Editor's Note: China's AI labs are becoming an increasingly difficult-to-ignore force in the global large model competition. Their advantages stem not just from abundant talent, strong engineering, and fast iteration, but from a pragmatic organizational approach: less talk about concepts, more action on building models; less emphasis on individual stars, more emphasis on team execution; less reliance on external services, more preference for mastering the core technology stack themselves.
After visiting several leading Chinese AI labs, the author of this article, Nathan Lambert, found that China's AI ecosystem is not entirely the same as America's. The US places more importance on original paradigms, capital investment, and the individual influence of top scientists; China is more adept at rapidly catching up in established directions, pushing model capabilities to the forefront quickly through open-source contributions, engineering optimization, and the massive input of young researchers.
What is most noteworthy is not whether Chinese AI has already surpassed the US, but that two different development paths are taking shape: the US is more like a frontier race driven by capital and star labs, while China is more like an industrial competition propelled by engineering capability, the open-source ecosystem, and a consciousness of technological self-control.
This means that future AI competition will not just be a battle of model leaderboards; it will also be a contest of organizational capability, developer ecosystems, and industrial execution. The real change in Chinese AI lies in the fact that it is no longer just replicating Silicon Valley, but is participating in the global frontier in its own way.
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Sitting on a modern high-speed train from Hangzhou to Shanghai, I looked out the window at the distinct, undulating mountain ridges dotted with wind turbines, forming silhouettes against the sunset. The mountains provided the backdrop, while the foreground was a patchwork of vast fields and clusters of tall buildings.
I returned from China with immense humility. To be welcomed so warmly in such an unfamiliar place was a profoundly warm and humane experience. I was fortunate to meet many people in the AI ecosystem whom I had previously only known from a distance; they greeted me with bright smiles and enthusiasm, reminding me once again that my work, and the entire AI ecosystem itself, are global.
The Mindset of Chinese Researchers
The Chinese companies building language models could be described as perfectly suited to being "fast followers" of this technology. They are built upon China's longstanding traditions in education and work culture, while also having a slightly different approach to building technology companies compared to the West.
If you only look at outputs—the latest, largest models, and the agentic workflows they support—and at input factors like excellent scientists, massive data, and accelerated computing resources, then Chinese and American labs appear broadly similar. The enduring differences lie in how these elements are organized and shaped.
I've always thought one reason Chinese labs are so good at catching up and staying near the frontier is that they are culturally very aligned with the task. But without speaking directly to people, I felt it inappropriate to attribute this intuition to something significant. After conversations with many excellent, humble, and open scientists at top Chinese labs, many of my ideas became clearer.
Building the best large language models today depends heavily on meticulous work across the entire technology stack: from data, to architectural details, to the implementation of reinforcement learning algorithms. Each component of the model offers potential gains, and combining them is a complex process. In this process, the work of some very intelligent individuals might have to be shelved to maximize the overall model in a multi-objective optimization.
American researchers are obviously also very good at solving individual component problems, but the US has more of a culture of "speaking up for oneself." As a scientist, you often succeed more when you actively advocate for your work; contemporary culture is also pushing a new path to fame: becoming a "top AI scientist." This creates direct conflict.
It's widely rumored that the Llama organization collapsed under political pressure after these vested interests were embedded within a hierarchical structure. I've also heard from other labs that sometimes you need to "appease" a top researcher, asking them to stop complaining that their ideas weren't incorporated into the final model. Whether this is entirely true or not, the message is clear: ego and career advancement desires can indeed hinder building the best models. Even a slight directional cultural difference between the US and China could meaningfully impact the final output.
Part of this difference relates to who is actually building these models in China. Across all labs, a stark reality is that a significant proportion of core contributors are students still in school. These labs are quite young, reminding me of how we organized at AI2: students are treated as peers and integrated directly into the large language model teams.
This is very different from top US labs. In the US, companies like OpenAI, Anthropic, and Cursor simply don't offer internships. Others like Google nominally offer internships related to Gemini, but many worry their internship might be isolated from the truly core work.
