Our phrase of the week is: “pilfered in passing” (顺手牵羊 shùn shǒu qiān yáng)

Context

One of the biggest stories being discussed in the Chinese media this week is a high profile plagiarism case involving the academic and writer, Jiang Fangzhou (蒋方舟).

Jiang is known as the “genius girl” (天才少女). She published her first book at the age of nine, and by the age of 12 was writing columns for Southern Metropolis Daily (南方都市报).

She was admitted to Tsinghua University in 2008 for her under graduate degree with a 60-point concession on her gaokao score. After a stint as deputy editor of Neweekly (新周刊), a respected current affairs magazine, Jiang went on to take a master’s degree in creative writing at Renmin University which she completed in 2019.

Since then she’s become one of the more recognisable literary personalities in China, especially among people born in the 1990s and 2000s.

But this has all come crashing down this month following accusations of plagiarism which resulted in her master’s being revoked by Renmin University.

The accusations were first raised by a retired Tsinghua University professor in August last year, who made a formal complaint to Remin University in April this year which triggered an investigation.

More recently, two “online sleuths” identified passages in her master’s thesis, and one of her books, which they alleged had been lifted directly from publications outside of China, asking the question:

“This makes me wonder:

how much of it did she write herself, and did she casually lift it from somewhere else?”

这让我不禁怀疑，多少东西是她自己写的，多少东西是顺手牵羊的。

And with that, we have our Sinica Phrase of the Week.

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What it means

“Casually lift from somewhere else” is our translation of the classical Chinese idiom which literally means to “leading the goat away in passing” (顺手牵羊 shùn shǒu qiān yáng). Although in the end we landed on a different translation which I’ll come to at the end.

But first, the long and winding history of this old idiom.

The earliest reference of the image of “leading a goat” is believed to be in The Book of Rites (礼记), the Confucian classic on ritual and etiquette compiled during the Han Dynasty (206 BCE-220 CE). The text instructs that when presenting a horse or a goat as a gift, it should be led in a certain way:

“When presenting a horse or a goat, lead it with the right hand.”

效马效羊者右牵之。

The first recorded use of the idiom in its modern form comes over a millennia later, in Single Whip Seizes the Lance (单鞭夺槊), a play by Shang Zhongxian (尚仲贤) who was a playwright of the Yuan Dynasty (1271-1368).

In one scene, a warrior boasts of capturing an enemy rider:

“I gripped his horse with my right hand, seized his eyelashes with my left, and led him back as easily as leading away a goat.”

是我把右手带住马，左手揪着他眼扎毛，顺手牵羊一般牵他回来了。

A century later, “as easily as leading away a goat” was immortalised in the Thirty-Six Stratagems (三十六计), a famous collection of military tactics, likely compiled in the late Ming Dynasty (1368-1644), althgough the writer is unknown.

“As easily as leading a goat” (顺手牵羊) is the twelfth stratagem. It teaches that when a large army is on the move it will inevitably expose weaknesses. A clever opponent takes advantage of every one of them, no matter how small, accumulating minor gains into major ones.

The idiom’s meaning has evolved over the centuries. Now in modern Chinese it describes casually or opportunistically pocketing something that doesn’t belong to you. It always carries a negative tone, which is exactly how it was used to describe Jiang Fangzhou’s writing.

According to the allegations against her, the work of other writers was conveniently within her reach, and helpfully written outside China making it harder to track.

So, according to her accusers, she casually lifted, translated, adjusted and dropped them into her own work. Which is why we landed on “pilfered in passing” for the translation of this ancient and storied idiom.

Andrew Methven is the author of RealTime Mandarin, a resource which helps you bridge the gap to real-world fluency in Mandarin, stay informed about China, and communicate with confidence—all through weekly immersion in real news. Subscribe for free here.

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Read more about how this story is being discussed in the Chinese media in this week’s RealTime Mandarin:

RealTime Mandarin

#286: Top academic in plagiarism scandal

This week we meet Jiang Fangzhou (蒋方舟) who is a famous writer and media personality in China…

Listen now

2 days ago · 3 likes · Andrew Methven

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• Xi expressed full-throated support for AI development, while indicating that regulation is moving up Beijing’s priority list.

Read together, the message was that China intends to compete hard on AI, but on its own terms, with stability ranked ahead of any near-term gains.

The bit that attracted the most attention was Xi’s reaffirmation of China’s commitment to open-source AI.

• There has been growing speculation that AI safety concerns may force Chinese regulators to disallow ongoing release of open-source models.

• But Xi’s message was that China isn’t going to walk away from open source.

That’s likely because the global popularity of Chinese open-source tools is a once-in-a-generation opportunity to bring the international community onto China’s AI stack – particularly in the absence of cheap, open US alternatives.

• Beijing intends to press that advantage for as long as it can.

Running alongside the open-source pitch, however, was a notable elevation of regulation, ethics, and governance in Xi’s remarks.

• The speech raised deep questions about practical and existential AI risk and called for new laws and risk-monitoring systems.

• Beijing was already an early and fast mover on AI regulation, but Xi’s talk indicated that China will double down on AI safety, potentially placing stronger controls on both the release of new open-source models and the distribution of open model weights.

Then there was the announcement of the World Artificial Intelligence Cooperation Organization, formally launched on the eve of the conference last week.

• This represents Beijing’s bid to compete with the US on global AI governance and offer the Global South an alternative to Western-led bodies.

• Whether it gains traction is an open question – China’s track record on tech governance organizations is patchy at best, and the recent past is littered with abandoned Chinese-led cooperative platforms.

Zoom out from the individual announcements, though, and a bigger picture emerges: Xi believes that while AI is deeply important and transformative, it should never become so all-consuming that policymakers lose sight of what really matters.

• Technology is not an end unto itself – instead, AI should serve social development and stability.

The implications of that framing are far-reaching. Beijing is prioritizing AI’s long-term development over a sprint race to build the most advanced frontier model.

• Viewed through that lens, the tighter regulations, willingness to slow model releases, and the hard line on NVIDIA chip imports all begin to make a lot more sense.

The bottom line: The AI race won’t be decided this year – it is a decade-long play for who owns the AI stack that the developing world runs on, who sets the rules of global AI governance, and ultimately, whose industries make the best use of these tools for economic gain.

Kendra Schaefer, Partner and Head of Tech Policy Research, Trivium China

What you missed

US-China

The Trump administration imposed tariffs of between 10% and 12.5% on 60 countries, including China, alleging that they hadn’t done enough to prevent the import of products made with forced labor.

• The fact that China wasn’t singled out and that the new duties merely aim to re-establish Washington’s previous tariff rate mean Beijing is highly unlikely to retaliate.

Xi Jinping looks headed to DC in September – at least for now.

• Trump has downplayed his July 16 comments claiming that China interfered in the 2020 US election, saying: “Well, we’re going to talk to them about it. It took place a long time ago.”

• On Monday, US Secretary of State Marco Rubio said, “we anticipate that the trip is happening in September,” while China’s foreign ministry says the two sides have “kept in communication on head-of-state interactions.”

Foreign affairs

Top diplomat Wang Yi met a delegation from the European Parliament’s Committee on Foreign Affairs visiting Beijing on Tuesday.

• Wang told the delegation: “China and Europe should…refrain from politicizing economic and trade issues and overstretching the concept of security in exchanges, and work for an upward and dynamic balance of trade from a long-term perspective.”

Econ and finance

Five of China’s largest state-backed insurers – China Life, PICC, Ping An, China Pacific, and New China Life – issued separate statements expressing confidence in A-shares.

• A-share valuations have collapsed over the past month, with the CSI 300 Index down more than 9% between its June 22 peak and July 17.

Total fiscal expenditure fell 11.9% y/y in June, widening from the 3.9% decline in May – but the scale of the decline is deceiving.

• Government-managed fund expenditure – which accounts for about 30% of total spending – was the main drag in June, plunging 43.7% y/y, largely due to base effects.

Commodities

China added 72.1 GW of new solar capacity in H1, a massive 66% y/y decline.

• Installation growth is all but certain to pick up in H2 as utilities rush to meet year-end grid connection deadlines. However, the structural bottlenecks constraining growth are unlikely to be resolved anytime soon.

Business environment

The finance ministry (MoF) has announced that, starting September 1, lithium-ion batteries – a core input for new energy vehicles (NEV) – will be subject to a 2% consumption tax.

• Innovative, early-stage battery products that have yet to reach mass-scale commercialization – including sodium-ion batteries and fuel cells – will be exempt from the tax until the end of 2028.

Tech

Chinese regulators, led by the commerce ministry (MofCom), are consulting domestic AI and chip firms on tightening export controls over advanced technologies.

• Regulators are also discussing ways to prevent offshore chip firms from fabricating chips designed by Huawei, Alibaba, or ByteDance.

Net zero

The macro planner (NDRC) and energy regulator (NEA) jointly released the 15th Five-Year Plan for renewable energy.

• The plan targets an increase in annual renewables output to 6,000 TWh by 2030 – a 50% increase from 2025 and roughly 35% higher than the US’s 2025 annual power output.

Zhang Xiliang – the chief architect of China’s national carbon market (ETS) – says regulators will tighten the screws on ETS compliance over the 15th Five-Year Plan (FYP) period.

• Zhang also hinted that policymakers are considering a carbon tax for energy-intensive industries not yet covered by the ETS.

As always, it was a busy week in China.

• Thank goodness Trivium China is here to make sure you don’t miss any of the developments that matter.

• Same standoff, squeezing from opposite ends of the pipeline.

Quick note: This episode is the first of a new, more frequent style of podcast we’re rolling out – shorter, faster-turnaround conversations to react to the news as it breaks, alongside our regular weekly deep dives.

• Look for more of these in your feed soon.

On this episode, Andrew Polk sits down with Kendra Schaefer (Head of Tech Policy Research) to unpack:

• Why MOFCOM’s reported talks with Alibaba, ByteDance, and Zhipu on export controls could reshape how Chinese model weights get released

• The three priorities Beijing is trying to balance: model competitiveness, security risk, and controllability

• How Kimi K3’s release has intensified US anxiety, with Treasury Secretary Scott Bessent floating possible sanctions over model distillation

• What Xi’s WAIC speech signals about China staying committed to open source while tightening the regulatory leash

Give it a listen and let us know what you think.

Transcript

Andrew Polk: Hi, everybody. Welcome to the latest Trivium China Podcast, a proud member of the Sinica Podcast Network. I’m your host, Trivium Co-Founder, Andrew Polk, and I’m joined today once again by our Head of Tech Policy Research, Kendra Schaefer. Kendra, how are you doing?

Kendra Schaefer: I’m good. I’m good. How are you?

Andrew: Yeah, glad to have you back on so quickly, which it’s a rare treat to get such a quick turnaround. I’ll talk about this a little bit in the housekeeping piece, but just for listeners — we’re doing a little bit of experimentation with putting out content more frequently, which means some of it will be shorter. I’ll talk through some of that in more of it in just a minute. But the reason specifically that I had Kendra on was there were some big developments, even since we talked last, in, of course, the China AI space.

The big news is sort of that Chinese regulators led by MOFCOM are reportedly in talks with some of the big hyperscalers — Alibaba, ByteDance, Zhepu, about potentially tightening export controls on AI models and chips going out of China, potentially even limiting overseas transfer of training data and model weights. And so, this is like a big deal in terms of potential restrictions from China’s side, but also we’re seeing potential restrictions from the U.S. side, which we talked about two weeks ago when I talked to Kendra.

And that has even ramped up further with the release of Kimi 3, Moonshot’s latest release, with now U.S. policymakers really worried. So, there’s like this very narrowing space where the U.S. looks like it’s going to increasingly restrict the Chinese models and China’s going to restrict Chinese models from going abroad. We’re going to talk a little bit about that, and then also add the context of Xi Jinping giving a big keynote speech at the WAIC. What does that stand for, Kendra? World AI Conference?

Kendra: Yes.

Andrew: Okay. Where he kind of doubled down on China’s commitment to open-source AI. A lot of just related topics. I want to get Kendra’s quick take on while it was top of mind for folks. But of course, we’ll do this quick. Got to do the quick vibe check. How’s your vibe today, Kendra?

Kendra: Well, I’m a little under the weather, actually, but I really wanted to do this podcast. So I’m all hopped up on DayQuil and ibuprofen. So I don’t know if overmedicated is a vibe, but if so, that is my vibe.

Andrew: It’s definitely a vibe. I love it. Well, this will be a trip then. I’m looking forward to it. My vibe is excited to kind of experiment with some of this more frequent, quick-hit content. So, excited about that. We will do a little bit of quick housekeeping reminder to anyone listening: if you’ve just got this or if this was forwarded to you, Trivium is not just a podcast. We are a strategic advisory firm and we work with businesses and funds to figure out China. So, we are a strategic advisory that helps businesses and investors navigate the China policy landscape.

That includes policy in China across a range of areas, but also policy towards China out of Western capitals like DC, London, Brussels, and others. So, if you need any help on that front, please do reach out to us at hq@riviumchina.com. Otherwise, listeners, please do leave us ratings and reviews on your favorite podcast apps. It really helps expand our reach and grow our business.

All right. The other housekeeping piece quickly. So, we are going to experiment with more frequent podcasts, more frequent content. It’ll be shorter, many of them. We’ll still kind of have our weekly anchor podcast kind of talking about bigger themes, but I just want to get more Trivium voices on, talk about stuff that’s really top of mind that people may be wondering about in the news. And so please look for us in your feeds more regularly. This isn’t just going to be just suddenly you see three or four Trivian pods. In a couple of weeks, we should be in your feed on a more regular basis.

So, make sure you subscribe to the podcast on whatever platform you use. All right, let’s get into it, Kendra. So, we’ll start with the MOFCOM piece. So, The FT in particular reported that Chinese regulators, led by MOFCOM, the Ministry of Commerce, are in talks with the hyperscalers about tightening export controls on AI models and chips. We’re talking again about potential overseas transfer of training data and model weights, and apparently even some discussion of preventing offshore firms from fabricating Huawei, Alibaba, and ByteDance design chips.

This all matters because, as we’ve talked about on the pod before, the whole reason that Chinese models like DeepSeek and Moonshot’s models have gotten global traction is precisely because they’re open weight, and anyone can download them and run them locally. So, if Beijing starts clamping down on that, it’s a huge deal. Why don’t you talk us through kind of, I know you’ve got a big picture understanding of what’s going on here and also kind of the different priorities that policymakers might be balancing when they’re thinking through this stuff.

Kendra: Yeah, totally. So we don’t know exactly what MOFCOM is going to do or specifically how they’re going to approach this. I’ll come back to my hypothesis on what tools they might use maybe later in the podcast. But we do know what considerations Beijing is trying to balance right now. It’s trying to balance three considerations. First, the competitiveness of models. Second, these sort of big, scary cybersecurity questions around models. And then on top of that, controllability.

And so, it’s probably helpful to paint a little bit of a picture in terms of what the state is thinking on each of those three aspects. So in terms of competitiveness, I mean, I think most of our listeners know that there’s been hope in China for 15 years that the country could sort of develop an alternative tech stack to the U.S. tech stack that would gain international traction. But prior to LLMs, you know, the platform economy and sort of cross-border e-commerce represented China’s best hope to do that.

TikTok was kind of the first big Chinese platform that American users loved and that really got a lot of traction in the U.S. And also, you’ve got some minor stuff like Temu and Shein with a mixed reception, but still relatively popular in the United States. But TikTok, Temu and Shein face direct competition from bigger, better established, better funded U.S. competitors. Temu and Shein have to go head to head with Amazon. TikTok has to compete with Meta and X. But look at what’s happening now in AI.

It’s crazy. Companies all over the world, including all these big companies and a lot of small firms like us in the U.S., want cheap AI technology that they can control. But there’s a huge demand for that. And right now there’s really only viable Chinese supply. There are like very few competitive U.S. models that meet that demand. U.S. models meet the demand for high-quality, very secure, bleeding-edge AI. There’s no great Chinese competitor in that space. And of course, there’s a huge market for that as well.

But they don’t meet the demand for cheap, controllable AI. Right? Chinese firms do. So, this is a once-in-a-lifetime opportunity for China. Even though Chinese tech firms have a sort of fraction of the resources that U.S. firms do, because they’re really the only viable players on the field in that particular segment of the AI market right now, they’re getting a ton of traction. My personal opinion is this isn’t even actually a U.S.-China issue. It’s just a supply and demand problem. And then China has this sort of additional incentive of, you know, it’s not just about can our firms gain market share in the U.S. or EU.

You know, Chinese tools also have actually probably much more long term durable prospects in the global south. Startups and government institutions and companies in countries where the startups don’t have that much money, where you know they’re scrappy, where there’s a lot of R&D firms that don’t have funding, of course, they’re going to want to use a model that’s cheaper, that’s easier to access, there’s lower barriers to entry, there’s not so much sort of subscription control or geofencing or any of that sort of stuff.

So, as you said if Beijing were to ban the export of Chinese open source models on a permanent basis that would essentially amount to ceding the most momentum China has ever gained due to risk aversion. So it would be a huge deal. So that’s one piece of the puzzle. And Beijing is certainly weighing that piece.

Andrew: That’s the competitiveness piece I’m talking about.

Kendra: That’s the competitiveness piece, exactly.

Andrew: So you said competitiveness, security, controllability. Okay, talk us through security then.

Kendra: We’re trying to do a faster pod here, so I’ll try to keep these shorter. But for safety...

Andrew: No, no, people will love it. It doesn’t have to be short.

Kendra: Okay. So, I think the safety concerns are becoming pretty clear to everyone.

I don’t know if you also saw the news just yesterday that this OpenAI was running some tests on new agentic system, and it lowered the guardrails on the system. My God, it was so terrifying. They lowered the guardrails on the system and basically told this tool to like do its worst, essentially, just to see what it could do. And it broke out of its box and went to Hugging Face, launched a cyberattack against the Hugging Face website, totally autonomously, right?

Andrew: Insane.

Kendra: Anyway, there was a big kerfuffle over this. OpenAI and Hugging Face have since cooperated to sort out the issue. But obviously, every week there’s some other terrifying headline about the cybersecurity risks of these models. You know, they’re doing things that humans didn’t expect them to do, etc. So, these safety concerns are very, very real. And open source tools have an even bigger safety problem than closed source tools, which is that if it’s a closed source tool and it is discovered to be hackable or breakable or the guardrails can come off or there’s something seriously wrong with it, the company that manages it can pull it off the market today.

But if you release an open source tool onto the market, you release open weights onto the market, it’s gone. It’s out of your control. That’s the end. And so, the risks for China to be the source of such a tool, let’s say a Chinese company releases a tool like that onto the domestic market. And suddenly there’s a bunch of attacks against Chinese critical infrastructure, even by domestic actors, even, right? Or by foreign state-sponsored actors, that is a direct risk for China, built by China to China.

But similar problem, even if those tools aren’t even used to attack China. I mean, the amount of geopolitical tension that would arise if a bad actor took a Chinese tool and then was using that to attack other countries and China was seen as this irresponsible global actor after they have, you know, talked up safety, etc. And then on top of all of that, It’s like Chinese regulators are some of the most risk-averse humans on the planet.

Andrew: Yeah. I was just quickly going to say, are you saying it’s like some sort of disruptive element escaped to the borders of China and caused a global issue? Have we seen something like that recently? That everyone would blame China for it? Yeah, I think we have. Yeah, well, so I’m sure. Yeah, absolutely. It caused a huge issue. But before you go to the controllability piece, just quickly, our team noted that Hugging Face use open source Chinese models to do the security postmortem to try to figure out what went wrong and fix it. Anything to take away from that?

Kendra: Yeah, that’s a whole nother rabbit hole. It’s like essentially what happened was Hugging Face tried to use in their own blog post when they were reporting on the incident, they essentially said we tried to use commercially available U.S. tools. Presumably, they’re talking about, you know, Anthropics tools or OpenAI’s tools. They tried to use the sort of leading edge commercial tools, and the guardrails on those tools couldn’t distinguish between a company trying to defend itself from a cyberattack and a cyberattacker.

And so those guardrails were triggered and they prevented them from using that tool. And so what happened was Hugging Face reached instead for Zhipu’s GLM 5.2 to do it because the guardrails were not preventative. So, I mean, need I enumerate all the different ways in which that’s scary? It’s also actually a really good argument for, again, China doesn’t really have anything to do with this argument. It’s just like people need, they want controllability. The market wants controllability over their tools, partially for this reason.

Partially because they don’t want, there are situations in which it doesn’t actually make sense or it’s actually unsafe or there’s a security risk to having a middleman tell you what you can and can’t do with the tools that you have access to. So, that’s a real tension, right? It’s the biggest tension, obviously, in the AI ecosystem at the moment.

Andrew: Thanks for that. Sorry for the quick detour, but okay, now let’s talk about the controllability piece, which you were about to go to.

Kendra: Yeah, so you and I have been talking about this for a while. A couple of months ago, we talked about the Meta-Manus deal. And around that time, we flagged for our clients that the Meta-Manus deal caused a shift in the way that China thinks about technology, right? Chinese regulators at the time hadn’t really had to consider the possibility that now that China’s making some bleeding-edge technology, that China needs the regulatory tools to control the outflow of that technology, right? That’s what the Meta-Manus deal sort of revealed to regulators.

And we flagged for clients at the time, this is actually going to be a major theme over the next couple of years, is that regulators are going to start giving themselves opportunities and options for saying no to specific deals, to saying no to a lot of different technology exports, to saying no to the transfer of our IP to foreign companies. And so, we expected to see that as well. And so now you have this interesting situation where Chinese models are starting to, with the release of Kimi, for example, walk up closer to the frontier.

I still think they’re relatively far behind the leading edge, but walk up close enough to the frontier that it starts to become a question of, well, these models are open weight. They don’t have a lot of restrictions on their use. There’s no framework for restrictions on their use. Does that mean a foreign company can just take those models and modify… what technology can be squeezed out of them that China doesn’t want to lose? Regulators don’t really know. That’s not really something they have considered before.

So, I think all three of those things are weighing on the state, are weighing on top leaders, and are weighing on MOFCOM as they consider what to do about open source Chinese models.

Andrew: All right. Well, thanks for laying that out. We’ll come back to how that may be impacting what they do next. But with that sort of context of where are the key priorities that policymakers are trying to balance, let’s now bring in Xi Jinping’s speech at the WAIC conference. It’s a lot to unpack. The headline really is that Xi Jinping recommitted or reiterated China’s commitment to open source, even though there’s been this reporting that China might be considering kind of putting on some restrictions. So, why don’t you just talk to us about the key themes that you saw from Xi Jinping and how this plays into what you just said about policymakers competing priorities.

Kendra: I think Xi’s speech was a perfect summary of all of the issues that China is currently dealing with that I just outlined, plus a bunch of stuff about international AI cooperation also at the end. But, you know, he basically said both, right? He said both things. He painted AI as a new industrial revolution, blah, blah, blah. We’ve heard that from… kind of most policymakers agree on that point. But he also really underscored some of the major risks that AI was presenting for China and for humanity. I’m actually going to read those questions here because I think they’re kind of interesting. He said, “When machines begin to think, how should humans coexist with them? When algorithms take part in decision-making, how can safety be guaranteed? When technology challenges ethics, how can governance keep pace? And when the divide keeps widening, how can inclusive benefits be achieved?”

And so, then he gets into his kind of proposals about how China may answer those questions. And his first proposal was that China should stay committed to openness. And again, I’m going to read out what he said because the devil’s in the details. He said, “Artificial intelligence is a new engine of global economic growth and an accelerator of the shift from old to new drivers of growth. It’s moving from the digital world into the physical world.”

In other words, robots are starting to be powered by AI. And he says, “We should seize this rare historic opportunity, encourage open source development and cooperative sharing,” there’s the money line, “and comprehensively promote AI innovation,” blah, blah, blah. So, he says straight up that the number one principle, right, and often these principles get listed in order of importance and priority. Right up front, he says, “We’re going to stay committed to open source and cooperative sharing.”

In other words, we’re reading that as China has no intention of taking a step back from open source technology. We still think that’s a great tool, and we intend to share Chinese open source models with the world, with other countries as well. But then he comes in from the other side. He pivots to addressing the safety risks. He said, “Second, we should strengthen risk awareness to ensure that AI remains safe and controllable. Artificial intelligence should be a trustworthy tool for humanity.

