Editor’s Note: Chinese AI labs are becoming an increasingly hard-to-ignore force in the global large model competition. Their advantages lie not only in abundant talent, strong engineering capabilities, and rapid iteration, but also in a pragmatic organizational approach: focus less on concepts and more on building models; emphasize team execution over individual stars; and rely less on external services, preferring to control their own core technology stack.

After visiting several leading Chinese AI labs, author Nathan Lambert found that China’s AI ecosystem differs from that of the United States. The U.S. places greater emphasis on original paradigms, capital investment, and the personal influence of top scientists; China, by contrast, excels at rapidly catching up in existing directions through open-source initiatives, engineering optimizations, and the substantial contributions of a large number of young researchers, quickly pushing model capabilities to the frontier.

What matters most is not whether China’s AI has surpassed that of the U.S., but that two distinct development paths are emerging: the U.S. resembles a frontier race driven by capital and star labs, while China resembles an industry-wide competition propelled by engineering capabilities, an open-source ecosystem, and a strong sense of technological self-reliance.

This means that future AI competition will not only be about model rankings, but also about organizational capability, developer ecosystems, and industrial execution. The real transformation in China’s AI lies in its shift from merely replicating Silicon Valley to actively participating in global frontiers in its own way.

The following is the original text:

Sitting on the high-speed train from Hangzhou to Shanghai, I gazed out the window and saw distinct ridges of hills dotted with wind turbines, silhouetted against the setting sun. The mountains formed the backdrop, while before me stretched vast fields interwoven with clusters of high-rise buildings.

I returned from China with great humility. Being welcomed so warmly to such a foreign place was a profoundly warm and deeply human experience. I had the privilege of meeting many individuals from the AI ecosystem whom I had previously only known from afar; they greeted me with radiant smiles and open hearts, reminding me once again that my work—and the entire AI ecosystem—is truly global.

The mindset of Chinese researchers

Chinese companies building language models are well-positioned to be rapid followers of this technology, grounded in China’s long-standing educational and work culture traditions, as well as distinct approaches to building technology companies compared to the West.

If you look only at outputs—such as the latest and largest models and the agent-based workflows they enable—and at input factors like outstanding scientists, large-scale data, and accelerated computing resources, Chinese and U.S. labs appear largely similar. The true, enduring differences lie in how these elements are organized and shaped.

I have always believed that one reason Chinese labs excel at catching up and staying close to the frontier is that their culture aligns exceptionally well with this task. However, before speaking directly with people, I felt it was inappropriate to attribute this intuition to any significant influence. After speaking with many talented, humble, and open-minded scientists at China’s leading labs, many of my ideas became much clearer.

Building the best large language model 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 has the potential to deliver improvements, but combining these improvements into a cohesive whole is a complex process. During this process, the work of some highly intelligent individuals may need to be set aside to maximize the overall model’s performance in multi-objective optimization.

American researchers are also clearly very skilled at solving individual component problems, but America has a stronger culture of "speaking up for oneself." As a scientist, you often achieve greater success when you actively advocate for your own work; and contemporary culture is promoting a new path to fame—becoming a "top AI scientist." This leads to direct conflict.

It is widely rumored that the Llama team collapsed under political pressure after these interests were embedded into a hierarchical structure. I’ve also heard from other labs that, at times, it may be necessary to “pacify” a top researcher to stop them from complaining that their ideas weren’t included in the final model. Whether or not this is entirely true, the message is clear: self-awareness and the desire for career advancement can indeed hinder the creation of the best models. Even a subtle cultural difference in this regard between the U.S. and China could have meaningful implications for the final outcome.

Part of this difference relates to who is building these models in China. In all labs, a straightforward reality is that a large proportion of core contributors are still students. These labs are quite young, which reminds me of how we organize things at Ai2: students are treated as peers and directly integrated into large language model teams.

This is very different from top labs in the United States. In the U.S., companies like OpenAI, Anthropic, and Cursor don’t offer internships at all. Other companies, such as Google, nominally offer internships related to Gemini, but many worry their internships might be isolated from the core work.

