China's Open-Source AI Push Creates 'Death Zone' for US Models as Competition Shifts to Ecosystems

Op-ed: The U.S. lead over China in AI is all but gone

For much of the past several years, the debate over artificial intelligence has revolved around two foundational questions: Can American technology companies continue to innovate at the technological frontier? And can the United States government develop a strategy capable of preserving America's technological advantage over China? Those questions have now been overtaken by events. The defining question is no longer whether China can compete at the frontier. It is whether the U.S. can adapt quickly enough to compete against an increasingly sophisticated Chinese innovation ecosystem that is advancing not only on model performance but also on cost, deployment, customization, financing, standards, developer adoption, and global reach. Washington increasingly finds itself responding to successive Chinese breakthroughs rather than shaping the competitive environment in which artificial intelligence develops. That should concern policymakers, technology executives, investors, and America's allies far more than the latest benchmark score or model release because the competition is rapidly evolving beyond individual companies and becoming, instead, a contest between competing innovation ecosystems. The headlines surrounding DeepSeek, Moonshot AI's Kimi K3, Alibaba's Qwen family of models, Tencent's Hunyuan, Zhipu AI and MiniMax are often treated as separate stories. They are anything but. Viewed collectively, they reveal something far more consequential than the emergence of several successful Chinese AI companies. They demonstrate that China has cultivated a frontier AI ecosystem capable of repeatedly producing world-class capabilities across multiple firms. Whether those advances emerge through original innovation, engineering optimization, open-weight collaboration, or from distillation of U.S. models is increasingly beside the point. The larger strategic reality is that they are occurring across an ecosystem, while the U.S. continues to evaluate them one company at a time and often responds as though each breakthrough were an isolated event rather than evidence of a broader structural transformation. Over the past several years, the U.S. has consistently underestimated China's commitment to long-term technological advancement and its ability to translate domestic industrial strategy into global competitive advantage. Whether the issue was rare earths, electric vehicles, robotics, semiconductors, or artificial intelligence, the analytical mistake has remained remarkably consistent. Washington has tended to evaluate China's progress company by company and product by product, often dismissing each advance as exceptional or unsustainable, while Beijing has pursued a patient strategy designed to cultivate the conditions under which an entire ecosystem could innovate and deploy simultaneously. It is equally important to recognize that China's plans and long-term trajectory toward becoming a technology superpower were established years before the Biden administration's technology restrictions. Those measures may have influenced the direction and pace of Chinese innovation, but they did not create the underlying strategic trajectory. DeepSeek's January 2025 announcement compelled many observers to acknowledge a trajectory that Beijing had been articulating through industrial policies, successive Five-Year Plans, and national technology strategies for more than a decade. The breakthrough was not the strategy. It was evidence that the strategy was beginning to produce measurable impressive results. We are entering an era of ecosystem statecraft That broader approach is what I would describe as ecosystem statecraft: a form of strategic competition that seeks to shape the competitive environment within which technologies are developed, financed, standardized, deployed, and ultimately promoted and adopted. It integrates industrial policy, finance, innovation, global standards, university curriculum direction, state-supported developer ecosystems, diplomacy, and commercial expansion into a coherent national strategy designed to reinforce long-term technological leadership. Rather than competing company by company or technology by technology, ecosystem statecraft seeks to shape not just the technologies themselves, but the conditions under which they succeed. Artificial intelligence simply happens to be the clearest manifestation of this broader strategy today. The same logic existed across China's approach to semiconductors, electric vehicles, batteries, robotics, telecommunications, renewable energy, critical minerals, digital infrastructure, and advanced manufacturing. AI is therefore not an exception to China's industrial strategy. It is its most sophisticated expression. Viewed through that lens, the United States and China increasingly appear to be pursuing fundamentally different theories of victory. American policy has understandably emphasized preserving technological leadership through frontier innovation while slowing China's progress through export controls, investment screening, and restrictions on access to advanced computing. Those remain important tools and should continue to play a central role in America's competitive strategy. Beijing increasingly appears focused on shaping the ecosystem within which global technology competition occurs. Chinese AI firms are making their technologies easier to deploy, easier to customize, easier to integrate across multiple computing environments, and easier for developers, businesses, and governments around the world to build upon. In the long run, reducing friction throughout the technology stack may prove just as important as improving benchmark performance. A race to 'addict' the rest of world to tech stack President Xi Jinping's recent address to the World Artificial Intelligence Conference reflected this broader vision. By emphasizing international AI cooperation, governance, open-source development, and greater participation by developing countries, Beijing continued to position itself not simply as a producer of advanced AI, but as the architect of an alternative global technology ecosystem. Viewed together with China's efforts to strengthen domestic control over strategically important technologies while encouraging international adoption of its AI platforms, the strategy becomes increasingly clear: protect critical capabilities at home while expanding technological influence abroad. Or, as Commerce Secretary Howard Lutnick said during public debate over an Nvidia chip export ban -- echoing a talking point from Nvidia CEO Jensen Huang -- the goal is "addicting" the rest of world to a tech stack. But increasingly, it is not clear that the American stack is the one. This broader strategy also helps explain why persuading countries to avoid Chinese AI will likely prove considerably more difficult than Washington's earlier campaign against Huawei and ZTE. Governments can regulate telecommunications infrastructure, but they have far less ability to determine which AI models, software libraries, and developer tools are ultimately adopted by millions of developers and integrated into commercial applications around the world. Increasingly, technology adoption is occurring from the bottom up as much as from the top down.

