Greetings from a world where…

Star City rocks

…As always, the searchable archive of all past issues is here. Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Who is Us: the globalization of innovation and challenges to assessing technological dependence

All the time, we hear about China's latest benchmarks and indicators to achieve technological autonomy (think: 70% domestic semiconductor production). Yet, what about the indicators that fade away?

I wanted to solve the case of the disappearing indicator: the foreign technology dependence ratio (FTD) [对外技术依存度]. Often seen as the launch pad for China's indigenous innovation drive, the National Medium- and Long-Term Plan for the Development of Science and Technology (2006–2020) outlined a target of reducing China’s FTD to 30% by 2020.

Curiously, in 2016, the Chinese government stopped using the FTD because of its “misleading nature”. Why? My new article “Who is us: the globalization of innovation and challenges to assessing technological dependence” endeavors to unpack this puzzle and address the broader question of how states assess technological self-sufficiency in a globalizing world.

In this article, I argue that globalization poses challenges for rising powers’ ability to assess technological self-sufficiency. Specifically, the hybridization of innovation — increased financial and high-skilled talent flows that connect advanced economies with emerging ones — has fostered firms like Alibaba, whose financial backing and some leadership are from countries different from the one where their headquarters and key operations are based. Notably, in some versions of China's FTD metric, Alibaba's R&D expenses (as a foreign-invested enterprise) would not count as indigenous innovation.

My theory builds on Robert Reich’s famous ‘Who is Us’ query. In 1990, when the U.S. and Japan were in the midst of a fierce high-tech competition, Reich compared the ‘nationality’ of two hypothetical companies. Corporation A is headquartered in the US, led by US directors, and owned by American investors; however, it conducts most of its critical operations abroad, where most of its employees are based. Corporation B is headquartered in a foreign nation and owned and managed by citizens from that nation; however, it conducts most of its critical operations in the US, and the majority of its employees are Americans. By positing that Corporation B could be the more ‘American’ company, Reich raised difficult questions about globalization and how states judge which firms are domestic and foreign.

Interestingly, in one of last week’s recommended reads, the Wi-Fi router company TP-Link made a similar “Who is Us” claim. A TP-Link lobbyist has used language similar to Reich:

[Greg Guice] argues that TP-Link’s strategy is good for the U.S. “Let’s say there is one company founded in the U.S. by a U.S. citizen and they then set up their manufacturing in China through PRC-connected facilities, as opposed to a company, like TP-Link, that is founded by a Chinese citizen who sets up his company in the United States and purposely chooses not to manufacture in China,” he told The Wire. “Which company is the better company?”

My version of the “Who is Us” query is slightly different. Both back then and today, Reich’s Corporation A and Corporation B remain conceptual possibilities, as opposed to empirical realities. This is borne out by statistics that show multinational firms locate the vast majority of their key operations in their home countries. In the three decades since Reich’s initial question, changes in the global economy have posed a different ‘Who is Us’ challenge. Enter Corporation C (Table 1 below): It is headquartered in China, and conducts most of its critical operations in China, where the majority of its employees are based; however, it is led by a mix of Chinese and non-Chinese directors, and largely owned by foreign investors.

The empirical case studies evaluate whether Corporation Cs make assessments of foreign technology dependence more malleable and subject to varying interpretations after the hybridization of innovation becomes an entrenched feature of the global economy. From the article:

I conduct comparative case studies of how policymakers assessed foreign technology dependency in China (1990–2005; 2006–2020), Japan (1970–1990), and India (2003–2020). Facing this new globalization trend in recent decades, Chinese bureaucrats adopted more unstable and malleable assessments of foreign technology dependence than their predecessors in the 1990s and early 2000s. Japan’s effort to nurture technological autonomy shares many similar features with China’s current indigenous innovation drive, with one key exception: It took place in a global economy unaffected by the hybridization of innovation. The Japan case analysis shows that this distinction made a meaningful difference in the ease and clarity of Japanese policymakers’ evaluations of foreign technology dependence. Lastly, a comparison to India’s efforts to measure technological self-reliance demonstrates the salience of my argument in a different context.

