The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see," Nadella wrote. Investors have been trading on that theme. MiniMax and Zhipu, the Chinese open-source AI lab, both surged on Monday as the Anthropic fight put a spotlight on downloadable models that companies can run themselves. The Anthropic-Fable news landed at an awkward moment late Friday. Roughly two hours earlier, SpaceX had wrapped up its first day of trading following the biggest IPO on record. SpaceX's xAI unit is a niche player in artificial intelligence, but CEO Elon Musk is an outspoken voice on the topic. Anthropic announced it had pulled access to its Fable 5 and Mythos 5 models to comply with an export control directive from the U.S. government that cited "national security authorities." Anthropic abruptly disabled the models for all of its customers in order to ensure compliance, but said all of its other models would not be affected. For developers who want full control over model access, there's another approach. An open-source model can be downloaded, run on a company's own infrastructure, and customized for its data and needs. When the model lives on a company's own servers, no political fight can switch it off. Yash Patel, CEO of Applied Compute, which helps companies train and run custom models, said the Anthropic fight "highlighted the significance of owning your own model." He said the shift has become much more mainstream of late. "What we've been hearing increasingly, probably more so in the last month than the entire year, is the fact that they want a multimodal future," Patel said. "They don't want to be locked into a single vendor." That could be a problem for the U.S., as the the open models winning the most adoption are from China, just as the world's two biggest economies are in a battle over controlling the future of AI. Models from DeepSeek, Tencent, Xiaomi, and MiniMax all rank among OpenRouter's most-used this month, even against closed competitors. Zhipu framed its latest release as a rebuttal of sorts to Washington. Cutting-edge AI, the company argued, shouldn't belong to a handful of players or be withdrawn at will. Cost could also speed adoption. As the price of state-of-the-art AI climbs, companies are already routing routine work to cheaper models and saving the most expensive ones for the hardest tasks. Patel said customers are reacting to what he called a "token-pocalypse" as AI products move toward usage-based pricing. "The era of token maxing is over," he said. Companies are now looking for "better, cheaper, faster models." That is pushing some enterprises to reconsider models they would have dismissed months ago, including open models from China. "Before it was just kind of like I don't even want to talk about it," Patel said. "Now they're like, OK, how good could it be, and if it's good, we'll figure it out." It's a reminder that the AI market is still in its infancy, with ChatGPT's public release occurring less than four years ago. For investors, that reframes who is actually leading the AI race. The winners may not just be the big closed model labs, despite what their current valuations suggest. Choose CNBC as your preferred source on Google and never miss a moment from the most trusted name in business news.

Anthropic's Fable fiasco leaves the door open for open-source AI, particularly cheaper models from China | Fortune

The U.S. government's decision to stop Anthropic from offering its Mythos and Fable 5 models to non-U.S. nationals may end up providing a big boost to the adoption of open-source models, including those from Chinese AI labs like DeepSeek and Moonshot AI. Users can download open-source models and run them on their own computers or cloud networks, effectively sidestepping the ability of both AI developers and governments to control access. These models can also be more easily fine-tuned by developers to tailor them for specific needs. Chinese labs are already claiming a public relations win from the Anthropic controversy. Shares in Knowledge Atlas, a Chinese AI lab better known as z.ai, surged by over 30% in Hong Kong trading on Monday after it released the latest version of its open-source model, GLM-5.2. (Knowledge Atlas's shares are up more than 800% since they debuted in January) "At a time when some frontier models can suddenly become unavailable, we choose to believe in a different path," Knowledge Atlas posted on social media, according to the South China Morning Post. In a clear reference to the Anthropic news, the company added that "frontier intelligence should not belong to only a few people, nor be subject to withdrawal by a handful of rules at any moment." Demand for Chinese models has already overtaken that for U.S. models on OpenRouter, a popular platform for accessing different AI models. Last week, the top four most-used