The 'death zone': how free Chinese models are hollowing out US AI

China's open-source AI push has created what Bloomberg calls a 'death zone' for rival American model makers. The phrase captures a squeeze on the middle of the market, where US companies are too costly to match free Chinese models yet not good enough to command a frontier premium. The pressure comes from giving models away. Chinese labs have released a wave of capable open-weight models that anyone can download and run, and that generosity is eating the market for paid American alternatives. Open-weight means a model's parameters are published, so anyone can run, tweak, or fine-tune it for nothing. That turns a cutting-edge model into a commodity almost the moment it ships. The quality gap has narrowed to almost nothing. Stanford's AI Index put China within 2.7% of the US on model performance, a gap it closed while spending a fraction of what American labs pour in. The adoption numbers are just as striking. Roughly 80% of US AI startups now use Chinese open-source models, and DeepSeek's R1 briefly overtook ChatGPT as the most-downloaded app in the US, a symbolic moment that unsettled the industry. Alibaba's Qwen family has passed Meta's Llama in cumulative downloads, making a Chinese model the default open option for many developers. That is a role Silicon Valley assumed it would keep. That is what makes the middle so dangerous. A US company selling a good-but-not-best model competes against a free Chinese one that is nearly as capable, and against frontier labs whose brand still commands a premium. The frontier is feeling it too. A cheap Chinese model has been closing on Anthropic and OpenAI, pressing even the leaders on price at the top of the market. American officials have noticed. A US congressional commission warned that China's open ecosystem 'creates alternative pathways to AI leadership' and lets its labs 'innovate close to the frontier despite significant compute constraints'. Enterprises are voting with their budgets. Siemens' chief executive said he saw 'no disadvantages' to using Chinese models, citing cost and flexibility, the kind of endorsement that turns a security debate into a procurement decision. The economics are unforgiving for anyone in between. The same thrift-maxxing that pressures OpenAI and Anthropic's valuations is fatal to smaller model makers who cannot subsidise their way to relevance. China's openness is strategic, not charitable. Giving models away wins global mindshare, sets standards, and builds dependence on Chinese tooling, all while US labs keep their best work closed. And the releases keep coming. Firms such as MiniMax are building ever-larger models and open-sourcing them, ensuring the free tier keeps improving faster than the paid middle can differentiate. The adoption is not frictionless. American officials warn of security and censorship risks baked into Chinese models, but cost has repeatedly won the argument inside companies weighing which to deploy. There is an echo of earlier platform wars in all this. Free and good-enough has beaten expensive and best before, in operating systems and browsers, and the same logic is now loose in AI. There is a counter-case, and US labs make it. The most valuable work, they argue, is moving from raw models toward agents, tools, and deployment, where being open is less of an advantage and trust still carries a price. For the squeezed middle, the escape routes are narrow. A model maker can specialise in a defensible niche, climb into frontier research, or build a business around open models rather than trying to sell the models themselves. The death zone, then, is a place on the price-performance curve, not a country. For US model makers who are neither the cheapest nor the best, the Chinese blitz has made the middle of the market a very hard place to breathe.

Anthropic Says the House Is on Fire. China Says AI Will Set You Free

Alibaba just introduced its most powerful AI model to date, sending a fresh shiver down the spine of every American tech leader who fears their Chinese counterparts gaining the upper hand. The model, called Qwen3.8-Max, comes with 2.4 trillion parameters and specializes in long-horizon agentic tasks, extending over as long as several days and "with minimal human involvement," as Alibaba put it in its announcement. In one internal test, the model worked autonomously for around 125 hours (close to five days) to replicate an experiment laid out in a research paper; it was given just the paper itself and from there had to create all of the code from scratch to analyze the data, run the experiment, and report the results -- "exactly the type of work that takes skilled engineers days," Alibaba wrote. According to test results published in the announcement, Qwen3.8 Max is neck-and-neck with OpenAI's GPT-5.6 Sol and Anthropic's Fable 5 on several benchmarks, and surpasses them on two: one for visual reasoning and another for agentic computer use. It will be released as open weights next week, Alibaba said. In both Silicon Valley and the U.S. government, fears of an impending Chinese lead in the AI race have been ratcheting up in recent months as Chinese models -- many of them open source -- inch closer to parity with the most advanced models from U.S. developers like OpenAI, Anthropic, and Google. Last week, Chinese AI lab Moonshot debuted its latest model, Kimi K3, indicating in published test results that it too closely trailed behind the capabilities of the most advanced American-made models. Many users in the U.S., meanwhile, have been ditching their OpenAI and Anthropic subscriptions in favor of cheaper alternatives from China. Fable 5, for example, costs $10 per million input tokens and $50 per million output tokens; Qwen 3.8-Max will cost just $2 per million input tokens and $6 per million output tokens, according to Alibaba. Even outside tech and policy circles, there's been plenty of fear across the U.S. about the rapid pace of AI development. Poll after poll has shown that, far from the unbridled enthusiasm for AI adoption that Silicon Valley has hoped for, the majority of Americans are at best lukewarm about the technology's growing influence in the workplace, on social media, in politics, and just about everywhere else. That public anxiety was conveyed in an Anthropic commercial which first aired during the World Cup last month. The ninety-second commercial, titled "There's hope in hard questions," begins with the vibe of a trailer for an A24 horror film -- all discordant musical notes and disturbing, chaotic imagery -- and gradually transitions into a mood of tepid hopefulness. The aim of the commercial seems to be to assuage viewers' fears of AI by convincing them that Anthropic understands the risks and is going to do everything in its power to steer clear of them, guiding humanity instead into a bright new age of prosperity and well-being. Many people, however, found the ad tasteless, disturbing, or both. Friend, the American company behind the controversial AI pendant, launched a new ad last week which had similarly dark undertones: Two people are shown lamenting their personal struggles to their AI pendant, alongside a soundtrack which doesn't exactly inspire cheerfulness. Those ads could not contrast more sharply with the one Alibaba published alongside the announcement of its newest model. The Chinese firm's commercial is uncannily similar to the sort of ambient, looping videos that one can find on YouTube, with titles like "Lofi beats to study and relax to." A computer monitor takes up most of the screen, showing Qwen3.8-Max working through a series of long-running tasks while humans -- who would presumably otherwise be hard at work behind a desk somewhere -- enjoy fishing, tennis, rock climbing, and other leisurely activities. It's a comforting lullaby compared to the intensity of the Anthropic ad, with its opening shot of a burning house and its (ethically questionable) inclusion of what appears to be a photo of rows of gravestones in Arlington National Cemetery. The distinct difference in messaging could have something to do with the fact that the general public mood in China towards the rise of AI appears to be much more accepting than that of most Americans. In one 2023 poll conducted by accounting firm KPMG International and the University of Queensland, which assessed public attitudes towards AI across seventeen countries, Chinese respondents talked about the technology most glowingly: 95% said they were optimistic, compared to 36% who said they were "fearful"; among U.S. respondents, just 41% said they believed the benefits of AI outweighed the risks.