In summary, this subtle cultural difference might enhance model-building capability in the following ways: people are more willing to do less glamorous work for the sake of the final model; those new to AI construction might be less influenced by previous hype cycles, thus adapting faster to new modern technical methods; in fact, one Chinese scientist I spoke to explicitly cited this as an advantage; lower ego makes organizational scaling somewhat easier because people are less prone to trying to "game the system"; abundant talent is well-suited to solving problems where proof-of-concept already exists elsewhere, etc.
This aptitude, more favorable to building current language models, contrasts with a known stereotype: that Chinese researchers produce less of the "0 to 1" academic research that is more creative and capable of opening new fields.
During several visits to more academic labs on this trip, many leaders discussed their efforts to cultivate this more ambitious research culture. Meanwhile, some technical leads we spoke to doubted whether such a reshaping of scientific research was possible in the short term, as it would require redesigning education and incentive systems—a transformation too large to happen under the current economic equilibrium.
This culture seems to be training a cohort of students and engineers exceptionally skilled at the "large language model building game." And, of course, their numbers are vast.
These students told me that talent drain similar to the US is also happening in China: many who previously considered academic careers now plan to stay in industry. One of the most interesting comments came from a researcher who initially wanted to be a professor because he wanted to be close to the education system; but he then remarked that education had already been solved by large language models—"why would students even come to chat with me anymore!"
Students entering the LLM field with fresh eyes is an advantage. Over the past few years, we've seen key LLM paradigms constantly shift: from scaling MoE, to scaling reinforcement learning, to supporting agents. Doing any of these things well requires absorbing a massive amount of background information extremely quickly, both from the broader literature and the internal tech stack of one's company.
Students are accustomed to this kind of work and are willing to approach it with humility, setting aside all preconceptions about "what should work." They dive in headfirst, dedicating their lives for the chance to improve models.
These students are also remarkably direct and free from philosophical musings that can distract scientists. When I asked them about the economic impact of models or long-term societal risks, far fewer Chinese researchers had complex views or a desire to influence these issues. They see their role as building the best models.
This difference is subtle and easily dismissed. But it's most palpable during a long conversation with an elegant, intelligent researcher who can express themselves clearly in English: when you ask more philosophical questions about AI, these fundamental questions hang in the air, met with a simple sense of puzzlement. For them, it's a category error.
One researcher even cited Dan Wang's famous judgment: compared to the US, which is governed by lawyers, China is governed by engineers. In discussing these issues, he used this analogy to emphasize their desire to build. In China, there isn't a systemic path to cultivate star influence among Chinese scientists akin to super-mainstream podcasts like Dwarkesh or Lex in the US.
When I tried to get Chinese scientists to comment on future economic uncertainty triggered by AI, questions beyond simple AGI capabilities, or moral debates about how models should behave; these questions ultimately revealed to me the scientists' upbringing and educational background (edited 1). They are intensely focused on their work, but they grew up in a system that doesn't encourage discussing or expressing how society should be organized or changed.
Zooming out, especially Beijing, felt much like the Bay Area to me: a competitive lab might be just a few minutes' walk or cab ride away. After landing, I stopped by Alibaba's Beijing campus on the way to the hotel. In the next 36 hours, we visited Zhipu AI, Moonshot AI, Tsinghua University, Meituan, Xiaomi, and 01.ai.
Getting around China via Didi is convenient. If you choose the XL option, you often get assigned an electric minivan with massage chairs. When we asked researchers about the talent war, they said it's very similar to what we experience in the US. Researcher job-hopping is normal, and where people choose to go largely depends on which place currently has the best vibe.
The LLM community in China feels more like an ecosystem than warring tribes. In many off-the-record conversations, I heard almost nothing but respect for peers. All Chinese labs are wary of ByteDance and its popular Doubao model, as it's China's only major frontier closed-source lab. At the same time, all labs deeply respect DeepSeek, seeing it as the lab with the most research taste in execution. In the US, sparks tend to fly much sooner in off-the-record chats with lab members.