In the China case (post-hybridization of innovation), in line with theoretical expectations, Chinese bureaucrats accept a wider “gray zone” between domestic and foreign companies. The Variable Interest Entity is the clearest example. It has also become increasingly difficult for the Chinese government to employ quantitative indicators to measure technological self-reliance. Returning to the fraught history of the FTD indicator, the article recounts: “Critics argued that the FTD masked China’s dependence on the R&D capabilities and technology transfer channels of foreign-invested enterprises (FIEs), including hybrid firms.” To give you a sense of the varied, inconsistent measures of foreign technology dependence, here’s a snippet from the article:

In contrast, in settings before the hybridization of innovation became an essential element of the global economy, we should expect governments to more easily and clearly track technological autonomy. Put simply, there should be fewer “Who is Us” issues. I tested this expectation by studying China’s efforts to evaluate indigenous innovation in the 1990s, with a focus on indicators of domestic production of computers, as well as Japan’s assessments of foreign technology dependence during its rise around the same period. In both contexts, the countries attracted very low amounts of FDI, and brain circulation of high-skilled talent was relatively limited. See below for some evidence from the Japan case:

In the last case study, I probe whether my theory generalizes to globalization’s impact on India’s efforts to assess foreign technology dependence. The following screenshot gives you a taste of some of the details in that case:

Why does any of this matter? I want to highlight two main contributions. First, we’ve chronicled this rise of technonationalist industrial policies. Naturally, China’s indigenous innovation drive has also claimed so much attention. Yet, a key point overlooked in these discussions is how does China and other technonationalism enjoyers measure success in the first place? If they can’t come up with stable measures, then the hybridization of innovation could greatly hinder the effectiveness of these policies. Second, and relatedly, this article wants to emphasize an important variable: the process of assessing foreign technology dependence. This is not an objective, straightforward process but rather a messy one that complicates state responses to globalization.

The full article is available open-access in Review of International Political Economy!

ChinAI Links (Four to Forward)

Below are four of the books that were especially influential in developing my thinking for this article:

• Ling Chen. (2018). Manipulating globalization: The influence of bureaucrats on business in China. Stanford University Press.

  • *Contains great details about how local governments pursue benchmarks and indicators that differ from narrow interpretations of indigenous innovation.

• Qiwen Lu. China’s Leap into the Information Age: Innovation and Organization in the Computer Industry. Oxford University Press, 2000.

  • *Lu’s work provided many of the historical details for the China (pre-hybridization of innovation) case.

• Aaron Friedberg. (1988). The weary titan: Britain and the experience of relative decline, 1895–1905. Princeton University Press.

  • *His analysis of British assessments of economic dependence in the late nineteenth century found that the process became dominated by a simplified indicator based on trade return data, which distorted policy debates.

• AnnaLee Saxenian. (2007). The new Argonauts: Regional advantage in a global economy. Harvard University Press.

  • *This text definitively maps out the brain circulation concept.

Thank you for reading and engaging.

These are Jeff Ding’s (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a world where…

the USMNT could go all the way!

…As always, the searchable archive of all past issues is here. Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: An AI that serves as a reliable “college preference form partner” is the one truly “worthy of trust”

Context: You may know about China’s national college entrance exam (Gaokao), which took place last week, but what comes after? This week’s feature Shuzi Lichang article (link to original Chinese) provides a window into the “college preference form” [志愿填报] process: after finding out their Gaokao scores, students have a few days to rank their top five colleges, alongside three to five majors for each college. It is considered the second-most stressful time for a kid, behind only the exam itself. Our entry point is Alibaba Qianwen’s launch of an AI advisor for the college preference form, which was made available for free to 12.9 million exam takers on June 10.

Key Takeaways: To understand why Chinese families might turn to an AI guidance counselor, let’s get a better sense of the process.

• Students and parents have to sort through an overwhelming amount of information, including school rankings, major specializations, enrollment quotas, employment prospects, and historical cutoff scores (The government sets cutoff scores that students have to score above to apply for elite colleges, but even if you’re above the cutoff, that doesn’t guarantee admission). As Jia and Li (2021) state, “Our findings suggest great room for improvement in [student-college matching quality] in this educational system.”

• When my parents went through the college admissions process, it was even more stressful. Under an older system, provinces required students to submit their schools before even knowing their Gaokao scores.

• Thus, there is a market for good advice on this potentially life-changing decision; unfortunately, families get taken advantage of. From the Shuzi Lichang piece: “Deceptive marketing tactics—such as claims of ‘insider admissions data,’ ‘exclusive institutional channels,’ or ‘one-on-one meetings with exam-setting professors’—are rampant.” The reporter recalls that a relative’s kid spent over 10,000 RMB on a college preference form consultant, who proceeded to just recommend the “hot” majors, completely ignoring the student’s preferences and wasting a hard-earned exam score.

• The stakes are high: if you are too conservative about your list of schools, then you could under-match and settle for a lower-tier college than your score would warrant. But if you are too aggressive, you could miss out on all your schools.

What does Qianwen’s AI advisor offer students?