models came from Chinese companies: DeepSeek, MiniMax, Tencent and Xiaomi. The Chinese open source models have proved popular not just within China but also in many other developing countries around the globe, where they are seen as a good trade off between price and performance. The U.S.'s ban on Fable and Mythos may also end up vindicating China's broader move towards tech self-sufficiency, which picked up in 2022 after the Biden Administration placed controls on the sale of advanced chips and chipmaking equipment. "It's a great move for China," says Neil Shah, vice president of research at Counterpoint Research. "Obviously they're not on the cutting edge because of the export controls, but they have their own silicon and their own software." Why go open-source? On Friday, Anthropic revealed that the U.S. Department of Commerce had ordered it to stop providing access to its frontier models to anyone outside of the U.S. The way U.S. export rules are interpreted also means the company cannot offer the models to any "foreign national" inside the U.S., including its own employees. In response to the government order, the company decided to suspend access to these models to all users. Anthropic had previously argued that its Mythos model was too powerful to be released to the public without safeguards, and had embarked on an early-access program, titled Project Glasswing, for key institutions to use the model to uncover security vulnerabilities. Institutions in about 15 countries, including U.S. allies like Japan and South Korea, eventually got access to Mythos through Project Glasswing. But the U.S.'s move against Anthropic raises the possibility that frontier models from other labs, like OpenAI or Google, might also get hit by export controls. In that event, non-U.S. organizations may be completely locked out from accessing the best U.S.-developed models. Open-source models could be an alternative, particularly for governments hoping to invest in sovereign AI, domestically-developed and -controlled AI models and infrastructure. The U.S.'s export controls on Anthropic only highlights the danger governments have from being locked in to one country's AI models. "It is the first time that a government has ordered a model developer to restrict access to a particular model based on nationality," says Paul Triolo, a partner at DGA-Albright Stonebridge Group. "Companies and governments will start reconsidering how they are approaching application development based on a particular model, and for governments, which companies they will want to partner with for sovereign AI deployments." "Until there is further clarity about what criteria the U.S. government will use in assessing and approving frontier models, companies and governments will definitely be exploring options such as non-U.S. origin models," such as those from Mistral, Cohere, and "capable Chinese open-source models," he adds. The Anthropic order will "push scale for Chinese open-source models," Shah says. "But we'll also see lots of ambitious and self-sufficient economies, like in the Middle East, who will try to build their own indigenous software models." Asian governments in particular have made a public push for "sovereign AI." South Korea, for example, launched a national state-backed competition to develop Korean-language AI models. "We need to advance our own technology as quickly as possible and become as self-reliant as we can," Sung Kim, the founder of Korean AI startup Upstage, said at a press conference on Tuesday, adding that AI was now a "strategic national asset." Japan, for its part, is suggesting that it might turn to Anthropic's arch-rival OpenAI to bolster its cybersecurity defenses. How good are China's open-source AI models? Neither OpenAI nor Anthropic make their models available in China, including the Chinese city of Hong Kong (which sits outside Beijing's internet controls). Both Anthropic and OpenAI have accused Chinese labs like DeepSeek of conducting "distillation" attacks, where their models are used to train smaller, more efficient models. Chinese models still lag models from the U.S. DeepSeek's most recent model V4 performs at approximately the same level as Anthropic's Claude Opus 4.6 and OpenAI's GPT 5.4. Those models were released in February and March 2026, respectively. The Chinese startup estimated it was three to six months behind state-of-the-art frontier models. However, while Chinese open-source models are not as powerful as their U.S.-developed peers, they are still significantly cheaper. DeepSeek's V4 Pro cost $3.48 for 1 million tokens of output; Anthropic's Fable 5 model cost $50 for the same output. (A token is the basic data unit that contemporary AI systems, most of which are large language models, process. It is equivalent to about a word-and-a-half of English text.)

