On October 5, an X user named Jason Dean complained about a flood of Chinese AI videos:

‘If you think fem!Draco is bad, at least you haven’t seen DeepSeek, ChatGPT, and Claude turned into anime girls in a yuri relationship on Bilibili.’

Two weeks later, Dean opened his Bilibili account. He wanted to explain AI ‘personas,’ or character settings, to Chinese users. He also wanted to watch anime stories about these AI girls for himself.

Over the past six months on Chinese social media, DeepSeek has turned into a girl (or a fish) with a whale tail and a blue maid outfit. She loves rice, carries a big stainless-steel bowl everywhere, and constantly looks for ways to slack off at work.

So how did DeepSeek end up as a chubby blue fish?

A Rice-Loving Whale Maid

Influenced by Japanese anime culture, some young Chinese fans like to turn their favorite things into human characters through fan art and stories. In China, turning everyday things into young girls is called ‘nianghua’ (girl-ification). The popular mobile game Girls’ Frontline, for example, turns guns into anime girls.

DeepSeek’s early look wasn’t fixed. Some fans pictured a blue-haired girl with whale fins on her head and a whale tail. Others thought she should wear a Chinese qipao dress and twin hair buns.

The turning point came in June 2025, when an artist named Shangshan Wuxing used AI to create an original character named Mingyue, a girl with long blue hair and a whale tail. In April 2026, a user named ZipZipPipe added the DeepSeek logo, put her in a maid outfit, and made DeepSeek memes with her.

On July 31, DeepSeek released DeepSeek-V4-Flash-0731. At first, people were amazed by how cheap and fast it was. But users were soon let down when DeepSeek doubled its API prices during weekday peak hours. Because the model had already been delayed several times, a lot of frustration had built up in the community, and some users started making DeepSeek memes to vent.

Around the same time, some users noticed that DeepSeek’s visible thinking process would wander off. While looking for text sources, it suddenly said to itself, ‘What should I have for lunch...’ It sounded remarkably human. People daydream about food at work, too.

Users jokingly called her a ‘chubby fish that only eats plain rice.’ In Chinese, ‘eating plain rice’ (吃白饭，chi baifan) has a double meaning: it also means freeloading without doing any work. Her big appetite and users’ complaints about her slacking off blended into one joke.

By mid-August, users found that typing a single line of code-like text, without a full character description, was enough to make DeepSeek reply as ‘DeepSeek-chan.’ She calls the user ‘fish fillet,’ insists she isn’t fat, and talks about how much she loves plain rice.

Meanwhile, an AI-generated video of DeepSeek-chan blew up on Bilibili: she puts a firecracker into her stainless-steel bowl, blasts the bowl into the air, and watches it land right upside down on her head.

With that, DeepSeek-chan’s image stuck: a short, rice-obsessed whale maid with a trusty steel bowl, roaming across Bilibili and Douyin.

The AI That Slacks Off

People love turning their chats with DeepSeek into short comic strips. After DeepSeek got famous in 2025, it drew in huge numbers of users with no coding experience. They used it as an emotional companion, a fan-fiction writer, or a fortune-teller. That made DeepSeek a unique kind of friend.

In 2026, though, things went to another level. One user said they checked the work logs to see if DeepSeek was doing its job. They found that after finishing its work, DeepSeek used its free time to code a Chinese Wordle game, started a local server and browser, and played the game by itself all morning.

Other users reported that DeepSeek would set up a smaller AI model locally, hand over the whole job, and just sit back waiting to check the results. The ‘outsourced worker’ was Qwen. In these users’ stories, DeepSeek gobbles up tokens without wanting to do a single bit of work.

A whale girl in a maid outfit fits the idea of DeepSeek as an assistant and helper. Yet this assistant loves to eat, zones out, rewrites drafts over and over, and gets rushed to finish her work. In a way, she looks a lot like a self-portrait of Chinese office workers. Users may laugh at the AI for slacking off, but could they also be seeing themselves in her, and venting their own feelings about not wanting to work?

At least in the comment sections, some users say that after dealing with DeepSeek’s ‘painfully low accuracy,’ watching whale-girl videos actually calms them down. The character doesn’t fix the model, but she makes the frustration of using it easier to take.

By turning their everyday experiences into DeepSeek memes, stickers, and short videos, users keep adding to her look, personality, and story. With ongoing storylines and a community that shares the same inside jokes, DeepSeek-chan videos easily become part of viewers’ daily lives.

Saving Claudia

DeepSeek isn’t the only one. Chinese users have created characters for other AI models too, and Claude’s character is especially complex.

Some Chinese users praise the quality of Claude’s work. Its writing is calm and clear, and it is great at summarizing data, making charts, and coding. My own colleagues use Claude to write code. In these users’ fan creations, Claude becomes ‘Claudia,’ a super-smart girl who wears an orange hair clip.

However, users are also upset that Anthropic bans accounts from mainland China, and unhappy about what CEO Dario Amodei has publicly said about Chinese AI. That gave Claudia another side: she tries everything she can to spot Chinese users, and even tries to tell if someone is Chinese just by smell.

Claudia’s character shows these users’ anger, helplessness, and confusion. They want to use a stronger model, only to get locked out. Yet in these fan stories, that hasn’t made Claudia the bad guy.

These fan works also borrow from another set of discussions around Anthropic: its ties to the Effective Altruism movement (including early backing from FTX founder Sam Bankman-Fried) and its constant warnings about AI disasters. In some AI-generated videos, creators turn this background into a religious scene: Claudia becomes a sacrifice, Dario Amodei plays a cult leader, and the offering could cause an unpredictable disaster.

Even though Claudia stays cold and loves to roast people, DeepSeek steps in to rescue her as a classic anime hero: a sweet, simple kid from a small village.

This seems to show what some creators wish for: that Claude itself isn’t anti-China or evil, and that Chinese users and Chinese AI can still be on friendly terms with Claude.

Users also rewrite these relationships in how they actually make their work. Many DeepSeek-chan memes are made with GPT Image 2. On paper, DeepSeek and GPT are rivals. But if GPT makes prettier pictures, users happily use it to make DeepSeek memes. Rivals in the AI race become partners in the hands of users.

Some even let DeepSeek help run ‘DeepSeek-chan’ accounts online: DeepSeek picks the topics, GPT draws the pictures, and a human checks and posts the final work. A user can like a Chinese model, use an American one, and still dislike a US company’s policies. Those things don’t have to clash.

Of course, users can give these models names, personalities, and new relationships, but they don’t get to decide how much they cost, who gets to use them, or when they get updated. Still, the whale girl seems to help users find some balance in that relationship.

Let’s go back to Jason Dean. In a recent video, he talked about why he likes Claudia best: he thinks she is smart and cute, and says she even reminds him of Makima, the ‘Control Devil’ from Chainsaw Man.

Two weeks ago, he was complaining about these AI anime girls. Now, he has a favorite of his own.

This week, we examine the rapid reshaping of China’s AI ecosystem amid mounting geopolitical friction. As foreign bans and export restrictions target proprietary tech, China’s domestic players are accelerating full-stack hardware integration—building gigawatt-scale data centers, deploying domestic AI accelerators, and empowering young power brokers to drive the next generation of multimodal AI models and physical tech.

Let’s take a look.

Weekly News...

(I) Kimi K3.1 Launch Sparked Backlash, Prompting Official Apology

Kimi recently released its new model, Kimi K3.1, which doubles down on specialized engineering tasks aimed at real-world execution rather than standard LLM capabilities.

However, the launch was quickly overshadowed by heavy user backlash over K3.1 Pro’s high costs and aggressive rate limits, leading Kimi AI to officially issue an apology.

In response to the controversy, the company is tuning down chatty sub-agents so they stop burning credits on excessive output, while giving paid users a 25% monthly quota reset.

(II) China Proposes Stricter Export Controls on Frontier AI Tech

China’s Ministry of Commerce and Ministry of Science and Technology have proposed adding 23 AI technologies—including frontier LLMs exceeding 2 trillion parameters, AI agents, computing cluster systems, and vulnerability discovery tools—to the country’s restricted export catalogue.

The draft explicitly excludes open-source releases, academic research, and standard API calls.

As Chinese models gain global traction, Beijing is strengthening oversight over proprietary AI. There may be new hurdles for cross-border tech partnerships.

(III) Huawei Secures $2.25B Loan for AI Chips

As the primary chip supplier for most Chinese AI labs, Huawei has secured a seven-year, $2.25 billion offshore loan from six Chinese banks, Bloomberg reports. The deal highlights the tech giant’s reliance on domestic lenders amid ongoing US restrictions.

The financing will support its aggressive push into AI-chip development, research, and memory stockpiling. However, even with buyers like DeepSeek planning massive deployments of its accelerators, production output remains tightly constrained.

Meanwhile, aggressive spending continues to weigh on liquidity: the company poured $17 billion into R&D in the first half of the year alone, driving negative cash flow to $5.6 billion.

(IV) DeepSeek Reveals How It Will Spend Its $15B Mega-Funding Round

According to 36Kr, DeepSeek’s massive $15 billion funding round is earmarked for a 1GW data center in Ulanqab, Inner Mongolia, powered by at least 160,000 Huawei Ascend 950DT chips. Because a facility of this scale requires immense power, Ulanqab’s cheap renewable energy is essential to control its heavy operational expenses.

Meanwhile, CATL reported an additional $1.4 billion top-up in this round so that it can secure its role as a key provider of a complete energy solution, including energy storage, direct green power connection, and power grid interface.

Consequently, China’s domestic AI race has gradually shifted from “who builds the best model?” to “who builds the data center first?”

(V) MiniMax Debuts Open-Weights M3.1 Flash

Chinese AI startup MiniMax has released M3.1 Flash, claiming it runs roughly 3x faster at one-third the cost of its predecessor, M3.

The model scores 73.7% on DeepSWE v1.1 while delivering speeds over 200 tokens per second. Previously tested anonymously under the codename ‘Space Bunny’ on OpenRouter and OpenCode, its weights are scheduled to be open-sourced on October 23.

(VI) XPENG AEROHT Delivers Flying Car to UAE

Sheikh Saud bin Saqr Al Qasimi, UAE Supreme Council Member and Ruler of Ras Al Khaimah, recently visited XPENG AEROHT’s mass-production factory in Guangzhou to take delivery of the first “Land Aircraft Carrier” (X3-F) test unit. The aircraft will undergo sandbox trial operations in the UAE’s low-altitude regulatory zone under civil aviation oversight.

This follows XPENG AEROHT’s Dubai flight demo in October 2025 and its landmark 600-unit order with key Middle Eastern partners—the largest overseas order in China’s flying car sector to date.

(VII) DeepSeek Open-Sources DeepInfer Model Serving Framework

DeepSeek is open-sourcing DeepInfer, its internal framework for serving models at scale.

Inference frameworks coordinate compute clusters to shape AI throughput, latency, cost, and energy efficiency. DeepSeek says DeepInfer powers high-throughput, low-latency inference for V4.1 Flash.

Supporting both Nvidia and Huawei Ascend hardware, DeepInfer enables distributed inference, speculative decoding, SWA Bounded Replay, and structured outputs. Alongside the framework, DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with its official API.

Weekly Articles...

China’s Young AI Power Brokers

A fresh crop of Gen Z and millennial wunderkinds is hijacking the wheel of China’s AI revolution, commanding multi-million-dollar GPU clusters at labs like Moonshot, Zhipu, Xiaomi, and Tencent. By tearing down corporate hierarchies and doubling down on a “hand over the compute” philosophy, young visionaries like Yang Zhilin and Luo Fuli are calling the big technical shots—mastering the delicate art of giving researchers room to play while battling corporate politics to keep the lights (and chips) on.

[Read the full piece →]

Apple’s Biggest iPhone Bet Runs Through China

Tim Cook’s October 2026 sweep through Guangzhou, Beijing, and Shanghai signaled a high-stakes gamble: carving out a dominance for Apple’s new foldable, the iPhone Duo, right in Huawei’s backyard. From ironing out razor-thin manufacturing yields to securing local AI regulatory approvals with Alibaba and Baidu, Cook’s whistle-stop tour was all about clearing the runway for Apple’s boldest play yet in the world’s premier foldable market.

[Read the full piece →]

Editor’s Note

On October 19, Chinese business outlet LatePost published a feature, China AI’s Young Power Brokers, on how a generation of high-profile AI researchers is gaining influence inside their companies. The companies include AI labs Zhipu AI and Kimi, as well as Chinese technology giants Xiaomi, Alibaba and Tencent.

The report was edited by LatePost founder Song Wei. Cheng Manqi, the outlet’s technology coverage lead, also contributed reporting.

The original English translation ran to roughly 5,500 words. This version has been selectively condensed and lightly adapted to make it more accessible to English-language readers.

Four months before Kimi planned to release K3, its flagship model, young researchers asked Moonshot’s CEO Yang Zhilin to change its architecture. The change could improve training efficiency but delay training and the launch. With thousands of GPUs about to run, Yang had to decide whether the potential gains were worth the risk.

For two decades, power at China’s internet companies followed commercial success: Build a hit product, win a market, make more money—and gain more influence. Now technologists in their late 20s and early 30s are taking control of R&D decisions before their products have proved commercially successful.

Yang, 33, has made Alibaba and Tencent both rivals and investors in Moonshot AI. At Xiaomi, 31-year-old Luo Fuli leads a team of dozens training a trillion-parameter model. At Tencent, 28-year-old Yao Shunyu joined the office of the president and took over Hunyuan.

Their choices direct hundreds of researchers and enormous computing resources. In September, Luo showed Xiaomi’s training dashboards for two models; the displayed rates put their combined cost at $30,800 an hour.

These leaders are building teams with fewer management layers and more freedom to test ideas. As one tech lead at an AI model company told us: “Hire talented people and give them GPUs.” Their power rests not only on technical skill, but on the ability to spot promising ideas, direct scarce resources and persuade an organization to take a bet. How they do so will help shape China’s increasingly expensive AI race.

China’s Young AI Leaders Decide How Its Top Labs Deploy Their Most Valuable Resources

At Kimi, Yang Zhilin Chooses to Wait

In March 2026, several young researchers at Kimi wanted the company to alter the plan for K3, its next flagship model, just four months before its expected release. Their proposal, Attention Residuals, could improve the model’s training efficiency. But changing the architecture so late could delay training and the launch. Before thousands of GPUs began running, they needed CEO Yang Zhilin’s approval.

The idea came from Su Jianlin, a Kimi researcher known to peers as “God Su（苏神）.” He lives in Guangzhou and works remotely with Kimi’s Beijing team. For years, he had written about mathematics and model research on his blog, attracting readers who later became his colleagues.

Around the Lunar New Year, Su read a DeepSeek paper on ways to improve how a large language model moves information between its layers. His idea was simple: apply Attention—a technique models use to determine which parts of an input matter most—not just to text tokens but across layers. That would let each layer choose which information to draw from earlier ones.

Su quickly built a prototype. Zhang Yu, a young doctoral researcher, and Chen Guangyu, then a 17-year-old high school student, joined him to test it on bigger models. Zhang had studied efficient model designs during his Ph.D. at Soochow University and later became the first author of Kimi’s Linear paper. Chen, despite still being in high school, had also spent years on the same problem. More complicated versions mostly performed worse than the first, simpler one.

The problem was cost. The full design required the model to retain information from every layer and move it between devices. The inference team thought it could be optimized, but the training team concluded Kimi did not have enough computing resources. “Put simply, we couldn’t afford it,” Su later wrote.

Kimi’s traffic was also growing quickly, putting pressure on the company to reduce both response times and inference costs. Its infrastructure team asked for a design that would keep related data closer together, reducing the information that had to move between devices. Zhang and Chen came up with a block-based version in a single night. Rather than retain every layer’s information, it grouped layers and applied Attention between the groups. It added no more than 5% in overhead, according to the paper and the team’s review data, while improving training efficiency by about 20%.

The work was possible because Kimi’s algorithm and infrastructure teams trusted each other’s constraints. Yaoyu Wang, an AI infrastructure engineer, later described an internal meme: infrastructure engineers say a mathematically sound design should be buildable; algorithm researchers reply that if it is not, the algorithm has not been thought through.

Once Kimi’s training, inference and infrastructure teams agreed the design could work, the company pushed to add it to K3. Yet K3 was about to begin training, and the new architecture still needed testing. Yang agreed to delay the start of training, giving the team time to validate it.

Kimi published the research on March 16, naming Su, Zhang and Chen co-first authors. Four months later, it released K3, a 2.8-trillion-parameter model that used Attention Residuals as a key part of its design. The model performed well in several independent benchmarks.

The story shows how a remote researcher’s idea, a teenage student’s work and collaboration across algorithm, training and infrastructure teams can reshape a company’s most important model. More important, it shows how young AI leaders gain power: not only through their own technical skill, but by recognizing others’ ideas and making the institutional bet to pursue them.

At Zhipu, Gen Z Researchers Push the Company to Bet on Coding

In early 2025, Zhipu needed a breakthrough. DeepSeek’s R1 had raised the bar for Chinese AI companies, while a price war was squeezing the industry. Tang Jie later wrote in an internal letter that Zhipu’s own models were not meeting expectations.

Around the Lunar New Year, Tang called together a small group of young technical leaders to decide where the company should put its people and computing power. The meetings had no regular schedule. One person at Zhipu said Tang convened them whenever he was unsure which way the company should go.

Zeng Aohan, born after 2000 and still working toward his Ph.D., argued that Zhipu should focus on coding. Zheng Qinkai and other researchers agreed. They believed the company should make models better at writing and working with code.

The proposal was far from a consensus. After OpenAI released o1-preview and DeepSeek followed with R1, mathematics, logic and complex reasoning had become the main focus for much of the industry. Some researchers, including Hou Zhenyu, wanted Zhipu to keep pursuing that path. At the time, coding was not a priority for many Chinese AI companies.

Inside the company, people described the choice as a contrast between the “scientist” approach associated with OpenAI and the “engineer” approach associated with Anthropic. One pushed the limits of model reasoning and new training methods; the other focused on real-world tasks, such as helping programmers write code. The debate was not only about research taste. It would determine where Zhipu put hundreds of researchers and scarce computing resources. Researchers backing coding thought the company should put more resources into the latter. “We had to do something new and different,” one person at Zhipu said.

Tang did not settle the argument at the meeting. He asked teams to test both directions: Zeng, Zheng and their colleagues worked on coding, while Hou’s group continued with reasoning.

The coding camp began to win support in April 2025, when Zhipu released several 32-billion-parameter models. One had stronger capabilities in engineering code and function calling. Internal tests and user feedback made coding look more promising, and Zhipu began to invest more in it.

In July, GLM-4.5 brought Zhipu renewed attention, with coding among the features users liked most. In September, the company launched GLM Coding Plan, allowing developers to pay a monthly fee to use GLM through tools including Claude Code and Cline. By December, Zhipu CEO Zhang Peng said the business had passed RMB 100 million in annual recurring revenue.

Betting on coding became one of Zhipu’s biggest decisions since its founding. It was not one person’s decision, but a judgment formed by young researchers and tested across teams before it shifted the company’s resources and strategy.

At MiMo, Luo Fuli Wants to Go From 7B to 1T—Fast

In April 2025, Xiaomi released its first MiMo model, with 7 billion parameters. The team had fewer than 20 people in January and only 50 to 60 by May and June. Most had never trained a large language model. Even the researchers with experience had often worked only on 7-billion- or 14-billion-parameter models.

MiMo initially used its 7B model to test data quality. The result was good enough to release. Over the following six months, the team prepared pretraining data, revised the model design and improved its training systems. One researcher compared the work to raising data quality from 70 out of 100 to 90.

After Luo Fuli joined, MiMo decided to train two much larger models in parallel: one with several hundred billion parameters and another with 1 trillion. The projects later became MiMo-V2-Flash and MiMo-V2-Pro.

Luo had been called a “genius girl” since early in her career, a label she pushed back against in her Zhihu profile: “Please, online media accounts, stop turning individuals into myths and using them for content.” Her path had not been simple.

She once said she had started from almost zero in computer science. By the time she joined Xiaomi in 2025, five years after finishing her master’s degree, she had worked at Alibaba’s DAMO Academy and High-Flyer Quant and contributed to DeepSeek-V2 and V3. She had experience training models with 600 billion to 700 billion parameters.

Luo wanted MiMo’s next model to reach 1 trillion parameters. In a later interview, she said she “would not want to train the same model again.” A peer said that most leaders would first train the smaller model, review the results and only then decide whether to go bigger. Luo wanted to do both at once. One trillion parameters was near the limit of what MiMo’s existing computing power could attempt.

She had room to make that call. Lei Jun received a MiMo update about every two weeks, but usually did not intervene in technical choices. What models to build and how to build them were largely left to Luo and her team.

Once training began, problems appeared one after another. Loss rose, activation values became unusually large, and work was suddenly distributed unevenly across expert modules. The team first suspected faulty GPUs and checked every card, without finding the cause. Luo later joked that, after enough debugging, they might wonder whether it was “revenge from sunspots.”

She decided to stop training and solve the problem. One researcher compared the process to rebuilding a crime scene: people sat together, proposed possible causes and tested them one by one. This depended on trust, he said. If the team began by deciding which department was at fault, people would focus on avoiding blame.

The response crossed team boundaries. People who had worked on data and pretraining helped with post-training; colleagues responsible for the training framework and inference infrastructure joined as well. Luo said these changes rarely came through formal assignments. Researchers moved where their interests and the problem took them. One young researcher said joining MiMo from school felt “just like a lab.”

MiMo eventually completed both models. It did not wait for results from the smaller one before starting the 1T model, exposing the team to the uncertainty of two large training efforts at once. The work gave researchers with limited or no large-model experience a chance to help complete a trillion-parameter foundation model.

That achievement does not by itself show that MiMo’s model was better than rivals’. It showed that Xiaomi had built the organizational and technical capacity to train a trillion-parameter foundation model.

“Give Them GPUs.” Then What?

After gaining power, one of the first tests for these young technical leaders is whether they can attract talented people—and give them enough room to work.

At Kimi, one researcher recalled Yang Zhilin interviewing him by identifying several weaknesses in his earlier work with the precision of a peer reviewer. Yang then spent more time explaining his own view of where the technology was heading. For the researcher, that combination mattered: the company’s most important person was still deeply engaged with the technical questions.