We must attach great importance to the various inherent and derivative risks that AI gives rise to and work to build systems of laws and regulations, technical monitoring, risk early warning, and emergency response so as to establish a firm safety baseline, guard against misuse and malicious use, and ensure that AI always remains under human control.” And then he kind of gets into some global cooperation stuff on how China will partner with the global south, etc. So, what matters there is they say they’re going to keep supporting open source, but they’re also going to put much more stringent safeguards in place relative to AI technologies.

Later in the speech, he reiterates that, and he says, “We’re going to continuously improve relevant laws and regulations, policies and institutions, norms and ethical guidelines to ensure that AI is safe, reliable and controllable so that this,” love this part, “so that this galloping steed of artificial intelligence runs both fast and steady.” I know. And then he goes on to say, “The more rapidly AI technology advances, the more firmly its direction towards goodness and the benefit of humanity must be anchored, the more precisely the measure of regulation and governance must be calibrated.” Blah, blah, blah.

So, all of this is centered around a clear message that there’s going to be a very sharp uptick in Chinese domestic regulation on AI and China’s efforts to control, likely control the pace of model releases. There’s no question in our mind that Beijing is seeking to do this. So, the question just becomes, right? the question just becomes, what is the tool they use to do it? They’re obviously going to do it. They’re obviously going to try to control probably some kind of technology exports or the release of models in some way or, or, or…

But the question is, what level of ban is that? How much regulatory involvement will that have? You know, we have some clues, but we’re not exactly sure yet.

Andrew: Well, can you talk us through the clues? Like what forms you think they might take?

Kendra: I think that when interests conflict in technology regulation, China almost always tries to kind of thread the needle. And I think, this is just my guess, I think this is just my hypothesis, but I think the best way to thread the needle in this case would be to sort of create a lag between the availability of an open model and the release of the weights. So, in other words, maybe you can access a model online through a provider right away. If you want to ping a hosted version of a new frontier model now or a new open model now, great.

But if you want to download the weights, the model has to go through some kind of very stringent security assessment or some kind of licensing or approval process before those weights actually become available online. And so that’s the really interesting piece. You said in the beginning that, and this is what we’re watching really closely, and I think I’m on the edge of my seat on this one, you know, there have been multiple reports that MOFCOM is leading the discussions on exactly how that might happen.

And if MOFCOM is leading the discussions, that tells us quite a lot because MOFCOM only has a couple of tools available to them. So, if they are the regulator that is going to move forward with controlling model releases, that tells us those releases may be controlled with the tools that MOFCOM already has. Most notably, MOFCOM handles China’s export control regime, right? And they do that through the same export control regime they’re using for rare earths. They do that through this regulation called Regulations on Export Control of Dual Use Items, which basically creates this centralized dual-use control list.

When MOFCOM puts something on that list, exporters have to apply for a license in order to export it, just like what’s happening with rare earths now. Something goes on the list, you’ve got to go to MOFCOM, get a license to export it. And there’s a few different kinds of licenses you can get. You can get a license to send it to just one company. That wouldn’t make a ton of sense here. You can get a license to provide it to a larger audience, etc. But that list has never been used to control something like this.

So, it would actually be super interesting if MOFCOM decides to use the export control regime because it opens a question, is posting model weights online an export? And I guess it could be considered an export under this regime. They also have this other tool called the Catalog of Technologies Prohibited or Restricted from Export. Basically, that’s not a dual-use issue. That catalog is managed by MOFCOM and the Ministry of Science and Technology together. I think, if I recall correctly, it’s under the foreign trade law, not under the export control regime.

And this is the list that China used to control or to kind of prevent the unapproved outbound transfer of TikTok under the TikTok divestiture kerfuffle. And that list too, if I recall correctly, has two sections. It’s got a prohibited section, like this is definitely banned from export under any circumstances, and then a restricted section, like you got to get a license if you’re going to export this. So, functionally speaking, MOFCOM has a couple of lists that they could use to stick model weights on.

Some of the language related to some of these lists, or at least the second one, could already be considered to apply to model weights. But I think they could just go ahead and make it very explicit, add model weights to maybe the export control list. And what that would do would be to give China a very granular pathway for controlling releases on a model-by-model basis. It’s a bit clunky, and I actually think it’s a terrible pathway because there are a hundred ways it could go wrong.

Andrew: Yeah. So I want to pick up on two last pieces then. One is what you see as potential unintended consequences. How can things go wrong? And then I want to circle back to the U.S., what the U.S.

is doing because we’re talking about the Chinese restrictions as China trying to restrict technology outflow and U.S. is trying to restrict Chinese technology inflow. So we’ll get to that piece in a second. But first, how could things go…? No, surely nothing could go wrong here. Nothing could go wrong.

Kendra: Sucks to be a Chinese frontier lab right now. Everybody’s gunning for you. So the first thing that’s on the top of mind is that MOFCOM doesn’t exactly have a history of granting export approvals in a timely manner. I mean, they are just terrible at granting export approvals under this particular regime in any sort of streamlined way. I mean, I think a lot of our listeners are probably watching the whole rare earths saga, even when top leadership, when MOFCOM itself, when the receiving party, when the selling party, in other words, even when every single party involved in a licensed transaction wanted that to happen smoothly, it did not.

That regime was beset by delays. That is one thing when you’re talking about a commodity. It is a very different thing when you are talking about a market that is moving so quickly that every two months, the entire shape of the market has changed. And it’s actually not just MOFCOM. I mean, every single export control regime I can think of, even those not controlled by MOFCOM, like cross-border data exports approved by the CAC was a snarl for two and a half years to the point where EU regulators were bringing it up with Xi Jinping at meetings saying, “Hey, we can’t get data, we can’t get access to data.”

So, I think that’s a potential where, in fact, I would almost guarantee it that if MOFCOM steps into the room, model releases will not be approved rapidly and there is going to be some kind of problem with the speed of exports. So that’s issue number one. The second problem is that MOFCOM doesn’t know anything about safe AI. In other words, at what point does MOFCOM issue an approval on what basis? It has to be the basis of some other kind of security test run by somebody else, right?

And those security tests, what constitutes safe artificial intelligence is still very much an open question globally. I mean, the U.S. is working with our… you know, we’re working with our best tech company as USG is working with, you know, sort of top AI companies to figure that out. And they’re going to be figuring that out for a couple of years. And so, if you put a bureaucrat in the room and you say, “This is exactly what happened with data exports,” you say, “Only allow people to export safe data, but then you didn’t tell them what safe data is.” It’s exactly what happened with data exports.

And regulators just stopped approving anything because they didn’t have a good definition and they couldn’t save their own butts. They couldn’t justify why they allowed something. Nobody wanted to be the one that rubber-stamped a major security risk.

Andrew: Yes.

Kendra: So I think that is the second problem. The third problem is I think China could definitely get too wrapped up in its own narrative about technology outflow too soon. We’ve already seen a couple of examples where the state made an ideological decision, ideological-ish bet that did not make sense from a purely tech competitive perspective. And here I’m talking about Beijing refusing to immediately allow the purchase of as many NVIDIA chips as its companies wanted as soon as the U.S. approved those exports, approved those sales. China sided on delayed for a while and focused primarily on self-sufficiency and said, “Look, we’re going to allow a couple of these.”

And meanwhile, a lot of the AI firms are going, “Please, please, please, we need as many chips as we can get. We’ll get them from anywhere. We’ll take them from anywhere.” So, it wouldn’t be unusual or strange to me if the state essentially talked itself into this idea where, look, we can’t allow too much technology outflow. We can allow foreign companies to access Chinese hosted models, but we can’t just freely and openly allow model weights to be distributed anywhere willy-nilly. We’re going to keep model weights domestic. We’re only going to da, da, da, da, da, which would absolutely destroy the one market advantage that China really has, which is that you can get the model weights…

I think China could just make a bad decision about over-securitization and talk itself into something dumb. And then finally, I think this is true for probably the U.S. and China. There could definitely be some kind of black swan event, where some, you know, we’re already seeing hints of that, just like this sort of OpenAI issue. Luckily, nothing terrible has happened yet. But there could definitely be some major security intrusion or breach or event that sends both China and the U.S. scuttling back into a crab shell, going, “Absolutely not, lock it all down. Nothing is released without approval.” And China being particularly risk averse, I imagine that we could see some serious sort of knee jerk security backlash to something like that.

Those are the four things that I think could derail any efforts by China to institute reasonable, rational safeguards, which are desperately needed, by the way, you know, on open-source model releases.

Andrew: Yeah. Like I was alluding to with the pandemic, don’t want to get into the hole; did China do this on purpose or not? I don’t want to go down those rabbit holes. But if there were an accident and some open-source model released from China started causing problems in the cybersecurity world globally, there would, of course, be an assumption that the CCP did it on purpose, right? And then that, I mean, you talked about geopolitical tension, I mean, that’s a war, I think, you know, waiting to happen.

Kendra: Yeah, 100 percent, 100 percent. I mean, China will avoid that at all costs. So, I don’t think it, but I’ve heard some of this chatter on X or whatever that people think China’s in control of every aspect of this. I mean, we are watching regulators in real time respond to like new catastrophes or new sort of angles on this question. Every week, there’s some other thing nobody thought of that has to be addressed and that regulators aren’t prepared for.

Andrew: Everyone’s flying or building the airplane while they’re flying it. Everyone everywhere, right?

Kendra: Exactly. Yeah.

Andrew: And the companies are trying to stay ahead of the regulators as is happening in the U.S., that’s a whole nother podcast. But let’s now turn to what’s happening in the U.S. So, already the U.S. was making a bunch of noise about potentially restricting access. I think we touched on it maybe in our last pod, potentially restricting access to Chinese models because a bunch of companies in the U.S. are increasingly using these models because of everything you said, because they’re cheaper. And a lot of times you just need kind of bread-and-butter type AI usages, right? You don’t need the bleeding edge models. That was before the release of Kimi 3, which caused a lot of waves.

We won’t go into the details, but basically it was much closer to the frontier of models that many people thought China was. And now, I mean, even today, Scott Bessent, maybe it was yesterday, the U.S. Treasury Secretary saying, “Oh, you know, we think there was a ton of distillation to train these models. We might sanction or entity list Chinese companies that are using mass distillation. So, talk about what you think of the U.S. discussion around restrictions on Chinese models. And then we’ll end by just talking about how there’s very clearly a pincer on both sides, China trying to restrict outbound access, U.S. trying to restrict inbound access, and what that might mean for the future.

Kendra: I mean, I think there’s a lot of angst, but I don’t think there’s a real strategy. And what Bessent said was, we’re going to take a look at whether or not, I mean, how I heard that was, we’re going to take a look at whether or not… I mean, how I heard that was we’re going to take a look at whether or not we can paint distillation as a major IP theft problem and go after Chinese models on that basis, because we’re not sure what basis to go after them on. We need some reason to go after Chinese models. We’re just not sure what that is. I mean, look, five years ago, D.C. was thinking about China’s digital technology space the same way that we think about Russia’s digital technology space.

And it’s helpful to look back on where people’s brains were at five years ago. The idea was like if China developed a digital technology that people liked, it was a massive fluke. It was an accident. And if you wanted to kill it, all you had to do...

Andrew: Or it must have cheated.

Kendra: Or they must have cheated, right? And so, China can’t innovate, they only steal and cheat. And so, all you have to do is sort of kill the international expansion of that one product or ban that one product and that one thing dies. And in some case, we’re always kind of fighting the last war. Against most countries, that has worked just fine. I mean, in 2024, I don’t know if you remember this, there was this Russian cybersecurity software made by like Kaspersky, which Russian cybersecurity.

Russia’s good at cybersecurity. And so, they created this really good software. It got some traction in the U.S. Commerce came in and said, “Absolutely not. We’re banning Kaspersky products in the United States due to national security reasons.” And not a peep before or since from any major Russian producer of software that the United States is clamoring to have. And that has been true for most countries, for most of Internet history. And it is no longer true relative to China.

When TikTok came out, we said it. We said this is not going to be the last digital technology that China develops that people want to use. They have a very strong digital technology ecosystem now. And so, we don’t have anything else except ban it, sanction it, whack-a-mole it, you know, kill it. Let’s find some way to kill it. And I don’t think that strategy is going to be effective here. But more than that, I think the problem is that without a U.S. alternative, again, it’s just a supply and demand problem. It’s just a supply and demand problem. We said this on the last podcast. I do not care at all if it’s a Chinese model or not.

I do not care at all. When we are deciding what model to use, we want one that’s cheap and we want one that’s controllable and good enough. That’s it.

Andrew: And sorry to interrupt you, but maybe also worth touching quickly, like it’s not that easy to ban these models. Like, once they’re on the Internet, that’s the whole point of open source, right? Like, if we were going to try to ban it, what do you think would be the…?

Kendra: I mean, there’s been a lot of very controversial conversation on that in the last couple of days. There’s been some arguments that all the U.S. needs to do is sort of create enough risk, create enough perceived risk. Like they don’t even have to ban it. They just have to create enough perceived risks for corporations, right? For major corporations to touch the models that they will sort of back away from them and won’t use them at all. That they don’t necessarily have to sanction or they have to take the worst step, but just try to create some kind of churn.

I actually don’t know if that will work considering this, again, just considering the supply and demand problem. You have a massive consumer base that desperately wants this technology and, you know, not a ton of alternatives. So, yeah, I mean, I think that’s really the critical issue.

Andrew: All right. We meant to do a short one, but there’s just so much to unpack here that I think… I mean, I’m glad we went a little bit longer and I’m sure listeners will have enjoyed it. I’ve certainly learned a lot. But last piece, like, I don’t know, we talked about, you know, this pincer, this like short or like narrowing space for Chinese models. It seems, at least in the U.S., used by U.S. users, if not more globally. Any thoughts on how to think about that or what that might portend to going forward to wrap this up?

Kendra: I mean, the last thing I’ll say just as a summary is I think that what is going to define the space of AI development going forward is the gap between Chinese regulations and how they choose to control their own model releases and then how the United States decides to approach disincentivizing U.S. firms from using Chinese models. But I also just I think I would be remiss if I didn’t say this. One lesson I’ve learned during my career trying to analyze technology and the direction technology is going to go is that very often the shape of our conversation turns out to be wrong.

That the actual issues that we’re dealing with turn out to be like something comes out of left field that we didn’t consider that we go, “Oh, that changes the entire shape of the conversation.” I’ll give you one example before we sign off. There was a massive panic over the 5G race. Right? But for the average consumer, the move from 4G to 5G didn’t end up being that impactful at all. And actually, where a lot of the impact was, was on the sort of enterprise side of 5G. But we talked a lot about what it was going to mean for China to have access to 5G technology before the United States and what it would mean for their ecosystem and what it would mean for ICDs.

And we worried that China had more base stations than we did and their rural population had more access to 5G than we did. I was giving a talk at a classroom a couple of weeks ago, and it was a college classroom, and I asked people to put their hands up if they knew what the 5G race was. You know how many people put their hands up? Zero. Zero people. Zero people under the age of 22 had ever even heard of that. And it was the only thing anybody was talking about for a long time. Other considerations entered that. We defined that conversation as a race between the U.S. and China on a couple of really narrow points.

None of those things ended up being that big of a deal. And then other considerations actually did end up being a really big deal that nobody was talking about. I suspect we will find something similar here. For example, I think there’s a possibility that the shape of this argument changes in a year. Here’s one hypothesis. The bleeding edge of artificial intelligence is going to keep moving forward for the foreseeable future. But most consumers probably aren’t going to need a bleeding-edge model starting quite soon.

What do we do with AI at our company? We do document processing en masse. That doesn’t require a very smart model. It requires a pretty good model. So, imagine a world a year and a half from now, I’m not saying this is definitely going to happen, but imagine a world a year and a half from now in which it doesn’t actually matter if a Chinese regulator takes six months to release an open-weight model because people don’t switch models anymore every month.

The frontier is moving forward, but most people just pick a model and stick with it for a couple of years because it does what they need it to do and that’s just fine. And so, none of the concerns about who’s approving faster or regulatory capture, you know, or where the frontier is, that becomes a cybersecurity and critical infrastructure problem and stays in that space. And the actual competitive consumer landscape for how companies are using AI is about cost, is about whatever stack you picked up two years ago, becomes a little bit more, kind of becomes boring, essentially.

Andrew: Totally. It does make sense.

Kendra: This whole conversation could change in two years, right? And it could just be about something completely different. So I’m just aware of that as we move ahead.

Andrew: I’m like so tempted to press on that point, because I think it’s a great point. And I think we could expound on that. But we’ll save it for another pod. I’m excited to get you on more regularly for some of these quick reaction discussions. The listeners need more Kendra Schaefer in their life. That’s like my constant feedback that I’m getting.

Kendra: I mean, if only I could shut up faster, we could get more quick reactions.

Andrew: This has been amazing. Amazing, amazing. So, thank you for the knowledge bombs. And yeah, we’ll look forward to seeing you again soon. Hope you feel better.

Kendra: All right. Thanks. Talk to you later.

Andrew: Thanks. Bye, everybody.

This week on Sinica, a rare treat: an in-person recording from Beijing with two dear friends who happen to be two of the very best in the business on technology and China — Samm Sacks and Paul Triolo, fresh off the exhibition floor of the World Artificial Intelligence Conference in Shanghai. We dig into Xi Jinping’s first in-person WAIC appearance and his most extensive statement on AI to date, the launch of the World AI Cooperation Organization, Moonshot’s release of Kimi K3, the Trump administration’s reported push to shut Chinese open-weight models out of the U.S. market, the coming age of agents, the ambiguous translations problem of ānquán, and what to expect from the first U.S.-China AI dialogue in September.

8:51 – The view from the floor: heat, humidity, robot boxing grandmas, WeChat-gated free water, and the “AI+” vibe — why WAIC 2026 felt less like an AI conference than a sector-by-sector snapshot of China’s entire economy being supercharged with AI, with attendance swelling to some 200,000 tickets

16:32 – Why Xi showed up: what the leader’s first in-person WAIC appearance and his most extensive AI statement to date signal, and why domestic drivers matter as much as geopolitics

18:01 – Chapter and verse: which phrases from the speech will be put to work in the system — “secure and orderly development” and the governance of agents, and Xi’s strikingly extensive language on AI safety after China was frozen out of the Paris process

22:26 – The ānquán problem: one word meaning both “safety” and “security,” the three buckets of AI risk, and how China’s safety community has moved from bias and deepfakes toward CBRN and loss-of-control concerns — Black Mirror versus Star Trek

28:14 – Shanghai’s baby: how WAIC’s ownership structure differs from the CAC-run World Internet Conference in Wuzhen, Chen Jining’s very visible host duties, and whether the center of gravity in AI policy is shifting to the Yangtze River Delta

30:05 – WAICO: what the new World AI Cooperation Organization with its 29 founding members is actually for, Xi’s concrete deliverables for the Global South — 5,000 AI training slots, regional cooperation centers, the MAZU early-warning system — and healthy skepticism about follow-through

34:39 – Kimi K3: what’s technically significant in Moonshot’s big new model, why it’s the fourth arguably frontier-class Chinese release in a single month, the two-way traffic in distillation accusations, and what it all says about the state of the frontier gap four years into export controls

40:33 – Washington reacts: the reported menu of options for shutting Chinese open-weight models out of the U.S. — entity listings, a draft executive order, supply-chain security authorities — and why none of the tools actually fit the problem

49:36 – Strange bedfellows: David Sacks versus the “closed lab duopoly,” the FUD strategy, why some 80% of Andreessen Horowitz portfolio companies reportedly run on Chinese open models, and how gating U.S. frontier models while Chinese weights flow freely supercharges the AI sovereignty argument worldwide

54:55 – The model is infrastructure, the agent is the product: the ByteDance–ZTE agentic phone, the CAC’s new initiative on agent trust and interoperability, and why agents fused into operating systems upend both super-app walled gardens and China’s data protection regime

1:02:27 – An exegesis of kěkòng: the many meanings of “controllable,” the long history of ānquán kěkòng in Chinese tech policy, and the unanswered question of who — CAC, NDRC, or somebody new — actually owns AI safety in either system

1:08:54 – The road to September: what to expect from the first U.S.-China AI dialogue, why Mythos tops the Chinese grievance list, the securitization feedback loop that starves trust-and-safety advocates of resources on both sides, and why recursive self-improvement makes this feel like a last, best chance

Paying It Forward

Paul nominates Tony Peng, whose Substack RecodeChinaAI offers sharp, well-written analysis of the application side of China’s AI industry — part of an impressive new generation of independent China tech writers.

Samm gives a shout-out to Professor Zhu Yue of Tongji University Law School, published in Science and doing pioneering work at the intersection of disability law and AI law.

Recommendations

Samm: The Land and Its People by David Sedaris — laugh-out-loud funny, especially the “Enough is Enough” chapter; Transcription by Ben Lerner, a perfect small novel about fathers, sons, memory, and technology as enabler or disabler of connection; and Didion and Babitz, on Joan Didion and Eve Babitz and the 1970s California rock scene.

Paul: The Party’s Interests Come First by Joseph Torigian — dense but beautifully written, and essential for understanding the current Chinese leadership.

Kaiser: A fiction-only summer! Stoner by John Williams, a small life told most grandly in some of the most beautiful sentence-level writing anywhere; Gilead by Marilynne Robinson, an epistolary novel dense with distilled wisdom from a dying Iowa minister; and Wang Xiaobo’s The Golden Age (黄金时代) in Yan Yan’s excellent new translation — bawdy, hilarious, and super Beijing-y despite its Cultural Revolution setting.

Transcript (courtesy of the fantastic CadreScripts) further down the page. Image by Keya Zhou. Listen in the embedded player above!

This week on Sinica, a rare treat: an in-person recording from Beijing with two dear friends who happen to be two of the very best in the business on technology and China — Samm Sacks and Paul Triolo, fresh off the exhibition floor of the World Artificial Intelligence Conference in Shanghai. We dig into Xi Jinping’s first in-person WAIC appearance and his most extensive statement on AI to date, the launch of the World AI Cooperation Organization, Moonshot’s release of Kimi K3, the Trump administration’s reported push to shut Chinese open-weight models out of the U.S. market, the coming age of agents, the untranslatable problem of ānquán, and what to expect from the first U.S.-China AI dialogue in September.

8:51 – The view from the floor: heat, humidity, robot boxing grandmas, WeChat-gated free water, and the “AI+” vibe — why WAIC 2026 felt less like an AI conference than a sector-by-sector snapshot of China’s entire economy being supercharged with AI, with attendance swelling to some 200,000 tickets

16:32 – Why Xi showed up: what the leader’s first in-person WAIC appearance and his most extensive AI statement to date signal, and why domestic drivers matter as much as geopolitics

18:01 – Chapter and verse: which phrases from the speech will be put to work in the system — “secure and orderly development” and the governance of agents, and Xi’s strikingly extensive language on AI safety after China was frozen out of the Paris process

22:26 – The ānquán problem: one word meaning both “safety” and “security,” the three buckets of AI risk, and how China’s safety community has moved from bias and deepfakes toward CBRN and loss-of-control concerns — Black Mirror versus Star Trek

28:14 – Shanghai’s baby: how WAIC’s ownership structure differs from the CAC-run World Internet Conference in Wuzhen, Chen Jining’s very visible host duties, and whether the center of gravity in AI policy is shifting to the Yangtze River Delta

30:05 – WAICO: what the new World AI Cooperation Organization with its 29 founding members is actually for, Xi’s concrete deliverables for the Global South — 5,000 AI training slots, regional cooperation centers, the MAZU early-warning system — and healthy skepticism about follow-through

34:39 – Kimi K3: what’s technically significant in Moonshot’s big new model, why it’s the fourth arguably frontier-class Chinese release in a single month, the two-way traffic in distillation accusations, and what it all says about the state of the frontier gap four years into export controls

40:33 – Washington reacts: the reported menu of options for shutting Chinese open-weight models out of the U.S. — entity listings, a draft executive order, supply-chain security authorities — and why none of the tools actually fit the problem

49:36 – Strange bedfellows: David Sacks versus the “closed lab duopoly,” the FUD strategy, why some 80% of Andreessen Horowitz portfolio companies reportedly run on Chinese open models, and how gating U.S. frontier models while Chinese weights flow freely supercharges the AI sovereignty argument worldwide

54:55 – The model is infrastructure, the agent is the product: the ByteDance–ZTE agentic phone, the CAC’s new initiative on agent trust and interoperability, and why agents fused into operating systems upend both super-app walled gardens and China’s data protection regime

1:02:27 – An exegesis of kěkòng: the many meanings of “controllable,” the long history of ānquán kěkòng in Chinese tech policy, and the unanswered question of who — CAC, NDRC, or somebody new — actually owns AI safety in either system

1:08:54 – The road to September: what to expect from the first U.S.-China AI dialogue, why Mythos tops the Chinese grievance list, the securitization feedback loop that starves trust-and-safety advocates of resources on both sides, and why recursive self-improvement makes this feel like a last, best chance

Paying It Forward

Paul nominates Tony Peng, whose Substack RecodeChinaAI offers sharp, well-written analysis of the application side of China’s AI industry — part of an impressive new generation of independent China tech writers.