In summary, these subtle cultural differences may enhance model-building capabilities in several ways: people are more willing to do less glamorous work to improve the final model; newcomers to AI development may be less influenced by previous AI hype cycles, allowing them to adapt more quickly to modern techniques. In fact, a Chinese scientist I spoke with explicitly viewed this as an advantage: lower levels of self-consciousness make organizational structures somewhat easier to scale, as fewer people try to “game the system”; a large talent pool is well-suited to solving problems that already have proof-of-concept solutions elsewhere, and so on.

This tendency to enhance the current capabilities of language models contrasts with a known stereotype: that Chinese researchers produce fewer highly creative, groundbreaking “zero-to-one” academic studies that open up new fields.

During several more academically oriented lab visits on this trip, many leaders spoke of cultivating a more ambitious research culture. Meanwhile, some technical leaders we spoke with expressed skepticism that this transformation of scientific research could be achieved in the short term, as it requires redesigning educational and incentive systems—a transformation too large to occur under the current economic equilibrium.

This culture seems to be training a large number of students and engineers who are highly skilled at the "large language model building game." Of course, their numbers are also extremely abundant.

These students told me that China is also experiencing a brain drain similar to that in the U.S.: many who previously considered pursuing academic careers now plan to stay in industry. One of the most striking comments came from a researcher who originally wanted to become a professor because he hoped to be close to the education system; he then added that large language models have already solved education—“Why would students still come to talk to me!”

Students entering the field of large language models bring a fresh perspective, which is an advantage. Over the past few years, we’ve seen key paradigms in large language models continuously evolve: from scaling MoE, to scaling reinforcement learning, to supporting agents. Mastering any of these requires rapidly absorbing vast amounts of background information—both from the broader literature and from the company’s internal tech stack.

Students are accustomed to doing this and are willing to set aside all preconceptions about what should work with humility. They dive in wholeheartedly, dedicating their lives to gaining opportunities to improve the model.

These students were also refreshingly direct, without the philosophical digressions that might distract scientists. When I asked them about the economic impact or long-term social risks of the models, far fewer Chinese researchers held nuanced views or expressed a desire to influence these issues. They saw their role simply as building the best possible models.

This difference is subtle and easily denied. But it becomes most apparent during a prolonged conversation with an elegant, intelligent researcher who can express themselves clearly in English: when you ask deeper, more philosophical questions about AI, these fundamental issues hang in the air, and the other person reveals a simple confusion. To them, it is a category error.

One researcher even cited Dan Wang’s famous observation: whereas the United States 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 is no systematic pathway to cultivate the star influence of Chinese scientists, as super-mainstream podcasts like Dwarkesh or Lex do.

I tried to get Chinese scientists to comment on the economic uncertainties sparked by AI, issues beyond simple AGI capabilities, or ethical debates about how models should behave; these questions ultimately revealed the scientists’ backgrounds and educational experiences (edited). They are extremely focused on their work, but they grew up in a system that does not encourage discussion or expression about how society should be organized or changed.

From a broader perspective, especially in Beijing, I felt it resembled the Bay Area: a competitive lab where the next breakthrough could be just a short walk or taxi ride away. After landing, I stopped by Alibaba’s Beijing campus on my way to the hotel. Over the next 36 hours, we visited Zhipu AI, Moonshot AI, Tsinghua University, Meituan, Xiaomi, and 01.ai.

Taking a DiDi in China is very convenient. If you choose the XL vehicle option, you’re often assigned to an electric minivan equipped with massage seats. When we asked researchers about the talent war, they said it’s very similar to what we’ve experienced in the U.S. It’s normal for researchers to switch jobs, and where people choose to go largely depends on where the current atmosphere is the best.

In China, the large language model community feels more like an ecosystem than a collection of warring tribes. In many private conversations, I almost always hear nothing but respect for peers. All Chinese labs hold ByteDance and its popular Doubao model in high regard, as it is the only leading closed-source lab in China. Meanwhile, all labs deeply respect DeepSeek, viewing it as the lab with the most refined research sensibilities in execution. In the United States, private conversations with lab members often spark immediate tension.