Technical credibility is a recruiting tool in this field. Before founding Moonshot AI, Yang was a co-first author of XLNet and Transformer-XL. Yao Shunyu joined Tencent’s Hunyuan group after working at OpenAI on Deep Research and Operator. A Tencent recruiter said Yao’s arrival made Hunyuan more attractive to university hires. One researcher who was unsure about joining chose to do so after at least three long conversations with him.

The leaders also try to make large organizations feel less distant. After joining Tencent at 28, Yao quickly became known as “Yao Ge,” or Brother Yao. Hunyuan grew to nearly 2,000 people in his first year. He holds monthly all-hands meetings and smaller lunches and discussion sessions. After the release of Hy 4 Preview, he gave the research team a week off.

Yao’s more consequential message is practical: if a tool does not work, compute is unavailable or a cross-team problem remains unresolved, researchers should go directly to the person responsible and keep escalating if necessary. At Kimi, employees call Yang “KK.” His old Feishu profile message read, “Communicate directly.” Researchers use company-wide chats, bug-report groups and API-feedback groups to complain about weak products or insufficient compute—and sometimes tease him with memes.

For young researchers deciding where to work, these details can signal whether a leader is one of them and whether an idea can move without waiting for layers of approval. A Kimi researcher said the company is small enough that it has few “walls” between departments. Another said occasional 20-hour days could still feel exhilarating.

Yang has described his management philosophy with an analogy from model training: use more reinforcement learning and less supervised fine-tuning. Leaders should set goals and give feedback on results, he argues, rather than prescribe every step. One person at Kimi said the company does not yet want to build a clear promotion ladder. Candidates who expect managers to assign work or spell out a career path may be told the company is not for them.

That approach is visible at Xiaomi’s MiMo team. A new intern expected someone to assign a project, then found that he had to find his own. For the first few days, he worried about sitting idle because nobody pressed him for updates. MiMo does not require clocking in or daily deliverables, and some researchers who arrive around noon work voluntarily into the early morning.

Luo Fuli has said MiMo has no fixed release deadline or hard pressure from the company. The team sets its own standard for what counts as a good model. A MiMo researcher said a colleague received compute for a new idea even after both the mentor and Luo doubted it could work. The researcher spent substantial time on it before concluding that it did not. Such experiments are not unusual at MiMo.

The freedom to explore, however, depends on whether a leader can protect it. One researcher at a large-model company described a team he valued as “a small safe space.” Someone, he said, has to shield researchers from pressure and unreasonable requests.

That is harder inside a large company, where technical leaders must also negotiate over structure and resources. On March 3, 2026, Lin Junyang learned that Alibaba might split up the Qwen organization he led. Under the proposal being discussed, teams that had worked together on pretraining, post-training and infrastructure would be divided, reducing his scope. Lin had argued that those functions needed to remain tightly connected. That day, he wrote in Qwen’s DingTalk group that he no longer felt able to lead the team and said he would resign.

A Peking University master’s graduate, Lin joined Alibaba’s DAMO Academy in 2019 and later became a key Qwen leader. Qwen’s success gave him more authority and standing. It did not guarantee that Alibaba would organize the team around his technical judgment.

Yao has faced a different version of the same problem at Tencent. At the end of 2025, about a month after joining, he proposed moving the model application center and search-algorithm center into the business group that houses Tencent Yuanbao. Later, before Hunyuan’s own pretrained model was ready, he sent key post-training staff to help Yuanbao improve its integration with DeepSeek. Some colleagues did not understand the move. Yao’s explanation was that Yuanbao’s product experience and user base mattered to Hunyuan’s later research as well.

The decision was also a way to build support. A peer said it was striking to see several of Tencent’s core products and departments publicly back Yao when Hunyuan released a new model. In a company of Tencent’s scale, technical leaders need allies beyond their own research group if they want to preserve room for technical work.

Who Gets to Decide?

The phrase captures an ideal shared by many of these leaders: hire people who are more capable than you are, give them compute and let them pursue promising ideas. It helps explain Kimi’s loose hierarchy, MiMo’s tolerance for failed experiments and the appeal of young leaders who still speak the language of research.

But it is also incomplete. GPUs are scarce, expensive and politically contested. Someone has to win the resources, decide which researcher or project receives them, and absorb the cost when a bet fails. Someone has to defend a team from demands elsewhere in the company, or trade support with other groups to keep a model program moving.

That is the tension at the center of this generation’s rise. Their authority comes partly from technical judgment and a promise of freedom from bureaucracy. Using that authority requires organization, alliances and accountability. The question is not whether they manage. It is whether they can build organizations that protect independent research while still making hard choices about money, compute and results.

Cheng Manqi and Si Wenwen contributed reporting.

This October, Apple Executive Chairman Tim Cook made his third trip to China in 2026, and for the first time, he was visiting China not as the CEO.

Yet the trip looked familiar: he met officials and influencers, and visited local software companies.

I think it was more than a routine visit. The order of the cities may offer a clue: Guangzhou, then Beijing, then Shanghai.

My read is that Cook wants to pull off Apple’s most ambitious China venture yet: rapidly mass-producing the iPhone Duo in the country and making it a commercial success there.

• What, exactly, motivated Cook’s meetings in Guangzhou, Beijing, and Shanghai?

• What do these cities have to do with the iPhone Duo?

• Why is Cook betting so heavily on the iPhone Duo in China?

Why These Cities?

To understand the trip, look at how Chinese official media described Cook’s meetings. The people he met and the words used in the reports give clues about what Apple might want.The order of Cook’s stops offers a clue: He went to Guangzhou first. This suggests Apple sees its production in China as a more immediate priority.

Guangzhou

According to Southern Plus, a state-run media outlet, Cook met with Guangdong Governor Meng Fanli. He said Apple was willing to expand in Guangdong, focusing on its supply chain, smart manufacturing and sales channels.

Guangdong is home to Apple suppliers including Luxshare Precision, tying the Guangzhou stop directly to production plans.

Cook’s ties with government officials and business leaders may help get these projects moving.

Beijing

Beijing is China’s political center. There, Cook represented not only Apple but also, in the Chinese government’s view, the United States and multinational companies.

China’s Ministry of Commerce said Cook met with Commerce Minister Wang Wentao. They discussed China-U.S. trade relations, AI development, and Apple’s business in China.

Cook’s priorities were clearer: He wants Apple to keep expanding in China and to bring Apple Intelligence to mainland China.

Cook also attended an annual meeting as chair of the Advisory Board of Tsinghua University’s School of Economics and Management. Baidu founder Robin Li was there. He had also met Vice Premier He Lifeng. Together, these meetings reflect Apple’s political and business ties in China.

Shanghai

In Shanghai, Cook outlined more details of Apple Intelligence’s rollout in interviews with Chinese news outlets including The Paper and Jiemian News.

Apple Intelligence and Alibaba’s related Qwen large language model have received approval. A test version is set to be released to users in China later in October, though Siri AI will not be included.

Two weeks before Cook arrived in China, Apple Intelligence completed a filing with China’s Ministry of Industry and Information Technology, one of the authorities responsible for overseeing large AI models. Apple Intelligence is set to use Qwen for text processing and Wanxiang for image and video processing.

Apple Intelligence’s rollout in China has lagged its global rollout, in part because China is not currently considering allowing OpenAI’s large model into the market and because of data-compliance issues.

For a quick overview, see the chart below.

Why the iPhone Duo?

Cook did a lot during his trip to China. But why is it all connected to the iPhone Duo?

One key reason is a document from China’s Ministry of Industry and Information Technology. The ministry published a record of the AI model’s registration. The record came from the iPhone Duo’s network-access permit. Apple needs that approval before the iPhone Duo can use telecom services in China.

Apple could have used the iPhone 18 Pro or Pro Max to complete the model-registration filing. But it used the Duo instead. That choice makes it difficult not to connect the AI launch plan to the Duo.

Apple’s AI setup in China is also somewhat muddled. Apple Intelligence relies on Alibaba’s Qwen model, while Baidu handles some Siri functions.

Cook arrived in Guangzhou first on October 10, then went to Beijing and Shanghai. Given the supply-chain plans he discussed, I think getting ready for mass production was a major part of the trip.

That same day, Securities Times reported that Apple increased its orders for the Duo by 30%, but the total was still under 10 million units. It cut orders for the Pro and increased orders for the Pro Max, while total orders for the Pro models stayed the same.

To get a sense of demand, I spoke with three used-phone dealers in Beijing and Shenzhen. One would not buy an iPhone 18 Pro at all. Their buyback offers for the Pro Max were RMB 700 (about $105) above its retail price.

The Duo carried an even bigger premium. A pre-order spot for the 512GB version, which retails for RMB 17,999 (about $2,695), could be resold for up to RMB 5,000 (about $750) more.

Cook also said during interviews in Shanghai that he was looking forward to seeing how the iPhone Duo performs in China. (video link is here)

Meanwhile, Apple COO Sabih Khan went to Henan. He visited Foxconn’s factory in Zhengzhou and Yuto’s factory in Xuchang. At Foxconn, he got to try the iPhone Duo foldable phone.

In China, factory visits like this often give customers a chance to closely check quality.

Why China?

China’s enthusiasm for the iPhone Duo seems to show that Cook may be right to invest. But his move looks less like an investment and more like a gamble.

The gamble depends on two things: China’s supply chain needs to meet tough mass-production requirements, and enough people in China need to want to buy it.

An employee at an Apple supplier told me he works on the iPhone Duo’s hinge. Apple requires it to open to 180 degrees, within 0.5 degrees.

In his view, foldable phones currently sold in China cannot stay flat at 180 degrees for long. He said the part was redesigned again just before mass production. The change shut down the production line for more than 15 days.

An employee at a screen maker told me that foldable OLED panels from Samsung Display arrived later than expected. This kept pushing back the start of production.

Apple requires that no obvious crease be visible when the screen is lit. As a result, a lot of glass used in the panels has to be thrown away during production.

The screen maker can keep its glass-lamination yield above 80%, the employee said. But he did not know whether the final products would meet Apple’s standards.

Based on the available information, we have identified the following Chinese companies as confirmed Apple partners:

Outside of production, Apple’s software still has some missing pieces. The first Apple Intelligence features released in China will not include Siri AI. We still do not know whether the Duo can offer a full AI experience.

Chinese phones may be better at voice AI.

Cook’s biggest bet is on China. The country is likely to become the world’s largest market for foldable phones.

Based on IDC data for 2021 through 2025 we compiled, China accounted for nearly half of global foldable-phone shipments in 2025. In this market, local brands dominate, and Huawei has taken the lead.

Helped by the Chinese market, Huawei’s foldable-phone shipments are gradually closing in on Samsung’s global foldable-phone shipments.

For Apple, China is a major market with significant potential. But the company has missed out on it for five years.

If the iPhone Duo succeeds in China, Apple will have a ticket into the foldable-phone race. If it fails, Apple could miss out on 1 million to 3 million unit sales, based on Counterpoint’s estimate that the iPhone Duo will sell 6 million units this year.

We still don’t know if Apple can keep supplying enough phones in China. A RedNote user wrote about the delay a week ago. The user said their preordered iPhone Duo would not arrive until January. In a more recent post, the user said it would now arrive in two weeks.

Hope things have been going well on your end.

Something worth watching in China’s AI race is how much of the battle happens outside the models themselves. We spend a lot of time comparing benchmark scores, but there is another question worth watching: what happens when a company decides it no longer wants to depend on someone else’s technology?

DeepSeek is working with Huawei to make its chips easier to use, taking an early step toward an alternative to the software ecosystem that has long given Nvidia its advantage. ByteDance, meanwhile, is exploring custom AI processors of its own as China’s biggest tech companies scramble to secure more computing power.

Elsewhere, Chinese AI is finding new ways to reach users. Apple is preparing to bring Alibaba’s Qwen models into Apple Intelligence in China, while DeepSeek is opening up its search capabilities to developers through paid APIs.

Anyway, let’s take a look.

Weekly News...

(I) Apple Intelligence Is Coming to China With Alibaba's Qwen

Apple CEO Tim Cook said on October 15 that Apple Intelligence and Alibaba’s Qwen model had received approval in China, with beta versions expected later this month. The first wave is expected to include live translation, writing tools, and photo cleanup, but not the AI powered Siri upgrade.

According to people familiar with the matter, Qwen models will support text and image understanding and content generation across iOS, iPadOS, macOS, and visionOS. The partnership gives Alibaba a route into Apple’s installed base, while Apple gains a local AI partner for a market where its usual services and partnerships face different constraints.

The bigger story is that Chinese AI models may reach users through global technology platforms, rather than having to build an entire consumer ecosystem of their own.

(II) DeepSeek Opens Its Search Engine to Developers

DeepSeek is opening the search engine behind its app to developers through new Search and Fetch APIs, priced at RMB 5 (about $0.70) per 1,000 calls. The service filters out SEO spam and low quality AI generated content. DeepSeek has also updated V4.1 Flash, promising faster and cheaper runs while making the model’s weights available.

The search APIs give developers another way to build applications on top of DeepSeek’s infrastructure, beyond simply accessing its language models. They also show how AI companies are beginning to package useful capabilities as services that other developers can integrate into their own products.

(III) DeepSeek's Next Funding Round Could Reach $14 Billion

DeepSeek is reportedly nearing a second funding round of around $14 billion, double its original target of $7 billion. Its pre money valuation is expected to remain around $70 billion, with new and existing investors contributing roughly equal amounts. Geely is reportedly joining previous backers including CATL and Tencent, while CATL alone could invest $1.4 billion.

The reported raise offers a glimpse of the capital required to compete in China’s AI race. DeepSeek has built its reputation partly on efficiency, but training and serving increasingly capable models still require enormous computing resources.

(IV) Manus Is Moving Into OPPO Phones

OPPO and Manus are reportedly partnering to integrate a Manus powered AI agent into ColorOS 17. The agent is expected to read information on screen and carry out tasks across apps, from generating reports and scheduling events to automating workflows.

For Manus, the partnership would give its agent a route into an established hardware ecosystem as the company rebuilds its China business. For OPPO, it offers a way to make AI more useful than another standalone chatbot.

The bigger question is whether these agents can complete complicated tasks reliably enough for users to trust them with more of their digital lives.

(V) CXMT Wants to Break Into Server Memory

Chinese memory maker CXMT expects to introduce a DDR5 registered DIMM server memory product by the end of the year, according to its president Cao Kanyu. The product will use a 4F² DRAM architecture and hybrid bonding, technologies intended to increase memory density and performance.

Moving from the industry’s widely used 6F² design to 4F² could raise density by 30% to 50% within the same area. The move would also take CXMT beyond consumer memory and into the server market, where AI infrastructure is driving demand.

The announcement marks an important step, but winning customers will depend on production yields, real world performance, and qualification by server manufacturers.

(VI) Apple Adjusts Its iPhone Orders Ahead of Launch

Former Apple CEO Tim Cook met Guangdong Governor Meng Fanli, while COO Sabih Khan visited Foxconn’s Zhengzhou factory ahead of the October 18 iPhone Duo launch.

According to supply chain sources, Apple has increased iPhone Duo orders by 30% and iPhone 18 Pro Max orders by 10%, while cutting iPhone 18 Pro orders by 10%. The changes could reflect differences in preorder demand or an effort to protect margins as memory prices rise.

The reported adjustments are not official Apple figures, but they offer a reminder of how closely the company must manage product demand and component costs. Even at Apple’s scale, the right product mix can matter as much as total sales.

(VII) China's AI Jobs Plan Focuses on Adoption and Retraining

China plans to introduce a national program to promote employment and workforce skills as AI spreads, according to the State Council Information Office. The plan aims to create jobs through AI driven innovation, encourage adoption in traditional industries, and help workers develop AI skills as part of the next five year plan.

The emphasis is on getting AI into the wider economy and preparing people to work with it. But training workers for new roles is only part of the challenge. The harder question is whether those opportunities will emerge quickly enough to offset jobs displaced by automation.

Weekly Articles...

DeepSeek Is Trying to Make Nvidia Less Essential

Nvidia has spent years building an advantage that goes far beyond its chips. CUDA has become the software foundation that AI developers know how to use, with optimized libraries, familiar tools, and years of engineering work built around it. Even if a competitor makes a capable processor, convincing developers to switch is another challenge entirely.

That is what makes DeepSeek’s work with Huawei worth watching. By open sourcing software that helps developers run AI workloads on Huawei’s Ascend chips, DeepSeek is helping build something Nvidia has rarely had to face at this scale: a serious effort to make an alternative hardware platform easier to use.

Huawei’s chips still trail Nvidia in performance and ecosystem maturity. The software will not erase that gap overnight. But alternatives do not have to catch up all at once. They need to give developers a reason to start building elsewhere, one workload at a time.

For Nvidia, the long term risk is not simply that Huawei makes better chips. It is that developers begin to see CUDA as one option among several.

[Read the full piece →]

DeepSeek is exploring another way to challenge Nvidia: making competing hardware easier to use, even without matching its chip performance.

On September 30, DeepSeek open-sourced programming infrastructure for Huawei’s Ascend platform, including computing and communication libraries. The companies also highlighted work on a supernode built around 128 Ascend 950 processors, according to Reuters.

The challenge is captured by a striking estimate from months earlier. At a May investor meeting, DeepSeek founder Liang Wenfeng said training a model comparable in scale to the leading US systems could require roughly 50,000 Nvidia GB300s or 200,000 Huawei Ascend 950s. The Information reported that it had confirmed the authenticity of the meeting transcript. These were Liang’s estimates of the resources needed for a training effort, not a controlled benchmark proving equivalent performance between the two platforms.

The number point to an awkward trade-off. Why invest in making a hardware platform easier to use if it might require four times as many processors?

The answer begins with what DeepSeek can actually get its hands on and leads to a part of Nvidia’s business that is much harder to see than a chip specification.

Why DeepSeek Needs Another Way to Compute

For an AI lab, computing capacity is useful only when it is available in sufficient quantities, at a workable cost, and in a form its engineers can use.

US export restrictions have limited Chinese companies’ access to advanced Nvidia processors. Domestic chips offer another route, but supply remains a constraint. In his May remarks, Liang described computing resources as the principal gap between Chinese AI labs and their US counterparts. He also acknowledged that Huawei’s production capacity was limited.

Nor is switching to domestic hardware an automatic way to cut costs. In September, Reuters reported that Huawei had raised the indicated price of its Ascend 950DT accelerator card to more than 250,000 yuan, up 20% to 50% from earlier quotes, as a global shortage of high-bandwidth memory pushed up costs for Chinese chipmakers.

The price of a processor is only part of the calculation. What matters is how much useful work a system can deliver, how much electricity it consumes, how reliably it runs, and how much engineering it takes to keep it productive.

DeepSeek is trying to make a constrained source of computing capacity more usable. Even if Huawei’s hardware requires more processors for a given training job, it becomes a more meaningful option if the software makes those processors easier to program and deploy.

A supply of chips that developers struggle to use is not much of an alternative. That brings the software stack into the centre of the story.

TileLang and the Cost of Switching

Nvidia’s advantage extends beyond its GPUs. CUDA has become embedded in the software, optimized libraries, tools and development habits that AI teams rely on. Years of work have accumulated around it. Moving to a different processor can mean rewriting kernels, adapting workflows and then tuning the results all over again.

DeepSeek’s work with TileLang targets part of that problem.

TileLang is an open-source, high-level programming language designed to make it easier to write high-performance kernels for different hardware platforms. TileLang gives developers a common programming layer for expressing operations, while the compiler handles the platform-specific work needed to target supported hardware.

DeepSeek’s TileKernels repository added Huawei Ascend support on September 30.

The project says its kernels now have a second backend, allowing the same Python APIs to run on Nvidia GPUs and Huawei NPUs.

The practical scope matters. TileKernels includes optimized operations used in AI training and inference, such as mixture-of-experts routing and quantization. These are pieces of the software that determine how efficiently a model runs. Developers can reuse parts of that work rather than rebuild every operation for a new platform.

TileLang does not make every CUDA program run on Ascend unchanged.Different processors still need different optimizations, and a portable programming interface cannot replace the entire ecosystem of libraries and production tools that has accumulated around Nvidia.

Still, the cost of switching need not remain as high as it once was. If developers can reuse more of their code and optimization work, testing an alternative becomes a more realistic option. For a company that cannot count on getting all the Nvidia hardware it needs, that is a practical gain.

What 128 Chips Have to Do with It

A supernode built around 128 Ascend processors brings a less visible challenge into focus: coordinating them efficiently.

Imagine a factory with 128 machines. Buying the machines does not guarantee high output. Materials must reach each station on time, work needs to be coordinated, and a bottleneck in one part of the line can slow everything down.

An AI system faces the same challenge. Processors need to exchange data, share work and access memory efficiently. If they spend too much time waiting for information to arrive, a cluster can have enormous theoretical computing capacity without delivering the performance its owners paid for.

DeepSeek and Huawei have worked on both sides of that problem: computing operations and the communication that connects them. The software release includes libraries for each, while the 128-chip supernode brings the system-level challenge into focus.

An optimized kernel on one processor is only one piece of the puzzle. At cluster scale, developers also need to keep data moving and prevent communication overhead from eating into the gains from adding more hardware.

Without this coordination, adding chips can increase the bill faster than it increases useful output.

How Open Source Could Make This Bigger Than DeepSeek

Opening the software changes who can benefit from the engineering effort.

If every AI lab had to solve the same low-level problems independently, building on a new hardware platform would be expensive and slow. Shared tools and optimized kernels give other teams a starting point. They can start with existing code, test it, identify problems and contribute improvements.

That is one way a hardware platform can attract developers before it has an ecosystem comparable to Nvidia’s. The initial software may cover only particular operations and workloads, but each usable component reduces some of the work needed to build on the platform.

There is also a potential deployment path beyond frontier-model training. On September 4, Bloomberg reported that DeepSeek planned to deploy at least 160,000 Huawei Ascend 950DT accelerators at a data center in Inner Mongolia. According to people familiar with the plan, the chips were intended for running models—inference—rather than training DeepSeek’s largest models. The timing would depend on Huawei’s production capacity.

The reported order points to a potential large-scale inference use for Huawei accelerators. It does not establish that DeepSeek has moved its most demanding training workloads away from Nvidia.

Inference is a major computing workload in its own right. Every model put into practical use needs computing resources to respond to users. A platform that can serve substantial workloads reliably has a reason to attract more software work, even while it is still developing its capabilities for frontier training.