Samm gives a shout-out to Professor Zhu Yue of Tongji University Law School, published in Science and doing pioneering work at the intersection of disability law and AI law.

Recommendations

Samm: The Land and Its People by David Sedaris — laugh-out-loud funny, especially the “Enough is Enough” chapter; Transcription by Ben Lerner, a perfect small novel about fathers, sons, memory, and technology as enabler or disabler of connection; and Didion and Babitz, on Joan Didion and Eve Babitz and the 1970s California rock scene.

Paul: The Party’s Interests Come First by Joseph Torigian — dense but beautifully written, and essential for understanding the current Chinese leadership.

Kaiser: A fiction-only summer! Stoner by John Williams, a small life told most grandly in some of the most beautiful sentence-level writing anywhere; Gilead by Marilynne Robinson, an epistolary novel dense with distilled wisdom from a dying Iowa minister; and Wang Xiaobo’s The Golden Age (黄金时代) in Yan Yan’s excellent new translation — bawdy, hilarious, and super Beijing-y despite its Cultural Revolution setting.

Transcript

Kaiser Kuo: Welcome to the Sinica Podcast, a weekly discussion of current affairs in China. In this program, we will get books, ideas, new research, intellectual currents, and cultural trends in China. Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China.

I’m Kaiser Kuo, coming to you this week from Beijing. As always, a quick reminder before we begin, Sinica is powered by your subscriptions. If you like the work that I’m doing with this show and with the writers and podcasts in the Sinica Network, the very best way to support my work is to head over to sinicapodcast.com and become a paying subscriber.

Subscribers get the complete show transcript, essays and reviews, the full range of offerings from our network, and more importantly, the warm glow of knowing you’re keeping independent, non-hysterical conversations about China alive. So, do it. You will feel great. So, I’ve been really looking forward to this show.

This week I am joined not across 12 time zones, but in person, in the actual flesh, sort of around an actual table — Actually spread out a little more than that — But right here in Beijing in the same room by two people who are not only among the very best in the business when it comes to technology and China, but whom I’m lucky enough to count as genuinely good friends, Samm Sacks and Paul Triolo. Both have been on the show multiple times, but this is still an absolute treat, and the timing could hardly be better.

They have just come up on a plane in one case, on a high-speed rail from another, from Shanghai, where they attended the World Artificial Intelligence Conference, WAIC. Fresh off the exhibition floor, lanyards barely off, opinions fully formed, but minds, of course, still very much open. Folks, it just doesn’t get any better than this. And there is a lot to talk about.

The headline out of Shanghai, of course, was that Xi Jinping himself made his first in-person appearance at WAIC and delivered what is, by some distance, the most extensive public statement on artificial intelligence from him to date. A few things jumped out at me. He opened remarkably, I thought, by actually crediting the United States as the birthplace of the field, Dartmouth University, the summer of 1956. That is not nothing, given the state of the relationship. He announced that the World AI Cooperation Organization, confusingly also WAICO, I think we’re pronouncing it WAICO, I don’t know, is now a reality launched in Shanghai with 29 founding member states alongside some concrete offers to the global south, which I thought were very important as well.

On open source, Xi Jinping said precisely one line that China should encourage open source, which, as our good friend, Matt Sheehan, notes in his excellent annotated read of the speech, I will link to that on the podcast page, restates the status quo rather than staking out anything new. And then there was the closing, which has been rattling around in my head ever since, a classical injunction to adapt as circumstances change followed by a call to make governance ever more precise and to “constantly refine measures to forestall loss of control.” We’ll be looking more closely at that speech and at that phrase in the conversation that follows. One more piece of news from the week deserves mention.

That, of course, is that Moonshot AI released Kimi K3, a big, big, big mixture of experts model with a million-token context window that by Moonshot’s own benchmarks approaches the leading American frontier models on a number of coding and agentic tasks without beating them across the board. And we’ll dig into what all that means. Anyway, with a full open weights promised by the 27th of this month, so around the time you’ll be hearing this. I’m going to leave the gap has closed, hyperventilating to other people, because that’s what the rest of the damn internet is for.

But this is a genuinely notable release, I’ll say that much. We will dig into what it does and doesn’t tell us and look a bit at the response that the Trump administration is now weighing, the different responses, possibly trying to shut Chinese open-weight models out of the U.S. market entirely. Let me introduce my guests properly, though most of you will need no introductions to either of them.

Samm Sacks was until recently a senior fellow at Yale Law School’s Paul Tsai China Center and at New America. And she is now, drumroll please, non-resident senior fellow at the ACF, the Center for America, China, and the Future of Global Affairs at Johns Hopkins School of Advanced International Studies, SAIS, with, of course, friend of the pod, Jessica Chen Weiss, who is just, you know, my lodestar.

Simply one of the sharpest people anywhere on China’s cyber, on data, on AI regulatory architecture, on cross-border data flows, and on the politics of what she’s called managed interdependence. She is at work on a book about data and trust in an age of strategic rivalry, and she has been a friend of mine and of the pod for many, many, many years.

Welcome back, Samm. Great to see you.

Samm Sacks: It is so nice to see you in Beijing, Kaiser. We’ve come a long way from my parents’ porch in Chapel Hill, North Carolina.

Kaiser: Indeed, we have. Indeed. Your folks are still there, right? I mean, I’ll be back. I mean, I missed the place already, but I’m sure I’ll see them.

Samm: Let us know when you’re back.

Kaiser: I will. Paul Triolo is a partner at DGA Albright Stonebridge Group, where he leads the technology policy practice, and he’s an honorary senior fellow at the Asia Society Policy Institute, working alongside my buddy Lizzi Lee, cranking out some really great stuff there. Nobody bridges the full stack better from fabs to high-bandwidth memory and interconnects, all the way up to export control policy and Track II diplomacy, which is what happens when you put an engineer’s training into 30 years or something like that in government service all into one analyst.

He writes the excellent Substack newsletter, AIStackDecrypted, which you should subscribe to after you subscribe to and pay for mine, of course. But Paul is also a dear, dear friend, a repeat offender on this program. His main offense, of course, being his tendency to plunge heedlessly into the technical and technological weeds without pausing to gloss all the terms that he should. Paul, consider yourself officially warned. You are enjoined to avoid doing that this time. No need, by the way, to dumb it down. I mean, just, you know, make sure that a semi-tech literate dope like me understands what the hell you’re saying so we can actually have a conversation.

Paul Triolo: Thanks, Kaiser. I’ll try to do that today. And I want to do make a point that you said that across the AI stack, I’m using the five-layer cake of Jensen Huang these days, and I want to be able to have my cake and eat it too.

Kaiser: Hey, you know, it’s now six layers because we’ve added a layer, thanks to Angela Zhang, I don’t know if you heard my conversation with her — Law is the sixth layer of the AI stack.

Paul: And I also talk about sprinkling that cake in the stack liberally with rare earths.

Kaiser: Samm, Paul, welcome back to Sinica. Welcome to Beijing. And thank you, guys, for doing this in person. It’s so great.

Paul: So happy to be here.

Kaiser: So first, the most important question and the one that everybody is wondering is, you know, how many humanoid robots one can actually watch fold towels before losing the will to live?

Paul: I think it’s the cooking ones that may do that to me, more than the folding the laundry.

Kaiser: Oh, really? Are they good? Did you try anything? I know you’re a very serious vegan, so probably no vegan dishes on offer from the robots.

Paul: No, it was all meat.

Kaiser: They feel no moral anguish when slaughtering animals, I understand.

Samm: I think if we want to judge capabilities, we can look at how the robot dog jumped up higher and did a flip this year versus last year, where it just trotted around.

Kaiser: But I hear it wasn’t just dancing robots this year, huh?

Samm: There were police dogs. There were boxers. And not just at the actual exhibition hall, but in the mall around it where the flocks of people went to escape the heat and humidity, they had a whole match going on between some grandmothers and robots in a boxing ring.

Kaiser: My money’s on the grandmas any day in China, at least. This is a significant upgrade from Rock’em Sock’em Robots, which I used to play when I was a little kid. You remember that, Paul?

Paul: I do. But I focus mostly on, I shouldn’t say, the semiconductor companies and sort of walked past the robots since I did a deep dive into the humanoid robotic sector in April and saw way too many really good humanoid robots.

Read more

• Coincidence? We don’t think so.

On this week’s Trivium China Podcast, Andrew Polk sits down with Dinny McMahon (Head of Markets Research) and Joe Peissel (Lead Macro Analyst) to unpack:

• Why the “national team” bought A-shares after last month’s selloff, and why this intervention was smaller than last year’s

• What SAFE’s move to regularize QDII quotas signals about RMB internationalization

• The quiet PBOC move to curb bankers’ acceptances, and what it tells us about Beijing’s deleveraging playbook

• What the latest GDP and trade data say about China’s widening “K-shaped” economy

Tune in for the full breakdown.
And while you’re at it, make sure to check out Dinny’s latest note on the pivot to deleveraging here. You won’t regret it!

Transcript

Andrew Polk: Hi, everybody, and welcome to the latest Trivium China Podcast, a proud member of the Sinica Podcast Network. I’m your host, Trivium Co-Founder, Andrew Polk, and I am joined today by two guests. First, our head of markets research, Dinny McMahon, and Trivium’s Lead Macro Econ Analyst, Joe Peissel.

Joe, how are you doing today, man?

Joe Peissel: Hey, Andrew. I’m good. Thanks, mate. And pleased to be here as always.

Andrew: Yeah, great to have you on, as always. Dinny, how about yourself, man? How are you doing over there?

Dinny McMahon: I’m doing good, mate. Doing good.

Andrew: Sorry, we’re laughing because Dinny was having some technical difficulties and getting quite frustrated. But we’re here now.

Dinny: Frustrating to me. I mean, it seemed to have made your day.

Andrew: Yeah, I was delighted by all of it. But we’re here now. We’re here now. And what we are going to talk about are some of the biggest developments in terms of the macro picture, as usual with these two guys. This is our monthly pod where we talk about the macro data, but we’re also going to throw in a couple of other things, which is why I also wanted to have Dinny on, also kind of have kind of a three-way conversation, a little bit more conversational than just me purely interviewing Joe.

But we’ll get to the macro data at the end with Joe really taking the lead there. We are going to start, though, with the deployment of the “national team” by Beijing to prop up A shares after last month’s tech-led share sell-off. Then we’ll get into the moves by SAFE, which is the foreign exchange regulator in China, to regularize the QDII quota, which is the outbound quota for foreign investment by Chinese institutional investors. And so, we’ll talk about what that means for outbound investment.

And then we will talk about the latest reports that the central bank, the PBOC, has told banks to raise the minimum discount rate on bankers’ acceptances. That’s a wonky thing, but basically, it’s a quiet but meaningful move on the deleveraging front, which we talked about in my last pot with Tiffany, so we thought it’d be a good thing to bring up. And then, of course, like I said, we’ll get into the macro data, talking about the K-shaped divergence running through China’s economy, which Joe has highlighted in some of our recent writing that our subscribers will have seen.

But of course, before we do that, we got to start with the customary vibe check. I’m already smiling, Dinny. How’s your vibe over there?

Dinny: Oh, mate, it was great. So, about 15 minutes ago, when I started to set up my camera and microphone and all that sort of stuff. Yeah, it was a beautiful morning. The birds, the sun is shining, the birds are singing. And then, you know, I had a workshop last night with Claude, exactly how I was going to set up my [inaudible 00:02:44] set up. And I don’t know, one of us clearly got it wrong, so I’m a little bit more on the irritated side of frustrated this week.

Andrew: Well, we’re glad to have you. Even an irritated Dinny is a good Dinny to have, and so hopefully we can push past it. Glad that you finally got everything set up. I can attest that technology issues, especially when it comes to sound, first happen way more than you would expect as a regular podcaster. It seems like the setup’s always changing. There’s always some sound bug. And secondly, they are incredibly frustrating when they do happen. So, you have my sympathies. Although I’m still going to laugh. Joe, how was your vibe, man?

Joe: My vibes are good. Thanks, Andrew. Although I’m, to be frank, I’m still smarting from the World Cup, from the Football World Cup from last week.

Andrew: Oh, right.

Joe: England’s depressing performance against Argentina.

Andrew: Yeah, we haven’t had a chance to discuss that.

Dinny: Yeah, France too.

Joe: Yeah, well…

Andrew: Well, it was bittersweet that was also a weird game. We won’t get into it; this isn’t a World Cup podcast, but that third-place game was a funny one. But yeah, sorry, sorry to all our British listeners out there. I watched that game. Sorry, yeah.

Joe: Well, English in particular. I think Scottish and Welsch listeners will probably be thrilled at the result, yeah.

Andrew: Yeah, fair enough. Fair enough. Yeah, good point. Well, my vibe, it’s 9.30 in the morning on Wednesday, July 22nd. I’m in a great mood. And watching Dinny’s foibles has sort of given me a little bit of a pickup further. So, I’m going to be carrying the good vibes into this podcast today. But we should probably get into it because that was kind of an extended vibe check. Although we also do have to do the quick housekeeping.

Just quickly, a reminder, we are not just a podcast here. Trivium China is a strategic advisory firm that helps businesses and investors navigate the China policy landscape. That, of course, includes domestic policy in China around a range of issues, whether that be tech, markets, etc. But it also includes policy towards China out of Western capitals like D.C., London, Brussels, and others. So, if you need any help on that front, please do reach out to us at hq@triviumchina.com. We would love to have a conversation about how we can support your business or your fund.

Otherwise, if you want more Trivium content, check out our website, again, www.triviumchina.com, where you can find the China policy intel option that you’ll need. We, again, have markets, tech, anything that you might need is there on the site as well. So, check that out. And while you’re at it, please leave us a rating or comment on the podcast and tell your friends and colleagues about Trivium. We always really appreciate the word-of-mouth recommendations. They really help us to grow our business. So with that out of the way, let’s get into it. Dinny, Joe, you guys ready?

Joe: For sure. Yep, let’s do it.

Dinny: Yep, let’s do it.

Andrew: All right. So we’re going to start with the stock market, the latest intervention on the stock market on July 19th. So that was just, I guess, on Sunday, a couple of days ago, basically. Two central government-owned investment firms, China Reform Holdings and China Chengtong, announced they’d increase their holdings of Chinese equities. These big state-owned investment firms coming in is what we refer to as the “national team.”

And that investment announcement came after a pretty rough month for A shares, the kind of benchmark CSI 300. Stock index is down more than 9% since its June 22nd peak. And the STAR market or the STAR50 dropped 17% just last week. So those two firms stepped in. And then even more recently, we had China’s biggest state-backed insurers, including China Life, Ping An Insurance, and China Pacific, all issuing statements pledging confidence in A-shares and committing to more equity investment as alongside 40 listed companies announcing buyback plans, stock buyback plans to support share prices.

So, Dinny, let me toss this to you first. What’s your read on the scale of this intervention, especially compared to previous interventions that we’ve seen. I think the most recent one was back in April 2025. So, talk us through kind of the scale of this thing and what to think about it.

Dinny: Yeah well so far it’s been pretty small. So when the Liberation Day tariffs hit, those two same state-owned firms deployed, well, they said they deployed 180 billion renminbi to buying ETFs and buying stocks. Whereas this time the numbers they’ve given us are only 70 billion. So, the scale of the intervention is much smaller. But I think the more sort of telling thing is that in the past, when Beijing is sort of really being worried about the stock market having fallen and it’s trying to get things back up again, its sort of modus operandi was to throw a whole lot of administrative measures at things.

So, typically, their go-to playbook would be they’d impose more a moratorium on all new IPOs so that you weren’t diluting liquidity among more stocks. Or you’d change margin trading rules so that it was easier to borrow to invest in stocks, or that you tighten up, make it harder for people to short sell off, stuff like that. But I think it’s quite telling that the two things that they’ve done this time in response to a pretty precipitous sell-off in certain stocks is one they’ve deployed the national team on a relatively modest scale.

And secondly, they’ve deployed the state-owned insurers not to buy stocks, at least not immediately, but to kind of jawbone the market up that kind of as a team they got together and said, “Look we’re all in this we’re buying more shares than we used to. We see value in the A-share market over the long term. We’re committed to this market as long patient capital.” And so, Beijing’s approach to this has fundamentally changed. It’s no longer about sort of trying to engineer a short-term bump to the market, but, as we’ve been arguing for a long time, kind of lay the foundations to a sort of a cultural change that can translate into what Beijing refers to as a slow bull market.

And so, I think that’s what we’re seeing here. To the extent that the national team is deploying capital this time around, it’s about putting a cushion under the market. It’s not about trying to reflate prices. It’s just trying to soften the fall, limit the degree to which stocks fall, and then try and rebuild confidence, genuine confidence, as opposed to just trying to engineer a rebound with various tricks and measures.

Andrew: Joe, let me bring you in here. What are your thoughts on this intervention? Do you think, well, first of all, I was going to say, do you think it’s going to work? But then I guess the question is sort of what defines success here in Beijing’s mind. Dinny just made the argument, they’re not really trying to throw everything at the stock market simply to pump up prices, but want to achieve this more of a slow bull market. So, will it be successful and what defines success in your view of Beijing’s view?

Joe: Yeah, so I think success is really about trying to attain this slow bull market. So, that there hasn’t been this aggressive knee-jerk reaction from Beijing like there has been in the past, I think it’s actually a positive thing. And they’re not really putting a floor on prices, right? It’s almost like they’re trying to put a floor in a panic. So, if stock prices drop too much, there’s these kind of these modest incremental measures, but nothing really aggressive, nothing really knee-jerk.

That’s consistent with trying to achieve this slow bull run of the market. What I think is quite interesting is that markets have stabilized. They’ve actually picked up very slightly. I mean, really modest. But I think the key takeaway here is markets have stabilized. The fall has stopped despite using two and a half times less fiscal firepower than they did this time last year, which, to me, suggests, or potentially suggests that maybe Beijing is building up credibility in its ability to put a backstop onto a market slide.

Andrew: Yeah, that’s a good point. Dinny, what do you think about that? I mean, I look at these interventions, and it just feels like we get one every 12 to 18 months now. So, what is your take on Beijing’s credibility here with the market that Joe pointed out?

Dinny: Yeah, I think the market’s starting to give it a better sense of what to expect from Beijing. I think it now knows that when Beijing intervenes, it’s not trying to reflate the market, but it’s trying to put a net under it. And I think that’s a very different set of expectations that for the state to intervene and try and engineer a bull market, which it has done in the past, the public has kind of lost faith in Beijing’s ability to do that.

But if it’s just the state intervening to go, we’re just going to limit the degree to which it falls, maybe even stop it falling. I mean, that’s a very different benchmark. It’s certainly something that the state is far more equipped to handle and so is conceivably something that the public is more willing to accept as something that the state can feasibly do and so sort of moderate their expectations.

Andrew: With that kind of context in mind, Joe, first to you, then over to Deneen, we’ll wrap this part of it up, should we expect more moves out of regulators and out of state-linked market participants on this front? I mean, it seems like in the past, there’s sort of been, I don’t know, usually like three, four weeks of kind of incremental moves that kind of add up to a package. But if they’re not really trying to pump prices, they’re just trying to create a net under them, maybe they don’t need a spate of moves. I don’t know. What do you think, Joe?

Joe: I expect regulators will respond to equity prices. So, markets have stabilized for now. I think if that stabilization prolongs for a period of time, then there won’t be any further moves. If the market fully resumes, then I expect there would be more intervention. Again, I don’t really think regulators are trying to put a floor under prices per se, but they do want to stop any sort of irrational market panic. Dinny, do you think that’s right? Do you agree?

Dinny: Yeah, absolutely. We absolutely watch the market there. I don’t think regulators are going in with a preconceived set of or a checklist of measures they’re going to roll out. They’re going to see how the market responds, and then they’ll adapt accordingly.

Andrew: Yeah. Well, we will obviously see in the coming days kind of how the market continues to react. This podcast is not investment advice. We have to say that, but it does strike me that there’s not an obvious signal. This is not the kind of situation where especially foreign entities would jump in and try to ride what looks to be an upward surge or a wave of price increases in A-share markets because that is not what they’re trying to do, according to you guys, which I agree with. But we will continue to monitor this and see how effective these moves are and if Beijing is becoming more effective.

So, I’m sure there will be plenty of opportunities to talk more about it in the coming weeks. I want to pivot now, though, to the second topic, which is about currency and capital flows. So last week, July 17th, SAFE, again, the FX regulator said it would start issuing QDII quotas, Qualified Domestic Institutional Investor quotas is I believe what QDII stands for. They will start issuing them on a regular basis going forward rather than ad hoc basis, which we’ve seen in the past, sometimes years apart, where the batches of quotas will just kind of be dormant for an extended period.

Walk us through why this matters, Dinny, what’s happening. Our super in the weeds listeners will know kind of what all this is, but just lay the groundwork for those who might not be experts in the space.

Dinny: So we were expecting something like this to happen, that Beijing would liberalize or open up the channels through which people could invest, the Chinese people in the mainland could invest in overseas capital markets. We thought this was happening, partly because there’s been a real push towards renminbi internationalization over the past year. Beijing kind of sees what’s happening globally with the role of the U.S. in the global economy, sort of shifting sentiments about the role of the U.S. dollar in the global economy.

And they kind of think, look, this might be a unique opportunity, a unique moment in time to promote sort of the internationalization of the Chinese renminbi. But on top of that, we thought something like this was sort of on the cards because a few months ago, Beijing cracked down on three Hong Kong-based brokerages, Futu, Tiger, and Longbridge. And what it was cracking down on is these three securities companies were kind of operating in a bit of a legal grey area. China’s people are allowed to move overseas $50,000 worth of cash, capital, however you like to put it, each year.

And they can do that year in, year out. And so, what these securities companies were doing is they were signing up people in mainland China, signing them up to brokerage accounts, having them move their money over to Hong Kong or somewhere, and then from there, putting the money into overseas brokerage accounts and allowing them to invest overseas. And Beijing doesn’t like that because it likes being able to control the channels through which money moves from the mainland into overseas equity markets, called capital markets. And so, you’ve got these formalized, I guess, these formalized channels through which capital flows.

You’ve got things like the Stock Connect, you’ve got Bond Connect, you’ve got various other programs, including things like the QDII program, which differs with what the Hong Kong brokerages were doing in the sense that people would put their money into a sort of a fund management company in mainland China. That fund management company was then able to convert funds into dollars, take the money overseas and invest in overseas capital markets. And that gives Beijing a degree control over where money is going relative to people doing it off their own bat. Now, of course, Beijing hasn’t completely crackdown on people being able to invest overseas based on their own discretion.

I mean, if you turn up in Hong Kong and bring your money over with you, you can set up an account in Hong Kong and invest overseas like that. You can invest in overseas insurance products in Hong Kong like that as well, and Beijing allows that door to remain open. But it didn’t like what Futu and Tiger and Longbridge were doing, and so they shut it down and there was a lot of bad questions at the time was, well, is this kind of like what Beijing did a few years ago with the tech sector, what it did with the education sector?

It kind of cracked down on a handful of firms, and it signaled a broader crackdown. So the question was by going after this small handful of firms, did it signal a broader crackdown on outward capital flows? And we did not think that was the case. We thought, given all the signals we’ve been given about renminbi internationalization, literally the opposite was true. That Beijing was cleaning up a gray area, a loophole that it didn’t like and had sort of put up with for too long.

But in doing so, it would then have the confidence to further broaden out and embrace, sort of broaden out those channels which it itself had sort of set up and which it had more confidence in. And that’s exactly what we’re seeing now. The State Administration of Foreign Exchange is saying that it will now regularize the issuance of new QDII quotas. Now, we don’t know how much each additional quota will be. We don’t know how regular or regularized actually is.

But the signal here is that we’re going to get these new quotas for outward investment on a more regular basis, which is one of the reasons why we think they were cleaning up the sort of grey channel conduits earlier in the year.