One of the most striking aspects of Chinese researchers' humility is that they often shrug at the commercial level too, saying that's not their problem. In the US, everyone seems obsessed with various industry-level ecosystem trends, from data vendors, to compute, to fundraising.
How China's AI Industry Differs from and Resembles Western Labs
What makes building an AI model so interesting today is that it's no longer just about gathering a group of excellent researchers in one building to jointly craft an engineering marvel. It used to be more like that, but to sustain an AI business, LLMs are becoming a hybrid: they involve building, deploying, fundraising, and driving the adoption of this creation.
Top AI companies exist within complex ecosystems. These ecosystems provide funding, compute, data, and more to continuously push the frontier forward.
In the Western ecosystem, the ways various input factors required to create and sustain large language models are integrated have been relatively well conceptualized and mapped. Anthropic and OpenAI are typical examples. Therefore, if we can discover that Chinese labs think about these issues in markedly different ways, we might see meaningful differences that companies could bet on in the future. Of course, these futures will also be strongly influenced by constraints in funding and/or compute.
I've compiled several of the biggest "AI industry-level" takeaways from conversations with these labs:
First, early signs of domestic AI demand are emerging.
A widely discussed hypothesis suggests the Chinese AI market will be smaller because Chinese companies are typically unwilling to pay for software, thus never unlocking a massive inference market large enough to support labs.
But this judgment only applies to software spending corresponding to the SaaS ecosystem, which has historically been small in China. On the other hand, China clearly still has a massive cloud market.
A key, unanswered question is: Will Chinese enterprise spending on AI resemble the SaaS market (smaller scale) or the cloud market (foundational spending)? This is being debated even within Chinese labs. Overall, I got the sense that AI is trending closer to the cloud market, and no one is truly worried about the market for new tools failing to grow.
Second, most developers are heavily influenced by Claude.
Although Claude is nominally blocked in China, most Chinese AI developers are enamored with Claude and how it has changed their software-building ways. Just because China has been less willing to buy software historically doesn't mean I would assume China won't see a huge surge in inference demand.
The pragmatism, humility, and drive of Chinese technical talent struck me as a stronger force than any historical habit of "not buying software."
Some Chinese researchers mentioned using their own tools for building, like Kimi or GLM's command-line tools, but everyone mentioned using Claude. Surprisingly, few mentioned Codex, which is obviously gaining rapid popularity in the Bay Area.
Third, Chinese companies have a technological ownership mindset.
Chinese culture, combined with a roaring economic engine, is producing some unpredictable outcomes. One strong impression I left with is that the sheer number of AI models reflects a pragmatic equilibrium among many tech enterprises here. There is no grand master plan.
The industry is defined by a respect for ByteDance and Alibaba—large incumbents seen as likely to win many markets with their powerful resources. DeepSeek is the respected technical leader, but far from the market leader. They set the direction but lack the structure to economically win the market.
This leaves companies like Meituan or Ant Group. Westerners might be surprised they are also building these models. But they clearly see LLMs as the core of future tech products, hence they need a strong foundation.
When they fine-tune a powerful general model, open-source community feedback strengthens their tech stack, while they can keep internal fine-tuned versions for their products. The "open-first" mentality in this industry is largely defined by pragmatism: it helps models get strong feedback, gives back to the open-source community, and empowers their own mission.
Fourth, government support is real, but its scale is unclear.
It's often asserted that the Chinese government is actively aiding the open LLM race. But this is a relatively decentralized government system with many layers, and no single layer has a clear playbook for exactly what it should do.
Different districts in Beijing compete to attract tech companies to set up offices there. The "help" offered to these companies almost certainly includes removing bureaucratic red tape in processes like licensing. But how far can this help go? Can different government levels help attract talent? Can they help smuggle chips?
Throughout the visits, there were indeed many mentions of government interest or assistance, but the information was far from sufficient for me to report details assertively or to form a confident worldview about how the government might alter China's AI development trajectory.