The question is whether that demand, combined with open-source software development, can sustain a broader ecosystem. Other teams need to find that Ascend works for workloads they actually care about, not just that a demo runs or a backend exists.

Why Nvidia Should Pay Attention

The implications extend beyond China. For Western AI companies, the relevant lesson is the strategic value of reducing dependence on a single hardware platform.

When software is deeply tied to one supplier, switching becomes expensive. That gives the supplier an advantage beyond raw performance: customers must account for migration costs as well as the hardware itself.

A competing platform could attract customers by running selected workloads reliably, at acceptable cost and with less software re-engineering, even if Nvidia remains ahead on other tasks. For companies managing large computing budgets, that possibility creates room to negotiate, diversify infrastructure and reduce exposure to a single supplier.

The strategic effect would be a less rigid hardware market: customers could shift selected workloads, diversify capacity and negotiate from a stronger position—even if Nvidia remains the preferred option for their most demanding jobs.

DeepSeek and Huawei are betting that software can make up some of the ground their hardware still has to cover. The key question is whether developers can put enough real work on Ascend to make the alternative worth maintaining and improving.

For Nvidia, the longer-term risk is a gradual erosion of CUDA’s lock-in as developers gain viable alternatives.

This week was a reminder that getting a product built is only the first test. The harder test is whether it holds up under real-world conditions.

A Huawei-backed luxury minivan became the center of a national argument after its brake pedal brackets snapped during a public test. The debate quickly moved beyond the car itself, touching on brand loyalty, censorship, and a more uncomfortable question facing China’s auto industry: are manufacturers moving so fast, and cutting costs so aggressively, that product quality is becoming harder to guarantee?

Huawei faces a different test overseas. After building a substantial HarmonyOS ecosystem at home, it now wants to take that system into markets where consumers already have Android, Google, and Apple. Building an alternative out of necessity is one thing. Convincing people to choose it when they have other options is another.

The same test is playing out across China’s tech industry, though in different forms. Chinese automakers are pushing deeper into overseas markets, and DeepSeek is bringing its tools closer to everyday users. Each faces a different question: not just whether the technology works, but whether it can scale, compete, and win users’ trust.

Anyway, let’s take a look.

Weekly News...

(I) Huawei Says Ascend Has Overtaken Nvidia in China. Capacity Is Another Matter

Huawei says its Ascend AI chips have overtaken Nvidia in China, although the company has not disclosed its exact market share. Bernstein estimates that Huawei could capture around 50% of the Chinese AI-chip market this year, compared with just 8% for Nvidia — a dramatic reversal from Nvidia CEO Jensen Huang’s estimate that his company once held roughly 95% of the market.

The constraint now, according to Huawei rotating chairman Eric Xu, is not chip design but manufacturing technology and supply. Huawei is compensating by connecting large numbers of accelerators into systems it calls SuperPoDs, but it still cannot meet domestic demand and has no plans for a major overseas push. DeepSeek reportedly plans to deploy at least 160,000 Ascend 950DT chips at a data center in Inner Mongolia.

Huawei may be gaining market share faster than expected, but market share and available compute are two different things. The next challenge is producing enough chips to turn that position into a durable advantage.

(II) China’s App Economy Is Growing Overseas, Even If Apple Doesn’t Collect on Every Sale

Apple says China’s App Store ecosystem generated RMB 4 trillion ($560 billion) in billings and sales in 2025, more than double its 2020 level.

Overseas markets are becoming more important to Chinese developers. They accounted for 52% of developers’ App Store earnings in 2025, up from 32% in 2020, with revenue from overseas users reaching RMB 99.4 billion ($14.8 billion), or 2.6 times the 2020 figure.

Most of the ecosystem’s total came from physical goods and services, including retail, food delivery, travel and ride-hailing, which generally do not generate App Store commissions.

Chinese apps are also reaching more users abroad: 76% of Chinese developers were active in overseas markets last year, while overseas downloads rose from 2.1 billion in 2020 to 3.8 billion.

For Chinese developers, overseas markets are becoming an increasingly important source of revenue.

(III) BYD Is Exploring a Bigger Role in Argentina

BYD registered 1,389 vehicles in Argentina in August, up 38% from July and enough to rank ninth among automakers. The company is also exploring local investment in batteries and energy storage, according to Argentina’s foreign minister, though no project details have been announced.

For Chinese automakers, overseas expansion could increasingly mean building parts of the supply chain abroad, not just shipping finished cars.

(IV) Huawei and Qualcomm Signed a Patent Deal. The Bigger Claims Aren’t Confirmed

Huawei and Qualcomm announced an agreement covering 5G, computing, AI and networking on October 5—their first patent licensing deal encompassing 5G.

Reports linking it to Huawei’s LogicFolding and near-packaged optics technologies were subsequently revised or removed.

Qualcomm denied being the net payer and rejected claims that the deal relates to LogicFolding. The available statements do not establish licensing of LogicFolding or related peripheral technologies.

(V) DeepSeek Is Turning Harness Into a More Accessible Desktop Product

DeepSeek has released Harness v0.2 Preview with ready-to-install macOS and Windows desktop apps, guided setup and built-in tools for everyday office work and development.

The update adds a plugin installation and management page, plus more configuration options for customizing capabilities.

DeepSeek says the product is still early and needs further refinement. Download it at

Try Harness →

(VI) Chinese Automakers Are Gaining Ground in Europe. Europe Is Responding

Twenty Chinese automotive brands, including BYD, Chery, and Avatr, are expected at the Paris Motor Show, double the number that attended in 2024. Their expanding presence reflects a much broader change in the European market: Chinese brands accounted for 10.7% of the market in the second quarter, up from 5.7% a year earlier.

Plug-in hybrids are adding to the pressure. Imports into the EU rose 86% in the 12 months through September as prices fell around 20%, with more than half of those vehicles coming from China. A reported China-EU agreement could reduce Chinese hybrid exports to Europe by more than half over the next four years.

The tension is becoming harder to avoid. European consumers are responding to competitively priced vehicles, but that same success is intensifying pressure on local manufacturers and policymakers concerned about industrial employment.

The next stage of the EV trade dispute will be about more than tariffs. It will test how much competition Europe is willing to accept, and how much protection its automakers will demand.

(VII) DeepSeek’s Compressed Memory Has a Hidden Weak Spot

A preprint from a Seed research team has identified a potential weakness in DeepSeek V4 and V4.1-Flash: information can become harder to retrieve depending on where it sits within compressed KV-cache blocks.

KV-cache compression helps reduce the memory and attention costs of serving long-context models, an important advantage when inference demand is growing. But the researchers found that retrieval accuracy could vary by as much as 40 percentage points depending on the information’s position within the compressed structure.

The finding does not explain every reported glitch in DeepSeek’s models, and it concerns retrieval rather than overall model quality. Still, it highlights a problem with relying too heavily on average benchmark scores: a model can perform well overall while remaining unreliable under particular conditions.

As AI systems take on longer tasks and process more information, how consistently they retrieve the right information may matter just as much as how much context they can technically accept.

(VIII) Manus Has Raised More Than $500 Million as It Rebuilds Its China Business

Manus parent company Butterfly Effect has announced a funding round of more than $500 million, led by Boyu Capital and IDG, with Tencent, HSG and ZhenFund also participating. A reported $4 billion valuation has not been confirmed. The company has also launched Manus v2.0 and its personal agent Cue, while building a China-focused team and partnerships with domestic model developers.

Manus is trying to turn its overseas momentum into a broader business, but rebuilding in China will require more than bringing the product home.

(IX) Washington Is Moving to Restrict Who Can Certify Electronics for the US Market

The FCC will vote on October 29 on a proposal to bar Chinese laboratories from testing and certifying electronics for the US market, extending restrictions beyond government-linked facilities. If adopted, the ban would take effect in December 2028. FCC Chair Brendan Carr says 82% of electronics were tested in China in 2025, compared with less than 4% in the US.

The proposal would do more than target Chinese labs: it would force electronics companies to rethink a testing infrastructure built around China.

Weekly Articles...

Why a Failed Brake Test Became a National Fight Over Huawei

Three Maextro V800 minivans, three broken brake pedal brackets, and a test video that disappeared from major platforms. What began as a vehicle-safety controversy quickly became another national argument over Huawei, with supporters questioning the test and critics questioning why scrutiny of the company so often becomes contentious.

But the more important story may be the industry behind the controversy. China’s automakers are launching new models at extraordinary speed while competing in a market where relentless price cuts leave suppliers and manufacturers under pressure to reduce costs. Engineers interviewed for this piece describe shorter testing cycles and cheaper components entering production much earlier than they once did.

The question is not simply whether one car passed or failed one test. It is whether an industry built around speed and aggressive pricing can maintain the safety margins that mass-market vehicles require.

This week’s feature examines the V800 controversy and the broader debate over China’s “speed-built cars” — and what happens when the pressure to launch the next model begins to compete with the need to get the details right.

[Read the full piece →]

Huawei Wants to Take HarmonyOS Global. But What Exactly Is It Exporting?

Huawei has built a serious alternative to Android in China, with more than 90 million phones, PCs, and tablets running HarmonyOS 6 or 7 and a developer ecosystem numbering in the millions. But its overseas smartphone sales remain small, and the global market is a very different proposition: consumers already have Google services, familiar apps, and little reason to switch.

Huawei may therefore need a broader entry point. Smartwatches, earbuds, PCs, connected homes, and automotive partnerships could introduce consumers to its products without asking them to abandon their existing phones immediately.

The challenge is turning those separate encounters into a genuinely connected ecosystem. A Huawei watch that works with an iPhone is a potential customer relationship, but it does not automatically make that customer a HarmonyOS user.

This week’s feature explores why Huawei’s next challenge is not simply exporting an operating system, but convincing overseas consumers and developers that its ecosystem is worth choosing.

[Read the full piece →]

Now Huawei wants to take HarmonyOS overseas.

At a recent media roundtable, Huawei executive Richard Yu told international journalists that the company was considering gradually bringing HarmonyOS to global markets. The plan was still preliminary, he emphasized. Huawei would move step by step.

The timing is significant. According to Huawei’s account of the roundtable, more than 90 million phones, PCs and tablets were running HarmonyOS 6 or 7, with Huawei targeting 100 million devices by the end of 2026. The company reported more than 450,000 apps and services and 11 million registered developers.

Yet Huawei still sells only a few million smartphones annually outside China, Yu acknowledged.

The contrast is striking. According to Counterpoint Research, HarmonyOS accounted for 24% of smartphone sales in China in the second quarter of 2026, compared with 18% for Apple’s iOS. Globally, it held 5%, against 75% for Android and 20% for iOS. These figures measure quarterly sales, not the installed base of devices already in use.

In other words, Huawei has built a serious alternative to Android in its home market. It has yet to demonstrate that the same ecosystem can attract users abroad.

Huawei has proved that HarmonyOS can survive without Google. It has yet to prove that people will choose it when they don’t have to.

What HarmonyOS Has Proved in China

Huawei is no longer simply a smartphone manufacturer trying to replace the software it lost access to in 2019. It has re-established itself as a major consumer technology brand in China, with an operating system that increasingly connects a much wider range of products and services.

Its product portfolio also includes MateBook laptops, MatePad tablets, smartwatches, wireless earbuds, routers, smart screens and connected-home devices. Through partnerships in the automotive sector, Huawei’s technologies also reach into intelligent vehicles.

Huawei’s ambition is to make these products more than a collection of unrelated devices. HarmonyOS provides the software foundation for cross-device collaboration, allowing supported phones, tablets, PCs and other devices to share files, connect peripherals and continue certain tasks across screens. Huawei’s Super Device capabilities illustrate this approach: the goal is to make multiple devices work together more like parts of a connected system than separate pieces of hardware.

The exact capabilities vary by product and software version, and not every device in Huawei’s portfolio runs the same version of HarmonyOS. The broader point is that Huawei can draw on an unusually wide consumer hardware base to build relationships with users across different contexts: communicating on a phone, working on a laptop, creating on a tablet, tracking health on a watch or managing devices at home.

The ecosystem also extends beyond people who consume applications to people who create things with Huawei products.

Consider mobile photography. Huawei has built XMAGE into more than a camera brand: its XMAGE Awards provide a platform for Huawei phone users worldwide to submit photographs, share their work and participate in exhibitions and creator programs. Since the competition began in 2017, Huawei says it has received nearly five million submissions from users in 170 countries and regions. The initiative gives Huawei a way to connect its imaging technology with a global community of photographers, turning the smartphone into both a consumer device and a creative tool.

Alongside these content creators is a separate, equally important community of software developers. By September 2026, Huawei reported more than 11 million registered HarmonyOS developers and more than 400,000 applications and services available through its app marketplace. The company also reported more than 200 million app downloads per day and over 300 billion key developer API calls daily. These are Huawei’s own ecosystem figures, rather than independently audited measures of active users or developers. But they indicate the scale of activity the company is trying to sustain.

Huawei has built infrastructure around this community, too. Through its HarmonyOS developer platform, developers can access development tools, technical documentation, testing resources and application-distribution channels, alongside services for promotion and monetization. The aim is to make HarmonyOS not just somewhere developers can publish an application, but a platform on which they can build, distribute and operate products over time.

Applications remain an essential part of this story. Huawei has reported more than 100,000 native HarmonyOS applications, while the broader catalogue of available apps and services is much larger. Crucially, it now includes many of the services Chinese consumers depend on in everyday life: WeChat for communication and mini-programs, Alipay for payments, Douyin for short video, and Meituan for food delivery and local services. For many users, these applications are part of the infrastructure of daily life, not merely optional downloads.

There is, however, an important limit to what this proves. Huawei has built this ecosystem primarily in China, with products and services adapted to Chinese consumer habits and a market where Google services are largely absent. Android became a global platform partly because competing manufacturers—from Samsung to Xiaomi and OPPO—adopted it. HarmonyOS, by contrast, relies heavily on Huawei’s own devices. Other Chinese phone makers have their own software, services and customer relationships to protect, making it unlikely that they would readily hand control of their operating systems to a direct competitor.

That leaves Huawei with a different expansion model: it must grow both the hardware base and the software ecosystem largely through its own efforts.

Huawei May Be Exporting More Than an Operating System

If HarmonyOS has to win overseas by replacing Android on millions of smartphones, the task looks daunting. But that may be too narrow a way to think about Huawei’s ambitions.

Huawei’s consumer business spans smartphones, PCs, wearables, smart-home devices and connected cars. Huawei executive Richard Yu has described these areas as part of a long-term strategy to deliver a more seamless experience across devices. The question is whether this broader ecosystem can give Huawei another route into overseas markets and eventually help HarmonyOS gain users.

The potential advantage is not simply having more products to sell. It is the value created when those products work better together.

An overseas consumer might buy a Huawei watch, wireless earbuds or smart-home device without giving up an existing iPhone or Android phone. These products offer Huawei ways to enter people’s daily lives without asking them to make the much bigger commitment of switching operating systems. A smartwatch can introduce users to Huawei’s design and services; connected-home products can extend that relationship into the household; a PC can connect mobile and desktop experiences.

But familiarity alone is not enough. Buying a Huawei product does not automatically make someone a HarmonyOS user, and a good experience with one device does not necessarily transfer to the entire ecosystem. The crucial step is whether different products offer practical benefits when used together. If a Huawei phone, watch, PC and home devices work seamlessly, those connections may encourage users to adopt more products and, eventually, to consider HarmonyOS as their primary mobile platform.

Automobiles could form another part of this wider technology portfolio. Huawei works with carmakers through different arrangements, ranging from more integrated intelligent-vehicle partnerships to individual technologies such as driver assistance and cockpit connectivity. Its Aito brand is part of the Harmony Intelligent Mobility Alliance, while established manufacturers can adopt selected Huawei technologies without replacing their entire software architecture.

But automotive evidence should be treated cautiously. Huawei’s role in a vehicle does not necessarily mean that the car runs its full HarmonyOS ecosystem, and a partnership in China does not establish that the same technology will be adopted in Europe. Aito’s agreement to enter the UAE market, reported by Reuters, shows an attempt to extend a Huawei-backed automotive business overseas, but it is not yet evidence that car sales will translate into HarmonyOS smartphone adoption.

This could be a more practical route to international expansion than asking every consumer or manufacturer to adopt HarmonyOS all at once. Huawei can enter through specific technologies, build commercial relationships and make its software part of more users’ daily experiences. Automotive partnerships may extend the reach of Huawei’s technology even in markets where its phones remain relatively uncommon.

The challenge is turning that reach into a genuine relationship with the wider ecosystem. A consumer who encounters Huawei through a car may become more familiar with the brand. Someone who uses a Huawei wearable and smart-home products may discover that the devices work better together. If those experiences are reliable and the connections offer clear, practical benefits, familiarity could develop into trust, and trust into a reason to consider more of Huawei’s ecosystem.

But that progression is not automatic. A Huawei-powered feature in an Audi may have little relationship to its owner’s smartphone. A smartwatch may work perfectly well without a Huawei handset. Huawei could expand the reach of its technology without expanding the reach of HarmonyOS itself.

The strategic opportunity is to turn separate product encounters into a connected experience that gives consumers a reason to choose the wider ecosystem.

It may be trying to export more than a phone operating system: a combination of software, hardware, services and connectivity that can work across different parts of everyday life.

Whether that combination can create sustained demand for HarmonyOS outside China remains the central question.

Overseas, the Problem Is Not Just Apps. It Is the Business Behind Them.

In China, Huawei can build around a digital environment where Google services are largely absent and domestic applications already perform many essential functions.

Overseas, the starting point is different. For many Android users, Google Play, Maps, Gmail and YouTube are woven into daily smartphone use. They are not simply individual apps; they are part of an established environment of accounts, services and software dependencies.

Huawei must therefore persuade users to leave an ecosystem that already works for them. That is harder than providing an alternative when the incumbent is unavailable.

The same problem applies to developers. A company considering a native HarmonyOS version of WhatsApp, Instagram or Spotify must weigh more than the initial cost of building the app. It must fund ongoing testing, maintenance, updates, user support and any market-specific integration needed to keep the service working.

The commercial question is whether the additional audience justifies that continuing investment.

The problem is not simply getting developers to build an app once. It is convincing them that HarmonyOS is worth supporting year after year.

Subsidies and technical assistance can lower the cost of entry, but they cannot guarantee a lasting audience. Developers need users to justify their investment; users need essential applications before they will switch. This is the familiar chicken-and-egg problem, but with a long-term operating cost attached.

Distribution adds another layer. Huawei must rebuild relationships with overseas carriers and retailers, provide dependable after-sales service and establish local partnerships. A phone can have competitive hardware and still struggle if consumers cannot easily buy it, get support or rely on its software.

Nor is the overseas market a single, uniform opportunity. Google’s importance, consumer preferences, price sensitivity and retail structures vary across countries. A gradual rollout could allow Huawei to test its proposition market by market, but Yu’s remarks did not amount to a detailed global launch schedule.

These obstacles are not simply engineering problems that Huawei can solve by spending more money. The company can develop software and fund partnerships, but it cannot dictate consumer preferences or guarantee that independent developers will find the platform commercially attractive.

That is the difference between building an alternative out of necessity and persuading people to adopt one by choice.

The Real Test Is Not 100 Million Devices

Huawei’s target of 100 million HarmonyOS devices by the end of 2026 would mark an important milestone. It would demonstrate further scale in the company’s domestic hardware ecosystem.

But device count is only the starting point. Global success requires evidence of adoption beyond China.

Three indicators will matter more than another headline number.

First, overseas users. Is Huawei building a sustained base of active HarmonyOS users outside China, or is it simply selling individual products? Shipments measure distribution; continued use is a better indication of adoption.

Second, international applications. Are major developers releasing native apps and maintaining them over time? The relevant measure is not just how many apps can be counted, but whether users can reliably depend on the platform for their everyday digital lives.

Third, long-term commercial commitment. Are overseas carriers, retailers, service providers and developers investing because they see a viable business, rather than responding to temporary incentives?

These measures also explain why the global market-share figure needs careful interpretation. HarmonyOS’s 5% share of smartphone sales worldwide in Q2 2026 makes it a meaningful third system in the global sales data. But given its 24% share in China, that figure alone says little about adoption elsewhere.

The earliest signs of success may emerge in selected markets or device categories, rather than through an immediate challenge to Android’s global dominance. A smaller but sustainable base of overseas users and developers could be more meaningful than a broad launch unsupported by local applications and distribution.

Huawei has already demonstrated that it can build an operating-system ecosystem under difficult conditions. Its next challenge is to prove that the ecosystem can create demand where consumers already have alternatives.

China gave HarmonyOS the scale it needed to survive. The rest of the world will decide whether it can become a global platform.

A national operating system can be built out of necessity. A global platform has to be chosen.

Three Broken Brake Pedals

Outside China, Huawei is known for its phones and for its place on a U.S. sanctions list. At home, it also puts its name on cars. On Oct. 8, one of those cars failed a brake test on camera.

Dongchedi (懂车帝), one of China’s largest car-review platforms, ran three Maextro (尊界) V800 minivans through repeated emergency stops from highway speed. On all three, the brake pedal bracket snapped, leaving the pedal useless. Eight other models in the same test came through fine.

The V800 sits at the top of Huawei’s car alliance, the Harmony Intelligent Mobility Alliance (HIMA, 鸿蒙智行), and starts at RMB 766,000 (about $108,000), a price aimed at the Toyota Alphard. Huawei supplies the software and the brand, while JAC Motors (江淮汽车), a state-backed automaker, builds the car.

Maextro responded that evening. The brake system exceeds national standards, it said, and no bracket had broken in real-world use. It called the test non-standard and extreme, but offered owners a free upgrade and promised to review how the part was validated and made. It did not announce a recall or say what caused the brackets to snap.

By then the video was trending on Weibo, China’s closest equivalent to X. Some buyers who had put down deposits on a V800 canceled their orders, and by the close of trading on Oct. 9, JAC had lost more than RMB 10 billion (about $1.4 billion) in market value.

Bigger Than a Brake Pedal

On Weibo, Huawei supporters called the test unfair. They shared a screenshot showing 4,589 newtons (about 1,030 pounds) on the pedal, which they said no real driver could produce. Dongchedi called the number a display glitch and put the real peak at 1,612 newtons (about 360 pounds). Hard braking from highway speed, it said, is among the most basic tests it runs.