Andrew: Thanks for that, Dinny. Great explanation. Joe, let me bring you in. So now seems like a pretty good time for a move like this because there is pretty significant capital inflow, right? They try to avoid liberalizing in any way of the capital account when there is depreciation pressure, capital outflow pressure from China. So, we saw in the first half of the year net non-bank inflows, so inflows from basically corporates and households, hit USD $247 billion. That nearly matched all of the 2025 inflow of $300 billion.

You think that kind of inflow strength gives safe sort of room here to do a little bit more capital account opening? Or do they need to be careful if kind of the dynamics go into reverse? I mean, I think, yeah, China’s export juggernaut and relatively weak dollar driving appreciation pressure that the PBOC is kind of fighting against. So, I think it creates a moment, but I don’t know, give us some context on how you think about this.

Joe: Yeah, for sure. I mean, I agree. I think it gives regulators, it gives Beijing confidence that they can pursue these policies now. Dinny touched on this a minute ago. We’ve been tracking really closely all of these policy measures aimed at RMB internationalization. And a prerequisite for that is to liberalize your capital accounts to an extent. And so, this is what we’re seeing. This is what the regularization of QDII quotas does. I think there’s kind of two important signals from this. So, the first is, and I’m really just kind of repeating what Dinny said, I think the timing makes perfect sense, right?

They crack down on some of this illegal or grey market capital outflow channels. And at the same time, they enlarge or liberate these legal controllable channels. So, the timing is really important. It signals that no, Beijing isn’t trying to crack down on capital outflow per se. I think it wants to crack down on those channels that it can’t control. The second thing is, is it normalizes capital outflow as a policy, which is really important. It’s something we haven’t really seen before because previously QDI quotas have always been issued on an ad hoc basis.

So, moving to this routine schedule, even though we don’t know the dollar amount at the moment, I actually just think the regularization per se is more important than the dollar figure here because as I say, it’s going to create routine and regularity in terms of capital outflow policy.

Andrew: Yeah, we actually had a client for years asking us to track the QDII changes, kind of trying to stay on top of this and see what it meant for capital account liberalization. And they were so irregular, we had to check it every day. But like I said at the top of this section, they often wouldn’t change for eight, 12 months or longer. So, I think people poo-poo the whole notion of renminbi internationalization.

Dinny: Who are these people?

Andrew: Yeah. Well, basically anyone in Washington, DC, right? No one will ever displace the dollar. China is never going to internationalize. And I mean, I kind of get it, right? People have been saying that China is going to open its capital account or liberalize capital flows since probably like the early 2000s, right? Definitely since I’ve been watching China starting in the mid-2010s or I guess late aughts. I don’t know. Last point here, Dinny, that they do seem to think this is a true moment of opportunity, right?

With kind of a lot of countries, especially that are not geopolitically aligned with the U.S., kind of looking for alternatives over time. Not that China’s trying to displace the dollar as a reserve currency anytime soon, but just to like create a somewhat more viable option at this sort of moment where people are looking at the dollar a little bit more askance. I mean, is that what’s happening?

Dinny: Yeah, absolutely. I think there’s a number of ways to look at the renminbi internationalization thing. I mean, even if you think that China will never be able to rival the dollar, or even rival the euro in terms of usage rates, the point is Beijing at the moment thinks it is something that it can push forward. It sees some value in it and it thinks it is in a unique moment in time where it can push that agenda. And so, the significance of that is we are seeing a lot of micro reform. And those micro reforms have knock-on effects which have unexpected consequences.

And so, I think it’s really worth watching what Beijing is doing, given Beijing thinks at least something is feasible. And so, it’s moving in that direction. And so, keeping an eye on what it’s actually doing is really quite important. And I think the other thing to keep in mind is with renminbi internationalization, I don’t think Beijing’s goal is to just displace the dollar. It looks at the United States and it says, “Look, if to have a global reserve currency is to lose control of our money supply,” because half of the dollars created in the world are created in the eurodollar market outside of the control of the Federal Reserve.

Given that the way that the U.S. ensures that there’s a sufficient supply of dollars globally is by running a massive trade deficit, Beijing looks at those conditions and goes, “We’re not particularly interested in that.” It sees that it can push renminbi internationalization, push the supply of renminbi globally through running a financial surplus, which is kind of a bit of an experiment they’re pursuing at the moment. But the goal here isn’t to displace the dollar or even to replicate what the U.S. has created to the extent that they want to internationalize the renminbi, they’re trying to come up with a way of doing it that suits their own domestic economic concerns and considerations.

Whether they pull it off or not, it’s a different question, but it’s certainly watching them do it and seeing the measures they pursue is going to have unintended consequences.

Andrew: Yeah, all those are great points. I think I 100% agree that China is not trying to replicate what the U.S. has done. And I just also, as you were saying that, just thought about the irony of people who, again, poo-poo RMB internationalization. “Oh, it’s never going to happen. They can never do it.” Their kind of implicit argument is because they’ll never run a trade deficit like we do. Meanwhile, they turn around and are like, “I hate your trade surplus, which I understand.” But I don’t know, it seems a little bit inconsistent logically. But anyway, we’ll leave that. There will be plenty more on that.

This is an issue where we do follow it very, very closely because we think that there is some action here. And so, these little micro moves add up over time. And we think, like Beijing, that kind of now is a moment where this is getting a little bit more traction than it has any time in the recent past. So, we’ll stay on top of that as well. And then the last thing I want to talk about before we dig into the macro data is particularly wonky.

But this went over to Dinny because it plays right into the deleveraging thesis, as I said, that you laid out in the pot a couple of weeks ago. So, Caixin, the Chinese financial media outlet, is reporting that in June, the PBOC told banks to raise their minimum discount rates on bankers’ acceptances to somewhere between 0.35% and 0.5%, depending on the bank. So, for listeners who may not know what those are, bankers’ acceptances are sort of IOUs that firms can use to pay suppliers. And then when those firms get the IOUs, they’re usually for a certain period of time.

But if you need cash, you can cash one in early at the bank. And that gets recorded on the bank’s books as basically a loan. But banks have been reportedly cutting the discount rates towards zero to juice their loan counts because credit demand has been so weak. And the PBOC is saying, “Stop doing that.” Dinny, before you get into the specifics of exactly what’s happening, why don’t you just take a minute and give a little bit more context around bankers’ acceptances, discount rates, give it to us in layman’s terms.

Dinny: Okay. Well, firstly, the reason we’re talking about this is because of what you said about deleveraging. When I was last on the podcast, I was talking about deleveraging in terms of the slowdown in credit growth, the government sort of peering back, it’s spending on certain stuff and pursuing policies that are resulting in less borrowing. And then I saw this was happening with the PBOC and I felt particularly vindicated because what’s happening with bankers’ acceptances is a way for the PBOC to reduce credit growth in a way that doesn’t necessarily impact the economy.

And I think that’s very important because it’s kind of almost like an administrative change that tweaks the numbers without having any real consequences. And this is kind of what we saw with the deep leveraging campaign back in 2016. Beijing was able to wring a lot of credit out of the financial system without it having much of an impact on economic growth because credit was being used in various sort of creative ways. Layers and layers of credit were being used to disguise what a central loan was being used for. But those layers of credit didn’t actually add any economic activity. They were just a kind of a wrapper or a disguise that allowed shadow banking to sort of exist beyond regulatory control.

And this in some ways is the same. Now, what bankers’ acceptances are, as you said, they’re IOUs. They’re a type of trade finance, almost like a trade receivable, an account receivable. So, let’s say I sell you something, and rather than you paying me in front of cash, you say, “Okay I’m going to pay you in six month’s time.” And I’m like, “Mate, I don’t trust you to pay me in six month’s time.” So, you say, “Look, I will pay you with a banker’s acceptance. So you don’t have to trust me. You can trust my bank.” So, you go to your bank, and you tell the bank, “Look, I’m going to pay this guy, Dinny, $100 in six month’s time. So I’ll need you to write the IOU and you give that to Dinny. And so, in six month’s time, he will be able to present that IOU to you or any bank, frankly, and you will give him the $100 that I owe him because I will pay you, the bank.” So, everything’s fine.

So that’s basically how it works. But if I, Dinny, am holding your IOU and I’m like, I need cash before the six months is up. I’ve got people to pay. I’ve got suppliers to pay. I can’t wait six months. So, I take your IOU and I take it to a bank and I present it. I say, “Look, I want my cash now.” And the bank says, “Yeah, we’ll give it to you, but we’re not going to give you the full face value. We’ll give it to you at a discount.” And that discount rate might be 4% annualized, might be 3%. But depending on what credit demand is, how hot the economy is running, that discount rate could really be anything.

But the point is, I present it, I need cash, and you don’t give me the full amount. You take a cut. And that’s the discount rate. Now, what’s been happening recently is that the discount rate has been next to zero. So effectively, I could turn up before the IOU matures, the bankers’ acceptance matures. I present it to the bank after a month, after two months, and the bank gives me the full amount or close to the full amount. So, the bank is really making no money on this whatsoever. And so, it begs the question, why are the banks doing it? And the banks are doing it because these bankers’ acceptance are recorded as corporate loans.

And so, when you look at their loan book at the end of the month, unless you’re looking at the details, all you see is a higher credit figure. And that’s kind of how the banks have worked for years and years, that there’s always been this prioritization of volume, that they’re all striving to generate robust credit growth every month. And the PBOC is sick of it. It’s been saying this for over a year at this point. It’s like, you know, size doesn’t matter anymore. We don’t want you to be pursuing credit growth just for its own sake. We want quality. And yet it hasn’t really been able to stop the practice.

And this year, it’s been particularly acute. I mean, over the last few months, there’s been months where credit growth would have contracted year-on-year, were it not for a surge in this discount in the bankers’ acceptances.

Andrew: Sorry, just quickly on that one, just a point to clarify. Correct me if I’m wrong, but the banker’s acceptances only show up as a loan at the bank after they are discounted, right?

Dinny: That’s right.

Andrew: Right. So, that’s why the banks would want to increase or lower the discount rate, increase the overall number of discounted acceptances, because when they’re not discounted, they’re just kind of off the balance sheet. And once they are discounted, then a bank can show it as a loan.

Dinny: That’s exactly right. That is exactly what’s happening here. And so when credit growth has been so weak with all other parts of the economy, companies aren’t borrowing as much, households aren’t borrowing as much. And so, banks are still in this mindset of like, well, we need to show credit growth. They are turning to discounting bankers’ acceptances, even though they’re making next to no money off these things. And so, the Caixin story was quite telling because it’s the PBOC now saying, look, you can’t cut this discount rate to zero. You’ve got to keep it at least 0.35 or 4% or 0.5%. And that’s kind of saying, firstly, you’ve got to make money off these things.

And secondly, there needs to be real economic incentive or reason for doing these sorts of transactions. And when we saw the story in Caixin, that kind of started ringing alarm bells for us because it started to make sense of comments that Pan Gongsheng had made at his Lujiazui speech in June. And Pan has really made a big deal of his speeches at Lujiazui. I mean, I think this is the third he’s given. Last year, it was all about women being internationalization. It really kicked off this sort of changing approach that Beijing has sort of been taking towards it.

The year before that, he kicked off a wave of interest rate reform with his speech. This year, there didn’t really seem to be a strong theme to kind of draw on. But now we’re starting to think maybe there was, and it just wasn’t immediately obvious. And it’s this idea of cleaning up credit, of improving the quality of the loans being made the system of not doing wasteful credit generation like meaningless discounted bankers’ acceptances.

He didn’t talk about bankers’ acceptances explicitly in his speech, but he was talking about raising the quality of credit growth and not pursuing credit growth for its own sake. All of the issues that sort of touch on what’s sort of going on here. So, we’re feeling a little bit validated because reducing the amount of bankers’ acceptances being discounted would be a way to reduce credit growth and kind of achieve that deleveraging goal that we were talking about without an overly detrimental impact on the economy.

Andrew: Yeah, let me bring in Joe here. That’s all great. Joe, you were going to jump in.

Joe: I want to jump in with a question for Dinny, if I may. So Dinny, my initial thoughts on this are, and I accept that bankers’ acceptances are inflated and the PBOC wants to crack down on that. Is there an argument to be made that they’re going to inadvertently crack down to the extent where banks actually pull back, offering bankers’ acceptances in cases where they’re actually useful, not just in the gimmicky cases, and in doing so squeeze working capital for smaller suppliers who rely on them? Is there a risk there, do you think?

Dinny: No. I think, if anything, smaller suppliers are already feeling screwed by this preponderance of trade finance that has been imposed upon them in ever-increasing volumes over the last few years. So, at the moment, it’s not just bankers’ acceptances. It’s commercial acceptances where large corporations are effectively issuing their own IOUs without the backing of a bank, which have really increased in huge volumes in recent years. I think at the beginning of this year or end of last year, BYD in particular was forced by the government to massively scale back the volume of corporate acceptance drafts.

Its own IOUs that it was issuing to its suppliers because its suppliers were barely seeing any cash. They were just seeing the scripts being issued by BYD. And then on top of that, you’ve seen sort of an expansion of accounts receivable, just normal trade receivables. And on top of that, you kind of have the pressures of local governments not paying their trade receivables or even their banker’s acceptances on time. So, I think there is a whole problem attached to the issue of suppliers and contractors being forced to accept some form of alternative payment other than cash because it’s just proliferated over recent years.

And it’s a way of firms, large firms, local government financing vehicles of large SOEs to push their own financial stress onto the supply chain because these sorts of dealings are effectively zero-interest-rate loans.

So, I think there’s a problem really with the proliferation of this sort of stuff. So, if the banks themselves stop discounting them in such large volumes, maybe you’ll find some firms are frustrated by the fact that they can’t cash in their IOUs, their acceptances earlier at lower discount rates. I’m sure some firms will find that frustrating. But it sort of belies a much bigger problem here where these sorts of arrangements have become just so ubiquitous in recent years to the detriment of smaller firms.

Andrew: Yeah, great question, Joe. Good answer. And I just wanted to follow up with you. I was actually thinking the exact same thing, right? Like, you know, isn’t this just hurting companies that need to get paid? But I guess there is a broader effort that obviously the three of us and others in Trivium have covered that to reduce the payment times for suppliers throughout the economy, reduce accounts receivable.

But more generally on the deleveraging piece, you come at this from a couple of different angles. One is you just edited Dinny’s client notes, going to go out here soon on this topic. And secondly, you are, of the three of us, probably the purest macro economist in terms of your thinking and analysis.

Joe: I’m anything but pure, Andrew.

Andrew: So, what do you make of Dinny’s overall kind of deleveraging argument here and how banker’s acceptances play into it?

Joe: Yeah, I feel like you’re tearing me up to make a shameless plug for Dinny’s piece. The client note is phenomenal. It’s phenomenal. I finished editing it this morning, and I’m serious, it’s so novel and insightful, this piece of work that Dinny’s worked on. And I know you talked about it in last week’s pod. But no, I think it makes sense. The policy signals that Dinny’s picked up on, it makes clear sense to me. I’m not convinced it’s going to work from Beijing’s perspective. I think a deleveraging sounds great in principle. It’s very hard to implement.

One of the main reasons is credit growth juices the economy in one way or another, even with decreasing efficiency over time. The minute you try and pare back credit growth, you’re actually going to impact GDP growth as well. So kind of unwittingly, your debt-to-GDP ratio can still end up rising. And there’s loads of case studies of this empirically. UK is a great example. So, I forgot your question, Andrew.

Andrew: Oh, just what do you make of the argument and how do bankers’ acceptances feed into it?

Joe: Okay, yeah. I got so excited talking about Dinny’s piece. Yeah, so again, yeah, I think the argument makes sense. I think it’s going to be really interesting to look at TSF versus nominal GDP growth in the coming year and see if the policymakers can effectively deleverage. Again, I suspect they’ll be unsuccessful, but there’s no doubt they’re trying to do it as Dinny has picked up on. And yes, banker’s acceptances are a small part of the puzzle.

I think how Dinny responded to my question actually makes a lot of sense that this is very much a low-hanging fruit. I asked just a minute ago, is there going to be pain for smaller suppliers? Is Beijing going to create a working capital problem for small suppliers? But I think Dinny convincingly argues no in most cases. And therefore, it’s a low-hanging fruit for policymakers. This seems like a very pain-free way to lower credit growth.

Of course, what comes later down the line necessitates more painful ways to lower credit growth. And I think that’s where we might start to see it filter into lower GDP numbers through things like lower infrastructure investment, less loans for consumer spending, things like this.

Dinny: If I could just jump in for a sec, because the whole idea of potentially hurting smaller firms, it’s quite interesting because at various times, particularly at the NPC, right? where delegates write documents making proposals for what they want to see policy-wise in the year ahead, a common theme of the NPC for the last few years is that somebody advocates for getting rid of bankers’ acceptances entirely because they hurt small firms so much.

So, regardless of what the discount rate is, they’re small firms hate bankers’ acceptances because they must prefer them than a straight-up trade receivable, right? it must be because trade receivable, nothing backs it except the goodwill of the company that you’ve sold something to. Bankers’ acceptances are better because they’re backed by a bank, you know you’re ultimately going to get paid. But no one really likes them because...

Andrew: You know what they like even more? Actual cash. Actually just getting paid.

Dinny: They see bankers’ acceptances as almost kind of like as an excuse to not get paid in cash, right? It’s like, well, if you’ve got an option, well, we could give you a trade receivable and pay you in six months, or we could not pay you in cash. Or we could give you a banker’s acceptance. It’s like, well, you know, okay, we’ll take the banker’s acceptance. So, no one actually likes these things. And you’re right, in an environment where the discount rate is zero, firms are probably a lot better off than they ever were in the past.

But it’s not necessarily translating sort of firms’ relationship or enthusiasm for getting paid with bankers’ acceptances.

Andrew: Yeah.

Joe: Andrew, can we add a link to Dinny’s client note in the podcast notes?

Andrew: Absolutely. We will do that for sure.

Joe: Again, I mean, segues into another shameless plug. A lot of listeners won’t be able to read it because it’s only available to paying subscribers. But I think it’s worth putting the link there for subscribers that listen to this podcast, then they can read Dinny’s report. Again, it is fascinating.

Andrew: Yeah, we’ll make sure to highlight that, and everybody should be on the lookout for that piece. I mean, I would love to actually just talk about this deleveraging piece more and more. I think there’s a lot here. We need to get onto the macro data, but I’ll just finish this up by saying, Joe, you partially sort of anticipated what would have been my next question, which we can now generally skip about whether or not there’s really a cost ultimately here to the economy of the deleveraging.

I will just say, again, we don’t want to get too much into it. The same argument was made in 2016, 2017, when Beijing started deleveraging the banking system. And the argument at the time was, “Well, we’re actually just unwinding some of the most speculative financial activity in the interbank market, where it’s really just been financialization, financial firms lending to each other, which has been running up credit growth, and it didn’t create any economic activity on the way up. And so, it’s not going to hurt economic activity on the way down.” And that actually what ended up being true for a period, right?

The low-calorie interbank loans that were really just about banks kind of betting on each other and kind of creating new financial instruments didn’t hurt economic growth when they were unwound. And I guess the thinking at the PBOC would be banker’s acceptances might be similar. Now, ultimately, that reduction in credit growth did have unintended consequences, which meant it was harder for private sector, especially small firms, to get actual credit that they needed. And they kind of got crowded out. And ultimately, a couple of years on, it really did impact private sector credit and overall economic growth.

So, I think your point stands, Joe, although also Beijing, I think could argue or regulators in Beijing could argue it was partially successful. The last time we tried something like this was at least partially successful in slowing credit growth without hurting economic activity. But we will see.

Joe: By the way, think about the property downturn that started in 2021. That was triggered by the three red lines, which was an attempt to deleverage the property sector.

Andrew: Exactly right. Yes. And there are definitely people who argue like the deleveraging then kind of tipped the first… it was a domino effect that got out of control and regulators couldn’t really control it. First, it went into the shadow banking sector that then reduced credit to the private sector, which then reduced basically credit to property developers, which then reduced credit and resources to local governments. And that none of this really was in the government’s control. So, that’s another way to argue it. So, that’s a good point, Joe. We will, again, definitely be debating this and following it, I think, for a while yet to come.

But it’s actually a great point to pivot to the current state of the economy, right? So, we’re talking about sort of the more contextual piece or the credit and financial environment within which this deleveraging effort is happening. And there’s no good time to deleverage. And Dinny has argued that actually part of the reason that they think they can do it now is because exports are so strong and inflation is up. But certainly, the rest of the domestic economy is not doing well. And you have, as we teased at the top, called this a K-shaped economy, and said that the Q2 data really puts that on display with GDP just growing 4.3% year over year, the slowest quarterly growth rate in three years.

Give us the lay of the land and the latest data in terms of what we’re looking at for China’s economy right now, Joe?

Joe: Yeah. So, I mean, it’s not… K-shaped, it’s not just me who’s calling it a K-shaped economy.

Andrew: Yeah, yeah.

Joe: It’s a pretty mainstream term. It’s quite satisfying to put the main economic indicators onto a graph because there really is a K there. And so, kind of the upward arm of the K is really China’s export sector. And this is driven by predominantly tech and clean energy exports. So, think of things like NEVs and batteries, solar. And that’s feeding into the manufacturing of tech and clean energy as well. And those parts of the economy are booming. They’re on fire. They’re doing really well.

But then the downward part of the K, the downward leg, is domestic demand. Both household consumption and general aggregate investment across the economy, whether that’s investment from government, from the private sector, from property developers, that’s all declining. And so, there’s this divergence. There’s one part of the economy that’s booming, quite a small subset of economic sectors really that are doing well. The rest of the economy not doing so well.

The consequence is, even when exports are at a record high, and China has this emerging clean tech and high tech, the emergence of this booming clean tech and high tech industries, GDP growth is still kind of stumbling along in the low fours, 4.3% in Q2. And that’s because despite parts of the economy doing really well, other parts of the economy are struggling big time.

Andrew: Well, talk to us about the parts of the economy that are struggling big time. I mean, I think we can pretty much guess them. But what’s the downward leg of that K look like?

Joe: Yeah, I think consumption is probably the most interesting thing to talk about because we got new data in the Q2 release. We got data from the Stats Bureau’s quarterly household survey, expenditure survey. So we can see what households are spending their money on. And they also report their income. So, we get estimates for income growth. So, throughout H1, so the first half of the year, income growth has hit a record low. It’s in the low fours, 4.something percent.

And outside of COVID, I should add, outside of COVID, that’s the lowest level on record. So, their income growth is slowing. They’re also spending less of their income. So, the propensity to consume, which is just the percentage of their income that they spend, that’s also hit a record low. Again, outside of COVID, kind of excluding those three COVID years where there’s lots of lockdowns. So, we’re talking about the lowest income growth on record and the lowest propensity to consume on record.

And as a consequence, consumption metrics are doing pretty badly. So, retail sales grew 1% year on year in June. That was following a 0.6% decline in May. So, it’s just a pretty dire picture on the consumption front.

Andrew: Is there anything that can turn consumption around at this point? I mean, they’ve been talking about it for years. We, and other analysts, have been highlighting the challenge for years. They just put out a five-year plan on consumption, which seemed to have no new ideas and didn’t seem to create much positivity among the analytical community. What can they do? What should they do? Is there anything to be done?

Joe: There’s no obvious near-term catalyst to boost consumption. And that’s because it’s a structural issue. It’s not just a cyclical downturn. And I think that it’s a structural issue. It’s caused by two things predominantly. There’s probably a bunch of other factors, but one is the overhang from the property sector downturn. So, household wealth is down, I don’t know, about 30% from this 2021 peak. Households just feel less wealthy. They have less money to spend.

And the second thing is a slow in income growth. As I say, income growth, lowest rate on record. That’s crazy. These are structural factors. This isn’t a cyclical downturn that is suddenly going to pick up in Q3. So no, in answer to your question, Andrew, there’s no obvious short-term policy fix, which is going to cause a sustainable increase in consumption. It’s structural issues, which means it’s going to take a long time to fix.

Andrew: And you’ve been highlighting this supply-demand mismatch for quite a while now. Others have as well, but you were definitely early on that. And now we’re seeing the structural divergence and kind of the AI versus the non-AI parts of the economy for a quick shorthand. But the PBOC, the China Central Bank, at its meeting earlier this month, actually named ‘structural divergence’ as a challenge for the first time as far as we have seen. Does the acknowledgement of that issue as a problem tell us anything about what the PBOC or any other policymakers might actually do here?

I mean, there’s admiring the problem, there’s fixing the problem. Where are we on that front?