What impresses me most about Chinese researchers' humility is that they often shrug and say it’s not their concern, even on the business level. In the U.S., it seems everyone is obsessed with industry trends across every ecosystem layer—from data providers to computing power to funding.

Differences and similarities between China's AI industry and Western laboratories

Today, building an AI model is so fascinating because it’s no longer just about gathering a group of brilliant researchers in the same building to create an engineering marvel. It used to be more like that, but to sustain AI businesses, large language models are now becoming hybrids: they involve building, deploying, funding, and driving adoption of this creation.

Leading AI companies exist within complex ecosystems that provide funding, computing power, data, and other resources to continuously push the boundaries of innovation.

In the Western ecosystem, the integration of various inputs required to create and maintain large language models has been relatively well conceptualized and mapped out. Companies like Anthropic and OpenAI are typical examples. Therefore, if we can identify clear differences in how Chinese labs think about these issues, we can discern potential meaningful distinctions that different companies may bet on in the future. Of course, these future trajectories will also be strongly influenced by funding and/or compute constraints.

I’ve summarized the key insights I gained from my discussions with these labs at the AI industry level:

First, early signs of domestic AI demand have already emerged.
A widely discussed hypothesis suggests that China’s AI market will be smaller because Chinese companies are typically unwilling to pay for software, thus never unlocking a large enough inference market to support major labs.

However, this assessment applies only to software spending corresponding to the SaaS ecosystem, which has historically been small in China. On the other hand, China clearly still has a large cloud market.

A key and still unanswered question is whether Chinese companies’ spending on AI will resemble the SaaS market—smaller in scale—or the cloud market—foundational in nature. Even internal teams at Chinese labs are debating this. Overall, I feel that AI is moving closer to the cloud market, and no one truly worries that the market around these new tools won’t grow.

Second, most developers are deeply influenced by Claude.
Although Claude is officially banned in China, most Chinese AI developers are deeply fascinated by Claude and how it has transformed the way they build software. Just because China has been less willing to purchase software in the past doesn’t mean I believe China won’t experience a significant surge in demand for reasoning capabilities.

Chinese technicians are extremely practical, humble, and motivated. This impression on me is stronger than any historical habit of "not paying for software."

Some Chinese researchers mention using their own tools for development, such as the command-line tools for Kimi or GLM, but everyone mentions using Claude. Surprisingly, few mention Codex, even though Codex is rapidly gaining popularity in the Bay Area.

Third, Chinese companies have a mindset of technological ownership.
Chinese culture is combining with a powerful economic engine, producing some unpredictable outcomes. One lasting impression I took away is that the sheer number of AI models reflects a pragmatic balance among many tech companies here. There is no overarching plan.

This industry is defined by respect for ByteDance and Alibaba—large incumbents seen as having the resources to dominate many markets. DeepSeek is a respected technological leader, but far from a market leader. It sets the direction but lacks the structural capacity to win markets economically.

This leaves companies like Meituan or Ant Group. Westerners might be surprised that they are also building these models. But in reality, they clearly view large language models as central to future technology products and therefore need a strong foundation.

When they fine-tune a powerful general-purpose model, feedback from the open-source community strengthens their tech stack, while they retain internal fine-tuned versions for their own products. This “open-first” mindset in the industry is largely defined by pragmatism: it helps models receive strong feedback, gives back to the open-source community, and empowers their own mission.

Fourth, government support is real, but its scale remains unclear.
It is often claimed that the Chinese government is actively helping to open up the large language model competition. However, this is a relatively decentralized government system composed of many levels, and each level lacks a clear set of guidelines defining exactly what it should do.

Different neighborhoods in Beijing compete to attract tech companies to set up their offices there. The “assistance” offered to these companies almost certainly includes streamlining bureaucratic processes such as removing licensing hurdles. But how far can this assistance go? Can different levels of government help attract talent? Can they help smuggle chips?

Throughout the visit, there were indeed numerous references to government interest or support, but the information provided was far from sufficient for me to report details with certainty or to form a confident worldview on how the government could fundamentally alter the trajectory of AI development in China.

There is also absolutely no indication that China's top leadership is influencing any technical decisions of the model.