And there was certainly no indication that the highest levels of the Chinese government are influencing any technical decisions about the models.
Fifth, the data industry is far less developed than in the West.
We had heard that Anthropic or OpenAI might spend over $10 million on a single environment, with cumulative annual spending reaching hundreds of millions to push the reinforcement learning frontier. So, we wondered if Chinese labs were also buying the same environments from US companies, or if a mirrored domestic ecosystem was supporting them.
The answer wasn't a full "there is no data industry," but rather that, based on their experience, the data industry quality is relatively poor, so often it's better to build environments or data internally. Researchers themselves spend considerable time crafting RL training environments, while larger companies like ByteDance and Alibaba can have internal data annotation teams to support this. All of this echoes the previously mentioned "build, don't buy" mentality.
Sixth, the hunger for more Nvidia chips is intense.
Nvidia compute is the gold standard for training, and everyone's progress is constrained by not having more of it. If supply were ample, they would obviously buy. Other accelerators, including but not limited to Huawei's, received positive reviews for inference. Countless labs have access to Huawei chips.
These points paint a very different AI ecosystem. Quickly overlaying Western lab operating models onto Chinese counterparts often leads to category errors. The key question is whether these different ecosystems will produce substantively different types of models; or whether Chinese models will always be interpreted as roughly equivalent to the US frontier from 3 to 9 months ago.
Conclusion: Global Equilibrium
Before this trip, I knew too little about China; leaving, I felt I had only just begun to learn. China is not a place expressible by rules or formulas, but one with very different dynamics and chemistry. Its culture is so ancient, so deep, and still completely intertwined with how technology is built domestically. I have much more to learn.
Many parts of the current US power structure treat their existing view of China as a key mental tool in decision-making. After formal and informal face-to-face exchanges with nearly every top Chinese AI lab, I found China possesses many qualities and instincts that Western decision-making processes struggle to model.
Even when I directly asked these labs why they open-release their strongest models, I still found it difficult to completely connect the intersection between "ownership mindset" and "sincere ecosystem support."
The labs here are very pragmatic, not necessarily absolute open-source purists; not every model they build is released openly. But they have deep intent in supporting developers, supporting the ecosystem, and using openness as a way to better understand their own models.
Almost every large Chinese tech company is building its own general-purpose large language model. We've seen platform service companies like Meituan and large consumer tech companies like Xiaomi release open-weight models. Their US counterparts typically just buy services.
These companies aren't building LLMs for visibility in the latest hot trend, but from a deep, fundamental desire: to control their own technology stack and develop the most important technology of the moment. When I looked up from my laptop and always saw clusters of cranes on the horizon, this clearly resonated with China's broader culture and energy of construction.
The human touch, charm, and sincere warmth of Chinese researchers are deeply relatable. On a personal level, the brutal geopolitical discourse we are accustomed to in the US had not seeped into them at all. The world could use more of this simple positivity. As a member of the AI community, I'm now more concerned about fractures emerging between members and groups based on nationality labels.
It would be a lie to say I don't wish for US labs to be the unequivocal leaders in every part of the AI tech stack. Especially in the open model space where I've invested significant time, I'm American—it's an honest preference.
At the same time, I hope the open ecosystem itself can flourish globally, as it can create safer, more accessible, and more useful AI for the world. The immediate question is whether US labs will take action to occupy this leadership position.
As I finish writing this, more rumors are circulating about executive orders impacting open models. This could further complicate the synergy between US leadership and the global ecosystem—something that doesn't fill me with greater confidence.
My thanks to all the wonderful individuals I was fortunate to speak with at Moonshot AI, Zhipu AI, Meituan, Xiaomi, Tongyi Qianwen, Ant Ling Guang, 01.ai, and other institutions. Everyone was so warm and generous with their time. As my thoughts solidify, I will continue to share observations about China, both on broader cultural levels and within AI itself.
Clearly, this knowledge will be directly relevant to the unfolding story of AI frontier development.