Joe: Yeah, we’re very much with the former as opposed to the latter. Symbolically, it’s important. Symbolically, it’s notable. But in the past, senior policymakers have publicly acknowledged weak domestic demand as a challenge. It doesn’t mean they address it. So yeah, to recognize it symbolically, maybe that’s important. But a week after that meeting, the State Council released its five-year plan for boosting consumption. And there was very little in terms of demand-side support, very much still thinking about how they can allocate resources towards supply-side solutions.

So, this explicit acknowledgement of structural divergence appeared in a PBOC report. I mean, that’s pretty consistent with this long-standing preference of policymakers to acknowledge the problem. They know what the problem is, but still prefer to invest in these supply-side levers to try and deal with it.

Andrew: Yeah. Dinny, any thoughts on where we are in the current economic environment and how that might fit in? I mean, we’ve been talking about the deleveraging piece. It’s hard to deleverage when your economy is struggling. And then, if the deleveraging further depresses GDP growth, as Joe argued, could end up backfiring. What are your thoughts on how the growth picture fits into all this?

Dinny: You know, I think when foreigners in particular talk about maybe it’s China’s consumption problem, it’s all about they need to throw more money at it. You know, they need to fund welfare or they need stimulus or something like that. We’ve talked about why Beijing won’t fund welfare out of debt before, but I think in terms of the stimulus side of things, Beijing’s increasingly at a position where it’s not willing to throw good money up at bad anymore. And it’s being a lot more realistic in its assessment as to what constitutes bad money.

And I think that’s what we saw with the peering back of subsidies for the consumer trading program this year. I mean, we’ve talked about this before how the consumer trading program had 300 billion reminiscences worth of central government funding last year. And it was incredibly successful. I mean, it was for to support purchases of big-ticket consumer items like cars, furniture, white goods, and household personal electronics. Did wonderful things for purchases of those goods. But the very nature of the program meant that you were always bringing forward future demand, right?

It was you were bringing forward next year’s demand and the year after that’s demand and so on and so forth. And so, to keep going on that path, to maintain last year’s sales, you needed to increase this year’s subsidies. So, it’s not even a question about growing the program. Just to kind of keep it at last year’s levels, you needed higher levels of subsidies this year. And instead, Beijing made the decision to reduce the subsidies. So, this year, the subsidies are 250 billion renminbi. When they did that, they clearly went in with their eyes open. There was never going to be any doubt that sales would fall.

And that’s what we’ve seen. I mean, Joe, I’m not sure if you have the numbers on your fingertips, but what… auto sales are certainly down. White goods, what? Furniture down 7% so far this year?

Joe: Yeah, something like that, yeah. And I think home appliances, white goods are like close to double digits.

Dinny: And so this is despite having put aside 250 billion RMBs worth of subsidies. But the thing is, Beijing is weaning itself off this program because it realizes exactly what Joe was saying, that the problem with consumption is structural. These sorts of programs are fantastic if they’re a band-aid, if it’s cyclical. If you go, “Well, consumption’s weak this year and it’s probably going to be weak this year, but we can get through it with this sort of temporary subsidy program.”

But we’ve had the program now for, what? I think we’re already at two years and there’s no end in sight. And there’s a realization that to keep this program, contributing the economy in the same way it has, It has to keep getting bigger and bigger. And so they’re now like, we’re not willing to throw good money up the bad. And I think that the increasing of profit remittances, centrally owned SOEs to the government, is kind of indicative of that as well.

Joe did fantastic work kind of trying to estimate just the impact of these increased remittances would have on fixed asset investment this year. And what was it, Joe, that it reduced FAI by about 2.3 percentage points, give or take?

Joe: Yeah, 2.3% is points of FAI growth from this policy. Yeah.

Dinny: Which is wild. But for Beijing to make that decision at the end of last year, at a time where investment across manufacturing and infrastructure and the property sector were all weak, to make a decision like that to prioritize fiscal revenue over investment kind of showed how Beijing’s priorities were shifting as well. So, I think that’s kind of where we are at. Beijing is like, “Look, maintaining investment for its own sake, it’s not really worth it anymore. Maintaining consumer subsidy programs just to maintain an arbitrary high level of consumption, it’s just not worth it anymore.”

And I think that’s kind of where we are. It’s almost a recognition that, yes, these problems are structural and these short-term fixes we’re deploying, they’re not sustainable anymore. I think that’s where we are.

Andrew: Yeah, that’s pretty dire picture. Joe, why don’t you take us home here? I mean, we’ve got the July Politburo coming up probably in about eight, nine days at the end of this month. And it will focus on the economy as it always does and kind of lay out economic priorities for the second half of the year and kind of last best chance to sort of make an adjustment to policy on the fly.

What do you think going forward in terms of what economic policy looks like and the economic trajectory? Are we going to see any change or is this K-shape what we’re in for, for the next six to nine months?

Joe: I’m not expecting any change to be announced at the July meeting, the end of July. Dinny wrote about this in his client note. We are expecting a modest infrastructure stimulus, sort of late Q3, early Q4, but that’s not going to be a game changer in and of itself. It’s going to be about trying to ensure that government spending on infrastructure at least remains equal to the level of last year. So that’s really about trying to prop up the economy as opposed to turbo boost it. Now, things we can look out for that would signal there’s a step change and Beijing is more inclined to try and boost domestic demand.

With the caveat, I’m not expecting any of this, but these are the sort of signals we might expect to see would be an expansion in the consumer trade and subsidies or potentially expanding it to cover new products or services. Large increases in infrastructure investment, a large policy push on the property sector to try and put a floor under prices. Again, not expecting any of this stuff, not expecting any of these moves to happen, but that will give us an indication that perhaps there is a step change in Beijing’s approach. Most likely outcome is really more of the same with a modest infrastructure stimulus towards the end of the year.

Andrew: Well, we will have our answer soon enough when it comes to the Politburo meeting, and we’ll talk about it on the back side of that. Until then, our listeners will have plenty to mull over. I say this pretty much every podcast, but we truly covered a lot of ground today, guys. I really appreciate a lot of expertise here, a lot of different topics, all of which are important. So, really appreciate both of your time. Joe, great to have you on as always, man.

Joe: Yeah, thanks, Andrew. And just one more reminder, let’s put a link to Dinny’s piece in the podcast notes.

Andrew: Absolutely. Yeah, will definitely happen. Thanks for that.

Joe: Sweet. All right.

Andrew: And Dinny, glad to see you got through the technical troubles to drop some knowledge bombs on us today. Appreciate the time, man.

Dinny: No worries. How many listeners do you think we lost at banker’s acceptances?

Andrew: Oh, no, no, no, that’s where they jump back on. That’s where they start sharing the pod. “Grandma’s got to know about this one. My wife’s got to know about this one. My uncle’s got to know about this one.” So, yeah, I’m expecting listener numbers to skyrocket. I love it, guys. Really appreciate the time. Great conversation. And of course, thanks, everybody, for listening as always. We’ll see you next time.

Bye, everybody.

For years now, we have highlighted the structural imbalances at the heart of China’s economy – weak consumption, over-reliance on exports, and a persistent supply-demand mismatch.

• In recent months, those imbalances have well and truly come to a head – welcome to the K-shaped economy.

The upper arm of the K is powered by AI and clean-energy exports. Semiconductor exports more than doubled year-on-year in June and computer hardware shipments grew by more than half.

• The manufacturing sectors plugged into global AI demand are humming – capacity is expanding, orders are strong, and profits are up sharply.

The lower arm tells a very different story. Domestic demand is barely holding on – retail sales of goods grew just 1.0% y/y in June, and household incomes are rising at their slowest pace on record, outside of the pandemic.

And with demand this weak, manufacturers without exposure to the AI export boom are pulling back sharply on investment:

• Auto manufacturing fixed asset investment collapsed in June, while investment in manufacturing facilities for furniture, footwear, and paper products also shrank.

The message from the domestic economy is unmistakable – firms simply don’t see any reason to build capacity.

The result is that Q2 real GDP grew just 4.3% y/y – the slowest reading in over three years – despite an export boom that would ordinarily have carried the economy to a strong quarter.

• The AI and exports story is real – it just isn’t enough to offset what’s happening at home.

Beijing, to its credit, has taken note.

• At its July meeting, the central bank (PBoC) formally named “structural divergence” as a challenge facing the economy for the first time.

• Separately, at an economic symposium, Premier Li Qiang pledged to “increase counter-cyclical adjustments” and boost consumption.

• And on July 13, the State Council published a five-year plan on expanding consumption, promising to “better leverage consumption’s foundational role in economic development.”

That all sounds encouraging – but read the fine print, and our enthusiasm quickly fades.

• The consumption five-year plan contains virtually no new demand-side policy support. Its central bet is on so-called latent demand – the idea that Chinese households want to spend more, but are held back by an inadequate supply of high-quality services and a lack of trust in domestic products.

• The prescriptions accordingly focus on supply-side fixes – enforcing product standards, expanding healthcare and education options, and building better consumption infrastructure.

• Fix the shelves, in other words, and the shoppers will come.

Herein lies the crux of the problem: Beijing continues prescribing a supply-side cure for what is fundamentally a demand-side ailment.

• Until Chinese households have more money in their pockets and stronger safety nets to fall back on, no amount of supply-side support is going to loosen consumer wallets.

So where do we go from here? Policymakers will have an opportunity to signal whether a more forceful policy response is on the way at the late-July Politburo meeting, which sets the economic policy tone for the second half of the year.

• We’ll be looking for any sign that Beijing is prepared to move beyond incremental measures and confront the demand-side weakness head-on.

But on the evidence of the past week, the smart money is on continuity – recognition of China’s structural divergence, without the decisive action needed to reverse it.

• For now, that leaves the K-shape – and all the vulnerabilities that come with it – set to define the rest of 2026.

Joe Peissel, Senior Macroeconomic Analyst, Trivium China

What you missed

US-China

In a July 16 address, US President Donald Trump accused China of carrying out the “largest compromise of election data in history,” alleging that Beijing obtained records on 220 million US voters during the 2020 election cycle.

• China’s embassy in Washington flatly denied the allegations, saying China “has never and will never interfere” ​in US presidential elections.

Foreign affairs

On July 10 and 11, respectively, Xi Jinping and Premier Li Qiang met with North Korean Premier Pak Thae Song.

• Notably absent from either meeting readout was mention of North Korean denuclearization.

Econ and finance

In Q2, China’s real GDP grew 4.3% y/y, down from 5.0% in Q1 and the slowest reading in over three years.

• Nominal growth – which incorporates price effects – accelerated to 5.9%, the fastest rate since early 2023.

• The divergence is driven entirely by cost-push inflation from the Iran war, with higher input prices artificially inflating the value of economic activity rather than reflecting stronger volumes or demand.

In a July 16 video interview with Qiushi – the Party’s leading theoretical journal – influential policy advisor Yin Yanlin pushed back against what he sees as a misreading of China’s weak demand.

• His message: Aggregate policy should be more decisive and forceful – and structural or long-term reform agendas shouldn’t dilute short-term countercyclical stimulus.

Commodities

The commerce ministry (MofCom) imposed a “temporary export ban” on helium, effective immediately and with no stated end date.

• With helium prices sky-high since March, and the reignited Middle East conflict threatening further disruption, Beijing is ensuring opportunistic distributors cannot export for profit at the expense of domestic supply security.

Tech

Xi Jinping delivered the keynote address at the World AI Conference (WAIC) in Shanghai.

• Xi championed AI’s benefits, backing open-source collaboration and calling for more innovation and wider real-world application.

• But he also leaned unusually hard into the risks, asking “as algorithms make decisions, how is safety ensured?”

China and 28 other countries signed an agreement establishing a new intergovernmental body – the World AI Cooperation Organization (WAICO) – in Shanghai.

• Founding members include Brazil, Indonesia, Malaysia, Cambodia, Kazakhstan, Pakistan, Russia, Serbia, Belarus, Cuba, Venezuela, South Africa, and others.

Politics

Xinhua published the official charge sheet against former Xinjiang Party secretary Ma Xingrui.

• Notably, the notice didn’t include any hint that Ma is in trouble for anything other than graft.

• By comparison, the only other two sitting non-military Politburo members purged since 2012, Bo Xilai in 2012 and Sun Zhengcai in 2017, were painted as political threats.

As always, it was a busy week in China.

• Thank goodness Trivium China is here to make sure you don’t miss any of the developments that matter.

Our phrase of the week is: “Odyssey Years” (奥德赛时期 ào dé sài shí qī)

Context

According to a recent survey, around half of recent graduates in China say they lack a clear sense of purpose or direction after university. With millions of people competing for a shrinking pool of opportunities, the pressure on them is relentless.

So, many graduates in China are opting to drift instead of following the usual script of entering the world of work and getting married.

In this column we’ve explored phrases which define this experience. From “involuted competition” (内卷) to taking on an underpaid “work horse” (牛马) job, and eventually giving up and “lying flat” (躺平).

This experience of rejecting the usual path and choosing to drift now has a name. It’s a phrase which has been circulating on the Chinese internet since February this year, and is explored in a recent article by current affairs magazine Sanlian Lifeweek (三联生活周刊).

The article opens with a familiar genre of street interview shared on social media, in which passers-by are asked why they’re out on the streets during working hours.

The answer to that question often contains our Phrase of the Week:

‘I’m going through my Odyssey Years.’”

“我在经历我的奥德赛时期。”

And with that, we have our Sinica Phrase of the Week.

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What it means

“Odyssey Years” (奥德赛时期 àodésài shíqī) is a phrase borrowed from English. Breaking it down: “Odyssey” (奥德赛), a phonetic transliteration of Homer’s Greek epic, The Odyssey, and the word for “period of time” or “years” (时期).

The phrase was first coined back in 2007 by New York Times columnist David Brooks.

Brooks argued that life once had four stages: childhood, adolescence, adulthood, and old age. He suggested there are now at least six stages. Including a new phase between adolescence and adulthood which he called “the Odyssey Years”.

This is a reference to how, after the Trojan War, Odysseus spent ten years enduring endless hardships before finally returning home to his wife and son.

Nearly two decades after Brooks coined it, the phrase has found a second life in China. It first began resonating in February this year through posts shared on lifestyle platform Red Note (小红书), and then started to go viral across the Chinese internet in April.

“The Odyssey Years” is how young people in China describe the drifting years in their twenties: switching constantly between jobs, cities, and studies, delaying marriage and children, and often still relying on parents for financial support.

It sits alongside related buzzwords describing the same generation, like “slow employment” (慢就业) and “gap year” (间隔年).

Part of why the phrase caught on is that it gives a name to this lack of direction. It reflects a growing rejection of the “social clock” (社会时钟), the expectation that life should happen on schedule, and echoes another popular saying among young Chinese:

“Life is a wilderness of possibilities, not a path set in stone.”

人生是旷野而非轨道。

Another phrase which has appeared alongside the Odyssey Years in these conversations in China is “emerging adulthood” (成人初显期), which is another imported term coined by American psychologist Jeffrey Arnett in 2000 to describe the same in-between stage of life.

But it’s the Homer reference, though, that has captured imaginations in China.

Invoking Odysseus makes the drifting feel less depressing, and offers hope that eventually a destination will be reached. But unlike Odysseus, China’s young people today might have no Ithaca waiting for them. So this new label is a way of romanticising that decade of confusion, tinged with resignation and a little bitterness.

The phrase’s popularity in China came well before and is unrelated to Christopher Nolan’s interpretation of the epic, starring Matt Damon as Odysseus, which opens in mainland China on August 14.

But with the hype around the movie already building, the phrase is likely to get a boost in the coming weeks, and could be an early contender for our Phrase of the Year in 2026.

Andrew Methven is the author of RealTime Mandarin, a resource which helps you bridge the gap to real-world fluency in Mandarin, stay informed about China, and communicate with confidence—all through weekly immersion in real news. Subscribe for free here.

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Read more about how this story is being discussed in the Chinese media in this week’s RealTime Mandarin:

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#285: The “Odyssey Years”: Understanding China’s latest viral slang phrase

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10 days ago · 15 likes · 1 comment · Andrew Methven

• But while US policymakers tend to view data primarily through a national security lens, Beijing is pursuing a much broader strategy aimed at unlocking data’s economic value.

On this week’s Trivium China Podcast, host Andrew Polk is joined by Trivium’s Head of Tech Policy Research Kendra Schaefer to explore why China has formally designated data as a “factor of production” – and how that idea is reshaping the country’s technology and economic policies.

The two discuss:

• Why low-cost Chinese open-source AI models are increasingly attractive to Western companies

• How restricting access to those models could undermine US competitiveness

• What Beijing means when it describes data as the economy’s fifth factor of production

• China’s efforts to make data easier to find, price, trade, and use as collateral

• Why Beijing views data security rules as necessary guardrails for a functioning data market

• How China’s approach could strengthen its AI ecosystem by increasing the supply of high-quality data

Andrew and Kendra also examine the absence of a coherent, pro-growth US data strategy – and why Washington’s overwhelming focus on security risks may be leaving significant economic gains on the table.

Overall, the discussion reveals that China’s seemingly disparate data policies are part of a much larger project: building the infrastructure needed to turn data into a more productive and widely traded economic asset.

Transcript

Andrew Polk: Hi, everybody, and welcome to the latest Trivium China Podcast, a proud member of the Sinica Podcast Network. I’m your host, Trivium Co-Founder, Andrew Polk, and I am joined today once again by a pod favorite, or a pod fan favorite, Trivium’s Head of Tech Policy Research, Kendra Schaefer.

Kendra, how are you doing?

Kendra Schaefer: I’m good. I’m good. How are you?

Andrew: Oh, yeah, I can’t complain. I’m excited for this discussion. Always good to get back in a rhythm with the pod after being off for a couple of weeks. So, I got to talk to Dine last week, get to talk to you this week. So. I’m excited about it. Thanks for coming on.

Kendra: Of course.

Andrew: I am going to talk to Kendra today about some of the research she’s been doing kind of on an ongoing basis for a while now, specifically around how Chinese regulators and Chinese policymakers think about data and how to sort of use data in the economy, how to govern data, all of that stuff. The framework is data as a factor of production. We’ll get into what exactly that means.

So, we’re going to do a deep dive on that. It’ll be wonky, but super unique research that Kendra has been doing that I’m excited to get into. Before we do that, though, we are going to talk a little bit about some of the latest developments in the kind of China tech space around AI, specifically around what’s happening with open-source models and more Western firms opting to use open-source models for cost purposes and potential restrictions coming both from the Chinese and U.S. side on those models.

So, we’ll touch on that briefly before we get into Kendra’s research. But before we do that, of course, we have to start with the customary vibe check. So, Kendra, how’s your vibe today?

Kendra: My vibe is actually really mellow. Nothing catastrophic has happened in the China space in the last 48 hours. And I’m pretty excited. I’m going to Taiwan. I think I mentioned the last time I was on the pod, I had an Asia trip coming up, and now it is imminent. I’m going in a couple of weeks to Taipei with the Brookings Institution delegation. So, I am pumped.

Andrew: Yeah, that’s exciting. Good to get back over to the Asia time zone. I know it’s been a minute since you’ve been over there. It’s always nice to get back on the ground and hear what people are saying. I know that’ll be a great trip. Very cool that Brookings is having you along for that. So excited for you. My vibe, similarly mellow. I feel like we’re sort of in the dog days of summer.

You know, it’s like you said, nothing catastrophic has happened. Our clients, in a good way, seem like they’re not having any fires they need to put out. And so, we don’t have people blowing up our email inboxes first thing in the morning. Oh my gosh, we need to figure this out, figure that out. So, I’m just kind of leaning into the casual summer vibe. So, we’ll bring that mellow vibe to the podcast today.

Kendra: I don’t know if I can promise that based on what we’re going to talk about.

Andrew: Well, I was going to say, Kendra Mello is sort of calm before the storm by definition. So, it actually makes me more nervous when you’re like, “Oh yeah, mellow.” I’m like, uh-oh, something’s coming. But no, we will channel your energy into the discussion today. So, that’d be great.

Kendra: Okay.

Andrew: Of course, before we get into the content, though, we also have to do the quick housekeeping up top. So, a quick reminder, we’re not just a podcast here. Trivium China is a strategic advisory firm that helps businesses and investors navigate the China policy landscape. That, of course, includes domestic policy in China, much of which we’ll talk about around tech and data factors today. But it also includes policy towards China out of Western capitals like D.C., London, Brussels, and others.

So, if you need any help on that front, on any of those fronts, please reach out to us at hq@triviumchina.com. We’d love to have a conversation about how we can support your business or your fund. Or if you just have comments on the pod content, reach out to us. We always love to hear feedback from our listeners. I mean, we prefer positive feedback, but we also will take constructive criticism.

Kendra: We’ll make fun of you in the office.

Andrew: Yeah. Behind your back, and then we’ll respond. No, we don’t do that. We never do that. Secondly, if you’re interested in receiving more Trivium content, check out our website, triviumchina.com, where we have a bunch of subscription products, both free and paid. They’re all sort of focused around Chinese policy intelligence. So, we’ve got a bunch of different options. Kendra’s team produces a daily tech policy update. We’ve got updates on policy impacting markets and impacting sort of the business landscape.

So, check out the site. You’ll definitely find the China policy intel option you need. And then, finally, please do tell your friends and colleagues about Trivium, both about the business and about the podcast. It really helps us grow the company. And I say it every week, but we truly, truly, truly appreciate the word-of-mouth recommendations. They mean a lot to us. And a word-of-mouth recommendation is so much more powerful than someone finding us randomly through a quote in the newspaper or whatever. So, we appreciate folks for spreading the word about Trivium.

While you’re at it, leave us a rating on your favorite podcast platform. That also helps us grow the visibility of the podcast. So, with that out of the way, let’s get into it. You ready, Kendra?

Kendra: I’m ready. Let’s go.

Andrew: Well, so like I said, I think I want to start just with some of the recent developments in the tech space. The number one theme or narrative I’ve kind of been looking at in this space for the past few weeks is companies increasingly thinking about or questioning the cost of AI investment, of building AI processes into their internal systems, partly because everyone thought, oh, well, we’ll be able to replace humans more cheaply with automated systems and AI.

But it turns out that it actually is quite expensive. And a lot of companies are finding out that their investments are actually having lower ROI than investing in humans. I think Alex Karp, the CEO of Palantir, had an interview, I believe it was on TV, where he talked about kind of the weak ROI and how it’s making companies rethink how they are approaching the issue. And his, I think, suggestion was that AI companies rethink their enterprise model.

I don’t know if that will happen. But that, I think, is also related to this idea and increasing reporting that a bunch of Western tech companies and startups in particular, partly because of this cost issue, are really basing much of their tech build out on the open source AI models, because they’re either close to the cutting edge or they’re good enough and miles cheaper that it makes sense from a cost perspective for them to rely on the Chinese models.

So, I just wanted to throw that over to you, Kendra. What do you think is happening here? How do you see the state of play in terms of these cost differentials and the dynamics of more and more Western companies taking a look at potentially employing Chinese models to a greater and greater degree?

Kendra: Well, this is an issue, as you know, that’s near and dear to my heart because I not only run our tech practice, like our tech analysis practice at Trivium, I also sit over our IT department. And of course, we are working with models internally. Have we talked about, you know, model cost on the pod before? Remind me.

Andrew: I don’t think so, actually. Yeah, let’s get into it. I mean, this is another one where it’s wonky and this is pretty inside baseball, but I think what we’re doing is actually quite illustrative of this bigger issue. So yeah, let’s talk about it.

Kendra: I mean, I think what we’re doing is the issue and it is sort of half the issue. So I think many of our listeners probably will have already used an LLM programmatically. They will have tried to interact with an LLM. They are coders themselves or are vibe coding apps and stuff like that. But there’s also a large subsegment of listeners, I think, who probably haven’t done that and don’t really understand what the cost issue is. We have had a sort of intimate experience with understanding where Chinese models are kind of winning the day and where they aren’t.

So, I want to not make that such a squishy conversation, but give a very specific example. So, for illustration’s sake, so we use LLMs for processing massive amounts of policy documents. So, just for illustration’s sake, and this isn’t exactly what we’re doing, but let’s just say we need to take a million policy documents and flag, you know, it would take a human countless hours to read all of those and figure out whether or not they’re related to a specific sector, autos, semiconductors, whatever it is, or if they have a subsidy amount in them and what that subsidy amount is, right?