• The below image depicts a report generated by the AI guidance counselor. Based on inputs of Gaokao scores and preferred majors, the model recommends six “high-potential” schools , eight “stable” schools, and sixteen “safety-net” schools. The report also includes a section about how AI could affect the preferred majors.

• Here’s how the article pitches the service: “It transforms college application guidance from a business based on information asymmetry into an inclusive public service, ensuring that having a ‘dedicated advisor’ is no longer a privilege reserved for the few.” This reminds me of Zebra English, the AI English teacher service that appealed to parents who had trouble finding or paying the high costs for in-person English tutors.

ChinAI #337: China's First AI English Teacher Earns its Stripes

Jeffrey Ding

·

November 24, 2025

Read full story

The question, going forward, for AI services in this market: how do you convince users to trust your AI with real, vital interests like the college preference form?

• As you’ll see in the full translation, I thought the last two sections were a bit too press-release-y, but the author seems to think Qianwen has developed this reputation of “getting things done” to earn the trust of the general public.

• But overdependence on Qianwen could raise issues. As one of the top WeChat comments on the article (26 likes) points out: “Aren’t you concerned that AI responses might become homogenized? If it recommends the same schools, or suggests the same preferences, to all students within the same academic tier, what would the ultimate outcome be?”

FULL TRANSLATION: An AI that serves as a reliable “college preference form partner” is the one truly “worthy of trust”

ChinAI Links (Four to Forward)

Should-read: Just Above the Exam Cutoff Score: Elite College Admission and Wages in China

Ruixue Jia, a professor at UCSD, co-authored this NBER working paper on the impact of scoring just above the cutoff to get admitted to an elite Chinese college on future wages. This paper provided a lot of the background information used above.

Should-read: Exporting U.S. Military AI Won’t Be Easy

In Foreign Policy, GovAI research scholar Jake Steckler studies how middle powers are making decisions about acquiring and developing military AI. One interesting detail: “France has pitched Mistral, in Europe and beyond, as a path to military AI that reduces dependence on the United States and China.”

Should-read: TP-Link’s American Dream

The Wire China is really starting to distinguish itself for in-depth reporting on China’s economy. By Noah Berman, this cover story on the router firm TP-Link weaves together so many threads, including geopolitics, globalization, American makeovers, and industrial policy.

Should-read: Life by Lu Yao, translated by Chloe Estep

This week’s feature article mentions the Chinese novel Life, which I had not heard of. Has anyone read it?

Thank you for reading and engaging.

These are Jeff Ding’s (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a world where…

it’s okay to sing a different tune [唱唱反调]

…As always, the searchable archive of all past issues is here. Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: My “Artificial Challenged Intelligence” Moment

Context: Back in June 2021, I wrote one of my favorite ChinAI posts on this phrase I had seen popping up in Chinese media: 人工智障, which I translate as “Artificial Challenged Intelligence.” It has the same first three characters [人工智] as the term for Artificial Intelligence, except switching out the last character turns the phrase’s meaning into one that’s used to make fun of AI failures.

ChinAI #144: Artificial Challenged Intelligence [人工智障]

Jeffrey Ding

·

June 7, 2021

Read full story

This week’s feature translation comes from Renwu [人物], a Chinese magazine that covers human-interest stories. As a fan of their reporting, I’ve previously translated their longform investigation on the algorithmic pressures faced by Chinese delivery drivers as well as an engaging profile of why some families seek out AI tutors for their children.

Renwu recently asked their readers to share stories about their encounters with Artificial Challenged Intelligence. When you read these stories, these slices of real people’s lives, maybe you’ll find them interesting. Or mundane. Maybe you’ll even see them as a challenge — as singing a different tune [唱唱反调] — to this narrative that all Chinese people are super optimistic about AI all the time.

At the end of the day, all that matters is you find them human.

Key Passages: 铅球(Qian Qiu), Male, 46, Beijing

I’m a fan of Chinese crosstalk comedy [xiangsheng, 相声] and particularly love the duo of Guo Degang and Yu Qian. One day, on a whim, I asked (Tencent) Yuanbao, “How many people are there in Deyunshe? (a famous Chinese crosstalk comedy group)” It quickly replied with the number of crosstalk performers and trainees, and thoughtfully gave me a “popular science explainer” on the apprenticeship hierarchy—the four cohorts known as “Yun, He, Jiu, and Xiao.” It was a bit like the generational naming conventions found in traditional family trees, and it even listed representative figures from each cohort.

I took a look and saw that “Shaobing” (Zhu Yunfeng) was listed under the “He” tier, while Qin Xiaoxian had “skipped a grade” into the “Jiu” tier. That messed up the generational order, so I quickly corrected it. I hadn’t actually expected a reply, but the eager-to-please AI generated a new list.