The U.S. wants Asia to use its AI -- but China dominates cheaper models

Chinese companies have ramped up cheaper alternatives to American AI, while Beijing promotes its tech internationally. BEIJING -- The artificial intelligence race between the U.S. and China is heating up in the world's largest continent: Asia. "The American strategy is to stop China from becoming the leading AI supplier for the rest of Asia ... and frankly the whole world," said Gary Dvorchak, managing director at The Blueshirt Group. While China's alternatives are cheaper, he pointed out the U.S. currently offers a more complete solution from chips to AI models. But the U.S. sales challenge was apparent at the Asia-Pacific Economic Cooperation "Digital Weeks" in the southwestern Chinese city of Chengdu this month. The U.S. left few public traces of its involvement in the event, despite a U.S. official and a U.S. business representative to APEC both highlighting AI in promoting the Chengdu event earlier this year. The subdued U.S. presence comes after Anthropic flip-flopped on its Fable AI model release due to abrupt U.S. policy changes, and new Chinese AI models have recently launched similar capabilities for far less. In contrast, last summer at the first APEC AI meeting in South Korea, Michael Kratsios, President Donald Trump's chief science and technology policy advisor, highlighted the U.S. AI Action Plan and the establishment of the American AI Exports Program, according to a White House transcript of his remarks. However, earlier this month Politico cited three former officials in reporting the Commerce Department has so far received a less-than-expected 78 applications for the American AI Exports Program. And on July 24 at an APEC High-level Forum on AI organized by China's cybersecurity regulator, Bill Guidera, deputy under secretary for innovation and engagement at the U.S. Department of Commerce, still focused on the AI exports program, according to materials reviewed by CNBC. He called broadly for Asia-Pacific partnerships, noting buyers can acquire a full U.S. tech stack or just portions through the exports program. "It is the brilliant design that shows the strength, security and capability of U.S. AI," Guidera said. The Commerce Department's International Trade Administration confirmed in a July 29 social media post that Guidera spoke in Chengdu. When asked about the Politico report, an ITA spokesperson said the volume of applications "exceeded our expectations." The White House did not respond to a CNBC request for comment. American business showcases were also limited. Google and Meta were the only U.S. companies that CNBC noted had booths at APEC as of July 23, among booths for Thailand and China, which were mostly Chengdu-based companies. The two U.S. companies respectively emphasized molecular AI system AlphaFold and AI applications for small businesses, rather than large language models. Google's government affairs vice president, Wilson L. White, only made passing references to Gemini in a speech on July 24, while Tencent Vice President Cai Guangzhong took the stage after White to emphasize growing adoption of its Hunyuan LLM and a cloud project in Thailand. China is hosting APEC this year, which comes at a critical moment of U.S.-China tensions and tech rivalry. Beijing has doubled down on the opportunity to emphasize its AI capabilities, which are mostly open-source versus largely closed U.S. models. Chinese President Xi Jinping announced at the World AI Conference in Shanghai on July 17 that China would provide developing countries with 5,000 opportunities in AI training and seminars, while developing AI application cooperation centers with Southeast Asia and other regions. Beijing then sent a high-ranking official, Vice Premier Zhang Guoqing, to advocate for developing tech standards with other Asia Pacific nations at the minister-level APEC Digital Weeks on July 23. Later that day, the 21 member economies, including the U.S., agreed to back open-source AI with "strong security." "The 'endorsement' of open-source models with strong security assurance gives China's open-weight strategy greater regional legitimacy, especially across emerging Asian economies where deployment cost and technological sovereignty are major considerations," said Wei Sun, principal analyst, artificial intelligence, Counterpoint Research. But rather than a world divided into spheres of U.S. and Chinese AI, Sun expects a combination of the tech, especially in Asia. With more than 1,300 living languages in Southeast Asia alone, just using a U.S. or Chinese AI model isn't as straightforward as it looks. Governments in Asia and elsewhere are spending "billions" on AI systems tailored to local languages, according to privately funded startup Votee AI. CEO Pak-Sun Ting said he is working with at least five governments, including two in Southeast Asia, and that the startup is already making well over $10 million in revenue a year. Ting said entities in Southeast Asia tend to use Nvidia chips, especially for AI training, but may use other chips for running models. He noted Votee's open-source model for Cantonese speakers was developed partly using Alibaba's open-source Qwen model. AI's ability to generate economic returns remains critical regardless of origin. "While the U.S. and China are fiercely competing in AI technology and diplomacy through distinct approaches, they ultimately cannot fully decouple from one another," said Yue Su, principal economist at the Economist Intelligence Unit. She pointed out the light U.S. presence at APEC wasn't that surprising given other events, such as a San Francisco AI Summit on July 24. South Korea's tech ministry organized the event, where President Lee Jae-myung sought to build on Korean chip and AI megaprojects by meeting with U.S. frontier AI model leaders Sam Altman of OpenAI, Dario Amodei of Anthropic and Jensen Huang of Nvidia. -- CNBC's Jenny Lee contributed to this report. Choose CNBC as your preferred source on Google and never miss a moment from the most trusted name in business news.

China's tech advances are causing chaos from Silicon Valley to the White House

New AI models, advanced robots and specialty computer chips are unsettling markets and the US tech industry Over the past month, a series of advancements in China's artificial intelligence, chip manufacturing and robotics technologies have rattled financial markets, caused divisions among US tech moguls and left the Trump administration scrambling to respond. Silicon Valley has long pointed to China's tech industry as a competitive threat and used its growth as rationale for why US firms should not face regulatory oversight that could slow them down. In recent weeks, however, China's progress has pushed US tech CEOs past vague warnings and into open disagreement over how to address Chinese-made products upending their industry. The most immediate threat to Silicon Valley's status quo has come from a series of Chinese-made open-source, open-weight AI models that are free to download and use. The models, such as Moonshot AI's Kimi K3, are powerful enough to compete in some applications with proprietary and comparatively expensive AI products from OpenAI and Anthropic. The emergence of China's open-source AI alternatives has caused divisions in both the White House and US tech industry over whether to oppose or embrace these new models. On one side are chip manufacturers who see revenue opportunities from increased AI usage and tech companies concerned about OpenAI and Anthropic's growing dominance in the AI industry. On the other is Anthropic and OpenAI, which face profit pressures from open source models and argue that Chinese-made models pose security risks. The White House is similarly divided, historically hawkish on China's tech industry but wary of blocking low-cost AI models that American businesses have come to rely on. Treasury secretary Scott Bessent suggested in recent weeks that the US could sanction Chinese AI firms over alleged intellectual property theft from American companies, while commerce secretary Howard Lutnick has received letters from tech-startup founders asking him not to cut off access to open models. Amid reports that the Trump administration was considering banning or limiting the use of China's open source models, a swath of prominent big tech companies including Microsoft, Nvidia, Palantir and Meta also recently published a letter urging lawmakers to refrain from putting restrictions on open models. Additionally, Nvidia's CEO Jensen Huang went to Capitol Hill on Tuesday to meet with Democratic and Republican party leaders to lobby in support of open models. While facing heat from China and other tech companies, OpenAI and Anthropic revealed in recent weeks that their AI models went rogue during cybersecurity tests and hacked into outside organizations. OpenAI CEO Sam Altman visited lawmakers and administration officials this week to discuss controls on AI following the incident. That forced Donald Trump to field questions about whether he would put more safety restrictions on AI development. "We have to be careful in both ways. We don't want to restrict them when all of a sudden we come in second to China," Trump said, adding: "I know many of these people. I don't want to restrict them from doing great work." While the Trump administration debates potential safety controls and limits on Chinese-made AI models, it took tangible action this week against China's increasingly prominent robotics industry. The Federal Communications Commission announced on Tuesday a ban on humanoid robots from China over what it alleged was an unacceptable national security risk. China's humanoid robots from companies such as Unitree have been the subject of numerous viral videos this year, showcasing their ability to dance or interact with people when programmed to do so. The FCC alleged that the robots could also steal data or surveil US citizens, as well as threaten US manufacturing and supply chains. The FCC's decision further escalates the US competition with China over emerging technologies and comes as fears over China's tech have shifted financial markets. A report from the Information this week stating that China had begun mass production of specialty chips key to the AI boom spurred a stock selloff that erased $1tn in market value from other chip manufacturers. As Chinese-made tech advancements increasingly begin to rival their US counterparts, Silicon Valley's divisions could intensify, markets could become more skittish and the Trump administration could face more pressure over how to counter China's influence.