China isn't trying to beat the U.S. at AI -- it's playing a completely different game | Fortune

In early 2025, a quiet evolution emerged out of Hangzhou -- a city of thirteen million famed for its historic West Lake, misty mountains, and poets who depicted it as "Heaven on Earth." Yet it was here that a Chinese company -- under-resourced by Silicon Valley standards -- released a reasoning-focused large language model in January 2025, a few steps behind that era's ChatGPT 4-class models. DeepSeek had been trained, its parent company claimed, for only $6 million -- pocket change compared to OpenAI or Google expenditures. For a global industry convinced that US large language models (LLMs) were unassailable -- built by companies with oceans of compute, elite talent, large capital infusions, and company valuations the size of small-nation GDP -- DeepSeek upended assumptions. The Chinese model emerged at a time that China's AI sector seemed beleaguered by chip bans and a slowing economy and against a backdrop in which private sector investment into the US sector was twelve times China's and twenty-four times the UK's. Analysts found that DeepSeek's parent, High-Flyer, hadn't spent its way into contention; instead, it had been crafty: focusing on targeted domains, lean training, and aggressive energy efficiency. While critics whispered about secret Nvidia chips, covert ChatGPT usage, or hidden costs, the signal was clear: US companies wouldn't hold a monopoly on advanced LLMs. This "DeepSeek moment" ignited a debate: "Are Chinese companies catching up in AI?" That framing misses the point. The AI stack is broad -- spanning energy, infrastructure, chips, foundational research, and application deployment -- and across that landscape the US and China possess different strengths. The real picture is that the world's two dominant AI markets aren't competing in the same race; they're running parallel ones shaped by different economic constraints, cost structures, and market demands -- and that divergence will shape global AI over the next decade. "If you look at the future of deep tech, it's clear that the US and China are like the two sides of [the traditional Chinese martial arts practice] tai chi, with its black-and-white yin and yang symbol -- each with unique strengths, each pushing the other forward," says Soul Capital's Herry Han, "It's not just competition. It's a dynamic balance." In the US, AI development follows a capital-intensive path: massive data centers, soaring talent costs, and billion-dollar frontier science bets. Well-funded US leaders will keep pushing toward artificial general intelligence (AGI) and increasingly sophisticated agentic systems designed to pursue goals through multi-step planning and self-directed action. Chinese companies face a different competitive reality: relatively tighter capital, more limited access to high-end computing power, and a smaller domestic profit pool. Its companies respond by leaning hard into open-source, cost efficiency, fast application layer innovation, and global markets for monetization. China's AI story isn't about catching up or winning the frontier model race but rather by industrializing the use of AI. Chinese AI firms are building an alternative ecosystem that's leaner by necessity, more open by design, and working to wire into the economy. This is prompting its adoption across start-ups, universities, and mid-market firms worldwide. As global industry leaders continue a preference for keeping their most capable models "closed-source" -- which keeps source code proprietary or secret -- Chinese models are diffusing through easy access and utility, quietly shaping global AI practices and creating a new, technical form of soft power. China's AI Efficiency Machine: Low Costs and Constraints An equivalent dollar in China goes farther -- a fact that reframes the AI investment picture. While the US dominates total AI funding by a wide margin, Chinese firms often produce more output per unit of capital. This efficiency begins with input costs. AI engineers earn about 402,000 RMB (about $57,000) per year, far below US salary norms. China also trains one-and-a-half to two times as many AI-relevant PhDs as the US, and many trained researchers are returning home, creating a large, affordable talent pipeline. Data centers benefit from cheaper electricity, discounted land, and aggressive local subsidies -- reinforced by national policies that treat large-scale computing as strategic infrastructure. In some provinces, electricity costs are halved for facilities using chips. These advantages are further sharpened by the constraints that characterize China's AI ecosystem. Foreign direct investment into China has fallen more than two-thirds since 2019, access to Western markets has tightened, high-end domestic GPUs lag behind imported ones, and regional grids are straining under data center loads. Moreover, China's business-to-business (B&B) market is characterized by a low willingness to pay for services; firms routinely build software tools in-house rather than buy them. The result is a domestic software market roughly one-quarter the size of the US's $237 billion industry, making it difficult for many AI start-ups to reach scale or charge premium prices. In addition, large corporation culture -- especially state-owned enterprises -- prizes headcount and incremental change, resists automation, and blunts AI's potential impact. Angel investor Jun Xu captures a core monetization constraint: AI's total addressable market -- the revenues available for a company's product or service -- tracks the cost of white-collar labor. "And that pool is simply much larger in the US and other developed markets than in China because salaries are much higher," Jun says. "China's AI problem isn't chips or models or supply -- it's demand. Demand is cheaper and smaller." These pressures have pushed Chinese firms toward efficiency and ingenuity. Nvidia's Jensen Huang said in 