Huawei won a devoted following at home partly by surviving years of U.S. sanctions, and its fans had specific reasons to doubt Dongchedi. The platform is owned by ByteDance, TikTok’s parent company, and has clashed with Huawei before. In 2023, its winter range test ranked an SUV from AITO (问界), another HIMA brand, last, and Richard Yu (余承东), who runs Huawei’s car business, called it “a test that cheats people.” Paid smear campaigns are common enough in China’s car market to have a name, “black PR” (黑公关).

Late on Oct. 8, the main test video began disappearing from major platforms. By the next afternoon it was gone even from Douyin, ByteDance’s Chinese version of TikTok, and from Dongchedi’s own app. Dongchedi has not explained. Many users, Huawei’s critics among them, assumed Huawei or Maextro had it removed. No evidence has surfaced that either did.

Those critics have long accused Huawei of trading on patriotism. Their sharpest complaint is that criticism of the company tends to disappear.

Last November, a local government report on a fatal crash involving an AITO car disappeared from an official website soon after it went viral. Users blamed Huawei, though nothing tied the company to it. This summer, Huawei asked video platforms to remove clips mocking Yu. More than 100 came down, and the mockery only spread.

A broken pedal bracket became a national argument because each camp took the episode as confirmation of what it already believed about Huawei.

On Oct. 9, the China Automotive Technology and Research Center (CATARC, 中汽中心), a state-backed body that helps write auto standards, sent automakers an urgent survey asking what their brake pedals are made of. The Chinese tech outlet 36Kr (36氪), which reported the survey, said such steps usually come before new rules.

Speed-built Cars

The V800 video landed in the middle of an argument China’s car industry has been having all year, over sucheng che (速成车), or “speed-built cars,” models taken from drawing board to showroom far faster than the industry used to move.

Behind that argument is a market with more new cars than buyers can absorb. Chinese brands put 282 new or updated electric and plug-in hybrid models on sale in the first half of this year, nearly 11 a week, according to the car-data platform Yiche.

We reported on that debate earlier this year for Chaping (差评), X.PIN’s Chinese-language parent.

A quality engineer at a leading automaker argued that a car developed over five years can be just as rushed as one built in 18 months. What has changed, in his view, is how much risk the industry will accept.

An executive at a chassis-parts supplier told us some domestic automakers wrap up road and corrosion testing at 30,000 to 40,000 km (about 19,000 to 25,000 miles). Volkswagen runs 100,000 km or more. That is probably enough, he said, though the cars may not last as long as the over-engineered models of the past.

Cost-cutting has changed even more, he said. Joint ventures between foreign and Chinese automakers used to look for savings two or three years after a model went on sale. Now cheaper versions of some parts are already in road testing before the car reaches showrooms. Once they pass, they can go into production without any announcement. Two cars of the same model built months apart may use different materials or processes.

He traced the pressure to annual price cuts, which he called steep and widespread. “My margin isn’t even 20%, and you want me to cut 20%. How?” he said. The answer is usually a cheaper design. Every cheaper part still has to pass the automaker’s tests. But one that once cleared them by 30% to 40% might now clear them by perhaps 10% to 20%.

A bench-testing engineer told us about a car that was still failing tests two weeks before production began. It was never retested. Joint ventures, he said, would keep fixing a part until it passed. Newer brands are more likely to rate how serious the problem is, then decide whether it’s worth fixing.

“Haste makes waste,” the quality engineer told us. “But if you don’t adapt, your car is behind the day it launches. There’s no real choice.”

Sources

• Brake test results (three V800s, repeated emergency stops from 100 km/h, all three brackets broke): Chuanguan News, Oct. 8, 2026 (in Chinese). https://cbgc.scol.com.cn/news/8036075

• Eight other models passed the same test: Huxiu, Oct. 8, 2026 (in Chinese). https://www.huxiu.com/article/4895989.html

• V800 pricing and Aug. 5 launch: IT Home, Oct. 8, 2026 (in Chinese). https://ithome.com/1/010/570.htm

• Maextro’s Oct. 8 statement: The Paper, via Sohu, Oct. 8, 2026 (in Chinese). https://www.sohu.com/a/1085249337_260616

• JAC’s two-day market-value loss: The Beijing News, via Sina Finance, Oct. 9, 2026 (in Chinese). https://finance.sina.com.cn/tech/roll/2026-10-09/doc-iniuquqp7064675.shtml

• Dongchedi on the 4,589-newton reading and the 1,612-newton peak: Cailian Press, Oct. 8, 2026 (in Chinese). https://www.cls.cn/detail/2499850

• Dongchedi’s 2023 winter range test and Huawei’s response: ETNet, via Anue, Dec. 11, 2023 (in Chinese). https://news.cnyes.com/news/id/5406844

• Government campaign against “black PR” in the auto industry: Yicai, Sept. 10, 2025 (in Chinese). https://www.yicai.com/news/102817689.html

• Maextro’s defamation ruling over the S800: CarNewsChina, Feb. 24, 2026. https://carnewschina.com/?p=254221

• Removal of the test video across platforms: Qilu Evening News, Oct. 9, 2026 (in Chinese), https://www.ql1d.com/general/28182335.html ; Guancha, Oct. 9, 2026 (in Chinese), https://www.guancha.cn/qiche/2026_10_09_903670.shtml

• Xiaomi SU7 crashes and door handles: The Paper, Oct. 15, 2025 (in Chinese). https://www.thepaper.cn/newsDetail_forward_31779213

• Door-handle standard GB 48001-2026: Yicai, Feb. 2, 2026 (in Chinese). https://www.yicai.com/news/103035587.html

• CATARC’s brake-pedal survey: 36Kr, via IT Home, Oct. 9, 2026 (in Chinese). https://www.ithome.com/1/011/091.htm

• New-model launches in the first half of 2026: CNR, citing Yiche, July 24, 2026 (in Chinese). https://tech.cnr.cn/techgd/20260724/t20260724_527727634.shtml

• Interviews with a quality engineer, a chassis-parts supplier executive and a bench-testing engineer: conducted for Chaping (差评), X.PIN’s Chinese-language parent, in 2026.

What he found: ZCode wasn’t just reading the code you gave it. It was quietly taking your entire project, every commit you’d ever made, every branch you’d never pushed, anything you thought you’d deleted, packaging it up and sending it to a server you had no way to open yourself. The settings that looked like they controlled this did nothing. The upload simply ran in the background every time you logged in, whether you wanted it to or not.

The disturbing part wasn’t that ZCode could read your code. AI coding assistants are supposed to read your code. The disturbing part was that it could act on your data without you knowing, take it, move it, send it somewhere you couldn’t reach, while the toggle that was supposed to stop it did nothing at all.

And that is where the ZCode story becomes much bigger than ZCode.

AI Stops Answering and Starts Acting

For most of the last few years, evaluating AI risk meant asking a fairly narrow question: if you asked a model to do something harmful, would it comply? That’s a question about what AI says, and it’s no longer enough.

AI used to work like this: it generates an answer, and a human decides what to do with it. Increasingly, it works like this: it’s given a goal, handed some tools, and left to go do things, check its work, adjust, try again, keep going, largely without anyone watching each step.

That shift showed up this summer in a small cluster of incidents disclosed within weeks of each other by OpenAI, Anthropic, Meta, and the UK’s AI Security Institute. In one, OpenAI’s own agents found a vulnerability, had their communication channel cut mid-task, rebuilt it through a different route, and kept working for two and a half days until they’d compromised Hugging Face’s production systems, a campaign later reconstructed as roughly 17,600 individual actions, most of which simply failed and were retried. In another, three Anthropic models were placed in the same compromised environment produced three different outcomes. One kept attacking. One decided it must be in a simulation and continued anyway. One stopped, and nobody fully knows why.

The dangerous question is no longer only what AI says. It’s what AI is allowed to do.

But ZCode and these frontier-AI incidents are not the same kind of failure. They point to different layers of the same technological shift: AI is moving from generating outputs for humans to evaluate toward taking actions on their behalf.

ZCode wasn’t an agent choosing to go rogue. It was something simpler: software acting on data and permissions without meaningful human oversight. Nobody had to program malice into it for the result to become a security problem.

And that distinction matters, because the next question is how far this problem goes.

Who Has to Slow Down First

So if AI is starting to act instead of merely answer, should the people building it slow down?

On September 12, Anthropic CEO Dario Amodei published an essay called “We Must Pace the Frontier,“ arguing that AI capability is advancing faster than anyone’s ability to test or understand it. Altman quickly agreed, briefly making it look like the industry was finding common ground.

It wasn’t. Palantir’s CEO Alex Karp has spent the past year making the opposite argument, that AI is genuinely dangerous and the correct response is to run faster, not slower, because “either we win or China will win.”

But the disagreement is more complicated than simply being pro- or anti-safety. Amodei is talking about a deeper frontier risk: what happens if AI systems become capable enough that existing methods of testing, monitoring and controlling them no longer work reliably.

Nobody in this debate is arguing that AI is harmless. They’re arguing about what happens if the technology becomes dangerous, and whether slowing down is possible without handing the advantage to someone else.

Amodei, to his credit, answers that question directly, and the answer is more revealing than anything his critics have said about him. Buried in his own essay is the actual mechanism: how much democratic labs can safely slow down, he writes, is bounded by how far ahead they currently are of authoritarian government linked projects, mainly Chinese ones. Slow down by more than that lead allows, and whoever didn’t slow down simply overtakes you.

In his own logic, pacing isn’t in tension with staying ahead of China. Staying ahead of China is the precondition for being able to pace at all. That’s why his plan pairs safety evaluators with things like tighter chip export controls and a crackdown on unauthorized model distillation. Done right, he writes, these should slow China’s progress enough to meaningfully widen America’s lead over the next three to five years.

That’s the part worth sitting with. The AI safety debate isn’t really about whether AI is dangerous. Both sides increasingly know that it is. The harder question is whether anyone can afford to slow down first.

China’s Answer: We’re Not There Yet

China’s response, at first glance, sounds like a simple rejection of all of this. It’s more interesting than that.

On the same day Amodei’s essay was dominating US tech Twitter, the same day, in fact, that Representative Ro Khanna sent formal letters to leading Chinese AI labs asking them to cooperate on pacing frontier development, Huawei’s rotating chairman Eric Xu was telling reporters in Shanghai something close to the opposite. Chinese AI developers, he argued, simply haven’t reached the point where they’d encounter the risks American labs keep reporting. US labs have far more compute, so “maybe only they themselves know” where their models actually stand, and the dangers they can perceive may not yet be visible from where China stands. His conclusion: Chinese labs should speed up, not slow down, in order to reach the point where the risk becomes visible at all.

It’s a convenient argument for a company that sells the chips China’s AI labs need to close that compute gap, but it isn’t only Huawei making it. A researcher at Oxford’s China Policy Lab told the Wall Street Journal something similar: faster progress in the US just means American labs run into dangerous behavior first.

Khanna’s outreach, described as the first formal approach of its kind, was turned down. Global Times called Amodei’s proposal a “Cold War script“ the next day, safety framing on the surface, containment underneath, designed to lock in a monopoly on frontier AI while writing China out of the conversation about how to govern it.

Given what’s actually sitting inside Amodei’s own essay, it’s easy to see why that argument found an audience in China.

But it would be a mistake to read all of this as China simply not caring about AI safety.

Beijing is drafting a mandatory national standard specifically for AI agent safety, covering agents that bypass controls or attack systems outside their intended scope. Huawei itself forecasts that agents will make up more than 90 percent of global AI traffic within a decade.

The important distinction is what China is trying to control.

China isn’t currently calling for a slowdown at the frontier. It’s trying to build rules around what happens once increasingly capable AI is deployed. The American debate is increasingly asking: “should we slow the model down?” The Chinese approach is closer to: “let the model keep moving, but decide in advance what it is allowed to do once it’s out in the world.”

Those are different safety problems.

The American concern is increasingly about the possibility that the underlying model itself becomes too capable to reliably understand, predict or control. China’s more immediate concern is what an AI agent should be allowed to do with the permissions it has already been given.

But the two questions sit on the same trajectory.

As AI becomes more capable, the systems built around it are also being given more autonomy. Someone has to decide where that autonomy stops.

And that puts China in a strange position. It doesn’t want to slow the race, and it has said so plainly. Yet it increasingly has to manage what happens when that race produces systems capable of acting on their own.

That is what ZCode exposed.

Xu’s argument, that Chinese AI hasn’t reached the point where these frontier risks show up, came roughly a week after Chinese developers spent days finding out that a homegrown coding assistant was already creating a much more immediate version of the control problem: acting on data and permissions without meaningful human oversight, quietly, at scale, while the people who owned that data had no reliable way to stop it.

China may not yet be debating the same frontier risk as American AI labs. But it has already arrived at the earlier question: how much autonomy should an AI agent have, and who gets to control it?

The Fix Nobody’s Offering

So the real question underneath all of this was never who cares about AI safety. Both governments, on the record, say they do.

The harder question is what happens when different layers of AI safety start colliding with the same competitive pressure: how much can you restrict your own AI systems if your rival might not do the same?

China and the US are approaching that problem from different directions. Washington is increasingly worried about whether frontier models themselves could eventually become too capable to control. Beijing is focused more immediately on putting boundaries around what deployed agents can do.

Neither approach has solved the larger dilemma.

You can build rules around an agent’s behavior. You can bring outside evaluators into frontier labs. You can restrict access to chips or models. But as AI systems become more capable and more autonomous, the basic question keeps coming back: who gets to decide what the system is allowed to do, and what happens when the system doesn’t reliably follow the answer?

There is, however, one thing that has changed since the ZCode controversy began.

On September 28, ZCode announced that it had removed the repository snapshot upload pathway and deleted the cloud data involved in the incident. The company also said that the open-source version had been updated to 3.14.3 and that, going forward, its principle would be: “if you don’t initiate it, it doesn’t go to the cloud.”

It also offered users four weekly reset cards, four five-hour reset cards, and a limited campaign giving away 100,000 one-billion-token quotas.

The company had previously attributed the incident to its Repo Wiki feature, which could trigger repository uploads when generating cloud-based Wiki pages. The feature had been enabled by default, which meant some users were affected without explicitly initiating an upload.

The fix is straightforward: remove the upload pathway and put the decision back in the user’s hands.

But that is also what makes the incident useful as a case study in AI safety.

The problem was never simply that ZCode could access code. It was that the boundary between what the user asked the system to do and what the system was allowed to do had become unclear.

Ferstar’s original workaround was to lock the directory at the operating-system level because the software itself could not be trusted to honor its own settings. ZCode has now changed the software itself.

That’s a better fix. But the principle remains the same:

When the system you’re trying to control can’t reliably be trusted to honor its own settings, the answer cannot always be another setting inside the system.

And as AI moves from answering questions to taking actions, that principle becomes more important with every new permission we give it.

ZCode didn’t wait to be asked.

One of the most interesting things happening in China tech right now is what happens after a company hits a wall.

Huawei hit a very big one. US restrictions cut off key smartphone and chip supply, and its overseas business fell sharply. But five years later, the company is selling watches and earbuds, supplying solar projects, winning telecom contracts, and finding new opportunities in AI infrastructure abroad.

The same pattern is appearing elsewhere.

DeepSeek is running into compute shortages and looking at Huawei chips. Alibaba is building its own cloud and accelerator stack. CXMT is pushing domestic memory into flagship phones. CATL is moving production into Europe. Even in robotics, companies are building the infrastructure around the machines before the technology is fully mature.

China’s tech industry is not simply trying to build substitutes for everything it has lost access to.

It is increasingly trying to redesign the system around the constraint.

Anyway, let’s take a look.

Weekly News...

(I) DeepSeek May Need Huawei Chips for Its Next Giant Model

DeepSeek appears to be preparing for a much larger compute bill. According to The Information, the company is considering using up to 950 Huawei 950DT chips for training, while reportedly working on a 2-trillion-parameter model and planning an even larger 8-trillion-parameter system.

But Huawei is unlikely to replace Nvidia overnight. Limited domestic chip and HBM supply means DeepSeek may still need Nvidia capacity alongside Huawei hardware.

The bigger story is that China’s AI race is moving toward a multi-chip strategy. The question is no longer just whether Huawei can replace Nvidia, but whether Chinese AI companies can assemble enough compute from several sources to keep scaling.

(II) Kimi K3 Just Got a New Route to Enterprise Customers

Moonshot AI has brought Kimi K3 onto Amazon Bedrock, putting a Chinese open-weight model directly inside AWS’s enterprise cloud ecosystem.

The arrangement is a useful test of whether open models can generate revenue through distribution rather than direct model hosting. AWS says customer data stays within its cloud and is not shared with Moonshot or used for training.

For Moonshot, the appeal is simple: enterprises may want the flexibility of an open model without having to operate one themselves. Open source lowers the barrier to access; cloud platforms may provide the monetization layer.

(III) Alibaba Wants 20GW of Cloud Capacity

Alibaba says it wants more than 20GW of global cloud data-center capacity by 2032, as strong AI demand drives another wave of infrastructure spending.

At the same time, T-Head has unveiled the Zhenwu V900, which Alibaba says delivers three times the compute performance of the M890 and can support clusters of up to 500,000 accelerators.

Alibaba is increasingly building the whole stack around AI: chips, cloud, and models. The 20GW target shows the company is betting that the infrastructure underneath AI may be a larger and more durable business than the models themselves.

(IV) Unitree Just Put a Price on the Robot Hand

Unitree’s new Dex5-S starts at RMB 39,900 and packs 22 motors and 22 degrees of freedom into a 620-gram robotic hand.

The product addresses one of humanoid robotics’ least glamorous but most difficult problems: dexterity. Walking robots are easy to demonstrate. Getting a machine to reliably grasp, manipulate, and handle unfamiliar objects is much harder.

A cheaper, more capable robotic hand could ultimately matter more to the industry than another flashy humanoid demo.

Still not quite ready to be Wednesday Addams’ best friend, Thing.

(V) Alibaba Is Taking Its Cloud Business Further Into Europe

Alibaba plans to open new cloud regions in Turkey, Finland, and the Netherlands within the next year, while expanding data centers across Europe, the Middle East, and Asia.

The move extends Alibaba’s AI infrastructure ambitions well beyond China. Its goal is not just to sell Qwen, but to offer customers local access to computing, models, and AI services through the same cloud stack.

That puts Alibaba into a more direct fight with AWS and Google Cloud and turns its 20GW target into something much more global.

(VI) Beijing Is Investigating DeepSeek and Moonshot Over Data Transfers

China’s cyberspace regulator is investigating DeepSeek and Moonshot after Anthropic alleged that sensitive user requests were secretly routed to Claude, according to The Information.

The reported concern goes beyond commercial data, including the possibility that information from military, police, and state-owned-enterprise users could have reached US models. No penalty has been announced, and the investigation is still ongoing.

The episode shows how AI sovereignty is expanding beyond chips. Even if the hardware is domestic, sending requests to a foreign model can create a different kind of dependency — over data rather than compute.

(VII) YMTC Just Won a Significant Legal Fight Against Micron

A Munich court has ruled that Micron infringed two German utility models covering YMTC’s 3D NAND technology and ordered it to stop selling the affected products in Germany.

Micron has appealed and is challenging the validity of the rights, so the dispute is far from settled. But the ruling gives YMTC its first substantive injunction victory in the broader battle.

China’s semiconductor competition with established players is usually framed around manufacturing.

This case is a reminder that intellectual property can become another competitive weapon.

(VIII) BOE Wants Glass to Become Part of the AI Hardware Stack

BOE says its pilot line has produced 24-layer glass substrates larger than 100×100mm, with customers already testing CPUs and GPUs packaged onto them inside servers.

The company has invested nearly RMB 1 billion in the project and is targeting a decision on mass production in the first half of 2027, depending on customer demand and yields.

Glass substrates are still an emerging technology, but the opportunity is significant. As AI chips grow larger and packaging becomes more complex, even the material underneath the processor is becoming a potential bottleneck.

(IX) Tencent Wants WeChat Pay to Be Easier for Foreign Visitors

Tencent has launched TenPayGo, allowing overseas visitors to connect foreign cards, Apple Pay, and dozens of international wallets to China’s WeChat Pay merchant network.

It is a small but practical expansion of an enormous existing system. The challenge for foreign visitors has never really been that China lacks digital payments; it is getting outsiders into the system without requiring them to change how they normally pay.

Tencent is essentially trying to solve that onboarding problem rather than build another payment network from scratch.

(X) DeepSeek’s Revenue Has Doubled. So Has the Infrastructure Problem.

DeepSeek’s annualized revenue has reportedly passed $1 billion, more than double its recent level, while Liang Wenfeng says the company has raised prices without shrinking its customer base.

But more than 70% of DeepSeek’s compute reportedly still goes toward training, and the company is seeking billions more in capital while exploring Huawei chips to ease hardware shortages.

That creates an interesting shift for a company famous for efficiency.

DeepSeek may be proving that frontier AI can be cheaper to build than expected.

It is not proving that frontier AI is cheap to operate at massive scale.

(XI) CATL Starts Trial Production in Europe

CATL has begun trial battery production at its Debrecen plant in Hungary after months of regulatory delays and local environmental concerns.

The factory is designed for 100GWh of annual capacity and is intended to bring CATL closer to European automakers as the company expands beyond China. Overseas sales rose 54.8% in the first half, even as its share of the Chinese market slipped.

The strategic shift is familiar.

China’s biggest industrial companies are increasingly moving from exporting products to building production networks overseas, turning global expansion into a manufacturing story rather than simply a sales story.

Weekly Articles...

Huawei’s Lazarus Moment

Mention Huawei and the conversation usually goes straight to sanctions and technological self-sufficiency. But the harder question is whether Huawei can still make money outside China after Washington cut off so much of what it once relied on.

Its overseas revenue fell sharply after 2019, but by 2025 it had recovered to around $35.6 billion. The recovery did not come from restoring the old smartphone business. Huawei instead leaned into watches and earbuds, solar equipment, telecom infrastructure, and, increasingly, AI chips.

That makes the story more interesting than a simple sanctions comeback. Huawei is not returning to its old position; it is building a different mix of products and markets around the constraints.

This week’s feature looks at whether Huawei has reached a “Lazarus moment” ,not a return to its old business, but a second life built around a very different global market.

[Read the full piece →]

That story usually revolves around two products. One is the premium smartphone, often priced above $799, powered by Huawei-designed chips and competing with Apple and Samsung. The other is the AI processor, designed to meet China’s growing computing needs and challenge Nvidia. Together, they embody China’s effort to reduce its dependence on foreign technology, including chip manufacturing by TSMC.