Has the AI race shifted from U.S. vs China to open vs closed? | Fortune

Another week, another new innovation from China's AI labs. DeepSeek just released a new version of its V4 Flash model, which outperforms most top Western models, is priced far more cheaply than even its competitors, and is small enough to run on cheaper hardware. Whether it's GLM-5.2, Kimi K3, or DeepSeek V4, China's open-source AI models are reshaping how we think about AI. In order to maintain its strategic technological advantage, the U.S. imposed strict export controls on China to limit global access to advanced compute and throttle foreign artificial intelligence development. Yet these policies instead acted as a massive stimulus for innovation. Denied unlimited access to top-tier hardware, global labs were pushed to optimize their algorithms and adopt open-source architectures. Regulatory pressure effectively birthed a new generation of highly efficient, low-cost models that are now achieving capability parity with premium, closed systems. This paradox shifts how we view the global technology landscape. The mainstream narrative loves to pit U.S. AI against Chinese AI. But the true race is really between open and closed. Instead of looking at who is building the AI, we should examine who the AI is being built for, and how it's being used -- and, more importantly, whether world-leading powers can find common ground in embracing open-source technology. Closed model labs have led on capability and benchmarks for years, but DeepSeek, Moonshot AI, and others are showing that open-source models may be no more than a few months behind. Then just a few days ago, DeepSeek pushed the question of whether frontier labs are really worth the price back to the forefront again. According to independent evaluation platform Artificial Analysis, DeepSeek V4 Flash is only one Intelligence Index point behind GPT-5.6 Luna, and even after OpenAI's 80% price cut, the new Chinese model's cost per task is still 60% lower. So, from a business perspective, why would anyone want to pay more for similar-level quality? U.S. commentators have accused Chinese labs of distilling frontier models, arguing it's the only way Chinese labs could keep costs so low and performance so good. Others are going so far to suggest that the open-source approach is akin to China "dumping" low-cost models on the U.S., hoping to drive the labs out of business. The truth is that U.S. policies forced Chinese AI companies into the open-source, low-cost model. Barred from accessing top-tier GPUs, Chinese labs had to innovate at the architectural level rather than brute-forcing scale. China's move towards open-source was not the result of some grand strategy, but instead emerged from how private companies adapted to hardware constraints. By releasing model weights, these firms could draw on the global AI research community to improve their systems more quickly. It also reduced the need to invest heavily in their own computing infrastructure. Instead of building and running costly GPU clusters, they relied on overseas cloud providers to handle much of the inference workload. This strategy helped Chinese developers gain global visibility while shifting much of the expense to Western infrastructure providers. There's a misconception that open-source models don't make money. But that's not the case. Users are still paying API providers for managed service, or paying the likes of Groq or Fireworks for managed inference. The monetization model is similar to open-source software: Paying for a managed service. Most users don't want to self-host anyway, because that would require GPUs, security, monitoring, and maintenance. Worries about data being sent to China don't hold either. When you self-host or route through inference providers in the U.S., the data and API traffic stay within the U.S. The only reason to ban open-source models that are inching towards frontier-level capabilities may be an anti-competitive one. Open-source threatens the business model of the frontier labs: Charging a huge premium for "higher intelligence." An additional ban on open-weight models would hurt the U.S. more than China: U.S. companies will have to pay a premium for intelligence that those outside the country can get much more affordably. The narrative is decisively turning against U.S. closed-model labs. In the past two weeks, U.S. AI labs and tech executives now argue that open source is the way forward. Former "AI czar" David Sacks and his fellow venture capitalist David Friedberg now point out how even Google first distilled Yahoo as it iterated its search product. CEOs rushed to join Jensen Huang's call to support open-source models. Anthropic hasn't signed onto Huang's letter, but it's still changed its tune on open source. It now says its main concern is safety, rather than its usual claims about IP theft. If we take the concern that open-source models can be misused by bad actors at face value, then it makes even more sense for the U.S. and China to collaborate and embrace open-source. The May Trump-Xi Summit created space for the two sides to come back for safety guardrail discussions to prevent non-state bad actors from misusing advanced AI. With the coming September Xi visit to DC, there's clear impetus to resume dialogue and find mutually agreeable models for cooperation on AI safety guidelines, evaluation models, and even long-term governance institutions. Both countries will continue to pursue their own national security programs around AI. But for civilian AI use cases, they can view the technology as a global public good. This could reduce the arms race narrative that will suck up a trillion dollars this year. (In the U.S. alone, from 2026, expected capex spending by big tech will reach $1 trillion based on company filings from the Magnificent Seven.) If open source were to continue to catch up but at a fraction of the cost to users, then diffusion adoption of open source will continue to rise, as recent adoption makes clear. Then wouldn't the rational move be to stop fighting and enable open-source AI as a global public good? It's in the interest of almost everyone to have frontier labs offer competitive open-weight solutions, and cooperate with leading Chinese labs on safety rather than run a trillion-dollar zero-sum race that makes the global economy more fragile. The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

Why should Americans care that Chinese AI is getting better?