2025 that restricting US chip sales to China would only accelerate China's domestic push. "Local companies are very, very talented and very determined," he said, "and the export control gave them the spirit, the energy, and the government support to accelerate their development." Altogether, the ecosystem has created a set of Chinese open-source LLMs operating along the "efficient frontier," delivering leaner architectures and stronger reasoning at modest compute levels. By late 2025, DeepSeek's parent announced that one million units of output could be had for about 3 RMB -- that's about fifty cents and a twentieth the cost of ChatGPT at the time. This combination of high efficiency, thin margins, and a smaller domestic monetization pool shapes how Chinese AI companies scale. Many are becoming what we call "skinny athletes" -- lean, fast, and relentlessly efficient -- which not only affects how they compete at home but also how and where they grow. For many, that increasingly means serving customers abroad, and the use of generative AI means that language, localization, and customer support are no longer decisive obstacles to global expansion. "All AI start-ups have an international strategy from day one in China, and Southeast Asia is top of the list," says Cindy Chow, CEO of the Alibaba Hong Kong Entrepreneurs Fund, which recently launched a new fund dedicated to AI application start-ups. "In fact, many Southeast Asian conglomerates and financial institutions are keen to invest with us, as they believe that the region won't catch up in AI development on their own, and that accessing advancements from China is key to staying competitive." The Chinese AI unicorn 01.AI shows what global expansion looks like when delivering value is the organizing principle. Founded by Kai-Fu Lee, an ex-Google China chief and president of leading tech VC Sinovation Ventures, the company started as a pure LLM developer in 2023 but quickly shifted toward enterprise AI agents, or what they call "super-employees" designed for functions such as insurance brokering, procurement, and logistics optimization. "Monetization challenges force AI to accelerate faster," says Ning Ning, 01.AI's vice president of international business and AI consulting. "The future of enterprise AI is not about selling technology but rather making AI accountable for business outcomes." In practice, delivering value means embedding "24/7 digital specialists" directly into enterprise operations. In one deployment with a Perth, Australia-based mining company, 01.AI's engineers positioned its AI agents as "teammates" alongside employees: a logistics scheduler to optimize rail and port traffic; a procurement agent that reads vendor emails and generates purchase orders; an operations planner to juggle trucks and crews. Each agent retrains itself as conditions change, says Ning, continuously improving its performance. China's Open-Source Bet: Diffusion and Collaboration China's open-source culture has become an accelerant. Hundreds of teams now openly release their model architectures and weights -- the blueprints and parameters that a model acquires through training -- creating a shared infrastructure anyone can examine, build upon, and improve. Chinese AI firms are pragmatic, says Chloe Fang, a founder in the text-to-image/video space. They aim for AI that delivers value and often use open-source "as a global hook to attract global users," she says. "They start building brand equity and word of mouth -- and then release better closed-source models later on." Chinese models now lead in several key generative media categories, accounting for five of the top ten image-to-video models globally and three of the top ten text-to-video and image-editing models. This open-source approach aligns with national priorities emphasizing "open ecosystems," deep integration with all industries and sectors of the economy and society, and "increased global cooperation"Chinese open-source LLMs -- led by Qwen, MiniMax, and DeepSeek -- now account for one-third of global LLM usage, up from virtually nothing in late 2024. Start-ups and companies from Silicon Valley to Africa and Southeast Asia now run Chinese models because they're accessible, transparent, and far cheaper to run than many US alternatives. You can remove anything you want, add anything you need. That flexibility and openness actually gains trust," says Sinovation Ventures founder Kai-Fu Lee, an ex-Google China chief and an influential AI voice. He points out many US institutions, students, and researchers are using Chinese models "not because they're Chinese but because they're open." Jian Wang, founder of Alibaba Cloud, argues that this moment in AI echoes the late 1990s: Netscape made its browser free and its code publicly available -- an open-source "watershed" that helped catalyze the commercial internet. Today's AI parallel is similar, he says: foundational tools are now open and collectively improvable, so the constraint is no longer whether source code itself is accessible. Rather, the industry is shifting toward what Jian calls " 'open resources,' especially model weights, data, and computing resources, which are indispensable to advancing this industry." As more of these resources become available, the more developers can skip over the massive costs of reproducing work already done by others -- a key variable in accelerating the spread of AI, he says. That said, models must also be "differentiable and sufficiently capable," researchers from Andreesen Horowitz find. In this fast-moving industry, users who find a model unreliable or a poor fit will quickly migrate, "finding value in a wider array of options, rather than defaulting to one 'best' choice. We expect the next wave of momentum to emerge at the application layer, with gains to society realized only as organizations and individuals adapt how they work. The potential of AI is universal; the challenge of capturing it remains distinctly human.