In the new list, Meng Hetang’s name appeared under both the “Yun” and “Jiu” tiers—but notably not the “He” tier. Yuanbao even added a parenthetical explanation: Meng belongs to the “Jiu” tier because...

It was bullshitting endlessly with a straight face…

匿名(Anonymous)

DeepSeek told me, while I was on my period, to go to sleep early and that my top priority for the night was stopping the bleeding.

大红薯(Big Sweet Potato)

I once experienced bleeding after wiping my butt during a bathroom visit and asked an AI about it. Since then, whenever I ask it health-related questions, it always mentions this: “Given your history of anal bleeding...”

Mm, Female, 31, Deyang

I wanted to find out a bit about my new boss, but the AI ​​sent me a message claiming the new boss was under investigation by the Central Commission for Discipline Inspection. It was written so convincingly that I nearly fell out of bed in the middle of the night from the shock.

Anonymous, 13 years old

I’m a student in the second year of junior high. A while back, I encountered a math problem where two different methods yielded different answers; I asked my teacher and found out the problem itself was flawed. On a whim, I fed the erroneous problem into DeepSeek.

Twenty minutes later, it produced a result. It looked orderly enough on the surface, but when I expanded the “deep thinking” process, I witnessed the AI’s internal conflict and hesitation. It was like a child screaming silently: “This is impossible! I must have calculated it wrong!” Yet, none of this struggle was reflected in the final result, nor did it ever mention the possibility that the problem itself was incorrect.

More recently, my teacher mentioned the trend of using AI to write essays and said they had specifically used an AI to check student essays. This piqued my curiosity: Could AI really distinguish between human-written and AI-generated essays? I turned to (ByteDance’s) Doubao and created an AI agent designed to detect AI-generated text. Then, using another agent on the same platform, I generated three essays and asked the first agent if they were AI-generated. Every time, Doubao insisted they weren’t, offering plausible-sounding analyses about the authenticity of the emotions and the vividness of the language. However, whenever I told it it was wrong, it would immediately change its tune. I even tried DeepSeek, but the results were much the same.

Often, AI acts akin to a “You might also like” feature; it simply panders to your wishes and gives you the answer you want, without ever questioning the premise of your question.

临安 (Lin An), Male, 39, Wuhan

In my view, the most “challenged intelligence” aspect of today’s AI is precisely its “smarts.” For all its brilliance—having digested everything humanity has ever created or is currently creating, and possessing a perspective that could be described as “omniscient”—AI is paradoxically trapped by that very omniscience. No matter the question, it never falls silent or admits, “I don’t know.”

Time and again, when I use AI to look up information, it insists on providing an answer—even to a flawed or unanswerable question. If I ask for real-world examples to back up its conclusions, it will fabricate one if none exists. If I press for further details, it will keep cobbling together more information. Even when I explicitly instruct it via keywords not to make things up, not too long after, it still reverts to fabricating cases; it seems pathologically incapable of telling the truth. Consequently, I approach AI research with extreme caution, verifying every detail myself. On several occasions, I’ve been frustrated to find that correcting the AI ​​took longer than simply researching the information myself.

I once asked the AI, “Can’t you just say ‘I don’t know’?”

It replied: “That is determined by the model’s mechanism. My answers are based on probability; I learn which words frequently appear together, but I don’t truly ‘understand’ truth or falsehood. When faced with an unfamiliar question, I simply select the words that ‘look most correct’ and keep the narrative going.”

Kevin Tsai (host of popular I can I BB debate competition show) once remarked: “In all my conversations with AI, the one thing it has never given me is silence, is space for reflection.”

Yet, therein lies the most terrifying aspect. As humanity’s reliance on AI deepens, information born of “AI hallucinations” can—if enough people believe it—morph into a kind of “fact.” It is the old adage: “A lie told a thousand times becomes the truth.”

…

We talk about “Artificial Challenged Intelligence” today because, despite its appearance of omnipotence, AI still frequently makes a fool of itself. I suspect that the day AI ceases to be “challenged intelligence” and instead becomes a symbol of omniscience and omnipotence—that is when the real disaster will begin.

Many other stories included in PARTIAL TRANSLATION: My “Artificial Challenged Intelligence” Moment

ChinAI Links (Four to Forward)

Must-read: China is getting worried about AI & jobs

I’m late to dig into this post by Matt Sheehan on growing Chinese worries about AI labor displacement. It is chockfull of insight, especially the section on the Wuhan Robotaxi Saga:

In late June of 2024, a Wuhan taxi company released a public letter decrying the shrinking margins for companies and declining income for taxi drivers. The letter actually spends more time on the way ride-hailing apps have hurt taxi drivers, but it was the parts about robotaxis that stirred up online debate.