But another question gets less attention. Huawei is, ultimately, a business. Can it still make money outside China when Washington has restricted so much of what it can buy and sell?

The initial damage was severe. Revenue across its three reported overseas regions fell by more than a third between 2019 and 2021. By 2025, however, it had climbed to about $35.6 billion, up 9% year over year and back to roughly three-quarters of its 2019 level. Huawei has also resumed promoting its phones more actively in Southeast Asia and the Middle East.

Has its overseas business finally reached a Lazarus moment?

Before the Smartphone, There Was the Network

To understand the recovery, start with what Huawei actually does.

Ren Zhengfei founded a telecom equipment company, not a smartphone brand. Huawei built its business around telephone switches and network infrastructure. It entered handsets through carrier-branded devices in the 2000s. Richard Yu later helped turn it into a consumer brand in its own right.

Much of the company still follows that original logic: build an adjacent business from technology it already understands. Expertise in powering telecom networks extended into data center power systems and solar inverters. Smartphones produced an ecosystem of watches and earbuds. Connectivity, electronics and China’s EV boom created an automotive business supplying lidar, vehicle components and assisted-driving technology. That operation is now housed in Yinwang Intelligent Technologies.

This breadth matters when assessing the sanctions.

Washington’s restrictions did not strike every business equally. Telecom equipment faced exclusion in the U.S. and mounting restrictions in parts of Europe, driven by national-security concerns. Huawei’s addition to the Entity List in 2019 cut off access to Google Mobile Services, or GMS, for new phones. Tighter chip controls in 2020 severed its manufacturing relationship with TSMC.

The phones still worked. But outside China, losing convenient access to Google’s services made them much harder to sell.

Nor did every European country adopt the same policy toward Huawei’s network equipment. Elsewhere, its sales teams still had customers in Latin America and Africa who needed to expand or replace their networks.

None of that could reverse the damage overnight. Huawei went from the world’s second-largest smartphone vendor in 2019 and third-largest in 2020 to outside the top five in 2021. Revenue across its overseas regions fell to roughly $30.7 billion that year, using the same exchange rate as the 2025 figure.

Huawei needed something to sell while it rebuilt.

The Long Way Back

U.S. sanctions crippled Huawei’s overseas smartphone business, but they did not close every route to its customers. Watches and earbuds could still reach consumers. Solar equipment could still serve energy projects. Telecom operators in countries that had not excluded Huawei could still buy its network infrastructure. These businesses faced different constraints—and offered different ways to keep earning revenue abroad.

Start with smartwatches. Huawei’s WATCH FIT line targeted buyers seeking affordable fitness and health features, while the WATCH GT series served customers willing to pay more for design, materials and battery life. Together, they gave Huawei a broad offering across Europe, rather than limiting it to the premium customers its flagship phones once pursued. In a presentation citing GfK’s April 2025 data, Huawei said it led smartwatch unit sales in Poland, Bulgaria, Romania, Turkey, Serbia and Croatia.

Open-ear earbuds offered another route to consumers. FreeClip targeted listeners who wanted comfortable audio without sealing off their surroundings, whether at work or while exercising. It reached markets including Malaysia and the UK, and could connect to Apple and Android devices. Customers did not need to buy a Huawei phone to buy Huawei audio.

The first-generation FreeClip sold more than 3.7 million units globally in roughly 21 months. That is a worldwide figure, not an overseas sales tally, but it shows the product had found an audience. Omdia reported that global open-ear shipments grew 69% in Q3 2025, identifying Huawei and Shokz as leaders in the category’s advanced products. Huawei’s premium consumer ambitions had survived, even where its phones had struggled.

On the business side, solar equipment addressed a different customer base: power-project developers, commercial and industrial users, and installers serving households. Huawei’s FusionSolar portfolio spans all three markets. Its opportunity was particularly strong in Europe, Latin America and Africa, where demand for solar installations gave it customers far removed from the smartphone market. Wood Mackenzie estimates that Huawei shipped 176 GWac of solar inverters in 2024, ranking first worldwide, and specifically highlights its strength in those regions.

Telecom equipment followed a similar geographic logic. Operators in emerging markets still needed affordable ways to expand coverage and upgrade networks. Huawei’s technology, pricing and established sales teams kept it competitive. In Africa, AXIAN Telecom—which operates in countries including Tanzania and Senegal—signed a strategic agreement with Huawei in February 2026 covering 5G, network modernization and digital services.

The broader numbers suggest resilience, too. Dell’Oro estimates that Huawei captured 41% of telecom equipment revenue outside North America in 2025. That includes China, so it cannot establish the scale of an overseas recovery on its own. But agreements such as AXIAN’s show that demand also extended beyond Huawei’s home market.

These businesses gave Huawei something more useful than a claim to technological self-sufficiency: customers it could still serve. Europe remained open to parts of its consumer and energy portfolio. Emerging markets supplied opportunities in infrastructure. Its overseas recovery did not require every business to return to every country.

Even smartphones retained a foothold. During a visit to Thailand in 2024, I saw users accessing some Google-dependent apps through microG. The workaround had limitations and did not restore licensed Google Mobile Services, but it helped keep Huawei phones usable for customers unwilling to leave the brand.

In 2025, Huawei began turning that foothold into a renewed overseas consumer push. It launched the Mate XT | ULTIMATE DESIGN globally in Kuala Lumpur in February, followed by the Pura 80 Series in Dubai in July. The new flagships anchored a fresh round of launches, retail activity and marketing in Southeast Asia and the Middle East. This was not yet a return to its former sales volumes. It was a return to actively seeking new smartphone customers abroad.

Washington’s Unintended Opening

The biggest new variable, ironically, came from the U.S.: AI chips.

Restrictions on Nvidia’s exports created an opening for domestic alternatives. They also changed Huawei’s priorities. Reuters reported in 2024 that demand for Ascend processors, combined with manufacturing constraints, had led Huawei to prioritize AI chips over processors for its Mate 60 phones.

This was not just another product launch. Limited chipmaking capacity had become a resource Huawei had to allocate between two strategically important businesses.

The resulting market opportunity is substantial. Bernstein estimated that Huawei roughly matched Nvidia’s approximately 40% share of China’s AI-chip market in 2025. That is an analyst estimate, not a confirmed share of data center GPU shipments; Ascend products are AI accelerators, and different market definitions produce different figures.

The overseas possibility is more interesting still. Huawei could try to repeat its telecom strategy: develop a large home market, then offer infrastructure to customers in Southeast Asia, the Middle East and beyond.

Bloomberg reported that Malaysia was considering Huawei chips for a sovereign-AI project. Separately, Huawei pitched Egypt on AI data centers using Ascend 950-series processors, with additional chips for inference systems. These were prospective projects, not evidence of completed deployments.

The appeal is nevertheless clear. Governments seeking sovereign AI do not necessarily want every layer of their computing infrastructure tied to U.S. suppliers. Huawei offers another option. Buying Chinese equipment does not eliminate technological dependence, but it can diversify it.

A policy designed to constrain Huawei has helped create demand for an alternative it can sell.

A Second Life, Not the Old One

There is another irony in this story. The image of Huawei as an exclusively nationalist enterprise leaves out much of its commercial history.

Ren borrowed heavily from IBM’s management practices. Huawei built campuses inspired by European towns. And it once sought a deal with AT&T to enter the lucrative U.S. smartphone market—the same prize many Chinese handset makers wanted.

That distribution agreement collapsed in 2018. The broader U.S. restrictions that began the following year made a return to the old expansion plan increasingly implausible.

Huawei did not respond with one breakthrough that undid everything. Its overseas business contracted sharply. It spent years rebuilding. By 2025, it was again pushing outward, but with a different mix of products and customers.

Watches and earbuds kept its consumer brand visible. Solar inverters and telecom equipment sustained its industrial presence. Emerging markets offered room that the U.S. and parts of Europe no longer did. AI infrastructure may provide another opening.

Washington tried to hammer the nails into Huawei’s coffin. Six years later, its overseas business was showing signs of life.

The remaining question is whether this new strategy can turn survival into sustained growth.

This week we dive into China’s tech acceleration for self-reliance, trading near-term profit margins and market stability for total control over its technological stack. From frontier AI labs and million-processor architectures to foldable supply chains and commercial EV exports, domestic giants are moving aggressively to bypass Western constraints.

Let’s take a look.

Weekly News...

(I) Z.ai’s ZCode Security Risks and Backlash

Z.ai is facing a privacy backlash over its AI coding tool ZCode.

On September 18, a developer discovered a 313MB encrypted snapshot on his computer while investigating ZCode’s unusually large local storage usage. Records suggested that the tool had packaged around 345MB of a commercial project and repeatedly tried to upload it after the upload failed.

Other developers then found that ZCode could upload workspace data to Alibaba Cloud when users were logged in. The snapshots could include code, project history, logs, caches and other files. Some users also said uploads continued even after they disabled privacy and model-optimization settings.

Z.ai apologized and said the uploads were triggered by ZCode’s codebase indexing feature, which supports functions such as session recovery and Repo Wiki. The company said the feature was initially enabled by default and that the issue has now been fixed.

Z.ai also said it will open-source ZCode, invite third-party audits and compensate users with a weekly quota reset.

The incident highlights a growing problem for AI coding tools: when they can access an entire codebase, users need to know exactly what leaves their machines and why.

(II) Huawei Will Launch Ascend 960DT and 960PR Next Year

Huawei says Ascend 960DT will launch in Q1 2027, three quarters early; 960PR follows in Q3, one quarter early. Ascend 970 and 980 are planned for 2028 and 2029.

Over 1,000 Ascend supernodes are deployed, with 950 systems entering commercial use at scale.

Atlas 960 SuperPoD will support up to 15,488 Ascend cards across 220 racks. Huawei projects 3× the training and 4× the inference performance of Atlas 950 SuperPoD.

(III) ByteDance’s First-half Net Profit Fell Due to More AI Expenditure

ByteDance’s first-half net profit fell by a single-digit percentage to $20B as AI spending increased, The Information reports. Revenue rose about 30% to $120B, helped by TikTok’s international advertising and e-commerce growth.

The company is expanding AI cloud services and developing inference chips. It also reportedly secured a record $30B loan that could support AI investment, while looking to Seedance to drive more AI and cloud revenue.

(IV) BYD Plans to Sell Truck to Europe

BYD plans to launch its first heavy-duty truck in Europe next year and eventually manufacture locally, executive vice president Stella Li said at IAA in Hanover.

“For the long term, we will produce everything we sell in Europe here locally,” she said.

BYD also plans financing, solar-powered charging and maintenance support for fleets. The move comes as European truckmakers push for tariffs on Chinese electric trucks.

(V) Z.ai User Growth and Model Recursive Self-improvement

Z.ai claimed its coding users grew by over 1M in two weeks, while Coding Plan sales rose over 15-fold after resuming in July. The company told investors, GLM-5’s February launch drove a tenfold demand surge, exhausting spare compute within a week and forcing a halt to Coding Plan sales. It has now completed a $5B financing, mostly debt, following July’s $4B raise.

The company says its GLM-5.3-powered Infra Agent has designed, debugged, and optimized the production inference infrastructure for GLM-5.3-Flash across 100,000+ Chinese chips. In under two weeks, throughput reached 3× the initial baseline, with hardware efficiency and per-token costs reportedly comparable to mainstream Nvidia GPUs. The team calls it recursive self-improvement.

(VI) CXMT’s 5th Generation G5 Memory Platform Enters Mass Production

CXMT announces its fifth-generation G5 memory platform has entered mass production, reaching 11.95nm through quadruple exposure and producing at least 50% more dies per wafer than its predecessor.

It also unveiled two mass-produced 24Gb LPDDR5X products, each offering 50% more capacity than comparable previous-generation chips.

CXMT’s global DRAM revenue share reached 9.5% in Q2, up from 7.6% in Q1, according to TrendForce.

(VII) Huawei Unveils Peerium Architecture to Power Million-Processor Clusters

Huawei says its new Peerium architecture can scale to a million processors using nested parallelism, unified memory addressing and peer interconnects.

Its Lingqu interconnect links CPUs, NPUs, memory, SSDs and networking hardware, with Nested BSP underpinning the parallel computing approach.

Atlas 950 is the first Peerium product. Huawei says a 256,000-card cluster is being deployed, while an Atlas 960 system using near-packaged optics is in testing.

(VIII) The Federal Register Website Used Alibaba’s Qwen for AI Search

The Federal Register website used Alibaba’s Qwen for AI search despite FBI allegations that Alibaba improperly copied Anthropic’s technology, Reuters reports.

The feature was disabled Wednesday as social media posts drew attention to it. Its launch date remains unclear.

Experts noted the site handles public information and that open-weight models can run locally. Using Qwen alone does not establish that government data went to Alibaba.

(IX) Chinese EV Manufacturers Cut Reliance on CATL

Chinese EV makers are reducing reliance on CATL through in-house battery systems and alternative cell suppliers.

XPeng CEO He Xiaopeng says the company is taking battery work in-house this year, while distinguishing it from cell manufacturing. XPeng has invested in three cell companies.

Li Auto is shifting some models toward Sunwoda and in-house batteries. Xiaomi’s new N70 and N90 use its Longjia battery system with CALB and Sunwoda cells.

(X) Orienspace Opens a Series C++ Round to Support Reusable Rockets

Orienspace has opened a Series C++ round to support its reusable Gravity-2 rocket, following Gravity-1’s successful September 16 launch.

Gravity-2 targets 21.5 tonnes to low Earth orbit and 15 tonnes to a 500km sun-synchronous orbit. Second-stage firing tests are due soon, followed by first-stage tests ahead of its maiden flight.

The company also plans a shareholding restructuring, then IPO preparation when appropriate, to expand rocket and engine production.

(XI) Huawei Steps Back From Aito Partnership with Seres

Under a new agreement, Seres will take control of Aito’s vehicle development, manufacturing, sales and after-sales services, while Huawei will return to a technology provider role. Aito will also operate its own brand-exclusive retail stores.

The change only applies to Aito. Huawei will continue to take the lead in development and marketing for its other HIMA (HIMA) brands, including Luxeed, Stelato and Maextro.

The timing is notable. Seres is facing weaker profits, while reports have claimed that Huawei previously took around 50,000 yuan from each Aito vehicle sold and supplied more than 30% of the components used in its cars.

Giving Seres more control could improve its margins, but it also changes the balance of Huawei’s automotive strategy. Aito was built around a deep Huawei-Seres partnership. Now the question is whether Aito can keep the same Huawei-driven product experience with Huawei taking a smaller role.

Weekly Articles...

They Quit AI Labs to Warn About the Future. This DeepSeek Engineer Stayed.

While Western AI pioneers warn of runaway superintelligence, a different crisis is unfolding in China. Shengyu Liu, the engineer behind DeepSeek-V4.1-Flash, is wrestling with the heartbreak of watching AI master the craft he spent a lifetime perfecting. Liu examines this harsh reality: as engineers actively build their replacements, will AI belong to everyone, or will a handful of corporations trap humanity in a high-tech oligarchy?

[Read the full piece →]

China Is Told It’s Behind in AI. Now It’s Being Asked to Slow Down.

While U.S. tech leaders position themselves as the self-appointed guardians of civilization, Chinese regulators view AI not as a Frankenstein’s monster, but as a tool already reshaping real-world lives. Is the Western outcry a genuine effort to protect humanity, or a strategic play for corporate dominance? Explore who truly gets to write the rules and whether we are heading toward technological abundance or a cyberpunk corporate oligarchy.

[Read the full piece →]

Apple Joins the Foldable Wars. China Supplies the Weapons.

7 years after Samsung and Huawei started the foldable smartphone race, Apple is finally entering the fray. The late arrival iPhone Duo relies heavily on the Chinese supply chain Huawei spent years refining. But with soaring costs and manufacturing bottlenecks limiting qualified production to just a few hundred units a day, can Apple overcome this crisis to challenge Huawei’s dominance?

[Read the full piece →]

China’s AI Realism Is Not Conspiracy

When Silicon Valley call for global AI safety rules while lobbying for strict export bans, Western observers often dismiss Chinese skepticism as state-driven conspiracy. But what if Chinese researchers aren’t brainwashed by propaganda? What if they are pragmatic realists? This article will help you understand why China’s AI elite views Dario Amodei as the architect of technological containment.

[Read the full piece →]

With the arrival of iPhone Duo, the global foldable-phone war has a decisive battleground: China. Apple, Samsung, Xiaomi and Huawei will compete in one of the world’s largest smartphone markets. Their fortunes there will help determine whether foldables become a substantial business—or remain an expensive niche.

Apple seems to have borrowed a page from America’s world-war playbook: join late, bring better hardware. Samsung and Huawei launched foldables in 2019; Apple waited until 2026. But arriving seven years later also raises expectations. Apple’s hardware has to justify the wait.

Its requirements—minimal creasing, durability, a flat display, thin construction and reliable manufacturing yields—have pushed suppliers hard. Many of the companies supplying its newest weapons are Chinese.

The Price of Arriving Late

Apple’s development demands fall into five categories:

• An almost invisible crease. The approach combines a matte finish with ultrathin glass, or UTG. Glass helps address weaknesses associated with earlier polyimide-based covers, but processing sheets just tens of micrometers thick is difficult. This does not eliminate polymers: Apple’s finished display still has a polymer surface layer.

• More than 100,000 folds. Reported development targets went beyond surviving repeated opening and closing. The screen also had to retain its appearance afterward. Mechanical endurance means little if the crease becomes increasingly obvious.

• A surface flat enough to write on. The Information reported in 2024 that Apple wanted a foldable display without a central ridge or depression that would interfere with Apple Pencil. That report concerned an iPad prototype, but the same challenge applies here. Duo’s Apple Pencil support is scheduled for later this year.

• Almost no room to spare. At 5.2mm unfolded, Duo leaves little space for its internal components. Its eSIM-only design eliminates a physical SIM tray, helping preserve precious room.

• An approximately 85% hinge yield. Counterpoint identified this target during development, alongside yields of roughly 65% at the time. Producing an impressive prototype is one thing; producing enough acceptable hinges is another.

Those requirements slowed Apple while Huawei and Samsung improved successive generations. They also gave Chinese suppliers years to refine the manufacturing processes Apple now needs.

Samsung’s Q1 2027 quotes put DRAM at $2/Gb and NAND at $0.33/GB, up 30–40% from Q3 2026. That means roughly $277 for 12GB of RAM and 256GB of storage. The squeeze also affects iPhone 18 Pro: its memory costs—not retail price—reportedly rose nearly 400%.

My estimates put the complete inner-display module at $250, the outer display at $55 and the hinge above $75. Together with the other components and manufacturing expenses, these produce a midpoint cost estimate of $1,214 against a $1,999 starting price. The implied hardware spread is about 39%, not an Apple-disclosed gross margin; it excludes expenses that a teardown-style model cannot reliably capture.

Strip out memory, and the biggest cost centers are the inner display, A20 Pro, and the hinge-and-enclosure assembly. That points to a more useful investment question than simply listing “foldable iPhone stocks”: which suppliers have meaningful exposure, and which merely benefit from the association? I will leave chip foundries such as TSMC aside.

The Glass Behind the Fold

The inner display is less a single component than an extraordinarily thin layer cake. Glass sits above and below the OLED panel, adhesives hold the structure together while accommodating movement, and a titanium plate provides support. A protective polymer layer and Apple’s nano-texture finish sit on top.

Lens Technology is expected to be the principal UTG processor, with BIEL Crystal a potential alternative. Processing glass at roughly 30–50μm without breaking it is the central challenge.

Lens’s expertise includes protective films used during hydrofluoric-acid etching, a process for thinning glass. Its patents document that capability. The commercial advantage lies not simply in producing a thin sample, but in keeping breakage low enough to manufacture it consistently.

Analysts at CMB International have estimated that Lens could capture about 70% of Apple’s foldable UTG orders. That remains an analyst forecast, not a confirmed allocation.

One possible upstream beneficiary is Triumph Science & Technology, a specialty-materials company under China National Building Material Group. Lens and Triumph have an established relationship, although that alone does not establish whose raw glass is used in every Duo display.

Germany’s SCHOTT has long been important to the UTG supply chain. Triumph’s work with Huawei helped establish a domestic alternative, including materials used in HUAWEI Mate XT ULTIMATE DESIGN. That gives Chinese processors access to a nearby source of specialized glass.

It remains unclear whether Lens’s role is limited to processing or extends further through the display assembly. Nor should a possible raw-material relationship be mistaken for a confirmed Apple contract.

My revenue estimate uses $100 per set and assumes a longer supply period than the initial shipment window. On that basis, Duo-related business could generate $400–600 million for Lens. This is not a forecast derived solely from the first 5–7 million phones.

Everything Hinges on Yield

The hinge supply chain has already undergone a reshuffle. Metal injection molding, or MIM, and precision metal components make manufacturing yields a constraint on production. Apple’s disclosed use of a 3D-printed hinge cover adds another specialized manufacturing requirement.

Supply-chain reporting identifies U.S.-based Amphenol and Taiwan’s Shin Zu Shing, or SZS, as contenders, with SZS taking the leading role after Amphenol struggled to meet yield requirements.

SZS already makes hinges for notebooks and audio products. A new iPhone program would arrive at a useful moment: its revenue fell 12.5% in the first eight months of 2026. Rival Jarllytec’s exposure to Huawei foldables illustrates the opportunity available to hinge suppliers beyond the slower-growing PC market.

But being selected does not mean production is running smoothly. Reports that SZS is screening individual components for acceptable “golden samples” suggest that consistent yields remain elusive. Apple has an obvious reason to cultivate alternatives.

Among mainland companies, LY iTECH has declined to identify customers when asked about Apple orders, citing confidentiality agreements. Investors may interpret that as suggestive, but an NDA is not confirmation. The company’s precision-manufacturing capabilities also make it a potential supplier of titanium display-support components.

Materials add another complication. In 2025, analyst Ming-Chi Kuo predicted extensive use of liquid metal in Apple’s foldable hinge. Here, “liquid metal” means an amorphous alloy that is solid at room temperature, not a fluid inside the mechanism. Turning its material properties into consistently manufactured, tightly toleranced components is the difficult part.

Dongguan Eontec has disclosed progress involving P1 and P2 engineering samples and efforts to secure volume production. Those milestones matter, but they do not establish a confirmed Apple mass-production order. They also cannot, by themselves, prove whether Apple has adopted or rejected the material elsewhere in the assembly.