But we can take that giant pile of documents, and we can pass it through an LLM and ask it to do that analysis and then maybe sell that output to a client or use that output in our research or whatever it is, or create a data product with that output. Processing a bunch of policy documents is a low-stakes, low-security use case. It doesn’t matter if the model is Chinese or just parsing boring open-source documents. There’s no client data going across that channel. There’s nothing, you know, even remotely sensitive that is sort of passing across those queries.

And we’ve tried these processes internally with both U.S. models and with Chinese models. And the bottom line is that the U.S. models are two to 10 times more expensive. And I think for one project that we ran some R&D on, it was like 20 times more expensive. That cost differential decides whether or not our product is profitable. Can we even build this? Should we even do this? That’s a huge difference.

It’s the difference between it costs us $100,000 a year to run this service, or it costs us a million dollars a year to run the service, and clients won’t pay for it. So, it’s really kind of that cost is a real make-or-break thing. There was one tech CEO, I think that was quoted, I think we quoted him in the Daily a couple of weeks ago, I think it was the CEO of Lindy, which is like an office productivity platform who announced on their blog that they’re using Chinese models for some of their features. And he just said, “I don’t need God to write my emails. I don’t need God to write my emails,” which is true for so many use cases, right?

And so that’s not a U.S.-China thing. It’s just a cost thing. There really isn’t a US alternative where the model’s pretty good. It’s good enough to handle those kinds of things. And then, in addition to that, the cost of it is cheap. So, there’s a thousand reasons that a company would choose cost over quality. R&D, you know, you’re just like testing a theory, you’re making a prototype, you don’t want to use the best equipment, you just want your proof of concept so that you can get to a place where maybe you switch to a U.S. model after that when you want a better quality, you know, or you’re kind of looking for top dollar.

Andrew: Yeah, that actually raises a point that I just want to throw in quickly, which is probably, I mean, is an obvious point to everyone like you who’s using these LLMs and to a lot of companies who are trying to figure this out, but maybe not to some people, which is there’s no perfect solution typically with this kind of thing. You’re constantly toggling or adjusting the dials between speed, costs, and quality, right? Quality of output. And so, at various times, you’re optimizing for different ones. Obviously, every company wants the highest quality, the fastest speed at the lowest cost, but sometimes you have to trade off on some of those things, and the Chinese models give you a different trade-off at times.

I guess one other question for you, if you can talk about a little bit is, you know, for what we do in terms of kind of looking at Chinese policy documents and other things in that area, are the Chinese models better with working with Chinese language material, or is that not right?

Kendra: Oh, a thousand percent. I mean, but our use case is so niche, it almost doesn’t matter. Maybe our listeners care. Definitely, the Chinese models are better at Chinese policy documents than the foreign models. But I think for most people, that’s probably not really that big of a consideration. But it’s like, I do think that for most companies, unless you are a coding firm, unless you are a bleeding edge tech firm, there is a lot that companies can do with LLMs.

I mean, and we’ve only started to scratch the surface of adoption, right? Corporate adoption really hasn’t filtered out. And we work with lots of companies who don’t use AI at all yet, right? So, it’s just there’s this huge space where you’re going to have companies who want to use all kinds of models for all kinds of purposes. It’s not like we use four different models in our work, and we just use the right tool for the right job. But if the only tool available is the top-of-the-line, most expensive tool off the top shelf, that is very problematic for our economics.

Andrew: Yeah. We can talk more about this on later pods. I’m sure folks would be interested in how these models inform our work and what some of the behind-the-scenes stuff is. I mean, I think it’s interesting. I think people think it’s interesting. But today, I don’t want to spend too much time because I want to get to the data factors piece. But before we do that, the additional piece of this is since there’s been sort of more reporting about how U.S. companies in particular are using more and more Chinese models, the U.S. government, of course, has taken interest in this issue.

And over the past week or so, there’s been rumors, particularly flying around on X and things like that in the policy space where people are saying the White House, in particular, the U.S. government is considering trying to restrict access to Chinese open source models. And there was a suggestion that an executive order to some effect on this might be coming out, but the White House has denied that. But anyway, I just wanted to get your thoughts on, you know, what you think about that as an issue, you know, whether or not the U.S. government should do that.

I can guess what your answer is to that, but also how that would kind of work and just, I don’t know, provide some context to us about that latest reporting.

Kendra: Well, yeah, I’m sure you can guess how I feel about it. Basically, unless there is a really also not just one good U.S. alternative that is a low cost and good enough alternative, but a robust ecosystem of competitive U.S. alternatives, it is a real bad idea to restrict access to the models that allow innovation to happen in small businesses, in the laboratory, right? All of those kinds of things. There are other reasons besides cost to choose an open source model.

That includes being able to download it and install it on your own machine at home or more likely in your own private corporate data center, which you can’t really do with U.S. models. So, the U.S. just simply doesn’t have a great alternative. And I think you said something to me earlier, which really rang true, which is like if the U.S. decides to try to ban access to Chinese models, and I’ll talk about how I think they might be able to do that in a second, but if they go that route, I mean, it’s basically the same route as saying, “Hey, we can’t manufacture a good NEV either. China’s got cheaper, better NEVs now, but we’re just not going to allow them into the market.”

Did you see the, I think the CEO of Ford a couple of days ago, you know, it was like one of the New York Times headline essentially said, “Look, we support the U.S. in blocking Chinese cars from coming into the market for now, but you absolutely aren’t going to be able to keep them out forever. And we have to be able. in the long term, to compete on a playing field with Chinese manufacturers.” And it’s the same thing here. It’s like, okay, well, you can ring fence the United States for a little while and let everybody else use cheaper open weight models. But the economics get real wonky the longer you hold that line if we don’t have a good alternative and we simply cannot be competitive.

So, I think that has to be addressed. If they want to do a ban, all right. But man, we better have a good alternative and a plan for how we’re going to offer cheap processing to domestic companies or I think it’s stupid.

Andrew: Yeah, well, and I hate the reaction being, the knee-jerk reaction to being we want to win XYZ part of the tech race. And so, we are just going to keep China out of our market. Like, it just strikes me as such sort of simplistic thinking, like we want to win. So, we’ll just kind of block them. And like the way you win is be the most competitive.

Kendra: Right. That’s what I mean. You don’t tie your opponent’s shoes together. That only gets you so far. You know, you might win a couple rounds doing that. But I just don’t think, over the long term, that’s not a sustainable strategy. We can’t just keep saying, “Well, okay, then you just can’t sell. You may not have a better one. You can’t sell that here.” I mean, it just isn’t…

Andrew: Well, and that doesn’t even account for, you know, what does that do for the global landscape? Like, you do reduce your competitiveness globally. And now, oh, great. Well, all U.S. companies run on really expensive U.S. models while the rest of the world works on just as good or nearly as good, very cheap Chinese models. Like, that’s not a positive outcome.

One quick thing before we finally pivot is you also, I said we weren’t going to get into this too much, but you’re unclear exactly whether or not the U.S. government can keep open-source models out of… how do you even enact a ban like that?

Kendra: So, I think from what I understand, there’s a couple of options under discussion. The first one and the most obvious one, although this has already been done to some extent, I think, is federal procurement bans, basically, right? Which is what they did with TikTok is the very first step the federal government took was that you can’t put this on a government device, which is just that’s very low-hanging fruit. But they could also say any government supplier can’t put it on, you know, can’t use it either, or you can’t be a government supplier. So, there’s those kind of that could extend in that way, or you cannot use this tool on a government contract, basically. So, they could go that route.

I think the main concern is that the Commerce Department is going to use the ICTS, like sort of supply chain restrictions toolkit that they’ve got. Basically, the USG has a rule that essentially says if a tech product or service comes from a foreign adversary and could be used to spy on Americans or sort of threaten U.S. national security in some way, then commerce can kind of ban it from the U.S. market or force changes to how that is used.

The problem is that this rule regulates transactions. So, it’s kind of awkward to try to characterize downloading open-source models as a transaction. So, the question is, which touchpoint would they go for? They could maybe go to cloud companies and say, “No U.S. cloud provider can host these models, which is mostly how people are using that.” It’s a large, not everything, but it’s a large chunk of how U.S. companies are using those models. They’re going through Amazon. So, you could do it that way.

They could try to go to like Hugging Face, which is where models are listed, where a lot of these open-weight models are listed and try to ban them from listing it in some fashion, which would make it difficult to download. People wouldn’t know where to go to get it.

Or it would be, I’m sure in two minutes, somebody would put up another website and just like post it.

Andrew: Yeah.

Kendra: So this is difficult to enforce.

Andrew: Our colleagues didn’t think my joke was funny, but obviously it’s just going to be on the dark web, which is where I’m most proficient.

Kendra: It’s where you hang out.

Andrew: Yeah, yeah, exactly.

Kendra: That’s where you hang out all the time. I mean, okay, and so there’s that. And they could also, I think, use the, what is it, the emergency powers, IEEPA, right? They could kind of declare it an emergency and go for it that way. So, there are things that they could essentially do. I very much hope that policymakers are weighing what it would mean for U.S. firms to not have access to that kind of technology. And what I would love to see is if the U.S. government focuses on how to incentivize the development and release of a cheap open-source U.S. model, all this goes away.

I don’t care if I’m using a Chinese model, to be perfectly honest. I’ll deal with like a slightly crappier… you know, if I don’t have to deal with any U.S. government problems, I don’t care if I’m using a Chinese model or U.S. model. I care if it’s cheap and good enough. That’s all I care about, right, as a developer. So, why don’t we just focus on figuring out some policy incentives to make sure we have one of those? I don’t understand why that’s not the primary topic of discussion. Or maybe I’m just not in those rooms, and maybe it is. But anyway, yeah, that’s my thinking on that.

Andrew: Yeah, well, we’ll leave that on the to-do list, figuring out a policy agenda to advance U.S. open source or U.S. developed open source models. I’m sure someone somewhere is having that conversation. But that’s all super helpful, very interesting stuff. We’ll, of course, stay on top of all of that as it develops because it’ll be an important part of not only what we do, but very important for our clients as well. I want to pivot now to your research. We’re going to get into your work on data factors or data as a factor of production.

And this is kind of evolving thinking in the Chinese side around how the government treats data, how everything from taxing data to, you know, data ownership, all that stuff. So, you’ve been doing this research for a long time. How long, you’ve been doing this? What? For like six years now?

Kendra: Yeah, I started in 2020. It’s been six years. I have been cornering people at parties about this and torturing them for six entire years.

Andrew: Well, that sounds like a fun party. Remind me not to go to any of your parties. So, the topic overall is what? How China thinks about data. Is that not something that sort of we already know the answer to? I mean, it seems like it should be relatively straightforward, but maybe I’m wrong.

Kendra: Yeah, no, you’re right. I mean, I think that’s a perfect place to start, because if you ask anybody in D.C., what’s the big U.S.-China data issue or how does China think about data, you’ll probably get something to the effect of China’s primary goal is to steal sensitive data from American citizens or the United States, and the U.S. has to prevent that from happening, right? That’s the vast majority of the D.C. conversation on U.S.-China data.

Andrew: Yeah, that’s a little mind-numbing for sure. I have had that conversation many times in Washington, but what’s the conversation more if you talk to people about this outside of the D.C. bubble? How are people thinking about this that aren’t so focused national security and policy and that kind of thing?

Kendra: I mean, I think the other group of people that we talk to about this is foreign companies that operate in China. They’re not obviously as worried about data exfiltration, but they’ll kind of tell you the biggest issue is cross-border data flow, right? China’s got one of the strictest cross-border data regimes in the world. And for the last five years, I think multinationals kind of been tearing their hair out trying to get their own information out of China. And so that’s basically what corporates are talking about.

So, DC is talking about China’s trying to steal our data. Corporates are talking about how do we get our data out of China and how do we comply with Chinese data laws without screwing up our R&D processes and stuff like that. But as far as I’m concerned, both of those views or both those conversations really only look at a teeny, teeny, teeny, tiny corner of the conversation that is happening inside of China about data. In China, the government has been having a very broad conversation.

They’ve essentially developed a sort of part theory, part national strategy about what role data plays in the economy, how to activate the economic power of data, how to use data to boost GDP and make gains, and how to kind of bolster technological competitiveness by increasing the supply of data. So, we saw this start kind of six years ago, and then we’ve just been watching that theory evolve over time. And it’s now driving this huge wave of Chinese tech policy.

And I think that wave is sort of flying under the radar a bit in the U.S. You don’t often hear people talk about how the Chinese government thinks about data.

Andrew: Why do you think it is so under the radar? I mean, if this is like the fundamental thrust behind the conversation in China, why isn’t it on, you know, more people’s agenda here?

Kendra: Well, that’s a good question. I mean, I think two reasons. One, you know, all of the data policies we’re going to talk about today, individually, if you look at them by themselves, they’re just deeply unsexy. It seems very uninteresting. They’re really interesting in aggregate, but they’re very uninteresting by themselves. And so, unless you can see what they mean in aggregate, looking at one particular piece of it, isn’t that fun?

And then, two, I think the way that China’s looking at this is so different. I mean, deeply different from how the U.S. talks about data that it kind of doesn’t even register. It doesn’t pattern match to anything in the U.S. policy conversations. We don’t see it.

Andrew: Well, that, I mean, I think is exactly why you and I wanted to have this conversation, right, is to start highlighting this. But why do you in particular think it’s so important at this moment that we, yes, the U.S. policy community start to see it for what it is now?

Kendra: I mean, I think the answer is pretty easy, right? Data supply is now a core input to AI development. The AI competition that everyone’s obsessed with is in part a data competition. So what we’re going to talk about today is a very heady idea, right? How the Chinese state views data. What is the long-term strategy? You know, what’s the big idea underneath these little policies, and what that means for the U.S.?

But that’s also now very intimate. Like five years ago when we started looking into this, that was a very squishy concept. But now it has this immediate economic impact because of how important it is or because of how critical and central data is to artificial intelligence.

Andrew: Yeah, good point. All right. Well, let’s get into some of the details here. Where do you want to start in terms of diving in?

Kendra: Okay, awesome. So this is me cornering you at a party now.

Andrew: Oh, no. Look at the time.

Kendra: All right. So this is kind of going to sound like a bait and switch, but I want to start this conversation with a concept that doesn’t seem to have anything to do with data at all, because getting into how China sees data sort of hinges on understanding the sort of econ 101 concept, which is what is a factor of production. And I think a lot of our listeners probably remember this from school, but I don’t know, you’re an economist, do you want to give us the 30-second refresher, remind everyone what is a factor of production?

Andrew: Yeah, I mean, I think I can do it in less than 30 seconds. I mean, traditionally, factors of production are land, labor, and capital, right? So think about the agricultural economy, you’d need land, labor, of course, humans, the people who’d do the work, and then capital being both money and equipment. So equipment, of course, matters in agriculture, but also in manufacturing.

So, basically, the fundamental inputs that you need to produce economic activity is what we think of as factors of production.

Kendra: Right. So, a factor of production is the input necessary for businesses or whoever to create economic value. And if they don’t have those things, they cannot create output. And there are typically, I think in traditional economics, there’s four, you said land, labor, capital, and then China calls the fourth one technology. I think the U.S. calls it entrepreneurship, but basically like IP know-how, you know, like...

Andrew: Yeah. Well, I would call it sort of productivity. Doesn’t matter. I won’t be potentially on that, but it’s really how those things interplay. Like, basically, productivity is how well humans use capital and land.

Kendra: Right, right, right.

Andrew: That’s like a little bit, but anyway, yeah.

Kendra: Right. So, if you’re going to do business, you need somewhere to operate. You need people to do the work. You need money to fund it. You need to know how to put it all together, right? So that idea of those are the inputs to the creation of economic value, that idea has essentially been stable for about a century, right? It’s the sort of periodic table of economics and nobody messes with it.

Andrew: Yes. And I feel like there’s a but coming here in the China context.

Kendra: But in 2020, China did actually mess with that idea. So this is a kind of interesting part. So, in 2020, the State Council released this high-level macroeconomic policy. And buried in that policy was something quite remarkable, right? The policy basically designated data as the fifth factor of production. So now, according to the sort of canon of socialist economic theory that China runs on, and remember, that’s like the foundational theory that the entire state apparatus uses to make policy, right? We’ve decided that this is the sort of economic theory. And based on this theory, we’re going to make some rules and we’re going to make some policy incentives.

There are five factors of production — land, labor, capital, technology or whatever, and data.

Andrew: Mm-hmm. And what’s the point of adding data? I think it’s somewhat obvious based on what we have talked about so far, like pretty obvious input. What do you think the point is of China to elevate data to that level in the canon, so to speak?

Kendra: Well, I think by doing that, what the state is formally saying is in a digitized economy, companies need data to produce economic value, right? As you said, in the agricultural economy, let’s say 300 years ago, if you wanted to create value, you need a plot of land and you need a dude to farm that land. So you need land and labor.

Andrew: Dude.

Kendra: But in the digital economy… a dude, a dude. But now, in the digital economy in the modern age, you need data as an input, or your company needs data as an input in the same way that they need financing. And so that sounds abstract, but it actually has these enormous practical implications because like, think about what that means. It means the state is taking responsibility. If the state names something a factor of production, they’re basically saying the state is responsible for making sure that companies can get this thing.

Andrew: Yeah, that does make sense. And I mean, in a way, with agriculture being such an important part of kind of how Chinese policymakers think of the economy, they would never actually drop land as a factor of production. But you can see for most modern economies, land is sort of less and less an important one. So, it’s almost like you could add data and take away land. Like, for our business, we don’t need land, but we do need data. But that’s just a quick point. But more like, what do you mean like in terms of people or companies getting data? What do you mean by getting it?

Kendra: Well, so, okay. So, it’s the state’s job to create a market environment where businesses can access the inputs they need to grow and contribute to GDP, right? So, if companies need labor, that’s fine. It’s on the state, then to kind of build an education system that produces the right workers or to write employment laws that like balance the needs of employers and employees so that talent can flow smoothly between firms and hiring and firing can happen while balancing everybody’s needs. So, it’s kind of on the state to create the background, the environment in which labor can get to companies, where they can acquire it and use it well.

And then if companies need land to build a factory, it’s kind of the same thing, right? It’s on the state to run zoning, to run deeds and titles, to write property and ownership laws. Those are things that we take completely for granted. It’s like invisible infrastructure of the market. We never even think of it. But those systems are basically what keeps factors of production moving throughout the economy and keeps them flowing into…

Andrew: Companies and enterprises. Yeah, that makes sense. So you’re saying basically that this same logic, at least in the Chinese context, now applies to data. The state is taking a role in making sure there’s an ecosystem that sort of curates and feeds data into companies, broadly speaking. Is that right? Do I have it right or is it different than that?

Kendra: Yeah, yeah, exactly. Exactly. The state’s saying, “Look, there’s already a capital market. There’s already markets for land and natural resources. There’s a labor market. And now it’s on us to build a data market, the systems, the regulations, the standards that basically govern how data gets bought and sold and traded so that it can sort of circulate through the economy and so that businesses can get our hands on it.” And in order to describe that idea, the state has basically formulated or coined this term data factors, meaning data when we view it as a factor of production, data as an economic input.

Andrew: Okay. Yeah, that makes sense. I guess the question then for me is when you talk about “building a data market,” you know, strikes me that data gets bought and sold all the time without the intervention of the state, right? And there are data brokers, there are entire industries already existing around this, both in China and elsewhere. So, why does the state need to build anything? Like, what specifically does it need to build?

Kendra: I mean, actually, that’s such a great question because I think there actually is a big open question about whether or not the state needs to do anything or needs to take an interventionist approach to this at all. But I think if you asked Beijing what the issue was, they’d say that for every other factor of production, humans have been trading it for, in some cases, hundreds of years, right? We’ve been trading land for hundreds of years. And so, the rules of the road are kind of ancient. I mean, we solved the fundamental plumbing problems that make those markets run to the point we don’t even see them anymore.

But none of that plumbing is there for data. Okay, so that all sounds squishy. We’ve been very squishy. Let me get very concrete. Let’s do a concrete example. So, imagine that you wake up today and you decide, I want to buy an acre of forest land in Washington state. So, what is the first thing you do after you’ve decided to do this?

Andrew: Well, either Google or ask an LLM or a ChatGPT, where do I buy land in Washington? I mean, no, I guess you sign on to some third-party site, like a Zillow for land.

Kendra: Right. You would know exactly what to do. You want to buy real estate, you open a real estate website. There’s a real estate market at your fingertips. You would open one of a dozen well-known sites, all of which are kind of pulling from these centralized property listing systems that have been there forever and you just browse what’s available. Consumers know where to shop, no bigs. Now, imagine you want to go buy access to regularly updated shipping container movement data. Now what do you do?

Andrew: Same answer, right? Google, ask ChatGPT. I don’t know. I mean, truly, that’s where I’d start.

Kendra: But there’s not like containerdata.com. Like containerdata.com is not like It’s a common marketplace where all data sales are happening.

Andrew: Website idea.

Kendra: Oh, there we go. We can just quit what we’re doing right now. So, there’s like, the real estate, there are well-worn pathways for discoverable real estate and not so much for other kinds of data, right? You’d like, you’d poke around online, you’d Google it, but there’s no… a business can’t wake up and say, I need this very specific kind of data and I know where to acquire it in most cases. Does the supply of data you want even exist? Who has it, right? And so, the reason data brokers exist is because you go hire these people to find data for you because there is no place that you can just simply go find it yourself in most cases, right? So, that’s one problem, discoverability. How do I discover the supply? Where is it? How do I get it? Does it even exist?

Problem number two, okay, you’re back on Zillow. You’re buying your acre of forest. How do you figure out what you should expect to pay for that data?

Andrew: Compare… well, see what’s out there, right? Look at what’s on the market and compare them to, I guess, decide the parameters of what you want and compare them to other comparable acres of land, houses, etc., whatever you’re trying to buy there.

Kendra: Yes, exactly. You look at comps, or you look at a house with the same… if you’re buying real estate, you look at a house with the same number of bedrooms and bathrooms that you’re looking for in the same street. And you’ll say, “Oh, with the same square footage,” and you’ll say, “oh, it usually sells at this particular price.” You found your million-dollar parcel, right? Whatever.

And then the value, whether or not that value is correct, basically gets confirmed through an appraisal in the process of buying your property. And it’s the same with the labor market. If you want to hire a senior engineer with 10 years of experience, you check Indeed or ZipRecruiter or Glassdoor, and you see what everyone else is paying for the same set of skills. And of course, capital markets have decades of these sort of established valuation methodologies. So, you can find the price for similar items easily, whether you’re buying or selling.

Now, if you’re buying or selling that shipping container data, what should you expect to pay for that? How would you know that you’re paying fair market value if somebody does quote you a cost? And if you are selling data, how do you even know what it’s worth or what you should be charging for it at all?

Andrew: Yeah, I mean, I guess no real answer. I don’t really know. But I mean, fundamentally, I guess it’s worth whatever someone’s willing to pay for it.

Kendra: Right. Yeah. 100%. There’s no real standard metric for valuation. This type of data is valued at this amount of money in general. Right? It’s very hard to do that. A, there are so many different types of data. But B, we just haven’t been selling it that long. And it’s hard to compare one data transaction to another data transaction right now. And so, I mean, I think this is very interesting, but the inability to put a very clear standardized value on data actually creates this sort of cascading set of downstream problems.

And here’s my favorite one. Let’s say you’re a small tech startup. You don’t really own that much physically. You don’t have any equipment, you don’t have real estate, you don’t have tractors or anything. But you’re sitting on a genuinely valuable data set, or you’ve collected or made some data that is worth a lot. You think it’s worth a lot. That data is your most valuable asset. Now you go ask a bank for a loan.

Andrew: And of course, They want like collateral or something to back the loan.

Kendra: Right. They want collateral. You don’t have physical assets. Physical assets work in collateral in part because they’ve got a clear value. The bank knows it can resell your equipment for a million dollars if you default. But if it takes your data, which is your only asset as collateral, what are they going to recoup on that? Where are they even going to put it? How would they offer it to…? They can’t price it. They don’t know what it’s worth.

And so, that creates this situation where data-rich companies that don’t have a lot of assets, which is to say like a lot of tech startups, become a sort of structural advantage when they’re looking for financing. They can’t use this valuable thing that they have.

Andrew: Yeah, I guess I had not thought about it from that aspect in terms of becoming a structural challenge for capital allocation. I mean, I think maybe the U.S. and the West broadly may be a little bit better at that through venture capital, but that’s like, basically, gambling is the wrong word, but you’re taking big bets on something you have no idea about. And China has obviously a venture capital ecosystem, but there’s a long-term problem that small companies, innovative companies can’t get capital. So this makes sense that it would feed into this issue of lending issues, capital allocation issues.