…the online conversation about robotaxis “stealing people’s rice bowls” continued to gain steam, becoming a top trending topic on Chinese social media.

Here’s where I heard something a bit different. The first time someone relayed this incident to me, they said taxi drivers had gotten together and led a “strike” against the robotaxis by repeatedly hailing them and then cancelling at the last minute, effectively paralyzing the system…

…the outcry over robotaxis in Wuhan led to a significant shift in how the government and policy community thought about AI’s impact on jobs. It led officials to take the threat of job displacement—and also the public’s fear of AI-driven job losses—seriously. I feel relatively confident about the Wuhan robotaxi incident contributing significantly to this shift, because multiple influential members of China’s AI policy community independently relayed that causal relationship to me at different times.

Should-read: How China is breaking apart a people and its culture

In the Financial Times, drawing on a satellite imagery, witness testimony, and local reporting, Alison Killing finds that “authorities continue to rely on widespread detention, short-term arrests and intimidation, even as the campaign enters a new stage centred on forced cultural assimilation, particularly in the heavily Uyghur areas of southern Xinjiang.”

Should-read: Two Chinese-language articles I considered featuring this week

1. How did Hangzhou become more hardware-oriented? A Huxiu piece tries to unpack why Hangzhou, known as a hub for AI software startups, has pivoted to growing a crop of startups focused on AI hardware (especially inference chips).

2. Today’s college students are living their university years like their final year of high school. This NetEase DataBlog article reports on why the relentless grind/rat race of preparing for college entrance exams doesn’t end when Chinese students get to college. Now, it’s all about squeezing out every tenth of a GPA point.

Thank you for reading and engaging.

These are Jeff Ding’s (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a world where…

welcome new readers: most issues are note-taking exercises on Chinese-language articles that interest me (not lengthy missives)

…As always, the searchable archive of all past issues is here. Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: Behind DeepSeek V4’s “Chip-Model Synergy”, the domestic computing ecosystem begins flywheel acceleration

Context: DeepSeek’s V4 made waves for something beyond pure capabilities: the validation of “chip-model synergy” between V4 and Huawei’s Ascend chips on a large scale. Previously, this was something that could only be achieved with Nvidia chips, in part because of the CUDA moat. As Lily Ottinger and Mary Clare McMahon described in a ChinaTalk article:

Nvidia’s position rests on what Doug O’Laughlin has called a “three-headed hydra” of leading hardware, networking capabilities, and, most importantly for this piece, a deeply entrenched software ecosystem. At the center of that ecosystem is CUDA, a proprietary programming framework that allows developers to efficiently map computations onto Nvidia’s GPUs. CUDA’s value lies not only in its performance but in its reach: an expansive set of libraries, optimized workflows, and tight integration with widely-used machine learning frameworks make it the industry standard. And, crucially, CUDA can only be used with Nvidia GPUs. That makes CUDA a core component of Nvidia’s competitive advantage, otherwise known as Nvidia’s moat.

In this week’s feature translation, QBitAI provides a readout (link to original Chinese) on Huawei’s alternative to CUDA, the Compute Architecture for Neural Networks (CANN), based on panels at the Kunpeng Ascend Developer Conference.

Key Takeaways: CANN has transitioned from its “infancy” into a “youth” phase, according to Qiuwu Chen, founder of Shanghai-based AI coding start-up AIGCode.

• From the article: “This ‘youth phase’ does not imply that the ecosystem has reached full maturity; rather, it signifies that developers are beginning to move beyond ‘vendor-provided babysitting support.’ Developers are now capable of independently resolving issues, contributing code, and driving the ecosystem's iterative progress.

• Chen recounted his company’s experience with Ascend over the past year or so. They used Ascend out of necessity, due to severe GPU shortages at the beginning of 2024. In early days, when they ran into issues when pre-training of a 7B-scale Mixture-of-Experts model on Ascend chips, they had to submit a support ticket to Huawei, which resulted in a four month wait. The entire ecosystem was “essentially a section of desert,” Chen recalls.

The three dimensions that dictate whether Huawei chips can overcome the Nvidia CUDA moat: adaptation efficiency, performance ceilings, and production-grade reliability.

• Regarding adaptation efficiency, as the previously cited Ottinger and McMahon article emphasizes, “adopting models to run on Huawei’s platform is also onerous.” If you have to depend on Huawei engineering support to fix issues, then it will take a long time (as the early experience of Chen’s AIGCode reveals). At the developer conference, a University of Science and Technology of China team claimed that it took less than a week for them to migrate a high-performance computing solver to Huawei’s Kunpeng CPU chips.