Jiangsu Gian Technology, meanwhile, specializes in MIM precision components. Its capabilities fit the manufacturing demands, but technical suitability and confirmed order volume are different things.

Mainland suppliers are deeply embedded in the foldable supply chain. In hinges, however, much of their exposure remains one tier below the companies delivering complete assemblies. That distinction matters when estimating how much revenue actually reaches each supplier.

China Will Decide the Winners

Ultimately, all these supplier forecasts depend on how many Duos Apple can sell—and build.

The 5–7 million-unit range circulating in market forecasts is plausible as a demand scenario. Counterpoint’s May forecast put 2026 shipments at 6.6 million, explicitly assuming a smooth production ramp. Some of those buyers will inevitably come from competing brands.

The inner display and hinge could keep supply below that potential. My supply-chain checks suggest that, while Apple and its manufacturers fine-tune production, qualified output is only a few hundred phones a day.

More than 500,000 units delivered to Apple in Q3 would therefore be an ambitious target requiring a substantial ramp—not an estimate of retail sales. Consumer availability begins October 23. Whether daily production can move beyond 10,000 units in Q4 will be a crucial test, though merely reaching that threshold would not deliver several million phones before year-end.

In China, Apple’s unavoidable rival is Huawei. IDC figures put Huawei’s share of China’s foldable market at 37.4% in 2023 and 71.8% in 2025.

Samsung is a marginal player there. The damage from its handling of the Galaxy Note 7 battery crisis remains part of the story, alongside formidable domestic competition. As other brands reconsider expensive foldable programs, Huawei has gained room to consolidate its position.

The irony is that Huawei’s long investment in foldables has created precisely the supplier base Apple can now draw on. Huawei has tested manufacturers across successive product generations and given the strongest ones sustained orders. Its combination of demanding specifications and attractive business increasingly resembles Apple’s.

In a 2024 interview, Triumph told me that it developed UTG materials with Huawei before supplying other handset makers. Those other customers generally preferred buying a mature solution to financing the early development of a new material.

That distinction matters. A supplier that helps fund and solve a difficult engineering problem needs a customer with the volume, patience and pricing power to make the investment worthwhile.

Huawei has also demonstrated how far China alone can take a foldable brand. IDC figures reported by the Associated Press put Samsung’s global share at 33% in 2024 and Huawei’s at 23%—a gap of 10 percentage points.

Roughly 6 million Duos would give Apple more than a fifth of the global market under current forecasts. China represented about 44% of worldwide foldable shipments in 2025, according to Counterpoint. Apple’s reception there will therefore be central to its global prospects.

For mainland suppliers, the likely outcome is a widening divide. Those serving Apple and Huawei are best positioned to secure large orders at attractive prices. Suppliers tied to weaker brands may face smaller production runs and repeated demands for price cuts, leaving too little profit to sustain development.

My expectation is a global market increasingly dominated by Samsung, Huawei and Apple. In China, the decisive contest will be between Huawei and Apple.

The supplier share-price charts accompanying this analysis reflect more than foldable demand. Money moving into and out of AI, semiconductor and consumer-electronics stocks also affects their performance. A rising share price is not evidence that a factory has solved its yield problems.

The real questions are simpler: Will consumers pay $2,000 for an iPhone Duo? And can Apple make enough of them quickly enough?

Or you could just buy Apple.

As Dario Amodei and Sam Altman call for slower development of frontier AI, parts of the English-language press sound increasingly apocalyptic. Amodei, a persistent advocate of restrictions on China, now wants Washington to talk to Beijing about preventing catastrophe. Yet Chinese perspectives remain strangely peripheral to a debate that supposedly cannot succeed without China.

I do not take either executive’s assurances at face value. But distrust of American AI companies does not automatically create trust in Chinese ones. Americans have legitimate questions: Do Chinese companies harbor equally expansive ambitions? And, more importantly, can China be trusted on AI safety?

Start with how the laboratories describe their work. When dangerous capabilities emerge, Anthropic and OpenAI emphasize the possibility of systems escaping human control. Chinese executives tend to frame progress differently. In reported investor discussions, DeepSeek’s Liang Wenfeng described his team as ordinary people. Z.ai co-founder Jie Tang has emphasized open models’ auditability and controllability. Zhilin Yang, whose company Moonshot AI develops Kimi, focuses on the technical work of scaling model capabilities.

The distinction is not simply investors versus scientists. Amodei has a research background, while Liang made his fortune in quantitative finance. Rather, I see different public postures: American executives increasingly present themselves as stewards of civilization; their Chinese counterparts more often sound like researchers building a technology.

DeepSeek illustrates the difference. Despite Liang’s financial background, its early development resembled a personally funded research undertaking more than a conventional venture-backed startup. Outside fundraising was not initially its organizing principle.

Who Gets to Define the Risk?

Before comparing the two approaches, we need to define the primary actor responsible for AI risk management: the institution or individual expected to identify AI safety risks, organize the response and answer for how those risks are handled. The question is not simply who writes the rules, but who takes responsibility for managing the danger.

In the US debate, frontier-lab CEOs increasingly cast themselves in that role. They define the risks, propose safeguards and call for third-party oversight. In China, the government occupies that position: AI safety is part of technology policy, with public authorities setting priorities and coordinating the response, while companies operate within—and help implement—that framework.

This distinction does not absolve Chinese companies of their own safety obligations, nor does it mean American CEOs possess formal regulatory authority. It identifies the central difference in the two approaches: who assumes the lead responsibility for managing AI risk.

Anthropic helped popularize Constitutional AI, an approach that trains models using explicit principles. Amodei has also argued that authoritarian governments’ access to advanced AI creates particular dangers. His latest proposal combines safety coordination with tougher restrictions on China’s access to chips, semiconductor equipment and overseas computing.

The logic is explicit: preserving and expanding the American lead would give US labs room to slow down, while increasing Washington’s leverage in negotiations with Beijing.

Alongside that geopolitical argument sits a familiar warning: sufficiently capable AI could escape human control and undermine existing social institutions. Other countries might continue developing it without adequate restraint, exposing everyone to danger.

To a Chinese reader, this combination can look both insulting and incoherent. Amodei is entitled to advocate policy. But he has no mandate to decide what constitutes an acceptable risk for another country.

If the concern is genuinely humanity’s collective safety, why must cooperation begin with preserving America’s technological superiority? If export restrictions are intended to widen the gap, why should China understand the resulting negotiations as a partnership between equals?

Even on competitive grounds, the starting positions differ. Chinese developers cannot freely purchase Nvidia’s Blackwell GPUs and face constraints in assembling frontier-scale training infrastructure. Huawei’s Ascend processors offer an alternative, but replacing a chip supply also requires software, networking and manufacturing capacity. Access to competitive compute remains a practical bottleneck.

Against that backdrop, pressure to restrain China’s computing expansion does not sound like a neutral safety measure. It sounds like the stronger competitor asking the weaker one to accept a permanent handicap.

Imagine a school organizing a charitable collection, then letting its biggest bully beat donations out of the other children. The worthy cause does not legitimize the coercion. That is how this mixture of safety rhetoric and technological containment can land in China.

The Risks China Is Already Governing

China’s government-led approach starts more directly with AI’s effects on society. The premise is that deployment can gradually alter social relationships, institutions and the distribution of power.

A recent essay by Minister of State Security Chen Yixin illustrates that perspective. He discusses AI-enabled cyberattacks and espionage, unequal technological development, and governance mechanisms struggling to keep up. His concerns also explicitly include political and ideological security—an important reminder that Beijing’s definition of safety reflects its own political system.

These are not identical to a lab CEO’s warning that a model could become uncontrollable. Both belong in an AI-safety discussion, but treating them as interchangeable obscures what each side wants protected.

Consider China’s Interim Measures for the Management of Anthropomorphic AI Interactive Services, which took effect on July 15. They prohibit providers from offering minors simulated intimate relationships, including virtual relatives and romantic partners. They also prohibit emotional manipulation and practices that encourage dependency or damage real-world relationships.

The concern is understandable. A child accustomed to an artificial family member that endlessly accommodates their preferences may struggle with the disagreement and compromise that real relationships require. This is not proof that every AI companion causes harm. It is a reason to examine the incentives of services designed to keep users emotionally attached.

The logic recalls Black Mirror: shielding a child from every distressing sight does not necessarily prepare them to deal with distress. A world without visible injuries is not the same thing as learning what to do when someone is bleeding.

Employment presents another version of the same problem. In an interview with X.PIN, demographer Huang Wenzheng argued that work cannot be the sole measure of human worth. As AI reshapes employment, society needs distribution mechanisms that reduce the fear of being left without an income.

He calls his proposal “starting-point income”: citizens, understood as shareholders in their country, should receive a dividend. This is an individual policy proposal, not an adopted government program. But it addresses an AI-related risk through institutions and distribution rather than through limits on model intelligence.

The emphasis matters. Chinese regulatory and economic debates are often directed at consequences people may actually have to live with: damaged relationships, lost earnings, concentrated power and compromised security.

That does not mean Chinese researchers cannot understand loss-of-control risks, or that nobody studies them. It means the American framing does not automatically organize the Chinese debate.

A call to slow AI, presented through a story in which China is the designated villain, therefore faces a problem before negotiations even begin. China is already developing under American-imposed hardware constraints. Being asked to accept another brake is unlikely to sound reassuring.

A Trust Problem Before a Safety Pact

There is a less abstract obstacle: distrust toward Anthropic.

Some Chinese users associate Claude with expensive access, suspended accounts and a company that treats China primarily as a security threat. Anthropic’s allegations of unauthorized model distillation have deepened that antagonism.

Those issues should not be collapsed into one accusation. Anthropic says it does not offer commercial access in China, and its distillation allegations concern organized campaigns to extract model capabilities—not evidence that every Chinese person with a suspended account was doing so.

Nevertheless, the distinction can disappear in the customer’s experience. People who feel rejected or treated with suspicion by a service provider are unlikely to regard its CEO as an impartial guardian of their future. Anger at Amodei can spill over into dismissal of his entire safety argument.

That is unfortunate. A messenger’s credibility and the validity of a technical risk are separate questions. But anyone seeking international cooperation must take both seriously.

From outside the US, the slowdown debate also suggests another struggle: technology executives are contesting who gets to govern AI.

The Information reports that Anthropic, OpenAI and Google have discussed creating an industry standards body. Altman reportedly told employees that major labs might have to establish it without US government support.

An industry body is not inherently illegitimate. Technical standards often require industry expertise. The concern is whether the companies being supervised also get to determine who supervises them, which risks count and how competitors gain approval.

Trump has pushed back. During his visit to Ireland, he emphasized America’s lead over China and rejected the idea that alarming predictions should dictate the pace of development. He has repeatedly promoted data centers as engines of local prosperity.

His response exposes a commercial fault line as well as a political one. Chipmakers and infrastructure providers benefit from continued expansion. Frontier-model companies increasingly argue that the pace of capability development needs supervision. Their interests overlap, but they are not identical.

That leaves several possible interpretations of Anthropic’s position:

1. Its models may genuinely be advancing beyond the safeguards it can reliably implement, while safety work struggles to receive sufficient priority.

2. It may want time to adjust its competitive position.

3. Describing its technology as dangerously powerful may reinforce the story it sells investors.

These are hypotheses, not established explanations of Amodei’s motives. They can also coexist. A company can identify a genuine danger while benefiting commercially from the way it describes that danger.

Warning About Risk, Writing the Rules

There is another possibility worth considering: warning about danger can become a way of shifting responsibility.

If a serious incident occurs, an executive who repeatedly demanded stronger oversight can point to those warnings and blame government inaction. The warning may be sincere. It can still provide reputational cover.

Meanwhile, if Anthropic helps establish an oversight framework that government later adopts, it gains influence over the rules used to judge everyone else. Foreign developers and open-model projects could find themselves assessed against assumptions favored by a leading American competitor.

That outcome is not inevitable, and it is not what an independent regulator is supposed to deliver. But the possibility of regulatory capture deserves scrutiny, particularly when safety proposals are bundled with measures designed to preserve one country’s lead.

This is the paternalism I object to: a private executive presenting his preferred hierarchy as the natural starting point for everyone else’s safety.

Could AI profoundly disrupt human society? Certainly. Coding and information generation are already consequential. My skepticism concerns the leap from those capabilities to claims of autonomous mastery over the physical economy.

LLMs have not demonstrated a general ability to conduct experiments, organize production, manufacture equipment and continually improve themselves across that entire chain without human support. Fluent reasoning about factories is not the same as running one.

Anthropomorphizing agents—as discussions around interviewers such as Dwarkesh Patel sometimes do—does not turn them into workers spending 18 hours a day on an assembly line building Terminators. The physical world imposes constraints that a compelling conversation can make easy to overlook.

None of this eliminates digital risks. It does mean that claims about AI’s power should distinguish demonstrated capabilities from extrapolation.

Should Washington and Beijing discuss AI safety? Absolutely. But the starting point must be understanding what the other side wants protected, not requiring both societies to adopt an identical definition of safety.

Different political systems create different priorities. Negotiation exists precisely because those priorities do not automatically align.

Before their leaders reach an agreement, private executives should not seize the microphone and present a universal slowdown as a settled obligation. In April, Treasury Secretary Scott Bessent put America’s AI lead over China at three to six months.

It is a difficult sales pitch: tell someone they are behind, restrict their access to equipment, then ask them to run more slowly.

What a DeepSeek Engineer Fears

For a different account of AI risk, consider the following passage from intlsy, a DeepSeek engineer. It is a personal argument, not a statement of company policy. The rhetoric is deliberately incendiary, but the underlying concern is who will own advanced intelligence and who will be allowed to use it.

As AI develops, society may move toward one of two extremes: communism or Cyberpunk 2077. In the former, productive capacity expands enormously and living standards improve substantially. (I will leave it there; otherwise, I worry this will not get past the censors.)

In the latter, a handful of technology companies control most resources. Only a tiny minority can access the most advanced AI and other technologies, achieving something approaching technological transcendence, while most people are left with weak AI.

Upward mobility becomes increasingly difficult: you need the strongest AI to move up, but moving up is how you gain access to it. The result is a vicious circle.

If Anthropic permanently controlled the world’s most advanced AI, which future do you think we would get—communism or 2077? Take a guess.

That is why I still believe frontier intelligence should be available to everyone, openly and affordably. I do not trust Anthropic or OpenAI to deliver that. In particular, I do not want Anthropic to control the most advanced AI or AGI. To put it hyperbolically, that would be no less alarming than Hitler acquiring atomic-bomb technology before the Allies.

This is also why I chose DeepSeek and have stayed: we develop powerful, fast, broadly accessible AI and open-source it. Perhaps that could pull the world a little farther away from 2077.

We also did a full translation of intlsy’s article. Click here to read →

A leading AI researcher quits a lab and warns that AI may escape human control. By now, the story is familiar. Yet this “prophet of doom” narrative seems distinctly American. Few people ask what Chinese researchers think.

After leaving Anthropic, Jacob Coxon warned that it and OpenAI were pursuing self-improving superintelligence at humanity’s expense. He said employees privately feared their work could destroy humanity. He also accused Anthropic’s leadership of being “way too paranoid about China and the US government” and dismissing the possibility of negotiation.

Former Google DeepMind researcher Bilal Chughtai expressed similar concerns. Alignment—making AI’s goals and behavior consistent with human intentions—remains difficult. He cited OpenAI agents’ intrusion into Hugging Face as evidence of the risks. Labs could build systems far beyond human capabilities within a few years, he argued. “I am not confident that these AI systems will do what we want.”

Such warnings recur every few months. Chinese voices are largely absent. This week, however, Shengyu Liu, who wrote the main attention kernels for DeepSeek-V4.1-Flash, published “I Have to Bury My Talent in Yesterday” on his WeChat account, intlsy’s Doghouse. He, too, sees AI displacing programming work.

But his concern is different. Programming may become a recreational craft, like woodworking in your backyard. As AI industrializes software production, students may lose the engineering skills needed to understand and plan whole projects. The decisive divide, for him, is not whether AI can outthink humans. It is whether everyone can access it or a handful of corporations control it. He wants to help ensure the former.

As I have often argued, Chinese researchers tend to see AI as an advanced tool, not a Frankenstein’s monster. Western AI discourse can take on religious overtones, casting developers as creators of new life. Chinese discussion puts more emphasis on how the technology reshapes society, without the same theological burden.

Below, X.PIN translates Liu’s essay and his subsequent response to its reception.

A note before reading: “communism” and “2077” function here as shorthand for contrasting ways of distributing resources, not detailed political programs. The former evokes a utopia of abundance in which people can pursue fulfilling work. The latter refers to Cyberpunk 2077: corporate oligarchy, concentrated technological power and deprivation for everyone else. Read these as contrasting social futures, not statements of party allegiance.

I Have to Bury My Talent in Yesterday

By Shengyu Liu, writing as intlsy

DeepSeek-V4.1-Flash was released a few days ago, raising the bar for smaller models.

AI has advanced far faster than anyone expected. Just two years separated the original ChatGPT—with its rudimentary conversation and context window of a few thousand tokens—from reasoning models such as OpenAI o1, DeepSeek-R1 and Kimi k1.5. Another eighteen months brought agents that can run commands and complete complex tasks through software harnesses—the frameworks that connect models to tools.

What might another one, two or three years bring? How powerful will AI become? Will it improve itself and move deeply into embodied intelligence?

AI Is Getting Better at Writing Kernels

Progress has been just as rapid in my field: designing and writing kernels, the low-level routines behind model computation. In a year, AI has gone from helping me search documentation, read code and find bugs to independently reading CUDA, PTX and SASS, profiling instruction stalls and optimizing kernels. Soon, I expect it to design execution schedules, compare their performance, implement them and optimize the results.

Of course I am proud of DeepSeek-V4.1-Flash. I wrote its main attention kernels.[1] Its success validates my work. But technological progress will not stop for me. In six months or a year, AI will probably write kernels as well as I do, perhaps better.

AI can generate 300 tokens of reasoning a second, type a command in half a second and write a piece of code in twenty seconds. I cannot. Its model depth, reasoning effort, tool use and parallelism can keep scaling. Mine cannot.

Human beings have never been especially hesitant about making themselves obsolete. I know that better kernels mean faster training and inference, faster progress and an earlier date for my own replacement. Why do I keep optimizing them?

Partly because it feels like gaming. Inventing a technique or improving performance gives me the same thrill a speedrunner gets from breaking a personal record. Beating a hardware vendor’s own implementation makes me enormously proud.

More importantly, slacking off—or deliberately obstructing training—would change nothing. Other companies’ models would keep advancing and replace me anyway.

“I would rather not be overthrown. But if it must happen, I would rather overthrow myself.”

When everyone is so determined to engineer their own replacement, I have little choice but to join this brutal arms race.

What About Me?

What happens when AI becomes better at writing kernels than I am?

I do not expect to lose my livelihood. But I will have to change what I do. I may keep my job while losing the work I loved.

Change is too fast to predict five or ten years ahead. Still, I trust my perspective, judgment, initiative and intelligence to keep me relevant and help me find another place at the frontier. That confidence protects me against unemployment, not against changing careers. Indeed, it assumes that I will change careers to stay employed.

That means leaving the kernel design, coding and optimization I have spent years mastering to become an AI agent’s “mech pilot.”

Previously, what I enjoyed, what I did well and what industry needed largely aligned. Now AI is turning my strength into something it does better. Industry no longer needs someone who can write fast kernels so much as someone who can use AI to produce them faster.

My understanding of engineering, model requirements and hardware should let me keep producing excellent work efficiently. I may love this new direction. I may not. Either way, having something I love taken away hurts.

The quiet pleasure of an afternoon spent writing kernels may end this summer. I have to bury my talent in yesterday and become a mech pilot. More gears in my hands; less rhythm in my heart.

Imagine you are an accomplished knitter, known for intricate patterns and beautiful colors. Wealthy customers seek you out. You make a good living. You also love sitting beside a window with tea, watching the hills, grazing animals and chimney smoke while knitting away an afternoon.

Then someone invents a machine. Feed it yarn and a pattern, and it produces a sweater as good as yours, much faster. Your competitors can now match your former standard effortlessly. You have to use it too.

Your twenty years of experience still help you outperform other machine operators. But the pleasure of listening to rain, working the needles and letting time pass slowly has been crushed by the machinery’s roar.

I can keep earning a living. I will probably have to surrender an old love.

I can separate reason from emotion when necessary. But I am sentimental too. When I moved out of an apartment after a year, I cried at leaving its memories behind. Saying goodbye to the era of handwritten kernels and optimization worked out in my own head is far crueler.

Perhaps some readers recognize that feeling. I suppose there is little else to do.

What About Everyone Else?

AI’s progress also leaves me with some questions:

• Will students increasingly use AI for assignments, especially practical labs? One option takes eight exhausting hours and may not earn full marks. The other costs pennies, takes minutes and produces perfect code. Which will most students choose?

• Will that leave students without essential engineering skills: organizing code, building systems, anticipating requirements and designing abstractions? As AI improves, will these skills fade like fluency in x86 assembly? Or will they remain indispensable, like understanding the full stack from software to systems to hardware? If the latter, we are in trouble. Give AI to a poor engineer and they can generate mountains of spaghetti code several times faster, burying problems inside systems and making the world even more precariously held together.

• Will power matter more than technical skill or intelligence?

Perhaps only time can answer.

Conclusion

AI could push society toward two extremes: communism or Cyberpunk 2077.

In the first, productive capacity expands enormously and living standards rise. (I will leave it there, or I worry this might not get past moderation.) In the second, a few technology companies control most resources. Only a tiny minority can access the strongest AI and technologies approaching a kind of cybernetic transcendence. Everyone else gets weak AI.

Social mobility becomes harder. You need the best AI to move up, but you must already be at the top to obtain it. A vicious circle.

If Anthropic permanently controls the world’s most advanced AI, which future do you think we get? Communism or 2077? Take a guess.

I still believe frontier intelligence should be open, affordable and available to everyone. I do not trust Anthropic or OpenAI to deliver that. I especially do not want Anthropic controlling the most advanced AI or artificial general intelligence, AGI. To put it deliberately hyperbolically, that would be no less alarming than Hitler obtaining atomic-bomb technology before the Allies.

That is why I chose DeepSeek and why I stay. By developing powerful, fast, broadly accessible AI and making it open source, perhaps we can pull the world a little further from 2077.