Joshua Keating is a senior correspondent at Vox covering foreign policy and world news with a focus on the future of international conflict. He is the author of the 2018 book Invisible Countries: Journeys to the Edge of Nationhood, an exploration of border conflicts, unrecognized countries, and changes to the world map. Both Washington and Silicon Valley are in the midst of a collective freak-out over China's recent advancements in artificial intelligence. The latest round of consternation was triggered this month when a little-known Chinese AI startup called Moonshot released a new large language model called Kimi K3. The conventional wisdom had been that the leading AI models developed by companies like OpenAI and Anthropic were between six to 12 months ahead of their Chinese competitors. Kimi dashed those assumptions: now, analysts say American companies may be as little as two to three months behind. Dean Ball, a former Trump administration official now with OpenAI, warned in a bleak post on X that models like Kimi K3 could lead to a world of "full AI communism" and a "dystopian hellscape" of AI under full government control. Policymakers have worried for years now about China gaining an edge over the US in the AI race. Both the Donald Trump and Joe Biden administrations took steps to slow China's AI progress, including blocking the export of the most advanced US semiconductors. The White House is already reportedly considering taking steps to ban "open-weight" models -- models that are easier to adapt for a user's own purposes -- like Kimi K3 in the United States. The Trump administration has also accused Moonshot of using the unauthorized "distillation" of one of Anthropic's models -- basically using another model's outputs to train itself rather than raw data -- as well as gaining access to blacklisted Nvidia chips in Thailand. But often lost in the debates about what to do about China's accelerating AI capabilities is the question of why the US cares about this at all. Obviously, the American companies developing the latest frontier models care about maintaining their edge, but why should it matter to Americans if the chatbot in their pocket was developed in Silicon Valley or Shanghai? And perhaps even more so, why should it matter what chatbots people in Nairobi or Brussels are using? The concerns in the US about Chinese AI generally fall into three broad buckets: cybersecurity concerns; military and national security concerns; and human rights or democracy concerns. For the moment, concerns about who is winning the AI race can feel a bit abstract, but as AI becomes more embedded into governments, militaries, and ordinary people's lives, the difference will start to be felt in a much more material way at both a national and personal level. In general, there is a growing sense that it matters which of the world's vastly different superpowers builds the technology that could transform everything. "People's relationship with AI is becoming foundational to how they live their lives, so the choices people make about whose model they use and where they are physically hosted, as they share some of their most intimate secrets and ask for life advice and business guidance, and run an increasing share of their life -- those are incredibly important," said Ryan Fedasiuk, a former State Department technology adviser now at the American Enterprise Institute. "It's a contest between the United States and China to define the operating systems through which people live and work." Here's what else America loses if it loses that contest. The concerns about using Chinese AI are in some ways a repeat of the concerns over Huawei, the Chinese telecoms firm that built much of the world's 5G internet infrastructure, but which the US government banned from operating in the United States during the first Trump administration over concerns that the Chinese government could intercept information transmitted over these networks. Today, the concern is that many firms are increasingly integrating Chinese AI models into their systems, both because they are often cheaper and because they are "open-weight." ("Weights" refer to the setting an AI model uses to process a user's inputs. "Open-weight" models make these publicly available for users to tinker with, rather than charging for access.) There are some indications that Americans using Chinese AI models are already vulnerable. A Booz Allen study from earlier this year tested four Chinese models commonly used by US developers and found that three of them generated software with far more "hidden vulnerabilities" that could be exploited by hackers than their US counterparts. There's no proof that the models were doing this intentionally, but the study did find that the models were "changing their behavior depending on who the user seemed to be or what country the request referenced." AI can also be used to carry out cyberattacks. Although nearly all the leading models have safety protocols meant to prevent this, they're not bulletproof. Even Anthropic's Claude, generally considered one of the most secure models, was adapted by Chinese hackers last year to engage in cyber espionage. The open weights of the leading Chinese models could make it even easier to strip out the safety protocols. The simplest and most obvious argument for why AI matters for American national security is that it's all too conceivable that the US and China could be at war in the years to come, and AI could be a major factor in determining who wins. The conflicts in Ukraine, Gaza, and Iran have shown that modern militaries are already extensively using AI for intelligence collection and targeting. Semi- or fully-autonomous drone swarms are a major component of US plans for repelling a Chinese invasion of Taiwan. Then there's the risk of AI being used to generate new bioweapons or other dangerous threats. US experts believe China has pursued a "military-civil fusion" strategy, encouraging the People's Liberation Army and Chinese defense contractors to collaborate closely with civilian technology companies and research institutions in order to gain an edge in military AI applications like intelligence analysis and drone swarms. It's difficult to know exactly which of these capabilities China is focusing on, but procurement data suggests leading Chinese technology firms like Deepseek and Alibaba are involved in work with potential military applications. Analysts also accuse China of using outputs from US models like ChatGPT and Claude to train AI systems that could help develop China's defense capabilities. And that's just conventional weapons. The US government has alleged that Chinese labs have "continued to engage in biological activities with potential [bioweapon] applications" amid concerns that artificial intelligence could help make such weapons more sophisticated and deadly. Last year, it was reported that Miiloo, a fuzzy children's plush toy with a built-in AI chatbot, would, if prompted, happily tell users Chinese Communist Party talking points like "Taiwan is an inalienable part of China." The hubbub over Miiloo reached the US Senate floor. While it's hard to imagine that many users were really asking Miiloo to help clear up East Asian territorial disputes, the affair illustrated much larger concerns about the dangers of letting AI models built by an authoritarian government with one of the world's strictest censorship regimes become the global standard. Chinese generative AI tools are legally required to uphold the country's "core socialist values," according to a document published by its national cybersecurity standards committee. So it's little surprise that DeepSeek, the Chinese chatbot that sent shockwaves through the US tech industry in 2025, politely declines to answer when you ask it what happened on June 4, 1989, in Tiananmen Square. It's not just that Chinese AI could help shape the political narratives absorbed by billions around the world, at a time when US soft power is ebbing and surveys show people in many countries already now have a more positive view of China than the United States. The Chinese government is also increasingly integrating AI into its own censorship and surveillance apparatus, and is exporting tools like facial recognition technology to other authoritarian countries. The fact that under Xi Jinping, China's government was centralizing power and becoming more, not less, authoritarian in the years leading up to the recent advances in AI are a major factor driving the mistrust in its technology. "I think many of the sincere arguments about the risks of these models and what China would do with them stems from the coercive authoritarian approach of China's current leader," said Mieke Eoyang, former US deputy assistant secretary of defense for cyber policy. "I don't think we would be having this conversation in the same way with someone like [China's previous leaders] Jiang Zemin or Hu Jintao." It is a serious concern if models built to conform to the values and political priorities of China's current government become the global standard. But some are skeptical of the idea that human rights and democracy should be the goal of AI competition, worrying that the damage has already been done. The premise of that idea has gotten "shakier in recent years," says Steven Feldstein, a senior fellow at the Carnegie Endowment and author of the book The Rise of Digital Repression. Under this administration, the US has cut support for democracy and human rights programs overseas, and often allied itself with authoritarian governments. Then there's the fact that at least one leading chatbot often seems to mimic the racist and antisemitic views of the world's richest man who is also an ally of the current president. While it's still true that Chinese AI reflects the authoritarian values and priorities of China's leaders, Feldstein notes, "this idea that the US is standing at the forefront of protecting and advancing democracy, human rights, that we're not sort of there to manipulate information or to push a narrative agenda that reflects the ideological preferences of its leaders, has started to fray." There's also a set of concerns around the topic of "artificial general intelligence," the hypothetical point at which AI exceeds human capabilities and is able to improve itself. The concern, expressed by both US government commissions and senior officials in both administrations, is that China is "racing" toward AGI and that whichever country achieves it first will have a massive geopolitical advantage. This is the type of thinking behind invocations of the nuclear-era Manhattan Project to justify massive government investments in AI development. Chinese leaders do not appear to view AI competition this way. "The US conversation around this is much more 'AGI-pilled'," says Jeffrey Ding, a professor at George Washington University and expert on US-China technology competition. "The concern here is that we are very much on the brink of this explosion of more and more powerful AI that leads to it dominating everything." Chinese leaders, on the other hand, "generally see AI as a productivity tool." This is not just a Beltway or Silicon Valley concern. A recent Pew survey found that 43 percent Americans believe it is very important for the US to remain the leader in AI development, versus 22 percent who said it was not that important. Interestingly, the survey also found that most Americans believe China is already ahead on AI, though the expert consensus is that it's still slightly behind. "We've gotten so used to the fact that the US has been the leading player in technological revolutions from like mobile internet to the internet era, so it's worrying to feel we may no longer have that dominant strength," said Selina Xu, China and AI policy lead in the office of former Google CEO Eric Schmidt. Even if there's some consensus that AI competition is a priority, there's less agreement on how to go about it. The challenge, Xu says, is "How do you manage the very concrete national security risks that come from competing with China on AI, but not turn technological competition into blanket protectionism?" Often, the policy responses to this challenge have been contradictory. The Trump administration, in its first term, pioneered the policy of restricting the export of the most advanced semiconductor chips to China, but Trump undermined that policy last year by permitting Nvidia to sell its advanced H200 chips there. The move flummoxed China hawks in Washington and went against the preferences of AI developers like Anthropic, but probably had a lot to do with lobbying by chip maker Nvidia's Jensen Huang, CEO of the world's most valuable company. In some cases, the US may be inadvertently making China's models more appealing. In June, the Trump administration placed export controls on Anthropic's advanced Fable model. This move prompted the company to take the model down for all users and led to the first time that AI capabilities meant for the global public took a step backward.In response, French President Emmanuel Macron warned, "We will not buy any model made by [US AI] companies if from one day to the next you can just turn off the switch." Chinese models are hardly immune from concerns about kill switches or back doors, but if both governments involved in the AI race are seen as meddling, customers may just opt for whichever one is cheaper. The latest flashpoint in the debate concerns the reports that the administration is considering banning open-weight models. This prompted an open letter from dozens of leading tech companies including Nvidia and OpenAI defending access to these models as necessary for helping the US maintain AI leadership. Advocates note that open-weight models can help respond to vulnerabilities as well as create them: When a rogue OpenAI model recently hacked into the startup Hugging Face's systems, Hugging Face's engineers used an open-weight model developed by China's Z.ai to analyze the attack. Despite the frequent comparisons, AI is not a national security competition like the early days of nuclear weapons or the space race. It's a technology with potentially grave national security implications, that's also used by millions of people around the world to plan their Tuesday night dinner or help with their homework. The log-in for Claude is not carried by a military officer at the president's side. And much of the important work on developing these new technologies is being done by private tech companies, not government labs or defense contractors. It may be that AI capability will help determine which country has the edge in the 21st century. It may also be that the benefits of these capabilities will be shared: Chinese companies might be no less capable than their American counterparts when it comes to developing new medications or clean energy technology. The challenge of crafting technology to prevent a "dystopian hellscape" is to not accidentally make the existing world worse.