• On performance ceilings, one key indicator is Model Flops Utilization (MFU), or compute utilization rate. How much compute can you squeeze out of the same chip? The article reports, “On the Ascend accelerator, AIGCode achieved a Model Flops Utilization of 65% during the pre-training of its MoE model. This figure is nearly double the industry average.” This dimension will also be shaped by the efficiency of scheduling and coordination in massive clusters (for example, a supernode of 384 Ascend chips and 192 Kunpeng chips).

• Lastly, the example of production-grade trust comes from a leading joint-stock commercial bank (think: an institution like China Construction Bank). At the developer event, this bank stated that they had directly integrated AI into its core risk management processes. The article implies (though it is not explicitly stated) that the open-sourcing of the CANN framework shaped this financial institution’s decision to entrust a core business operation to AI models that run on Huawei chips.

Could an open-source developer ecosystem drive CANN’s development going forward? This is one of the most interesting questions.

• Huawei open-sourced the core code for CANN, including runtime environments and compilers, last December. The article lists a few indicators that may point to a sustainable ecosystem that drives flywheel growth: Over 70 major AI models are now supported immediately upon their release, the Kunpeng and Ascend developer communities both surpass 4 million members, and groups like AIGCode and the aforementioned leading bank are contributing features to various communities. *Notably, the bank contributed 34 optimizations to vLLM-Ascend, a project to adapt one of the most popular inference engines for Ascend NPU chips.

• Back in May 2025, Ottinger and McMahon found that “Huawei’s Ascend developer portals — both in English and Chinese — exhibit low engagement, with sporadic posts and limited public debugging activity.” To be honest, it was difficult for me to figure out whether this has meaningfully changed. On the surface level, the Ascend forums seem to have more activity. I guess I’m just used to an open-source framework being posted on a Github repository, and then most of the issues being sorted out there, instead of a separate community forum website. And on my quick scan, it seemed like CANN was posted on Gitee (China’s domestic alternative to GitHub) and then switched to Huawei’s own site.

• In any case, the key trend to watch is whether CANN can actually benefit from an organic community of implementers and complementary innovators that contribute back into the framework.

FULL TRANSLATION: Behind DeepSeek V4’s “Chip-Model Synergy”, the domestic computing ecosystem begins flywheel acceleration

ChinAI Links (Four to Forward)

Must-read: Can Huawei Take On Nvidia’s CUDA?

Written for ChinaTalk in May 2025, Lily Ottinger and Mary Clare McMahon’s in-depth analysis of CUDA vs. CANN was essential background reading for this post. Definitely read it if you are interested in this topic.

Should-read: China Is Testing Its State Surveillance Model Abroad

In NYT, David Pierson and Berry Wang report on Beijing’s efforts to export its security model to the Solomon Islands, which signed a security pact with China in 2022. The pilot was suspended after local backlash. H/t to Kirsty Needham for sharing and who reported on this in a 2024 story.

Should-read: Simon Willison thread on thinness of AI-failure stories

Good discussion and back-and-forth on this thread about whether some AI-failure stories are overblown. The context behind this is a flurry of reporting about companies saying that they’ve spent too much on AI.

Should-attend: June 2026 Wonky China

If you’re in the area, I’ll be joining the Wonky China team for a happy hour at Chang Chang on June 10th. Come for the hot takes, stay for the scallion bubble pancakes.

Thank you for reading and engaging.

These are Jeff Ding’s (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a world where…

church is in session

…As always, the searchable archive of all past issues is here. Please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Anthropic’s Dogma on US-China AI Competition

I’ve been going to church since I was a kid. Early on at the University of Iowa, when I identified as an evangelical Christian, I went on mission trips overseas and led prayer meetings. Over the years, my faith has morphed and faded and deepened and changed. Today, as fellow “exvangelicals”, my wife and I still attend a Lutheran church almost every week, and we go to a young adult Bible study on Wednesday nights.

All this to say that I am relatively sensitive to dogmatism: an unfounded confidence in asserted opinions not based in fact. As I read more and more of Anthropic’s policy papers and CEO Dario Amodei’s blog posts, the elements of religious dogma abound: infallible faith in a set of unquestioned beliefs; justification of doctrine based on appeals to exclusive access to divine wisdom; an unwillingness to consider contradictory evidence. Sound familiar?