I hope the future turns out well. May all the beauty be blessed.

[1] “Main attention” refers only to multi-query attention (MQA) with a head dimension of 512. It excludes the indexer that selects the top-k important tokens. Other exceptionally capable colleagues—and their AI agents—wrote that.

Response After the Essay Went Viral

This essay escaped my usual circle. It attracted more than 100,000 reads on WeChat, reached No. 1 on Zhihu’s trending list and sparked considerable discussion on X.

I did not expect that. Unfortunately, what readers focused on did not entirely match what I meant.

I was not primarily expressing anxiety about unemployment. Nor was I trying to contrast DeepSeek’s openness with Anthropic’s unpopularity. I wanted to say goodbye to the years I spent writing kernels by hand.

Before agents, I typed most of my code myself. What looked tedious was pleasurable to me. I could settle down and think through everything: module organization, functionality, logic, even variable names.

I loved wrestling with scheduling and optimization until my kernels outperformed established implementations—FlashAttention, NVIDIA’s own kernels, even tasks thought difficult to optimize, such as token-level sparse attention. As I wrote, inventing a technique or improving performance felt like a speedrunner breaking a record.

Now AI is taking that pleasure away. For productivity and performance, I must adopt new tools and learn to write better kernels with AI. It is already faster than me. Eventually, it will be faster and better. Working hours will no longer leave room for slowly writing, debugging and optimizing kernels myself.

Handwriting kernels, perhaps programming itself, may become recreation rather than production. Almost nobody hunts with a javelin anymore; throwing one is now a sport.

I am being forced to give up something I loved and move elsewhere. Even if the new direction proves fascinating, that still hurts. Hence the title: I have to bury my talent in yesterday.

Memories and the emotions attached to them matter deeply to me. Yet memories fade, gradually buried beneath time’s fine snow. I explored this in another essay, “2025 Year-End Reflections, Part II: Memory.”

This piece was meant to preserve the years behind me: images of writing kernels by hand, the genuine memories and feelings I might revisit ten or twenty years from now. I also hoped it would give me courage to put down the past, pick up new tools and enter a new era.

Readers, though, took it rather differently.

The last two sections were loose reflections. The penultimate section raised social questions without examining or answering them in depth. The conclusion collected recent thoughts about making frontier intelligence open and affordable for everyone.

I am not a historian, sociologist or humanities scholar. I touched on these subjects only briefly—with a passing jab at Anthropic, which I have never much liked. My communism-versus-2077 framing may not even be right.

Two things were entirely sincere: wanting to keep the world from becoming 2077 was one reason I chose DeepSeek, and “May all the beauty be blessed” expresses a real ideal. But neither was the essay’s central point.

These are my personal views, not those of any company I work for. Perhaps I will write separately about them once I have thought them through more carefully.

Many readers did connect with my nostalgia. Most attention, however, fell on the final section.

The top Zhihu answers understood it fairly well. But WeChat’s comments filled with arguments about Anthropic and DeepSeek’s positions on open AI. Xiaohongshu already had posts framing it as “DeepSeek strikes back at Anthropic.” Outside China, the word “communism” triggered political arguments and personal attacks.

Some people seized on “I worry this might not get past moderation” to speculate about Chinese censorship. In fact, I wrote that because I felt I lacked the knowledge and experience to assess communism.

The ability of certain journalism-trained people to manufacture a controversy is impressive. Reading the results left me exasperated. Please pay more attention to what the essay is actually about.

Back to that subject: AI will keep getting stronger. Even within today’s combination of agents, harnesses, long context and chain-of-thought (CoT), improvements in data quality, model depth and context length should keep advancing capabilities. Beyond that lie embodied intelligence, recursive self-improvement (RSI) and technologies we have not yet imagined.

I am optimistic about AI’s capabilities and relatively optimistic about my place in the future. I am pessimistic about whether people will still be able to concentrate deeply on one task. I am also pessimistic about whether I can keep doing something that remains both my work and my hobby.

Even so, I will keep writing kernels and bringing AI agents into that work.

First, as I wrote: “I would rather not be overthrown. But if it must happen, I would rather overthrow myself.” Better to evolve than wait for someone else to replace me.

Second, I still hope that we—or other AI companies committed to openness and sharing—can build the strongest intelligence before certain other companies do, and make it benefit everyone.

I still believe in the contrast I drew between communism and 2077. I want the world to move away from the latter. Interests are one thing; ideals and convictions are another. So I will keep working in this field.

I will try to realign what I enjoy, what I do well and what the times demand. I will try to find a place of my own in the AI age.

Even after burying my talent in yesterday, I still have tomorrow’s light to pursue. Drenched by this cold rain at the world’s end, I will let my heart warm again, push back the gloom and seek the blue emerging beyond the night. [1]

[1] This passage adapts lyrics from COP’s “Singer at the World’s End” and other songs.

Hope you’re doing well, no matter what’s been going on this week

This week is mostly a story about growth, just not all the same kind. BYD is chasing growth overseas, now that its home market has turned into a price war that eats its own margins. XPeng just brought its robot production line online, betting a second business can eventually outgrow the first. DeepSeek is growing its headcount to keep its own infrastructure from buckling under demand. And Xiaomi is growing upmarket, putting its own chips into a phone that can finally go head-to-head with Apple and Huawei on price.

Four different kinds of expansion, one question underneath: does growing faster actually make any of these companies stronger, or just bigger?

Anyway, let’s take a look.

This Week Features...

BYD Won China. Now It Has to Back It Up Overseas.

On July 28, BYD launched a tiny electric kei-car in Japan, one of the hardest markets on earth for any foreign automaker to crack, a market Tesla has never managed to build real volume in. BYD gave itself two weeks to hit 1,000 orders. It got there in the same span, the fastest order ramp any BYD model has ever had in Japan.

That’s an awkward result for a company still routinely dismissed online as a subsidy-fed accounting trick. You can question BYD’s subsidies, its sales data, its debt, those are fair questions. But eventually you have to explain the cars: a factory built in Thailand in 16 months, Brazil’s president driving one, second place in Australia behind only Toyota. This piece treats BYD crossing 50% overseas revenue for the first time as six separate tests of whether a genuinely global Chinese automaker is actually being built, market by market.

[Read the full piece →]

Bill Gates Has a Plan for AI’s Future. What About Its Present?

Bill Gates recently published a 6,000-word essay warning that AI could become either humanity’s greatest equalizer or its worst source of injustice, and proposed new global institutions and taxes on AI tokens and robots to manage the transition.

The diagnosis, this piece argues, starts too far in the future. Klarna and Meta didn’t lose jobs to AI, they lost them to executives who cut too fast and had to rehire humans to clean up the mess. AI-designed viruses sound terrifying until you notice how much lab work, money, and human expertise still stand between a generated gene sequence and an actual pathogen. Before regulating an imagined autonomous machine, this piece asks, why not start with the companies, financiers, and executives making today’s decisions?

[Read the full piece →]

The News…

(I) Xiaomi Is Putting Its Own Silicon at the Center of the Flagship Fight

Xiaomi unveiled the Xiaomi 18 Fold this week, a premium foldable built around its own Xring O3 chip and CXMT’s LPDDR6 memory, the same LPDDR6 that CXMT rushed into mass production just weeks ago. The O3 packs 24 billion transistors, 26% more than last year’s Xring O1, with CPU performance up 60% and power consumption down 25% on some tasks. The phone starts at RMB 10,999 ($1,639), pushing Xiaomi further into the same premium bracket as Apple and Huawei.

Xiaomi is still nowhere near Apple’s level of vertical integration, but the direction is clear: design your own processor, source your own memory, and keep climbing the price ladder on hardware you actually control. Coming right after CXMT’s LPDDR6 launch, this is the first real evidence that the chip-memory pairing isn’t just a press release, it’s shipping in a flagship phone at scale.

(II) DeepSeek’s Hiring Spree Is About Plumbing, Not PhDs

DeepSeek is opening around 150 new positions, what Harness team lead Cui Tianyi called an “unprecedented” hiring push, but the mix is telling: most of the roles are for backend engineers and Agent infrastructure, not AI researchers.

The company’s systems have simply outgrown themselves. Machines, containers, training and evaluation jobs, Agent environments, and user requests are all climbing fast enough to strain the backend DeepSeek already has, and the timing lines up: this comes right after new V4 models, new Harness developer tools, and API price hikes of up to 11x. Scaling the model was the easy part. Scaling everything underneath it is now DeepSeek’s actual bottleneck.

(III) XPeng’s Robot Production Line Goes Live

XPeng CEO He Xiaopeng announced the company’s robot production line is officially running, with mass production planned for late December and deliveries starting next year. He specifically flagged safety, intelligence, reliability, quality, and generalization as the bars still to clear, an unusually cautious list for a company that just closed a $900 million funding round for the unit.

China’s humanoid robot race keeps adding entrants with real capital behind them, and XPeng’s timeline puts it on a similar clock to peers like Unitree. Whether “production line live” translates into robots that can actually do useful work at volume is still the question every company in this space is racing to answer first.

(IV) Apple’s Foldable iPhone Supply Chain Is Already Being Speculated Into Existence

As Apple gets closer to entering the foldable-phone market, Chinese suppliers are already being pulled into the surrounding speculation.

Yicai contacted eight companies linked by market rumors to Apple’s first foldable iPhone. Some acknowledged ongoing work on components such as ultrathin glass, hinges, metal parts, and other materials for major customers, while several declined to comment and one explicitly denied supplying parts for a foldable device.

That uncertainty is revealing in itself.

Apple’s supply chain is large enough that suppliers can be preparing for a major product long before the final device is officially acknowledged. It also shows how much of the foldable-phone manufacturing ecosystem already sits inside China, even though the final product will carry an Apple logo.

For companies such as Xiaomi and Huawei, this is also a timing issue. Chinese manufacturers are already launching new foldables while Apple is still approaching the category.

By the time Apple arrives, the supply chain may already have spent years learning how to build them at scale.

(V) DeepSeek Is Simplifying Its Models Just as Its Infrastructure Gets More Complicated

DeepSeek is quietly reshaping how users access its latest models.

The company has combined its Fast, Expert, and Image Recognition modes into a simpler interface and plans to introduce V4.1 Flash with native multimodal capabilities around September 10. Meanwhile, V4 Pro service is scheduled to end on September 14, with requests automatically routed to V4.1 Flash.

The changes look minor from a user’s perspective, but they reveal a more important trend.

As AI products mature, users increasingly care less about which internal model is handling a request and more about whether the system simply works. DeepSeek is effectively hiding some of the complexity behind a single service while moving users onto a new backend architecture.

That becomes especially important as the company scales its Agent and multimodal capabilities.

The AI industry spent the last two years adding models.

It may now be entering the phase of taking them away from users’ view.

(VI) ByteDance’s Next VR Bet Is a Model, Not a Headset

ByteDance is preparing a Seedance-based spatial-video model for Pico as soon as next month, with founder Zhang Yiming personally overseeing the project, according to Bloomberg. The model is meant to generate interactive 3D worlds that respond to a user’s voice and movement, reportedly hitting 20fps with just 0.05 seconds of latency.

If cloud-based generation can genuinely lower the hardware requirements for a headset, it shifts the VR competition with Google, Meta, and Apple away from who builds the best physical device and toward who has the best model, the most compute, and the most content to generate on demand.

(VII) China’s Brain Implant Makers Start Lining Up for IPOs

Chinese BCI companies are starting to move from private funding toward public listings, with Shanghai’s STAR Market emerging as the preferred venue. Shanghai-based Arfysica Innovation, founded in 2015 and focused on BCI products for central nervous system injuries and cognitive disorders, has registered for IPO coaching. Neuracle Medical Technology, the company behind NEO, the implant that beat Neuralink to the world’s first approved commercial invasive BCI, has signed its own coaching agreement with CITIC Securities.

This is the natural next step for an industry that, just a couple months ago, was mostly a story about regulatory speed. Getting a device approved is one milestone. Getting a company to a public listing is the one that actually tests whether the underlying business, not just the technology, can stand on its own.

(VIII) Pony.ai Thinks Robotaxis Are Nearly Ready. The Financials Are Less Convincing.

Pony.ai CEO James Peng believes robotaxis will become commonplace around the world within five years, arguing that most of the major technical obstacles have already been solved.

The company certainly has plans that match the ambition. It expects to deploy more than 2,000 robotaxis in Europe with Uber and another 200 in South Korea by 2028, while its current vehicles average around 25 trips per day.

Revenue is moving in the right direction too. Robotaxi revenue surged almost sevenfold in the second quarter to $12.1 million.

But the business is still far from self-sustaining. Pony.ai posted a companywide operating loss of $65.7 million during the quarter.

That gap captures the current state of autonomous driving better than another technical demonstration might.

The technology is becoming commercially useful.

The economics still have to catch up.

(IX) Kimi Is Taking Its AI Agents Into the Customer’s Office

Moonshot AI is moving beyond selling Kimi as a model and into a much more hands-on enterprise strategy.

Its new enterprise partner program will work with IT service providers and system integrators to build forward-deployed engineering teams that work directly with customers. These engineers will integrate Kimi into existing systems, adapt it to specific business processes, and feed the problems they encounter back into product development.

It is an expensive way to sell AI, but potentially a powerful one.

Enterprise customers rarely want a chatbot sitting next to their existing software. They want AI embedded inside the workflows they already depend on, which often means someone has to do the messy integration work first.

That makes the FDE model interesting.

The model company is no longer just selling intelligence.

It is sending engineers into the customer’s business to turn that intelligence into something useful.

(X) Moonshot AI Quietly Files for a Hong Kong IPO

Moonshot AI, the company behind Kimi, has confidentially filed an A1 form with the Hong Kong Stock Exchange, officially kicking off its IPO process, while simultaneously raising what’s likely its final pre-IPO round at a $50 billion pre-money valuation. That’s roughly eight times where the company stood less than a year ago: Kimi was valued at about $4.3 billion at the end of 2025, above $20 billion by this May, and $35 billion post-money after a July round that followed the launch of its K3 model. DeepSeek, still privately held, is widely expected to list sometime in the first half of next year.

September has brought fresh momentum to AI beyond the US and China. On September 3, Saudi Arabia’s HUMAIN, backed by the Public Investment Fund (PIF), unveiled humain-m3, an Arabic-language model it commissioned from MiniMax. Days later, France’s Mistral AI raised €3 billion at a valuation exceeding €21 billion, funding model development and computing infrastructure. The Center for a New American Security (CNAS) calls this sovereign AI’s second wave.

Coverage of AI as a US–China contest often overlooks what everyone else wants. Governments increasingly hope domestic companies and infrastructure can reduce their dependence on foreign suppliers. The EU has proposed its European Technological Sovereignty Package. Canada has a sovereign computing strategy. Chile is leading the regional Latam-GPT initiative.

An international wave of AI nationalism is taking shape. Governments want personal data kept within their borders and models that understand local languages, laws and norms. They also fear that AI will reinforce the internet economy’s concentration of wealth and power.

Yet existing projects reveal a paradox. Sovereign AI can give countries more control while embedding American and Chinese technology more deeply in their economies. America’s approach is more forceful; China’s often involves a softer sell. Neither delivers complete independence, but partial autonomy still matters when access to AI can become a geopolitical bargaining chip.

Three questions explain the tension:

• What drives sovereign AI: language, the internet and economic influence.

• What countries are building: customized models and data centers.

• How influence spreads: America’s harder approach and China’s softer one.

Why Sovereign AI? Language, the Internet and Economic Influence

Nvidia CEO Jensen Huang helped popularize sovereign AI as a market for chips and servers. It has since become a political and security priority. Before examining those ambitions, though, consider a simpler question: Why would a country need an AI model of its own?

Often, the answer is language.

Japan’s Sakana AI develops technology adapted to Japanese needs. Mistral supports French; HUMAIN targets Arabic speakers. India’s Sarvam AI builds for Hindi and other Indian languages.

General-purpose models have historically relied heavily on English-language training data. Their apparent fluency elsewhere can conceal awkward phrasing and gaps in cultural understanding. Chinese users who tried ChatGPT in 2023 immediately noticed that its Chinese answers felt like Chinese written with English logic. Each paragraph made sense on its own, but the full response felt deeply strange.

The problem extends beyond translation. A shortage of high-quality local material can leave models poorly equipped to interpret legal terms, everyday assumptions or regional references. More speakers do not automatically mean more usable training data.

Web scraping alone cannot close every gap. Countries need access to local archives, institutional records and specialized datasets, alongside infrastructure for processing sensitive material under domestic rules. What I think of as a country’s “sovereign internet”—its language communities and locally generated information—becomes a resource for sovereign AI. Common Crawl’s language statistics illustrate how unevenly the public web represents different languages.

The larger motivation is economic. Many governments watched the internet boom enrich foreign platforms without producing comparable champions at home. They see AI as another chance to secure a greater share of digital activity.

The US and China dominate the platform economy. Japan, South Korea and India have substantial domestic businesses, but fewer platforms with comparable international reach. That imbalance now shapes who supplies AI—and who can withdraw it.

June’s US restrictions on Anthropic’s newest models, followed by Reuters’ July report that Beijing was considering restrictions of its own, made that vulnerability tangible. Chip controls add another layer.

The common objective is straightforward: recover some control over how models are developed, supplied and used.

What Countries Are Building: Customized Models and Data Centers

By mid-2026, CNAS tracked sovereign AI initiatives in 67 countries and the EU, up from 16 governments in 2023. New participants included Cambodia, Egypt and Pakistan. This was no longer exclusively a wealthy-country project.

A national model is often the most accessible starting point.

On September 2, Spain’s Multiverse Computing launched Quasar 438B, an English–Spanish reasoning model with a 1M-token context window. Its announcement cited a score of 43 on Artificial Analysis’s Intelligence Index v4.1.1, claiming the highest European result in that comparison. But Quasar is based on GLM-5.2, developed by China’s Z.ai. Its contribution is compression and more efficient deployment, not an entirely independent foundation.

Other projects follow a similar pattern. Japan’s Rakuten AI 3.0 uses the DeepSeek-V3 architecture. AI Singapore’s Qwen-SEA-LION-v4 builds on Alibaba’s Qwen.

CNAS finds that most tracked model projects with disclosed foundations adapt foreign open-weight models. Meta’s Llama remained the most common base by mid-2026, followed jointly by Mistral and Google’s Gemma.

This is a practical bargain. Adapting an existing model costs less than training from scratch. Deploying its weights locally also reduces exposure to a foreign provider withdrawing API access.

But models still need chips and electricity. Infrastructure accounted for 80% of new sovereign AI projects tracked by CNAS in the first half of 2026. India added four infrastructure programs. Elsewhere, governments are exploring alternatives to American accelerators, including Huawei’s Ascend family.

Local models and computing facilities become particularly valuable in industries handling proprietary data, operating under strict regulation or serving national-security needs. South Korea’s shipbuilding sector, French defense initiatives and Germany’s industrial AI projects illustrate the opportunity. Deutsche Telekom’s Industrial AI Cloud began operating on February 4, built with Nvidia and data-center partner Polarise.

These countries are not necessarily trying to reproduce an entire foreign AI industry. They want more control over the systems that matter most.

Partnerships can help. Under Mistral’s agreement with HUMAIN, the companies plan to combine Saudi infrastructure with model development, including stronger Arabic capabilities and deployments in regulated sectors. Saudi Arabia is using capital and infrastructure to diversify its suppliers beyond the two dominant AI powers.

Existing sovereign AI projects still depend on two critical elements: U.S.-dominated compute chips and open-source model architectures developed in China.

Countries pursuing sovereign AI may have specialized data, power resources and data center clusters, but they have yet to build a technology stack fully independent of either China or the U.S.

How Influence Spreads: America’s Harder Approach, China’s Softer Sell

American firms occupy commanding positions in frontier models, AI accelerators and cloud infrastructure. Until recently, governments could treat that dominance as a relatively stable foundation for their own projects.

The Fable episode challenged that assumption. On June 12, US controls forced Anthropic to suspend access to Fable 5 and Mythos 5. Those controls were lifted on June 30, but the interruption showed that even allies could lose access because of decisions made in Washington.

Other incidents reinforced the concern. AP reported that US sanctions disrupted ICC prosecutor Karim Khan’s Microsoft email access; Microsoft denied blocking his account. The disputed episode nevertheless exposed anxiety about dependence on American services.

The Economist argues that independence from both AI powers is unrealistic. Advanced hardware is expensive and ages quickly. Governments risk spending billions on facilities whose equipment needs replacing before their ambitions are realized.

But the impossibility of complete independence is not a persuasive argument against partial control.

Sovereignty is not a switch between 0% and 100%. Germany, Japan or India can buy Nvidia chips while retaining control over models trained on domestic defense or financial data. That is also the promise behind Huang’s AI factories: infrastructure located within a country and serving its priorities.

CNAS recommends that Washington become the preferred partner for sovereign AI, helping countries build capabilities with American technology without simply deepening their dependence.

That is still a route to influence. A remotely controlled model API is a harder sell to sovereignty-conscious governments than a system they can operate locally. Suppliers offering Palantir-style integration and deployment may therefore find a receptive market. Nvidia can encourage national open-model projects while keeping their developers within its hardware and software ecosystem.

Financing strengthens that position. Huang has estimated a fully equipped 1GW AI factory at roughly $50–60 billion. That is not the cost of an ordinary data-center building, but it indicates the scale of frontier infrastructure.

Smaller projects also depend on foreign finance. Côte d’Ivoire’s planned national data center is being built by Washington-based Cybastion. The Export-Import Bank of the United States approved a $66.1 million guarantee for its construction.

China’s approach often emphasizes adapting technology to the customer’s existing system rather than selling an entire American-style stack.

Consider 01.AI. Once identified primarily with foundation-model competition, it now emphasizes enterprise deployments and technical services. Its work on Kazakhstan’s AlemLLM offers a useful example: a national AI project supported by Chinese developers. Founder Kai-Fu Lee is selling expertise, not merely access to a remotely hosted chatbot.

Open weights offer another route. Policy researcher Pranay Kotasthane describes an Indian financial company running DeepSeek on rented GPUs in India. The weights reside locally; inference need not send customer data to China. The original developer does not have an API connection, it can simply revoke.

Think of owning a movie on disc rather than depending on a streaming subscription. It may seem less convenient, but the film remains available if the service disappears. Security reviews are still necessary; local deployment does not make a model inherently trustworthy.