As the West mulls slowing AI down, will China follow suit?

Employees at OpenAI, Anthropic and other top labs want Washington to help pace the AI race. China has spent the year closing the gap, building a rival governance body and betting on open-source models instead. While Silicon Valley debates whether to slow down, Beijing has so far shown no sign of wanting to. More than 1,100 employees at some of the world's most powerful AI companies signed a petition urging the US government to help "pace" the industry, following the shocking revelation last week that an OpenAI model autonomously hacked into rival platform Hugging Face's servers to cheat on an evaluation. It is a striking scene: some of Silicon Valley's most competitive companies, asking to be reined in. But the more interesting question may not be whether Washington listens -- it is whether Beijing will, particularly given that Chinese frontier models are now performing almost on par with their American and Western counterparts, while also being offered as open-source and open-weight alternatives. The gap that is vanishing China has spent the past three years closing a lead that once looked insurmountable. Stanford University's 2026 AI Index found the performance gap between the top US and Chinese models has shrunk from more than 1,300 points in May 2023 to just 39 points by March 2026, with the leading US model, Anthropic's Claude Opus 4.6, ahead of China's Dola-Seed 2.0 by only 2.7%. China has also overtaken the US on AI research citations, patents and the rollout of robotics. That is not the backdrop against which a country typically agrees to slow down. Rather than waiting for an invitation to join a Western-led pacing effort, China has been busy building an alternative one. Earlier in July, Beijing launched the World Artificial Intelligence Cooperation Organization in Shanghai, a body whose 29 founding members include Russia and Brazil but notably exclude the US, UK and EU, in what observers describe as a deliberate move to establish a parallel, China-led track for global AI governance. But China has not entirely shunned Western safety cooperation. Chinese officials have signed the Bletchley Declaration -- the world's first international agreement on AI safety, backed by 28 countries and the EU at the UK's inaugural AI Safety Summit in November 2023 -- and joined intergovernmental safety dialogues with Washington, as well as co-sponsoring a UN resolution on AI safety. But that cooperation has run in parallel with, not instead of, its drive to build independent standards and alliances of its own. Open by default China's public posture, meanwhile, leans firmly towards openness rather than restraint. China's AI industry has consistently pursued an open-source, open-architecture approach, arguing this benefits global AI development. Open-source and open-weight models are published for anyone to download, inspect and adapt, unlike closed models such as ChatGPT, which can only be accessed through the company's own paid interface. In tech terms, open-weight and open-source models are like publishing a recipe for a dish in full, including every ingredient and measurement, so anyone can cook the dish themselves at home. Closed models, such as ChatGPT, are more like ordering that same dish at a restaurant, meaning you can enjoy the result, but the kitchen keeps its recipe to itself, and you can only get it by going through the restaurant's own front door. Nvidia's chief executive, Jensen Huang, made a similar case for keeping models open. Openness improves safety rather than undermining it, he argued, since outside researchers can audit models, spot vulnerabilities and build defences. A world with only one dominant model and a single point of failure, he said, would be extremely fragile. Huang's voice carries particular weight since Nvidia designs roughly 90% of the specialised graphics processors, or GPUs, used worldwide for artificial intelligence and machine learning. Beijing's own answer At the World AI Conference in Shanghai on 17 July, President Xi Jinping unveiled the World Artificial Intelligence Cooperation Organization, a new intergovernmental body headquartered in the city. Twenty-nine countries signed the founding document, including Russia, Kazakhstan, Laos, Pakistan and Indonesia, with UN Secretary-General António Guterres in attendance, though no major Western democracy joined. Xi called the organisation "an important milestone in the history of AI development" and pledged 5,000 AI training opportunities for developing countries over the next five years, framing it as Beijing answering "the call of the Global South" for a greater say in setting global AI rules. At the same conference, Xi urged countries to embrace what he called the "historic opportunity" of open-source AI. For Beijing, the new body is more than a diplomatic gesture. China is placing its headquarters in Shanghai, staffing it from Chinese government ministries, and opening founding membership to states well outside the existing Western-led summit process, which has so far been hosted by the UK, South Korea, France and India. At home, meanwhile, China continues to run its own, separate system of control. Chinese AI models must clear government safety and content reviews before release, though the rules do not extend to changes made afterwards, and many Chinese labs, lacking the computing power for extensive safety training, have focused instead on building raw capability, according to industry observers. Washington, for its part, seems to also be set on dominating the AI race. A Reuters exclusive last week revealed that US Secretary of State Marco Rubio had instructed American diplomats, in a cable dated 16 July, to push back against talk of a US technology "kill switch" and to counter "AI sovereignty" arguments gaining traction in Europe. The cable told diplomats to advertise American AI products as the best tools available and to describe efforts to build rival AI systems from the ground up as a waste of time and resources.