On May 14, Anthropic laid out its beliefs about U.S.-China competition in a new paper titled “2028: Two scenarios for global AI leadership.” This paper envisions two scenarios for the world in 2028: in the first, the U.S. has a commanding lead in AI; in the second, China’s AI ecosystem remains neck-and-neck, which results in “authoritarian AI leadership”. Their policy prescription is to intensify efforts to prevent Chinese labs from accessing export-controlled chips. Taking on the position of a religious skeptic, let us question seven of this post’s unfounded assumptions.

Assumption #1: Because transformative AI arrives in two years, the actions that decide the US-China competition will only occur in this limited window.

In January 2025, Dario posted a version of this “two scenarios” essay, in which he warned that transformative AI — “AI that is smarter than almost all humans at almost all things” — would arrive in 2026-2027. What explains the updated timeline? Why did we go from 2026 to 2028?

Looking back at my rebuttal to Dario’s post, it’s funny (and sad) how neatly this point still applies to the recent Anthropic paper: “It’s really important to note Dario’s assumed one-to-two year timeline. I’ve been in governance of AI circles since 2017. In that time, I’ve consistently heard some iteration of: AGI is two years away! It’s the Bruno Caboclo of technologies: always two years away from being two years away.”

I can’t wait until next year’s paper when Anthropic tells us that we only have a limited window of time before 2030, when the company will open up a box, and transformative AI will transform everything.

Assumption #2: Transformative AI capabilities will diffuse almost immediately.

In line with this two-year window, the Two Scenarios paper argues that frontier AI could quickly reshape the military balance in China’s favor: “These capabilities will not diffuse slowly. When a new model reaches a new capability in autonomous targeting, vulnerability discovery, or swarm coordination, for example, the regime that controls it can put it onto the field in weeks, not years.”1

History teaches us that this is not the timeframe by which general-purpose technologies like AI diffuse across economic and military systems. My book Technology and the Rise of Great Powers verified these protracted timelines across more than ten different general-purpose technologies. Based on research into how electricity affected the military balance of power, I also found that electrification transformed militaries in a gradual, decades-long process, in which the main impacts materialized not through the eye-catching weapons systems (e.g., the drone swarms that attract Anthropic’s attention) but instead in communication, transportation, and logistics applications.

To be sure, AI could be different from past GPTs, especially if recursive self-improvement accelerates capabilities development and diffusion. Well, consider OpenAI’s fascinating reflections on “harness engineering,” based on their effort to built a software product with “0 lines of manually-written code.” What resonated with me about this early taste of the challenges associated with “AI building AI” is the organizational adjustments that OpenAI had to make. For a time, they spent every Friday cleaning up “AI slop.” They also needed to readjust their software engineering team’s primary job toward designing environments, specifying incentives for AI agents, and building feedback loops to monitor those agents. This was an experiment with a small team of three engineers. Imagine the challenge of diffusing these organizational adjustments across the entire economy or military. This will take time.

Before moving on to the next unfounded premises, I want to underscore why it’s important to question these assumptions. This is not a navel-gazing exercise. If the timeline of when transformative AI systems actually make an impact on national power is not 2026 (or 2028, or 2029) but, say, ten years from now or later (which is more in line with the diffusion marathon of past GPTs), then the basis of policy debates changes. In a past critique of the U.S.’s export control policy, I’ve made this short-term versus long-term distinction:

We haven’t even mentioned other downsides such as the risk that the controls accelerate a) China’s development of an independent high-end chip capacity and b) the reorientation of chip supply chains in a way that insulates them from future controls. If you are operating in a world where AI does not make its mark until after at least a decade, these downsides become more salient and the benefits of the controls become more muted.

Zooming out, the fast timeline assumption fits with Anthropic’s rhetorical strategy — “we have only a limited period of time to set the conditions of the competition,” asserts the paper — to not quite manufacture a state of emergency but strategically deploy one for its own purposes. It is an approach reminiscent of some evangelicals: claiming that the end times are near, some Christians justify war (including the current military operations in Iran) because it fulfills an end-times prophecy to trigger Christ’s return. Note: Coincidentally, at church a few weeks ago, our congregation studied Acts 1:7. When apostles ask Jesus when he will restore the kingdom to Israel, he responds, “It is not for you to know the times or periods that the Father has set by his own authority.”

Assumption #3: If China’s AI models are close to US models in capabilities, China will win on adopting these capabilities across economic, military, and technological domains.

In Dario’s 2025 blog post, he makes a similar claim: “Even if the US and China were at parity in AI systems, it seems likely that China could direct more talent, capital, and focus to military applications of the technology. Combined with its large industrial base and military-strategic advantages, this could help China take a commanding lead on the global stage, not just for AI but for everything.”