Kendra: Yeah, exactly. I’m going to give one more example just to give a little bit more meat on the bones. So let’s say you bought your land, you have purchased it, and now you go to closing, and it’s time to take ownership of that land, right? There’s a mechanism for doing that that is very well worn. The deed gets transferred into your name, and that transaction and whose name is on the deed gets registered with some kind of county recorder’s office so that forever after, if anybody needs to verify who owns that land right now, they can check the registry.

There’s nothing like that for data. We don’t really even conceive of data as something you would need to register in that way, right? That you would need to kind of confirm that you have the rights to buy and sell and the right to own and the right to use, that there would need to be some kind of allocation. Beijing does think that that is probably necessary. So, you can kind of see these four issues pulling back a little bit, right? All of these things are related to trade, these kind of invisible pieces of it, discoverability, valuation, can you figure out how much it’s worth? Collateralization, can you turn something into an asset that can be used as collateral?

And registering or confirming ownership or rights to ownership over some kind of property. Those are four of the many unglamorous, invisible plumbing problems that have basically been solved for every other factor of production and just don’t exist at all for data.

Andrew: So, you’re saying that basically establishing those four things for data is the underlying project that the Chinese state or policy apparatus is trying to achieve here? Do I have that right?

Kendra: Yeah, that’s the whole project. I mean, not just those four. There’s probably about 20 different unglamorous plumbing problems like that, that the state has identified and gone, OK, we’re going to have to launch a sort of policy initiative to do that. But yeah, I mean, when Chinese policymakers say data is a factor of production, what they’re really committing to is just what we said, define the fundamental rules and processes and systems surrounding transactions so the market can grow.

And the theory of the case is if we make data easy to find, if we make pricing standard and predictable, if we let companies legally sort of establish and protect their rights to data so that they can trade it, then more companies will want to sell data. More companies will buy data. That means more companies will acquire and use data, empowering the data economy and share data and trade data. And so, supply goes up, and circulation goes up. That’s generally, that’s the fundamental data theory.

Andrew: Okay. Yeah, makes sense. All right. So, thanks for laying that out. I think that kind of sets the sort of theoretical and sort of contextual piece of this. But let’s kind of go a layer down. What can you talk about, like an actual policy here, something sort of more concrete that solves one of these problems that the Chinese policy apparatus is putting forth?

Kendra: Yeah, actually, I’ll give you three. I’ll talk a little bit about how the state is actually trying to solve those three problems, like those couple of the problems we just talked about. So, first, the registration and ownership problem, right? How do you confirm you have the right to sort of use a specific data set in a specific way? What we’re seeing now is that the NDRC, China’s big sort of macroeconomic agency, is piloting what they’re calling a data property registration system.

So, you can think of that, well, the way they’ve described it is a land registry or like a patent office, a securities depository, but for data, where data owners and users can register their claims, log rights to use, and then trace the history of ownership of specific types of data. So, in other words, I could basically say I made this data set. I’m putting it on this registry. I think they’re talking about the underlayer maybe being built on blockchain or something like that.

But I’ve got this registered that I’m the owner of this data set. And then let’s say I’m transferring… It’s not really actually with data about transferring ownership. It’s, I’m going to allow you to use my data set for the following purposes. And the right to use the data in that way is then logged in this registry. And the end user can then take that data and use it without worrying that there’s going to be some kind of… you know, there’s like a clear transaction that they can point to and a clear rights document that they can point to that is sort of part of a sort of central depository.

So, that’s the general idea with that. And they’re already kind of trialing that at the local level. Shenzhen in particular is actually running a trial that’s supposed to go national in a couple of months. And last year, we actually saw the NDRC’s National Data Administration put out this call for research proposals on how to construct, basically asking researchers for ideas on how the base construction of that system should be run nationally. So, we see a lot of movement, right? Early movement on constructing a system like that, meaning that companies in China in five years, three years that acquire data, that sell data, that use data, that leverage data in any way, will probably have to transact with this registry.

Andrew: So, this is like the county recorder office registration system, but for data sets, you’re saying?

Kendra: Yeah, exactly. That’s exactly right. So, the second issue, right, discoverability, the where do I even shop problem? This one’s pretty simple. We’ve been watching this for many years. There’s been this sort of wave after wave of state-backed data trading platforms established. They call them data exchanges. Usually, it’s a local government that stands one up. It’s basically a platform where you can browse available data sets.

Most of the companies listing data sets on there are state-owned companies, indicating that the private market is not really that interested in transacting on these state exchanges. So, I don’t know that they’re the best idea, you know, but the state has been essentially doing that. There’s one in Shanghai, there’s one in Beijing, there’s one in Shenzhen, there’s one in I think Guiyang still, where it’s essentially just a centralized marketplace where people can go and kind of shop for the data that they need, or at least that’s the fundamental idea.

Andrew: Okay, got that. But I guess a follow-up question would be, what are they doing, that sort of resource allocation issue that we talked about before, or how to get a bank loan based on your data assets? How are they looking to solve that issue?

Kendra: Oh, well, this one’s actually my favorite because it’s really concrete. State banks are running pilots that let companies use their data as loan collateral. And so, we’ve studied quite closely the structure of those pilots because I think they’re pretty interesting. It’s a three-party structure. So you have the bank that’s making the loan. You have a data-heavy and asset-light company that wants a loan. And then the third party is usually one of those state-backed data trading institutions, so like a data exchange, that independently certifies the value of the company’s data assets, like an appraiser or a data appraiser.

And so, then the bank sets the loan rates based on the value of the company’s data assets. So, there’s like one example, I think from August 2024, when the Chongqing branch of Huaxia Bank partnered with this data trading platform locally and offered a 1.3 million renminbi, so not a big loan, to a company in Chongqing that was doing smart city development. And so then the trading institutions certified the data’s value, the bank priced the loan’s interest rate off the certified value. And that’s how the money was issued. So, these aren’t big numbers. 1.3 million renminbi is not like a massive loan or anything like that. But it’s interesting just to watch them kind of see, does this work? Can we proceed here? Yeah.

Andrew: I mean, that strikes me that that whole system depends on the bank or someone else, some third party, whatever it is, being able to credibly say what the data is actually worth, right?

Kendra: Yeah, yeah, exactly. So there’s another piece, right? Another piece of unglamorous policy plumbing. So, the Ministry of Finance has basically been supporting research into standardized data valuation methods. And we saw a couple years ago in like 2023, there’s this body called the China Appraisal Society, which is like an industry association tied to the Ministry of Finance.

They usually just do physical asset appraisals. And so now they’ve been publishing guidance on conducting data asset appraisals, right? And so, they ask people to look at basically creating a sort of framework for determining for how an appraiser should be able to set a price on data. It’s very interesting stuff.

Andrew: Okay, let me step back for a second. So that all makes sense in terms of domestic flow of data, right? Kind of trying to boost the infrastructure behind the pricing of data, how data can be used as collateral, where and how you can sell it, where and how you can exercise the rights to data. But, I mean, as we talked about before, foreign companies who we work with, non-Chinese companies, are primarily interested in cross-border data, right?

Getting their data, in particular, out of China. And China’s regulatory regime on that front is incredibly strict. So, we’ve worked with these companies trying to get their data out of China for months and months and months. So, it strikes me as actually quite normal for China. But talk to us about that dichotomy where, yeah, we want stuff flowing freely internally, but we don’t want it to go across the border. What’s going on with that?

Kendra: Well, so I’m glad you brought that up, right? Because actually, I think this is the single biggest miscalculation in how D.C. reads China’s data security regime. I mean, the D.C. read is China’s data security rules are digital protectionism and that’s it. China wants to build a wall to hoard data inside of China’s borders while they steal data from everybody else’s. That’s kind of the standard, right?. That’s kind of the standard framing. But from Beijing’s perspective, the data security regime isn’t a wall around the market. It’s actually the guardrails that make the market possible.

Like, it’s not unusual for markets to have guardrails, even really, really heavy handed guardrails for cross-border trade. Capital markets have a zillion guardrails for cross-border trade. Labor markets have a zillion guardrails. I’m not necessarily cross-border, but there’s some. And so, the logic runs if the state clearly establishes what kind of trading is not allowed and where the safety risks are, and a lot of those risks are bigger in cross-border trade, and if it clearly defines which categories of data cannot be traded and starts there, then everything outside of those lines can sort of flow more freely and with more confidence.

I urge listeners, anybody who cares enough to, after you finish this episode, go read the actual text of China’s data security law. Go read it. I think DigiChina has a really good English translation. And I promise you it will read differently than you remember if you’ve read it before. There’s all this language in there about how data security is the fundamental building block of data trade, and that security has to be strong before data trading can occur. And that’s how all these data security rules are about enabling the safe trade of data, and the state’s job is to enable the safe trade of data.

And I think we just kind of gloss over that because we don’t, again, it’s not really on our radar that this is the plan. I do actually want to say one other thing, though. So that’s the plan. But China’s data security regime is still over-calibrated. I think they do want trade, but they have significantly overshot on the let’s secure this before we allow trade to the point where the current regime is not serving its own goals. There’s like a genuine desire to enable safe data flows, but the state is kind of its own worst enemy with this like, knee-jerk over-securitization. And so, what we’re watching right now is the state kind of actively hunt for a balance point.

How do we balance development and security? We heard that a thousand times, right? And we’ve watched the pendulum swing really hard towards security. We’ve watched it swing back a couple of times. It’s a live negotiation.

Andrew: Yeah. I mean, that’s not shocking, right? Like the security versus development debate, to the extent that it’s even a debate or finding that balance is an ongoing endeavor among Chinese policymakers, like in a range of areas, right? Data, technology, supply chains, you name it. They’re always trying to strike that balance. So, that’s not shocking to me. And it’s also not shocking to me that they’ve leaned a little bit further into the security side than the development side, which also is normal for governments everywhere, but also in particular for China.

But I think you’ve done a really good job here of laying out kind of the main rationale that China is using to put forth this data governance regime. Some of the specifics around the very concrete plumbing and flowing issues or flow issues that Beijing’s trying to solve. But flip that around. What do we as people who are in the policy community in the U.S. to make of that, what should Western policymakers or policy thinkers take away from this discussion?

Kendra: Yeah, I mean, I definitely don’t think that the United States needs to adopt the idea that data is a factor of production and rush into Beijing’s footsteps and do exactly as they have been doing. That’s definitely not the point. I think the biggest takeaway is that like when you lay China’s approach to data policy next to America’s approach to data policy, on our side, there’s this kind of massive gaping hole where a proactive pro-growth U.S. style data strategy ought to be. China has a pro-growth strategy, so we need a pro-growth strategy.

Every major U.S. ally has already done this. We are the outlier, right? The UK, Japan, the EU, Canada, Australia, all of them have looked at this issue. How can we use data to foster growth? What are the problems we need to solve? What are the pathways we need to take? What are the incentives we need to put in place? And we simply have not done that. And I think it’s because, as I mentioned earlier, when the U.S. talks about data, it’s almost exclusively as a security issue. And when security is all we talk about, then security is all we do.

I mean, just look at the last five years. We’ve done a ton on security. We have secured telecom equipment, smart car software, port cranes, cellular modules. There was the TikTok fiasco. We went after WeChat. We’re doing ICVs, preventing Chinese cars from coming into the U.S. because they collect data on this. So all of those actions was fundamentally about preventing the exfiltration of sensitive American data. And that’s just the entire American policy portfolio right now.

Andrew: Yeah. Okay. So I understand that. I guess the question then to me, actually, I was thinking of this, as you were talking through the Chinese side, is it that the U.S. has just decided like we don’t need a growth strategy for data per se? Or is it like, does the government need to be involved to the extent that China is involving itself here, meaning like our U.S. policymakers just saying like the market will figure this out or which I think it would be…? You know, that might be also an appropriate way to go. I don’t know. How is that conversation happening in the States? Is it just like a growth strategy is nice to have? Should it be left to the market? And where do you land on all of that?

Kendra: I think every time I have heard policymakers talk about this kind of sort of pro-growth strategy in the U.S., it has been talked about like, like those are the Montessori kids. Like that is a kumbaya, get out the guitars and sing together. Let’s all talk about data sharing. Let’s all talk about… like almost it’s taken on this like hard left kind of, I don’t know, let’s all hold hands and share data kind of initiative, right? It’s just got this very strange overlay in the U.S. that I haven’t really seen it take on anywhere else. I’m exaggerating.

There are certain initiatives that have made some progress. But there’s been a lot of that. I mean, there was some government data sharing initiatives where the U.S. decided to try to push more government agencies, is another thing China’s doing, to release more of their data in a format that researchers could use to the general public. And that was treated as this like… you know, there’s some open data laws about what research was supposed to do. Get government departments to share more data with each other so that they could be more, you know, efficient and improve bureaucratic efficiency, all this kind of stuff. But these don’t have any staying power. They die. They go to the back burner.

They get treated as not important. I think because, my personal take on that is that in order to see the value of initiatives like this, you’re looking at a 20-year investment. You’re looking at a 20-year investment in research. You’re looking at a 20-year investment in changing the way that the bureaucracy functions, you’re looking at a 20-year investment before you see any returns. And in a four year or an eight year administration. We’re not good at that, we’re not good at making… I mean, that’s one of the US’s weak points unfortunately. We’re just not great at making investments that we hope will, you know, prioritizing investments that we’re going to reap the dividends in two decades.

We’re great at let’s reap the dividends next year, but we’re just not really good at those kind of long-term goals. And so, I think that’s why, security strategy, you can implement within the span of a single administration. You can ban TikTok in two years. Or I guess not. I guess you can’t. You can try to ban TikTok in two years.

Andrew: Yeah. That took three administrations, technically.

Kendra: That was a bad idea. You can institute semiconductor export controls or whatever. You know, you can put out an executive order in a minute. Generating more efficiency and growth economically from data is like a little bit of a squishy idea and it’s a little bit of, it’s too long-term. And actually, I just want to say, this is real money. This isn’t just a sort of wishy-washy, oh, gross. But there are actually numbers there, right?

The OECD kind of concluded back in 2019 that data access and sharing, if you increase the supply of data in the economy, that it can generate benefits worth 1.5% of GDP if you’re just talking about public sector data. In other words, if you just make governments release more data, then you can really generate a bunch of significant economic benefit out of that because companies will jump on that data and they’ll make new businesses out of it. There’s more data available, let’s make an app that like uses that data to do something, and then that creates jobs and then that creates productivity.

And then if you also account for private sector data, if you basically get companies moving their data around between market actors more than they do, instead of sitting on it or hoarding it or being afraid to share it or can’t be bothered to sell it or whatever it is, then, you know, the range gets a lot bigger. You can get a bump of like between 1% and 4% of GDP. So, it’s like really leaving, actually leaving potential gains on the table in a way that’s pretty detrimental, I think.

Andrew: Yeah. Can you talk actually just a little bit more about the channels through which you see and, again, Chinese policymakers or others, non-Chinese policymakers see like what avenues are there for data to be a growth driver, generally speaking? I think that’d be interesting for listeners as well.

Kendra: Yeah, I’ll give a couple more examples. So, I just kind of mentioned one of them, which is job creation, right? I mean, I think some of the studies that are coming out now are basically showing, as I just said, data is available to startups, to innovators, to entrepreneurs. They come up with cool ideas for creating businesses with the data. If the data is not available, then they don’t do that, right? And that’s especially interesting because you have a lot of situations where the government or a large company or a collective of companies is the only body capable of putting that data together.

I’ve got one example I cite a lot, which is, so in 2017, Deloitte did a cool study. They looked at what happened when Transport for London released real-time transit data through APIs. And I think it was free. If I recall correctly, I don’t remember exactly, but I don’t think they charged for it. But like Transport for London put this out. 600 apps got built off the back of that data. 500 jobs were produced. And the economic savings for the city were like 130 million pounds. That was one data set.

This is one data set on the market. And if you aggregate that across the entire economy, what you could do with that is like pretty cool. There’s some early research indicating that you will get a small productivity boost when firms invest in collecting and using their own data. So, if you basically encourage a company to go acquire data and then transform that data and make, I mean, we’re seeing that in our company right now. We’re using data more than we did before. And there’s a lot more, like we’re doing bigger things faster, right?

We can see it kind of in the way that we’re working at the moment. So, the productivity is a way that you can kind of get growth out of that. And then third, it’s like the government itself kind of gets better. The bureaucracy gets more responsive. People get better public services, right? And China’s a really good example here too. Nobody really liked how China responded to COVID, but they responded really fast. And that, you know, epidemic control was all totally data-driven, built on 20 years of investment in data sets for public health, for transportation, that they just leveraged the minute this disease kind of appeared.

They took all these existing data sets and they pulled them, and started drawing insights on disease spread. And that’s kind of how they did the entire epidemic control measures. And they did that in just a couple of weeks because they’d made that investment already. Right? And finally, now it’s, of course, it’s talking about this a bit, but it’s AI. The big issue in AI is like AI researchers and small AI startups, like specialized AI startups and niche industries really need a steady supply of this high quality data, especially data that’s hard to get.

So, that would be things like, imagine what you could do if you had an entire data set of all of the mechanical equipment failures in smart factories across manufacturers, not just one manufacturer’s data, but every manufacturer’s data. Could you improve uptime, productivity, production speed of machinery? You know, what insights could you gain from that? So, tons of things like that across in almost every sector. And so, you know, health care, another great example. Hard to get good health care data because of various privacy restrictions, etc. But you get tons of benefit from that.

You can cure diseases with that kind of stuff. And so China’s made that producing that supply, this is where we come back to factors of production.

If data is a factor of production, then making sure that supply exists so that these things can happen, it’s a state’s job now. It’s a state’s priority. They’ve taken on that responsibility. They’ve decided to move that ball forward. Right? So anyway, that’s the game.

Andrew: China is obviously pursuing that. And you would say that U.S. policymakers are just kind of leaving that on the table in terms of not having a national strategy for data development and supply.

Kendra: There was a couple of mentions of data in the Trump administration’s America’s AI Action Plan. And when I read those, I got real excited about them. Some of those are really good, right? They’re actually really good ideas. And they have not at all been prioritized as much as all of the securitization stuff in that plan, right? The funding has not gone to those initiatives yet. Tick tock, tick tock. It’s that kind of stuff. It’s like somebody will recognize that, yes, mostly those initiatives were about funding consortiums that pool sort of high-quality data and compute for leading-edge researchers so that researchers were solving that access to research data problem for AI specialists and stuff.

So, it’s not that somebody hasn’t written it down. It’s not that somebody hasn’t said, hey, we ought to do this. It’s that when you look at where policymakers’ time and energy and attention is going, that’s not what anyone’s talking about. When you walk into a room where they’re talking about AI and DC, nobody’s sitting around saying, “How can we really squeeze economic value out of data? What proactive, positive, long-term roads can we lay down so that we really get benefit from data?” That’s not the conversation that’s happening. So, it’s not that it’s not recognized. It’s just not prioritized.

Andrew: Yeah. Well, and again, I just sort of anticipate listeners saying, you know, “Well, that’s not the state’s job.” And I guess my thought would be, of course, the U.S. is never going to take the same state-heavy interventionist approach that China is.

Kendra: Totally.

Andrew: But that doesn’t mean there’s no role for the government to help kind of build this ecosystem. I mean, of course, like as you talked about, the government, whether it’s city government, county government, national government, has taken a role in governing and overseeing transactions and putting guardrails around all the other factors of production, but we just don’t seem… I mean, you know, we haven’t caught up in terms of kind of treating data fundamentally as so structurally important to the economy. I mean, you know, the old, obviously, cliche is data is the new oil, but we’re certainly not acting like it, right?

Kendra: Yeah, yeah, exactly. Exactly.

Andrew: Well, this has been super, super interesting. Obviously, a ton of work that you’ve done on this. And just in case it’s not clear, the work that Kendra has done on this, in case it’s not clear to listeners, was specifically with an eye towards informing U.S. policy. So, everything we do at Trivium is kind of trying to understand China, but this was like an effort to understand what China’s doing in order to kind of make strategic recommendations on how the US might want to be thinking about these issues. And so that’s one of the reasons that we kind of leaned so heavily in the last part of the conversation on what the U.S. is not doing here. I think this is great.

I hope that this work gets some uptake from policymakers and people in that space. We will keep sounding the drum or pounding the drum, sounding the alarm. I don’t know.

Kendra: Sounding the gong.

Andrew: Yeah. And yeah, well, I’m sure there will be a lot more opportunities to talk about these kinds of things. It’s always good to kind of take a step back and do kind of a wonkier, a higher-level… wonky higher level; those are maybe at odds. Anyway, I’m rambling now. But this was amazing. We’ll just leave it at that. Thank you, Kendra, for the time and for walking us through that. I found it super helpful and fascinating. I’m sure our listeners did as well.

Kendra: Awesome. Well, always good to be here.

Andrew: All right. Well, thanks so much. And thanks, everybody, for listening. We’ll see you next time. Bye, everybody.

• But while many analysts are asking why Beijing isn’t doing more to stimulate growth, this week’s Trivium China Podcast explores a different question: why are policymakers deliberately choosing not to?

Pod host Andrew Polk is joined by Trivium’s Head of Markets Research Dinny McMahon to examine why Beijing may be quietly embarking on its first genuine economy-wide deleveraging effort in years, and what that could mean for China’s growth model.

The two discuss:

• Why recent weakness in investment, consumption, and the property sector won’t trigger a major stimulus package

• Whether Beijing’s annual fiscal “stimulus” has become more theater than meaningful economic support

• How slowing credit growth could signal a deliberate shift in macroeconomic strategy

• Why strong exports and rising inflation may have created a rare opportunity to reduce leverage

• Why policymakers appear to be prioritizing future borrowing capacity over stronger short-term growth

Andrew and Dinny also explore what slower credit growth means for businesses and investors and how Beijing’s evolving priorities could complicate trade negotiations with Europe and other major trade partners.

Transcript

Andrew Polk: Hi, everybody, and welcome to the latest Trivium China Podcast, a proud member of the Sinica Podcast Network. I’m your host, Trivium Co-Founder, Andrew Polk, and I’m joined today by Trivium’s Head of Markets Research, Dinny McMahon. Dinny, great to have you back on the pod. How are you doing, brother?

Dinny McMahon: Doing good, mate. Great, as always, to be here.

Andrew: Yeah, it’s been a couple of weeks since we’ve had a podcast. So, I was on vacation with my in Hawaii. Nice to get away. Come back to the very, very hot DC weather, although it’s cooling off a bit now, but I’m glad to be back in the groove and glad to have a fan favorite here, Dinny, to kick off the second half of the year with me. We are going to talk, of course, about the Chinese economy. And specifically, we’re going to talk about what’s happening with credit growth in the economy, which may sound, I don’t know, wonky, but it’s hugely important to the overall trajectory and pace of growth in the Chinese economy.

And it’s traditionally how the Chinese policymakers either stimulate growth or sort of pull back on growth rather than using the monetary lever per se. It’s really more of the credit impulse. That, of course, has changed more towards a fiscal impulse in the past, say, four or five years. But credit growth is hugely still important to the, like I said, the overall management of the economy, the trajectory of the economy. Dinny has some really unique, and I think… well, I mean, unique in a good way, like out of consensus views on what’s-

Dinny: Sounds like unique and courageous.

Andrew: Yeah, yeah, yeah. Shall we say unique views? No, I think really thoughtful views on what’s happening here, how credit policy relates to overall macroeconomic growth, and kind of what that means around economic performance in the second half of the year. So we’re going to get into all of that. And it’s going to be an exciting conversation.

But of course, before we do that, got to start with the customary vibe check. Dinny, it’s July 9th, 3.20 pm in the afternoon Eastern time. And you are recording from the Eastern time zone, which is not normally the case. You used to be in the Central Time Zone. How’s your vibe? Welcome to the East Coast.

Dinny: Mate, I mean, look, no shade on Chicago. I was there for nine years, and I love the city. But dude, North Carolina is doing good things for my vibe. I mean, no one can see this. We’d hope to record this, but we haven’t kind of got our act together yet. But like my back window opens onto a forest. I mean, I’m loving the warmth. I’m loving the humidity. I’m loving the greenery. This place is doing some good things for my soul. So, I might still be in the honeymoon period, but I’m doing great, mate.