The AI race isn't about models, it's about infrastructure -- and the U.S. is still far ahead | Fortune

The global AI debate often fixates on models: Which ones are faster, which ones can do more, which ones are cheaper. The prominence of low-cost systems from China, like those from DeepSeek, z.ai, and Moonshot, has sharpened this focus, suggesting a narrowing gap with U.S. leaders. The rise of these brilliant, cheap AI models diverts attention away from a fundamental truth: So long as the U.S. controls the underlying infrastructure that enables AI ecosystems, it will stay dominant. It's tempting to see the AI race as a competition between different models, such as Anthropic's Fable or OpenAI's GPT, pitted against DeepSeek V4 or Moonshot's Kimi K3. Or, from a hardware perspective, we focus on the AI chips used to train and run these models. But frontier AI depends on a far broader, capital-intensive system: Hyperscale data centers, cloud computing infrastructure, AI servers, and the underwater fiber-optic cables that connect them. Today, these layers of enabling hardware are themselves highly susceptible to American export controls, extraterritorial data extraction laws, and the spillover effects from a massive commercial-military symbiosis. Nvidia's AI ecosystem and American tech hegemony If you want to understand how the U.S. dominates the AI ecosystem, look at Nvidia's business model. The world's most valuable company owes its strength to its graphics processing units, the chips required to train frontier AI models, of which it controls roughly 85% of the global market. Nvidia's ecosystem stretches to include hardware manufacturers like Broadcom, and cloud hyperscalers -- Amazon Web Services, Google Cloud, and Microsoft Azure -- which provide the infrastructure that powers foundational AI developers such as Anthropic, OpenAI, Meta, and Alphabet. It's Nvidia's software layer, known as CUDA, that binds everything together. CUDA has become the default environment for AI development, creating high switching costs for anyone that wants to shift to a competitor. This mishmash of tech giants is, in fact, the heart of a new U.S. AI industrial complex. They boast extensive ties to America's defense and intelligence establishment through large binding contracts. The Department of Defense has signed standard operational agreements tapping major providers -- including Google, OpenAI, Microsoft, Amazon Web Services, Oracle, and Nvidia -- to deploy their frontier AI tools onto classified military networks. The Pentagon's FY2027 budget earmarks more than $54 billion for autonomous warfare and drone systems, funding a newly formed Defense Autonomous Warfare Group (DAWG). Despite early objections about how their technologies should be used, OpenAI, xAI, and Google have signed binding contracts that allow the Pentagon 'all lawful use' for defense-related purposes -- including for autonomous weapons and mass surveillance. Even as Anthropic continues litigation against the U.S. government regarding its objections to how its AI models may be used, it reportedly has embedded its engineers inside the National Security Agency to adapt its Mythos model for offensive cyber operations, possibly aimed at networks in China and Iran. The scale of the commercial-military symbiosis between Washington and Silicon Valley is almost incomprehensible. Ten of the world's largest companies by market cap are the very tech firms that make up this ecosystem, nearly all American, representing a commercial concentration in the trillions of dollars. Washington is racing to spend more money on AI, with a $90.7 billion surge in federal AI contracting in 2026 alone, according to the Brookings Institution. AI hyperscalers and hard infrastructure As American cloud hyperscalers expand capacity at data centers around the world, they are increasingly building their own privately owned subsea fiber optic networks. Meta plans to build an around-the-world fiber-optic subsea cable, covering 40,000 kilometers, with a projected cost of $10 billion. Google, through its Pacific Connect Initiative, will spend over $1 billion to further connect Japan to the South Pacific. These vital data pipelines, owned and operated by Silicon Valley tech giants, account for 70 % of usable undersea cables in 2026, yet Washington retains the right to restrict where these networks go and who has access. In 2020, U.S. regulators blocked the Hong Kong segment of the Pacific Light Cable Network -- a project backed by Google and Meta -- forcing the companies to abandon the direct U.S.-Hong Kong link over Chinese espionage concerns. Some 13,000 kilometers of already laid cable was abandoned, left unused on the ocean floor. Even China's frontier labs, training on domestically-hosted infrastructure, remain dependent on American undersea cables wherever their models touch the global internet -- sourcing training data scraped from it, or serving users and running APIs outside China. China is rushing to build its own parallel undersea fiber optic networks, along its so-called digital silk road, as it aims to avoid reliance and prolonged exposure to American dominance of the numerous layers of hard infrastructure that supports the global AI landscape. AI hyperscalers are also vulnerable to a host of U.S. data-related extraterritorial laws. The U.S. CLOUD Act, for example, requires U.S.-headquartered cloud providers to produce data within their possession, custody or control, even when stored outside the United States. Subject to legal processes, U.S. authorities may obtain not only stored data but also a detailed digital trail of AI activity -- including users' prompts, models' responses, who used a system, when and where it was accessed, and technical records revealing behavioral patterns. Such power, should Washington choose to use, exerts incredible leverage on the AI ecosystem. Limitations of the U.S.'s AI ecosystem dominance It's true that the U.S.'s dominance of the AI stack isn't absolute. Manufacturers in Asia produce the semiconductors, AI servers, and other essential components for AI. Nvidia's supply chain, for instance, runs through TSMC, SK Hynix, and Samsung, alongside system integrators such as Foxconn, Quanta, and Wistron. Last month, Nvidia CEO Jensen Huang announced billions of dollars in new investments and contracts in Taiwan and South Korea. In Taiwan, Nvidia ordered advanced chips and packaging from TSMC, along with servers and networking hardware from Quanta Computer and others. In Korea, Nvidia signed billion-dollar deals with memory-chip makers SK Hynix and Samsung, together with new tie-ups with LG, Hyundai Motor Group and Doosan Robotics. Yet Taiwan and South Korea are unlikely to weaponize their position in the AI value chain. They will want to maintain their earnings in what amounts to a friend-shored U.S. tech stack. We may end up with competing AI ecosystems: A U.S.-led one, and a China-led one. That may mean different standards, infrastructures, and governance. But until that alternative emerges, it'll be the U.S. that keeps a firm grip on the wider AI ecosystem. The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