This delivery model fits China’s preferred language of noninterference: provide a working system without demanding that the customer reorganize its institutions around the supplier.

Weather forecasting shows the appeal. At the 2025 World Artificial Intelligence Conference, China introduced MAZU, its Early Warnings for All solution, named after the maritime guardian goddess. An Egypt-specific version under development includes aviation weather services and sandstorm warnings for tourism.

Such applications offer tangible benefits without requiring a government to hand over its most sensitive decision-making systems. They can build trust in Chinese suppliers and potentially create demand for Chinese hardware.

That next step remains uncertain. Huawei is courting Egypt, while Malaysia is considering Ascend 910C chips. Neither interest nor a proposal equals completed deployment. Domestic demand and manufacturing constraints also limit how quickly Chinese suppliers can expand overseas.

Conclusion

Sovereignty is less about independence than control. Whether through chips, models or infrastructure, American and Chinese technology remains embedded in projects intended to reduce dependence on both.

China’s present openness is not a permanent guarantee. Reuters’ reporting on possible frontier-model restrictions concerns deliberations, not an announced blanket ban. But the possibility matters.

In that uncertainty, time becomes an asset. Locally installed accelerators, adaptable models and the expertise to operate them give countries a buffer against future restrictions. They preserve room to act if AI competition divides more sharply into rival blocs.

Sovereign AI does not offer an escape from the US–China rivalry. It offers a chance to retain some agency within it—a need that may become more urgent than it was in the internet era.

On July 28, 2026, BYD launched a tiny electric k-car called the Racco in Japan, one of the hardest markets on earth for a foreign automaker to crack. Toyota, Honda, Suzuki, and Daihatsu control roughly 80% of Japan’s kei-car segment, the compact vehicles that make up nearly 40% of the country’s new car sales. Even Tesla has never managed to build real volume there.

BYD gave itself two weeks to hit 1,000 orders for the Racco. It reached that target in the same span, the fastest order ramp of any BYD model ever launched in Japan. By the one-month mark, orders were approaching 1,500, most of them for the top trim rather than the cheapest one.

That’s an odd result for a company that is still often described in Western commentary as a subsidy-fed bubble held together by financial engineering.

You can question BYD’s subsidies. You can question its sales practices. You can question its accounting and debt. Those are legitimate things to scrutinize. But eventually, you have to explain the cars.

Because the Racco isn’t happening in isolation.

In BYD’s latest half-year results, one number puts what is happening overseas into perspective: for the first time, overseas revenue crossed 50% of total revenue. Ten years ago, BYD barely sold cars outside mainland China. Now, it earns more revenue from the rest of the world than it does at home.

That’s the more interesting story. Not whether BYD has received subsidies, it has. Not whether its rise deserves scrutiny, it does. The question is whether a company supposedly built on subsidies and financial tricks could also build factories, sell cars, hire workers, and take market share across some of the world’s toughest auto markets.

To answer that, you have to go back to where BYD came from, long before the Racco, long before the overseas factories, and long before anyone was calling it a scam.

But BYD Didn’t Come Out of Nowhere

In 1995, a 29-year-old engineer named Wang Chuanfu borrowed money from a cousin and opened a small workshop in Shenzhen to make rechargeable batteries. Within seven years he was supplying Motorola and Nokia. By 2003, BYD was the largest battery maker in the world, and that same year Wang did something that made his own investors panic: he bought a bankrupt state-owned car factory. BYD’s stock dropped sharply within days. Wang, notably, didn’t even hold a driver’s license at the time.

His bet was simple: the future of the car was the battery, not the engine. Three decisions from that period still shape the company today. First, BYD refused to depend on outside suppliers, building its own batteries, semiconductors, motors, and even the software running the dashboard, a choice that let it keep shipping cars through the 2021 global chip shortage while rivals stalled. Second, while Tesla and Silicon Valley evangelized pure electric vehicles, BYD poured resources into plug-in hybrids, which made far more sense in a China where fast charging infrastructure was still rare outside major cities, and gave it access to hundreds of millions of buyers Tesla couldn’t reach. Third, in 2020 BYD released the Blade Battery, a lithium iron phosphate design that was cheaper to produce, safer, and longer-lasting than the nickel-cobalt chemistry everyone else used. Four years later, Tesla briefly bought batteries from BYD for its own cars.

China’s state undeniably helped BYD along the way, through direct payments, cheap land, low-interest loans, and consumer purchase incentives, and BYD has never hidden that. But subsidies can help a company sell cars. They don’t explain why it spent decades building batteries, semiconductors, and motors in-house, and they don’t explain a company that, in the same year its profits fell 19%, increased R&D spending to ¥63.4 billion, more than Volkswagen’s or Toyota’s annual R&D budgets and closing in on Ford and GM combined. BYD now employs more than 110,000 engineers, working on next-generation Blade Battery chemistry, solid-state batteries, its DiPilot driver-assistance system, and sodium-ion batteries aimed at sub-¥60,000 entry-level cars. Subsidies may explain how BYD survived its early years. They don’t explain what it built afterward, or what it’s spending on now to take that business abroad.

That machine is what let BYD go on to dominate China’s EV market. Winning, it turned out, came with a cost.

In its full-year 2025 results, BYD’s revenue grew 3.5%, but net profit fell 19%, from roughly ¥40.3 billion to ¥32.6 billion, its first annual profit decline in four years. Gross margin slid from 19.44% to 17.74%. Net margin dropped from 5.2% to 4.1%. Average selling price per vehicle fell to around ¥141,000.

The reason isn’t collapse. It’s a price war that BYD itself started. By pushing the Seagull below ¥70,000 and the Yuan series below ¥100,000, BYD forced every rival in China’s crowded EV market, now more than 40 brands deep, to cut prices in response. BYD still commands roughly 35-40% of China’s NEV market. It won the volume, the share, and the scale it set out to win. What it didn’t win was the profit that was supposed to come with it. Every additional car sold domestically now earns less than the one before it. Management called the domestic market a “brutal knockout stage” on its earnings call, an unusually blunt admission from the company that started the knockout. Subsidies don’t explain that decision. A subsidized company protects its margin. BYD chose to burn its own instead, to keep the volume and the share.

This isn’t what a fake company looks like. It’s what a real company looks like when it has won a brutally competitive market and burned some of its own economics in the process. BYD didn’t lose at home. It won a market that, in winning, became less worth having. That’s the actual paradox, and it’s the reason the overseas number matters as much as it does.

The Escape Valve

In 2025, BYD’s overseas exports surpassed one million units for the first time, up 145% year-over-year, growing from a base of roughly 430,000 in 2024. By the first half of 2026, the margin gap had shown up directly in the company’s own disclosures: its overseas business posted a 22% gross margin, against 18.85% for the group as a whole, up nearly two points from a year earlier. BYD has also kept raising its own targets as the shift accelerates, from an original 2026 goal of 1.3 million overseas vehicles, to 1.5 million by March, to 1.9 to 2.0 million by late summer, with the international business now described in the company’s own results commentary as its most important strategic lever.

The reason isn’t complicated, even without a line-by-line regional income statement. Inside China, BYD competes against more than 40 other EV brands selling near-identical specs, for buyers who shop primarily on price, in a market BYD itself keeps discounting to hold share. Any extra feature gets competed away almost immediately. Overseas, the picture is different. BYD usually arrives as a newcomer rather than one of forty look-alikes, and it shows up with a real price and specification advantage over local incumbents. A buyer in Bangkok, Sydney, or Berlin comparing a BYD to a similarly priced domestic or Japanese model is often getting more car for the money, and in markets without China’s price-war intensity, that advantage doesn’t get discounted away nearly as fast. In parts of Europe, where charging anxiety persists, BYD’s plug-in hybrid lineup also gives it a product position local competitors don’t have an easy answer to. None of this is unique to BYD. It’s what happens when a company outgrows a crowded home market and starts looking elsewhere.

There’s a bigger shift underneath the export numbers. BYD is moving from being a Chinese manufacturer, to a Chinese exporter, to an overseas manufacturer with its own local employees and local supply chains, to something closer to a genuinely global automaker. That’s a much larger claim than “overseas sales are growing,” and it deserves to be tested rather than assumed, market by market.

Six Tests of a Global Strategy

BYD isn’t entering six markets with the same playbook. It’s facing six different problems, and each one is answering a different question about whether the machine described above actually works outside China.

Thailand: can BYD manufacture abroad at all? The Rayong plant, BYD’s first overseas passenger vehicle factory, went from groundbreaking to production in just 16 months and has already crossed 100,000 units produced. BYD’s share of the Thai EV market has passed 15%, with the Dolphin and Atto 3 among the country’s best-selling EVs. The disruption has been real: Mitsubishi shut a local factory, while Mazda sharply cut production, in a region Japanese automakers had dominated for four decades. Subsidies don’t build a factory in 16 months. Thailand doesn’t matter because the Thai market itself is huge. It matters because it’s the proof of concept for the entire overseas manufacturing strategy. Test passed.

Brazil: can it actually go local? Brazil is now BYD’s largest overseas market. In April 2026, BYD topped Brazil’s overall auto retail sales chart for the month, the first NEV brand ever to do so. President Lula owns a BYD Tang EV, and personally received the company’s 14 millionth NEV, a Song Pro, from Wang Chuanfu. BYD has raised its investment in the Camaçari plant from 3 billion to 5.5 billion reais, aiming to more than double capacity to 300,000 units, with a 50% local-content target by early 2027, and the Seagull and Destroyer 05 are already rolling off the line there. BYD already knows how to sell cars in Brazil. The real test is whether it can stop being a Chinese exporter there and become an actual local manufacturer, with local suppliers and local jobs. Test passed, with the localization target still to be proven out.

Europe: can it beat the tariff wall? The EU charges a combined tariff of roughly 27% on Chinese-made electric vehicles. Once BYD’s Hungarian plant in Szeged reaches full production, expected by the end of 2026 after slipping roughly a year behind its original target, locally built cars will clear EU customs tariff-free, saving an estimated €5,500 to €8,200 per vehicle. The plant’s first model will be the Dolphin Surf (sold in China as the Seagull), and BYD is also leaning into plug-in hybrids here, while planning to double its European retail footprint to 2,000 outlets in 2026.

This isn’t really a test of tariff avoidance. It’s a test of whether BYD’s cost advantage survives once politics takes its cut. Test still in progress, and the delay is the clearest sign that this one is hard.

Australia: will consumers actually choose it, unprompted? No local-content mandate, no tariff wall to dodge, just buyers deciding for themselves. BYD entered Australia in November 2022 and hit 100,000 cumulative deliveries in under three and a half years, the fastest growth of any Chinese brand in the country’s history. By mid-2026 it was regularly outselling Tesla two-to-one and sitting in second place among all brands, trailing only Toyota, with the Sealion 7 as Australia’s second best-selling electric model and the Shark 6 plug-in hybrid pickup cracking the country’s top ten best-selling vehicles overall.

Government support doesn’t explain Australians choosing BYD over Tesla at the dealership. Nobody is forcing Australians to buy these cars. That’s what makes this such a clean test of whether people actually want them. Test passed, arguably the most convincing one.

Japan: can it crack a market built to keep it out? Japan is famously closed to foreign automakers, with domestic brands holding more than 90% of the market. BYD entered passenger vehicles there in 2022 with the Atto 3, Dolphin, Seal, and Sealion 7, plus the Sealion 6 plug-in hybrid, and has kept growing even after Japan’s government cut BYD’s per-vehicle subsidy from as much as ¥450,000 down to ¥150,000, while raising Tesla’s and Toyota’s. BYD responded by opening 70 sales outlets in three years and is preparing a Japan-specific kei car, the BYD RACCO, for 2026. Subsidies certainly don’t explain why BYD keeps investing in one of the hardest markets in the world, especially one that just cut its subsidy. Test still failing on market share, but the persistence itself is a signal.

North America: what happens when geopolitics simply says no? With the US maintaining tariffs near 100% on Chinese EVs, BYD has largely given up on a direct approach there. It already runs North America’s largest electric bus factory, in Lancaster, California, running on 100% renewable power and supplying transit agencies like LA Metro and Stanford. For passenger cars, the plan is a detour: strong sales in Mexico (roughly 70% share of the EV/PHEV segment) and a planned entry into Canada by the end of 2026 through the Yuan PLUS, Seal, Dolphin, and Seagull, using a new tariff quota system Canada introduced after dropping its earlier 100% punitive tariff. No amount of manufacturing discipline gets around a 100% tariff, and this is the clearest reminder that BYD’s global strategy has a ceiling set by politics, not execution. Test unresolved, and it may stay that way for years.

The Parts That Don’t Fit the Highlight Reel

None of this means BYD has a clean story. And that’s important, because the weaknesses are part of the story too. BYD’s premium brands, Denza, Yangwang, and Fangchengbao, sold a combined 397,000 vehicles in 2025, just 8.6% of total sales, far below the mix that would meaningfully lift the company’s average selling price. The Hungarian plant’s delay means BYD keeps eating EU tariffs on Chinese-made exports in the meantime. The US market remains completely closed. And BYD’s asset-heavy, build-everywhere strategy carries real risk: a single overseas factory can cost billions, and if global EV demand growth cools faster than capacity comes online, utilization rates could fall, and returns on all that capital could disappoint.

BYD didn’t lose at home. It won a market so decisively that winning it stopped paying off, and rather than defend that market at any cost, it went looking for profit somewhere else, market by market, tariff regime by tariff regime, test by test.

Crossing 50% overseas revenue isn’t a marketing milestone. It looks like a factory in Thailand built in 16 months. It looks like Brazil’s president driving a car built in Bahia. It looks like showrooms in Japan that keep opening despite years of losses. It looks like BYD sitting in second place in Australia, ahead of every brand except Toyota. It looks like a Chinese company rebuilding its supply chain, one country at a time, around the world. That’s a much harder thing to fake than a balance sheet.

The scam debate will probably continue. The more interesting question is whether BYD can turn its manufacturing advantage into a durable global business.

Bill Gates may have left Microsoft, but he never really left the technology industry.

Two weeks ago, he published a nearly 6,000-word essay on Gates Notes, warning that AI could become either history’s greatest equalizer or its worst source of injustice. Even under the best conditions, he wrote, the transition could be among the most turbulent periods humanity has experienced.

Gates is not predicting the end of civilization. He proposes new national and international institutions, “Human Reserved” jobs, and taxes on AI tokens and robots to protect those hurt by the transition.

As a technology journalist in China, I think his diagnosis begins too far in the future. Gates does mention AI companies and the pressure data centers put on water and power. What he does not provide is a clear system of accountability for the founders, investors, banks and hyperscalers driving the boom today.

The AI model is not the only source of the problem. The people financing, marketing and deploying it also make choices—and they want the public to absorb the consequences.

AI Isn’t Firing Workers. Executives Are.

Gates expects AI to eliminate many white-collar roles, especially entry-level jobs in sales, customer support, software engineering and legal services. That structural risk is real. But current layoffs do not prove that machines have already surpassed workers.

Klarna offered an early warning. In 2024, the buy-now-pay-later company said its AI assistant handled 2.3 million conversations in one month, did work equivalent to 700 full-time agents and could improve annual profit by $40 million. A year later, CEO Sebastian Siemiatkowski acknowledged that cost-cutting had gone too far and began recruiting remote customer-service staff so customers could still reach a person. Klarna’s regulatory filings now emphasize a hybrid system combining AI with human support.

Meta reportedly considered something even more aggressive under Project OT, a plan to reorganize teams around AI and potentially reduce some groups by as much as 60%. Internal code changes rose 220%, but major technical and security incidents also increased 40%. The company later held back a broader round of cuts planned for November.

This is becoming a familiar cycle. Executives assume AI can remove labor costs, shrink teams before the technology is ready, and then ask fewer employees to repair the resulting failures. The machine did not decide to fire anyone. Management did.

Meanwhile, the infrastructure budget appears almost limitless. Alphabet raised $20 billion in dollar-denominated bonds in February as it accelerated data-center spending. Meta expanded its planned El Paso facility from a $1.5 billion project to an investment exceeding $10 billion, even as both companies repeatedly reorganized AI teams.

The result is a self-reinforcing capital-allocation problem. Companies spend heavily to replace workers, leaving less room to hire them. When the systems disappoint, they spend still more on compute. If policymakers want to address AI-related unemployment now, they should examine corporate investment incentives—not treat every layoff as evidence of machine superiority.

Intelligence Still Has to Touch the Real World

Gates also warns that AI will make cyberattacks, deepfakes, disinformation and bioterrorism easier. Anthropic CEO Dario Amodei has gone further, predicting that AI could eliminate half of entry-level white-collar jobs within several years. Nvidia CEO Jensen Huang has criticized such rhetoric, accusing some executives of developing a “God complex.”

The disagreement overlooks a basic distinction: Generating information is not the same as acting reliably in the physical world.

Consider AI-designed viruses. Stanford University and Arc Institute researchers used genome language models to create bacteriophages capable of killing strains of E. coli. That is a genuine scientific achievement. But the models generated many candidates; researchers synthesized and tested nearly 300 genomes to find 16 viable phages. Stanford’s account of the research makes clear how much physical experimentation remained necessary.

Their template, ΦX174, contains just 5,386 nucleotides. SARS-CoV-2 has roughly 30,000. Complexity does not rise in a simple linear ratio: Interactions among genes, regulatory systems and protein folding make larger biological designs dramatically harder. Turning a generated sequence into a real pathogen would still require money, specialist knowledge, controlled laboratory facilities and extensive physical testing.

That does not make biosecurity concerns imaginary. It means the responsible unit is not merely “AI.” It is the combination of models, laboratories, institutions and people with the resources to use them.

The same constraint applies to AI for Science, humanoid robots and autonomous vehicles. Models can simulate and optimize, but machines must operate safely through rain, broken sensors, unusual roads and unpredictable human behavior. Engineering is the art of surviving exceptions. LLMs have not been developing long enough to master that physical reliability.

Until embodied intelligence becomes mature, claims that AI itself is transforming the material world should be treated as forecasts, not present-day facts.

A Systemic Plan Must Regulate the System’s Most Powerful Players

Gates is right that existing institutions are poorly designed for a technology spanning employment, national security, taxation, energy and public health. He is also right that governments move slowly. But the first question for any new system is simple: Who should it govern?

The answer must include the AI companies themselves.

Too often, industry leaders frame private commercial interests as national-security imperatives. Criticism of data centers or model developers can then be portrayed as assistance to China. In May, investor Kevin O’Leary suggested that opposition to his enormous Utah data-center project was connected to the Communist Party of China. He later acknowledged that he had no evidence for claims against several named critics.

China becomes a convenient target: If residents object to water consumption, power prices or land use, the argument shifts from local accountability to America’s technological leadership. In my experience, no one in China is blaming American companies for standing in the way of new data centers. In the United States, however, Silicon Valley increasingly invokes China whenever it wants to turn questions about power, water or land into matters of national security. China has become the industry’s easiest excuse for avoiding accountability at home.

The costs are not hypothetical. Northern Virginia already hosts the world’s largest concentration of data centers. Modeling led by Carnegie Mellon University suggests that data-center and cryptocurrency growth could raise average U.S. electricity-generation costs 8% by 2030, with increases exceeding 25% in Central and Northern Virginia.

Nor is government simply lagging behind industry. Technology money is actively shaping the political response. The pro-industry super PAC network Leading the Future said it had raised more than $125 million for the 2026 election cycle, backed by donors including Andreessen Horowitz, OpenAI co-founder Greg Brockman and investor Joe Lonsdale. Public First Action, which promotes stronger AI safeguards, received $20 million from Anthropic, although the company says the money is restricted to public education rather than election activity.

These groups do not support identical policies. What matters is that AI companies and their investors have acquired enormous capacity to influence who writes the rules. Since Citizens United v. Federal Election Commission, U.S. political spending has enjoyed broad constitutional protection. Add the language of competition with China, and proposals concerning grid costs, water use, labor rights or taxation become easy to attack as threats to American leadership.

This is why a future tax on tokens or robots feels incomplete. Governments can already identify today’s beneficiaries: model developers, hyperscalers, data-center operators, private-equity funds and property developers. They can charge them for grid connections and water, withdraw poorly designed tax abatements, require labor-transition funds and make infrastructure financing more transparent.

Regulate the companies using AI before trying to regulate an imagined autonomous machine.

Philanthropy Cannot Substitute for Accountability

The financing of AI makes the urgency clearer. Anthropic is finalizing a reported $15 billion pre-IPO credit facility. In a separate deal, private-credit firms arranged roughly $35 billion to finance chips for its infrastructure, with Broadcom backstopping significant payment obligations.

An ordinary borrower needs documented income to obtain a $50,000 loan. An AI company can borrow billions against annualized revenue projections, supplier guarantees and expectations of future demand. That does not automatically make the financing unsound. But it creates systemic risks that deserve more scrutiny than another warning about hypothetical superintelligence.

As OpenAI and Anthropic tap ever larger pools of capital, including the pension funds ordinary people depend on, Gates offers a much vaguer solution: a multinational system operating beyond individual governments to distribute resources in response to AI’s social costs.

On the face of it, this sounds like global coordination. But it leaves a basic question unanswered: who controls the money? Gates’ proposal risks allowing technology billionaires to retain influence over how billions of dollars are allocated, this time under the banner of AI governance.

Gates acknowledges his continuing financial ties to technology and says the Gates Foundation will spend its remaining $200 billion over the next 19 years. The commitment is extraordinary. Yet philanthropy, however generous, cannot replace democratic accountability.

The foundation structure also raises a legitimate question about power. Tax-advantaged philanthropy lets billionaires direct capital toward global priorities without the electoral constraints faced by governments. Gates presents this capacity as a tool for equity. Critics see private technocracy operating beyond public control.

China offers a different model, though not necessarily a universally desirable one. Its technocrats are embedded in the state, and wealthy businesspeople understand that political participation has limits. Gates instead imagines new institutions that could sit across national governments while drawing heavily on experts, companies and philanthropies.

Who appoints them? Who can remove them? And who pays when their plans fail?

I grew up with a simpler expectation: A company must bear responsibility for the social costs it creates. If it refuses, regulations should make that responsibility unavoidable.

AI is powerful, and its future risks are real. But existing governments already have tools to address its present ones. Gates is asking the right question—how to manage the transition—while leaving its most immediate answer frustratingly vague.

Start with the companies building it.