Again, my book and past research on China’s “diffusion deficit” thoroughly debunks this assertion. Take, cloud computing as an example. China has strong cloud computing giants like Alibaba Cloud, but its overall cloud adoption rate, across all businesses, is half the U.S.’s rate. As for military deployment, the U.S. military likely leads the world in leveraging private technology firms to support cloud computing adoption. In a forthcoming paper in Security Studies, I take aim at this assumption that China’s civil-military integration outpaces the United States: “When applied to China’s ‘military civil-fusion efforts,’ the four-channel measure reveals that China lags significantly behind the U.S. in terms of the efficient use of common technologies, facilities, and personnel for military and industrial purposes.”

Assumption #4: There are only two scenarios. Support Anthropic’s leadership to secure democratic AI, or authoritarian AI will rule the day. I mean, come on, it’s almost too easy to make the connection to the false dichotomies of religious fanatics. We are good, everyone else is evil.

Look, I think we should be concerned about AI’s impact on surveillance. I’ve consistently covered this issue in these pages (from ChinAI #29 to #357). But it’s a big leap from China keeping pace in AI to the entrenchment of authoritarian repression around the world. There’s some dangerous technological determinism in this thinking, which likely repeats the same mistakes of those who thought that the Internet would bring about widespread democratization — just in the other direction. As Internet governance scholars Christopher S. Yoo and Alex Mueller point out:

The premise that China threatens to increase international acceptance of authoritarianism by exporting related values through Internet infrastructure warrants greater skepticism. It appears to reflect the same type of soft-technological determinism as the United States’ early “Internet Freedom” agenda that saw the Internet an unstoppable vehicle for democracy. Just as the existing Internet failed to liberate China, Russia, and Iran, a cyber-sovereign Internet should be no more likely to increase the level of digital repression among governments that have not already embraced authoritarianism.

Core to the “two scenarios” vision is the assumption that there is a full-stack American AI and a full-stack Chinese AI. In fact, in their policy proposal to “champion the export of American AI,” Anthropic is trumpeting a plank of the Trump administration’s “America’s AI Action Plan.”

If, however, you make some effort to comprehend how AI is developing at the ground level, the notion of an all-American or all-Chinese full-stack AI system becomes fanciful at best and purposefully ignorant at worst. Top U.S. AI startup Perplexity, for instance, permits subscribers to use Chinese startup Moonshot AI’s Kimi K2.5 model; previously, Perplexity also supported a version of the DeepSeek-R1 that had been post-trained to remove censorship. I could keep going with examples. Read more about SGLang and inference engines, if you’re interested in the complex web of interdependencies that characterize the AI stack.

Here’s the thing. When talking to policymakers and pundits who aren’t steeped in this field, Anthropic can get away with these flimsy assumptions. When it tells them that the end is nigh, and we need democratic AGI to save the world, it is all too easy to just blindly trust the company’s divine technical wisdom. However, if you’ve done your homework (in other words, the equivalent of reading Acts 1:7 or any number of Bible passages that underscore no one knows the time and hour), you can see right through it.

Given space constraints, I’ve included rejoinders to the following three assumptions in the footnotes:

• Assumption #5: If a PRC AL lab had developed a model like Mythos, the CCP would have had first access to a system that could autonomously discover and penetrate critical American infrastructure.2

• Assumption #6: Chinese AI labs depend on large-scale distillation attacks that illicitly extract the innovations of American companies.3

• Assumption #7: Neck-and-neck competition risks disincentivizing responsible AI.4

The broader purpose here is not to debate every single point line-by-line but rather to show the lack of reflexive thinking in this recent Anthropic paper.5 Good research and good argumentation — and good faith, for that matter — must exist with a healthy amount of doubt and uncertainty. I’ve just finished grading 47 research papers, and the number one piece of feedback I give to my students is consider how an opposing perspective might answer their research questions and then either cede some ground back or engage with hypothetical counter-arguments. When I read good writing about AI policy, the authors are open to updated assumptions and disconfirmatory evidence. I don’t see these qualities in Anthropic’s paper. It’s dogma all the way down.

This is a peculiar moment. When one of the world’s most valuable private companies publishes a geopolitical thesis with so many holes, I would have expected it to generate more productive debate in these past two weeks. There are many smart people who have a better mix of expertise and insights than me to give critical takes on this paper.

It’s just that many of them are now working for Anthropic (including several former Biden administration officials who shaped AI policy) or hope to be hired by Anthropic in the future.

The Church of Anthropic is growing, and they pay handsomely. Call me a nonbeliever.

Thank you for reading and engaging.

These are Jeff Ding’s (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

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