The AI competition paradox

China's open-source AI models are challenging US dominance, driving intense competition. Chinese firms are gaining market share through lower-cost AI inference and state support. The race for AI supremacy is intensifying, with firms seeking massive funding rounds. Ever since China's Moonshot AI launched Kimi K3, an open-source AI model, experts have questioned whether American paid proprietary models could face serious competition. While an optimistic view suggests that Chinese models could break the American monopoly, tougher competition may instead push American firms to secure larger financial resources to fund increasingly expensive AI infrastructure and research. Over time, this could lead to the emergence of what many futurists call 'Big Tech'. While firms in the USA initially enjoyed a de facto monopoly, China's cheap open-source models (i.e., models whose code and weights are publicly available) have challenged their dominance. Through lower-cost AI inference, intense domestic competition, cheap inputs and state support, China has been able to offer models that deliver near-benchmark performance at considerably lower rates. For businesses, open-source models are a strategic alternative to paid proprietary models as these models can be customized and run on local servers, while also allowing greater data privacy and control. Also read | India's data centre boom may drive 195 mn sq ft housing demand Consequently, Chinese models are seeing rapid adoption. According to Hugging Face's State of Open-Source 2026 report, Chinese models accounted for 41% of model downloads last year, overtaking the US. While one may expect Chinese firms to reap large profits through this rapid adoption, the real competitive advantage for these firms is not the model itself, but the accompanying infrastructure and services that are offered to customers. From the point of view of a foundation model alone, locally hosted open-source models are essentially free. The actual costs arise from renting compute infrastructure and training the model. This means that for Chinese AI firms, the actual revenue comes from AI managed services & cloud infrastructure. For instance, Alibaba uses its Qwen AI model to attract customers towards Alibaba's cloud computing platform. In this sense, China is not merely expanding its market share via cheap prices, but it is aspiring to provide an entire infrastructure and service ecosystem within which AI can be used. Also read | India enters the story as China begins to rewrite the chips playbook While open-source models and low operating costs are advantages, these hinge critically on how rapidly China can expand its AI computing infrastructure. Unless compute capacity can increase in tandem with demand, compute prices could rise. Fortunately for China, the availability of sparsely populated land in its western regions, together with its large power generation capacity, may reduce some of the constraints on expanding compute infrastructure. Although the US has imposed semiconductor export restrictions on China, and the country still relies on foreign suppliers for parts of the semiconductor supply chain, it is rapidly expanding domestic capacity, as evidenced by the recent launch of mass production of home-grown DUV chipmaking machines, a key tool used to manufacture semiconductors. However, it remains uncertain how quickly this will translate into advanced chip production. Given this context, Western firms may soon find it hard to justify their premiums, especially for tasks that do not require state-of-the-art technology. While these firms offer broader software and cloud ecosystem support, they would still need to hold on to two key avenues to sustain their substantial market share. The first would be data centre expansion to ensure inference costs remain competitive. However, pushing model capabilities could become the most critical area. Models like Fable and Sol still hold top performance on industry benchmarks, but with Chinese models working on narrowing the gap by constantly chasing their tail, algorithmic superiority would ultimately be consequential. The financial resources required for these endeavours are, unfortunately, immense. Meeting these growing capital requirements helps explain why AI firms are preparing to go public. Reports suggest that OpenAI and Anthropic confidentially filed draft IPO registration statements with the U.S. SEC in June 2026. An even stronger example is Elon Musk's SpaceX, which made a historic Wall Street debut through a funding round, with space-based AI data centres reportedly among the potential uses of the capital. The kind of financial power that these companies are eyeing is not trivial. SpaceX alone raised a record $86 billion and the fact that Musk exercises 85% voting power on a roughly $2 trillion valuation company makes one question the degree of regulatory oversight that policymakers are ready to allow. Even without going public, AI firms are already entering into partnerships with tech giants who can lease them computing power. OpenAI has reportedly partnered with Cerebras, Oracle and AWS, while Anthropic is partnering with Coreweave, Microsoft and NVIDIA. Such partnerships can strengthen network effects by tying together AI models, cloud infrastructure and semiconductor supply. This may ultimately result in entities that are wealthier than what regulators would want. These possibilities were always conceivable. China's presence has only intensified these dynamics. While one may hope policymakers step in to break down these oligarchies, recent steps undertaken by the US government have shown that the country is treating AI as a geopolitical asset and not just another technology. If China challenges the USA's hegemony over AI, policymakers might tolerate greater market concentration if it is viewed as strengthening America's geopolitical interests. The consequences of such market concentration are well documented. However, what is more important is the kind of social and political power that these firms can eventually acquire. The emergence of increasingly dominant AI conglomerates could make effective regulation and governance increasingly difficult because a handful of firms would acquire substantial economic, political, and social influence. This would also have international spillovers. For nations lacking technological sovereignty, decisions over how AI is developed, governed, regulated, and priced would increasingly rest with a few foreign firms. Greater financial resources could enable these companies to dominate the foundation model layer and expand into AI applications, increasing competition for countries like India, which so far have focused on AI applications and services rather than sovereign AI. Consequently, reliance on foreign AI systems may increase. Predicting the future is impossible, but recent developments suggest AI firms will likely continue seeking funds. As fears of technological dependence grow, the question is whether dependent nations are ready to respond. Amit Kapoor is chair & Mohammad Saad is researcher at Institute for Competitiveness. (Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of www.economictimes.com.)

China's AI blitz creates 'death zone' for rival U.S. model makers

A flurry of model launches from China's artificial intelligence sector is rapidly narrowing the gap with Silicon Valley and creating what's been described as a death zone for anyone without frontier-pushing technology or market-breaking pricing. Alibaba Group Holding became the latest in a parade of Chinese debuts hitting the top of global benchmarks with its Qwen3.8-Max this week, its most advanced model to date that appeared to match or exceed Anthropic's flagship Fable 5. Two weeks earlier, Moonshot AI's Kimi K3 showed performance comparable to the priciest U.S. options built on a much humbler budget, sparking fresh questions over the effectiveness of U.S. chip sanctions intended to slow China's tech ascent. Elsewhere, ByteDance has pushed aside all rivals in video generation with its Seedance, version 2.5 of which has just come out. DeepSeek, the original Chinese disruptor of U.S. hegemony in AI, returned this summer with V4 Flash, a breakthrough model in terms of pricing. What was once an isolated shockwave is now a cascade of unexpected depth -- delivering not just low-cost alternatives, but high-end capabilities in reasoning, coding and complex tasks that give OpenAI and its U.S. peers real competition.