Fifth, the data industry is far less developed than in the West.
We had heard that Anthropic or OpenAI spends over $10 million on a single environment, with cumulative annual expenditures reaching hundreds of millions of dollars to push the frontiers of reinforcement learning. Therefore, we are curious whether Chinese labs are also purchasing the same environments from U.S. companies, or whether a mirrored domestic ecosystem supports them.

The answer is not that there is “no data industry” in the absolute sense, but rather that, based on their experience, the quality of the data industry is relatively poor; thus, it is often better to build internal environments or data in-house. Researchers themselves spend considerable time creating reinforcement learning training environments, while larger companies like ByteDance and Alibaba can maintain internal data annotation teams to support this work. All of this aligns with the previously mentioned mindset of “building rather than buying.”

Sixth, demand for more NVIDIA chips is extremely strong.
NVIDIA's computing power is the gold standard for training, and everyone's progress is limited by the lack of additional computing capacity. If supply were sufficient, they would clearly purchase more. Other accelerators, including Huawei among others, have received positive evaluations for inference. Countless laboratories have access to Huawei chips.

These points illustrate a very different AI ecosystem. Applying the operational model of Western labs directly to their Chinese counterparts often results in category errors. The key question is whether these distinct ecosystems will produce fundamentally different types of models, or whether Chinese models will always be interpreted as resembling U.S. frontier models from three to nine months prior.

Conclusion: Global Equilibrium

Before this trip, I knew very little about China; by the time I left, I felt I had only just begun to learn. China is not a place that can be described by rules or formulas—it is a place with entirely different dynamics and chemical reactions. Its culture is so ancient and profound, yet still completely intertwined with the way technology is built domestically. I have so much more to learn.

Many elements of the current U.S. power structure treat their existing view of China as a key psychological tool in decision-making. After engaging in formal and informal face-to-face discussions with nearly every leading AI laboratory in China, I found that China possesses many qualities and instincts that are difficult for Western decision-making frameworks to model.

Even if I directly ask these labs why they openly release their most powerful models, I still struggle to fully connect the dots between a mindset of ownership and genuine support for the ecosystem.

The lab here is highly pragmatic and not necessarily an absolutist when it comes to open source—not every model they build is openly released. However, they have a strong commitment to supporting developers, fostering the ecosystem, and using openness as a means to better understand their own models.

Almost every major Chinese tech company is building its own general-purpose large language model. We have already seen platform-based companies like Meituan and large consumer tech firms like Xiaomi release open-weight models. In contrast, their American counterparts typically only purchase services.

These companies build large language models not to gain visibility in trendy new phenomena, but out of a deeper, more fundamental desire: to control their own technology stack and develop the most important technology of today. Whenever I look up from my laptop, I see clusters of cranes on the horizon—clearly aligned with China’s broader culture and energy of construction.

The humanity, charm, and sincere warmth of Chinese researchers make them incredibly approachable. On a personal level, the harsh geopolitical debates we’re accustomed to in the U.S. simply don’t permeate them. The world could use more of this simple positivity. As a member of the AI community, I’m now more concerned that fractures are emerging between members and groups based on national labels.

If I said I didn’t want U.S. labs to be clear leaders in every part of the AI technology stack, I would be lying. Especially in the field of open models, where I’ve invested a great deal of time—I’m American, and this is an honest preference.

At the same time, I hope the open ecosystem itself can thrive globally, as this can create AI that is safer, more accessible, and more useful for the world. The current question is whether U.S. labs will take action to assume this leadership role.

As I finished writing this article, more rumors were circulating about how executive orders could impact open models. This could further complicate the synergy between U.S. leadership and the global ecosystem—and it hasn’t increased my confidence.

I thank all the outstanding individuals I had the privilege to speak with at Moonshot, Zhipu, Meituan, Xiaomi, Qwen, Ant Lingguang, 01.ai, and other organizations. Everyone was so enthusiastic and generously gave their time. As my ideas take shape, I will continue sharing observations about China, encompassing both broader cultural aspects and the AI field itself.

Clearly, this knowledge is directly relevant to the ongoing story of advancements in AI.