Andrew: Well, that’s great to hear, dude. I’m glad you like it down there. And a chilled Dinny is a happy Dinny is a happy Trivium is a happy Andrew. So, I love it. And you can bring your chilled but still intensely thoughtful vibes to the podcast. And I, meanwhile, super rested, had a couple of good weeks off with the family, just no work, all relaxation. So ready to get back to it, second half of the year. I had a good night out with a handful of my China nerd friends last night in D.C., which was good to catch up with people.

So, I’m ready to get back to it. That’s my vibe is raring to go. So, with that out of the way, we also have to quickly do the housekeeping up top. Just firstly, a quick reminder, we’re not just a podcast here. Trivium China is a strategic advisory firm that helps businesses and investors navigate the China policy landscape. That, of course, includes domestic policy in China in a range of areas — autos, tech, macro, econ, as we’re going to talk about today, commodities — you name it, we do it.

But it also includes policy towards China out of Western capitals like D.C., London, Brussels, and others. So if you need any help on any of those fronts, please reach out to us at hq@triviumchina.com. We’d love to have a conversation about how we can support your business or your fund. Otherwise, if you’re interested in receiving more Trivium content, check out the website, www.triviumchina.com. We’ve got a bunch of different subscription options, both free and paid. You can definitely find the China policy intelligence option that you need on our website, www.triviumchina.com.

And finally, always say it, but tell your friends and colleagues about the podcast. It’s how we grow our business. We truly, truly, truly appreciate those word-of-mouth recommendations. And like and subscribe as well to the podcast on your favorite podcast app. That helps us get more visibility as well.

All right. With that out of the way, Dinny, we are going to get into the latest on the macroeconomic front, and specifically what’s happening with credit growth and the credit environment, I think we can sort of safely say that after a really good start to the year, the past few months, so really Q2 have been pretty terrible for China’s economy. Fixed asset investment. So overall kind of capex in the economy fell 12.5% year over year in May. So negative growth, actually significant contraction of 12.5% year over year. Last month, May is the most recent data we have. That was a sharper contraction than April’s 9.4% decline. So, not only is the economy weak, it seems to be weakening. And one of the key issues is that deterioration is broad-based.

So, it’s manufacturing investment down 4%, infrastructure down 9.5%. And the property sector, where we had been sort of hopeful that the worst was over, even it took a pretty clear return for the worst in May with new home sales by floor space falling about 12%. That was down from the 9% year-over-year drop in April. So, just kind of across the board looking terrible. On the retail\consumer side of things, the dynamics are just as bad. Auto sales down 16% year-over-year last month. Home appliances sales down about 16%, furniture down 9%, even mobile phone sales, which people replace pretty regularly, and which had been holding up in recent months, they just eked out 0.7% year-over-year growth.

So, pretty terrible profile after a good start to the year, but it all sort of raises the question, why aren’t policymakers doing more to support or stimulate the economy? And I know you’ve got a lot of thoughts on this, specifically, as I said, when it comes to the credit side of things. So, Dinny, I’ll just throw it over to you. Can we expect more support, lay the land on kind of what you’re thinking here in terms of what policymakers’ approach is?

Dinny: Well, I think the real question isn’t exactly support because, I mean, we’ve held the position for ages that interest rate cuts aren’t coming. I mean, monetary policy isn’t really in the toolkit at the moment in terms of sort of dealing with economic weakness. They’ll tweak here and there, but that’s not really what’s going on. The question is, are we going to see stimulus, straight up fiscal stimulus? And I think the answer is yes and no. So, every year for the last few years, in the last few months of the years, we’ve got some sort of stimulus.

So, what we had last year in October was an extra 500 billion RMB worth of special purpose bonds for local governments to issue. And some of that had to go into infrastructure. And some of that was just broad fiscal support to help local governments with their budgets. Some of it was for paying down arrears. And I think it’s likely we’ll get something like that again, probably around 500 billion again. The question is, though, should we consider that as being stimulus? Now, what I mean by that is that last year, local governments were permitted to issue 4.9 trillion RMB worth of special purpose bonds, And that was broken up into a 4.5 trillion RMB quota that they got in March at the legislative session, the government work report.

And then they got that additional 500 billion at the end of the year I was talking about. Now, this year, they got the same 4.4 trillion quota in March. So, we could get an additional 500 billion top up by the end of the year. And that would be the same as last year’s total. So, the question then becomes, is that stimulus or is that just the bare minimum necessary to maintain economic activity at last year’s level, right? For local governments just to be spending as much as they did in 2025, do you need an additional 500 billion RMB? And I think in this economic environment, the answer is probably yes.

So, it’s also, the other question here in addition to that is, is it stimulus if the government’s doing the same thing every year, if it’s providing that same top-up on government spending in October every year? Which is what it’s been doing. To me, that doesn’t strike me as stimulus. That strikes me as state management, right? They kind of lay the expectations earlier at the beginning of the year. We’re going to let government borrow this much. And then, lo and behold, everybody chill. We’re going to provide stimulus in the last few months of the year.

But if they’re doing that every year on a comparable amount, that doesn’t really feel like stimulus. That kind of feels like drum roll, please, everybody. Everybody chill. There’s more stimulus. Everybody be cool. It feels like smoke and mirrors as opposed to a real injection into the economy. And I think that’s particularly pertinent this year because I don’t think we should be watching the stimulus because I think it is smoke and mirrors.

I think what we need to be watching is what happens with total social financing, which is the government’s measure of the total amount of credit being put into the economy. Because I think what’s happening this year is that Beijing is striving to start deleveraging. So, regardless of whether we get stimulus, the amount of credit being pumped into the economy this year, I think is likely to fall pretty significantly. And what Beijing is trying to embark on is a conscious effort to start deleveraging the economy.

Andrew: Okay, well, so you’ve broken it out well. It’s a good point around if stimulus is every year, is it truly stimulus, right? Or is that just the new baseline? I mean, I’m trying to go back to my economics 101, macro 101, a permanent expansion in aggregate demand versus a temporary expansion in aggregate demand via fiscal stimulus. Very Keynesian idea. And the short-term stimulus tends to have more of a policy effect, whereas longer-term perpetual stimulus, typically prices just adjust, right? And people adjust to this new level without really seeing it as a way to kind of boost their short-term economic prospects.

So, without getting way too into that, and I’m sure someone will call me out on that. I’m sure I got part of that wrong. But point being, if it’s permanent, people and businesses, which matter ultimately in the economy, think about it differently. And it doesn’t really provide that stimulatory effect exactly that you’re talking about. On the credit side, which is so important, I want you to get into that a little bit more. The last piece you talked about was sort of deleveraging, which I think a lot of people argue about is, we’ve been arguing really since 2017 when China first started its financial prudence or financial cleanup efforts, now a decade ago. What is deleveraging in China? Are they truly trying to outright reduce leverage?

Where in the economy are they trying to reduce leverage? Talk to us about what you mean by that concept.

Dinny: The thing that it isn’t, it is not the total amount of debt declining. It’s not having $100 worth of debt yesterday and having $90 worth of debt today. What it’s about is we’re talking about reducing the debt to GDP ratio. So, GDP here is nominal GDP. And that is the important thing because it reflects the capacity of the economy to sustain and service its debt. So, it’s about the size of the debt pile relative to the size of the economy. And Beijing has wanted to bring that down or at the very least stop it from growing for a very long time.

So that cleanup campaign that you mentioned, I mean, that was called explicitly a deleveraging campaign. And they launched at what, a tail end of 2016, really sort of launched, got going in earnest 2017. And at the end of the day, it was more of a de-risking campaign than a de-leveraging campaign. But that said, over the, about an 18 month period over 2017 and ‘18, That debt to GDP ratio did come down. And that’s because they were unraveling shadow banking. And there was a lot of additional debt in the system that was just unnecessary. It was kind of like just layers of debt to kind of obscure what was really going on in the heart of the financial system. And about that time, the debt to GDP ratio was about 240%.

And then it started rising again in late 2018. And in early 2019, the then Premier Li Keqiang, he set a formal target for deleveraging. He said that on a year-on-year basis, what they were aiming for is for credit growth, as measured by total social financing, to roughly, the expansion of credit, as in TSF, to roughly equal the growth of nominal GDP. So those two things would be expanding roughly at the same time. If nominal GDP was growing at 8%, then total social financing would be growing at that pace as well. Now, according to the official data, we did get a period, a short stint of deleveraging in 2020 and 2021.

I mean that was during the pandemic. Credit demand collapsed. But I mean at the time, the GDP data during that period is a little bit sus. I mean, I think there’s a bit of a consensus that the economy probably went into recession in that period but it doesn’t really get borne out by the data. So, whether there was deleveraging then or not, the data says there was. It’s probably fair to assume that there perhaps wasn’t. but what is clear is that the ratio started rising again in a very meaningful way after the housing market peaked in the middle of 2021.

Now, back then, the debt to GDP ratio was about 255%. And by the end of last year, according to the Bank for International Settlements Data, they reckon it had gone up to 300.1%. So that’s an increase of about 45 percentage points. Now, there’s a think tank inside Peking University, which also keeps track of this. It puts out its own numbers. It’s put out more recent data than the BIS. It reckons at the end of the first quarter, the debt to GDP ratio was already at 309%. So, anything over 300% is really high.

I mean, this is a club that includes Japan, Canada, France, the Netherlands, some global financial hubs because they’re doing a lot more sort of financial activity globally relative to the size of their economies, but it’s a pretty small club. And certainly, for developing economies, I mean, you have India’s, Brazil’s, they’re well below 200%. So, over 300% is way, way up there. But as I said, we’ve had this real increase since the housing market peaked. And frankly, that’s perfectly reasonable.

A collapse of economic activity of that scale really requires borrowing to ramp up, usually borrowing by the state. Now, in China, it wasn’t purely by the state. It was the government, and it was also corporations who borrowed and pumped a huge amount of money into manufacturing and industry, particularly around the time, really kicking off just as the pandemic was starting. And that increase in credit, it wasn’t just about making up the shortfall because there was this shortfall of economic activity as investment in property, the property sector contracted. But it was also in aid of achieving Beijing’s growth target, which the economy has consistently been able to do over the last few years.

But here’s the thing. Governments of economies that have experienced a crisis, they often want to pare back their support, their fiscal support, prematurely because they freak out after they see the fiscal burden rising. They start to worry they’re borrowing too much money. They start to worry about overall debt levels. And so, they cut back before the economy can sustain itself. Now, I don’t think Beijing is necessarily freaking out.

I think it’s being a little bit more opportunistic. I think they believe they can… This is a moment in which they can start deleveraging on a sustainable basis because of resurgent inflation and because of strong net export growth.

Andrew: Yeah, thanks for that explanation. I want to get into the inflation and export piece, but I do have to say my piece on the 2017 de-risking campaign. I think you nailed it. That’s the right way to describe it. In fact, I had to train myself for years to call it a de-risking campaign, not a de-leveraging campaign, because every time I call it de-leveraging, people would say, “Ah, they’re not actually de-leveraging. Their total credit’s rising.” I’d be like, yeah, okay. Well, it’s growing more slowly.

And the most important thing I would always point out is as they were de-risking the riskiest, most vulnerable parts of the system, which primarily was the interbank market, that part of the economy, that part of the financial system did deleverage, right? The interbank market, total lending in the interbank market contracted for, I think, 18 months or so. And that was really the riskiest part of the system. So, it was a deleveraging campaign, but just for semantics, probably easier to call it a de-risking campaign.

It was quite successful, and that’s put us in this sort of new world. Sorry, just that point of privilege that I had to touch on. But back to inflation and net exports, talk to us about how those two pieces fit into this deleveraging picture you’re talking about.

Dinny: So the way to think about it is this. As I said, you achieve deleveraging when credit growth is slower than the growth of nominal GDP. So, nominal GDP is real GDP, which is the number that we care about every quarter or so, plus inflation. So, if prices go up, nominal GDP goes up. So, in this current environment, nominal GDP is going up because inflation is back. But credit growth, even at that same moment, credit growth is slowing because credit demand is weak. So, total social financing growth was 7.7% in May year on year.

And that is the slowest pace step ever. So, credit growth is coming down even as nominal GDP is going up because of inflation. So that’s why inflation is such an important part of this picture. The other important condition here is exports. Now, exports are important because deleveraging really requires a growth driver that doesn’t require much debt. The old economic growth model in China, the old property-driven growth model, it was all about debt. You couldn’t drive the economy through property without more and more borrowing.

I mean, people borrowed to buy homes. And the expansion of that borrowing, that was kind of the bedrock of economic growth model. But exports are different. They rely on global demand. It doesn’t require anyone inside of China borrowing more. So, with exports, growth can increase without exporters needing to take on really much additional debt. And so that’s why Beijing feels like it has a moment here to de-lever because it can add growth through exports without taking on much additional debt and inflation is pushing up nominal GDP.

So, if we break down the numbers and what Beijing might be sort of aiming for by the end of the year, it has a real GDP growth target of between 4.5 and 5%. So, if full-year growth comes in at the upper end at 5% and inflation comes in at, I mean, say 2%, then deleveraging starts once total social financing growth gets down to 7%. As I said in May, it was 7%. So, if it gets down to 7, maybe 6.8, 6.9, well, it’s conceivable that that’s the point at which deleveraging begins. Now, of course, if the real economy only grows at 4.5% and inflation is 1%, then total social financing would need to slow to 5.5% by the end of the year, which is far less feasible.

So, the real question is what pace of inflation is likely? I mean, in June, CPI, commercial prices were up 1%, but producer prices were up 4.1%. So, it’s really a bit of a toss-up as to where it’s going to land by year-end.

Andrew: Well, that’s a pretty big call, I mean, especially given how bad the domestic economy is. So, when you think about it, I mean, now doesn’t seem like the time to start deleveraging. You want to deleverage when you’ve got a tailwind to your economy, an upward economic trajectory. So, can you kind of, I don’t know, justify a little bit further or not justify, but expound on why you think they’re not going to kind of try to come in and pump things up?

I mean, I know we haven’t seen much of it in the rhetoric, but typically, even when they’ve been pretty reticent on stimulus or pretty measured on stimulus, when things get this bad, they usually step in with some kind of additional support, as you talked about at the beginning of the podcast. I mean, you know, the debt-to-GDP ratio has been rising for years, right? And they haven’t undertaken a concerted deleveraging campaign. So, just talk to us a little bit more about why you think that term is coming now.

Dinny: Yeah, I think it’s a combination. So, on one level, they’re being opportunistic because of what I said about exports and inflation. They’ve got a moment, they’ve got an opportunity to do something that they’ve always found quite difficult to do. But I think they’ve also got one eye on the long term. And here, the real issue is China’s demography. Because sometime in the next 60 years, the number of retirees in China are going to exceed the working age population.

And I mean, that’s 60 years in the future, but the burden on the state from the transition to that point, as the balance moves against the working-age population, the state is going to need to borrow more and more to fund the healthcare and pension needs of retirees. Now, I’m not sure when Beijing is going to have to sort of start borrowing to meet those obligations, but it’s almost inevitable. And when it does start, borrowing will go on for decades and it will just keep increasing.

So, when that day comes, when Beijing needs to start borrowing to meet the welfare needs of the retirees, it needs as much fiscal space as possible. So, if it starts borrowing when the debt-to-GDP ratio is at 300%, it will have far less runway than if it starts at 250%. But crucially, it’s not about where the level is at the moment. It’s where the level will be if they don’t start deleveraging now, right? Because if the ratio keeps increasing at the pace it has since the housing market peaked, it’ll hit something like 240% within a decade by 2035.

So, something has to give. They either, at the very least, need to stop that ratio increasing as soon as possible because the more it goes up now, the more they really need to pull it back or put it to reverse to kind of prepare for that sort of that demographic decline that’s on the horizon. And that’s far more costly than sort of putting a line under it now and then sort of incrementally pull it back over time. So, something has to give. And, you know, I know I’m talking about total economy-wide debt to GDP ratio. I’m talking about the 300%. And perhaps the more relevant ratio to talk about is the government debt to GDP ratio, because by BIS calculations, it’s about 100%, which, relative to the U.S., is pretty decent. I mean, the US level is 120%.

But the problem is with China, when you’re talking about government debt levels, you’re wading into a morass, because so much of the corporate borrowing is in some way state-related. Most of the corporate debt is either borrowed by state-owned enterprises or it’s borrowed by some local government financing vehicle or some other entity borrowing on behalf of some level of the state. And so, these are implicit liabilities. And we’ve seen over the last few years that implicit liabilities do become explicit in times of financial stress. I mean, we’ve seen this migration of local government debt from LGFEs to local governments. It’s ongoing. We don’t know where the end is. So, looking at that formal debt-to-GDP ratio doesn’t really help as much.

So, I think looking at the overall levels of debt in the economy kind of give us a little bit more of a sense of sort of the potential stresses that the economy could be under and what Beijing is dealing with. Now, the irony of all this is that Beijing’s been aware of this for years. I mean, as I said, they called the cleanup campaign in 2016 a deleveraging campaign. Li Keqiang set that target of you know keeping nominal growth and credit growth in line back in 2019. And this whole new economic growth model, new quality productive forces, which we’ve talked about heaps on this podcast, that new model is, by design, supposed to be debt-light, right?

New quality productive forces is all about generating superior sustainable growth that is driven by productivity gains not by borrowing. So, they’re trying to bake it into the system, they’re trying to overhaul the economy in a way that it will grow on a sustainable basis over the long term with less debt. But because Beijing is still dealing with the fallout of the property bust, we’ve still got this debt rising and rising, and so we’re at this point of what does Beijing do about it? Because on one level, it needs to keep borrowing until the fallout of the crisis is properly dealt with.

But if it does allow debt to continue rising, then it’s really storing up real problems for a not-so-distant future when its hands are tied and it is going to have to ramp up borrowing.

Andrew: Yeah, that makes sense. I mean, truly like trying to not borrow from the future or to maintain your headroom for borrowing later. That makes a lot of sense. I think it all makes sense conceptually, right? I think you made a really compelling case, but I guess the follow-on question for me is what are you seeing that really indicates Beijing has pulled the trigger on deleveraging? And I know you’re going to talk a little bit about credit growth slowing.

But I think the natural sort of skeptic of this argument would say, “Well, credit growth is slowing. Maybe that’s more of the balance sheet recession idea. It’s not about Beijing’s desire to reduce leverage. This is just businesses being in such dire straits that they don’t want to put any more new borrowing on their books. And a lot of this is all outside of Beijing’s control,” right? That would be the bare argument that this is all an imposed reality of years and years of overborrowing and financial expansion. So, tell me what you’re seeing in terms of why you think they’ve pulled the trigger and then respond to that kind of preemptive counter argument.

Dinny: Yeah, I mean, it’s a really good point. I mean, if you looked at the debt to GDP ratio of China’s households, I mean, they’ve been falling really since the peak of the, a bit after the peak of the market in 2021. I mean, households are deleveraging. There is certainly an element of a balance sheet recession going on there, at least with Chinese households. Credit demand is incredibly weak. No one wants to borrow. But of course, I mean, China’s response to these sorts of moments when there is weak credit in the past is always for the state to have stepped in with some sort of measure policy shift that results in an expansion of borrowing. In the late 1990s, when China was dealing with the Asian financial crisis, that’s when we got the housing reforms.

And that sparked off a 20-year boom in housing investment. Global financial crisis; China’s response was a massive expansion in investment in infrastructure and public works. And even when we saw the peak of the housing market in 2021, sparked by government policy changes, the three red lines and whatnot in 2020, the response was a state-initiated and led massive expansion of investment in manufacturing and industrial capacity. So, every time a slowdown in one aspect of the economy was met by the government initiating an expansion of credit somewhere else.

So yeah, you can argue, “Oh, this is just inevitable. This is what happens.” The way that Beijing has dealt with such inevitabilities in the past is to find some way to ramp up credit. And yet it feels like it’s different this time. And we’ve talked about this a little bit in terms of Beijing’s increasing focus on services and trying to get more growth out of low debt parts of the economy. But there’s three things that have really struck me this time, and the first is Beijing allocated less money this year to the consumer trade-in program.

Now, we got these numbers back in March at the NPC. Last year, 300 billion renminbi were allocated to support consumer purchases of big ticket items — cars, furniture, white goods, home appliances, and personal electronics. This year the quota came down to 250 billion. But that decline is far more significant than it looks like at face because the way that this program worked was by pulling forward future demand, right? So, to be able to sustain last year’s consumption at 2025 levels, not even expanding it, Beijing would have had to have increased the quota.

So, to ensure sales stayed at last year’s level, that 300 billion in subsidies they provided needed to have gone up. Now, instead, they reduced it to 250 billion. So, they cut and sales plunged. I mean, you outlined the degree to which sales were down in May at the very beginning of this. I mean, I think auto sales are down 16%. Furniture sales, white goods sales are down double digits as well. So, Beijing made a decision not to throw good money after bad and accept a sharp drop in retail spending in favor of less debt. So that’s the first thing. I mean, that’s a conscious decision like, okay, we’re not going to stimulate the way that we used to.

We’re going to pare back debt because this is not doing what we’d hoped it would. Second thing, SOE remittances, state-owned enterprise remittances. Now, I’m not sure we’ve spoken about this on the podcast, but we’ve written a heap about it. At the end of last year, the central government ramped up the remittances that centrally owned state-owned enterprises are required to pay to the government. So, as a percentage of their profits, it went up from almost all centrally owned state-owned enterprises by between 10 and 15 percentage points.

Now, that’s great for government revenue, right? But it is a meaningful hit to investment. And that’s because SOEs, they use profits as seed money for new projects. Any new investment project requires a certain amount of equity before the firm that’s making the investment can go out and borrow. It’s usually about 20% of the value of the project. So, if SOEs have fewer retained profits, it means that they have less seed money for investments. And so that seed money gets massively leveraged up by borrowing.

And so, it means having the government take a bigger share of SOE profits make meaningfully less investment. We estimate it could reduce fixed asset investment this year by 2.3 percentage points. Now, that’s a back of an envelope calculation, and there’s a whole lot of caveats on that. But the point is that Beijing here has chosen to increase fiscal revenue, over boosting economic through investment. In fact, it’s not even about boosting economic activity. It chose fiscal revenue while knowing that the trade-off would be less economic activity through investment.

So, I think the thing to take away from both of these things is that Beijing realizes that the drag on domestic demand is structural. There are no band-aids that will tide things over until the economy recovers. So, the acknowledgement that you need to keep doubling down on the consumer subsidy program because consumption isn’t going to come back until the underlying problems are fixed. Specifically, the property bust is over, that local government fiscal shortfall is dealt with, and perhaps industrial overcapacity is dealt with.

And so I think that’s where it is, there’s this recognition of like, well, look, borrowing more and more, it’s not fixing anything. All it is, is it’s good money after bad. It’s just about trying to get an extra percentage point of growth, and ultimately it doesn’t really fix anything. And so, yeah, those are my first two, which kind of brings me to the last thing.

Andrew: Well, what’s your last thing?

Dinny: So my last point is it’s less about government policy and it’s more about what the PBOC Pan Gongsheng, PBOC Governor Pan Gongsheng said at the Lujiazui Forum in June. So, you know, Pan has given a speech at this thing annually for the last few years and he usually uses it partly to make some big policy decision announcements, a real shift in the way that the central bank is doing things, and/or to kind of outline a shift in the way government is approaching a particular issue. Last year, it was all about renminbi internationalization. It kind of very much was a signal that Beijing was kind of seeing a significant shift in the way that the world perceived the dollar and kind of saw, okay, this is now a moment for us to do more to promote the RMB’s internationalization.

And in the year, since then, we’ve seen a huge amount of new changes sort of supporting that effort. Now, the really interesting thing, I think, that came out of Pan’s speech this time round were his comments about credit. Now, the first thing he did was that he noted that the severity of the decline in borrowing by property developers and local government financing vehicles since the peak of the property bubble makes it difficult to maintain the pace of growth.

And he said the remaining loans must first fill this decline before they can be considered as incremental growth. And this is, I thought, was really important. He’s like maintaining the previous growth rate for all credit is difficult and unnecessary. And so, rather, this is what he said, rather than trying to maintain high levels of new credit which inevitably leads to some wasteful investment, Pan said this slowing down and improving the quality of loans may become one of the new normal modes of macroeconomic operation.

So, in short, Pan seems to be laying the groundwork for even slower credit growth and acknowledging that the payoff for keeping credit growth high is declining.

Andrew: Well, so, you know, I guess the final question, so you’ve made the case, right? I think, again, convincingly, like this sort of needs to happen. There are structural drivers that the government sees, pushing them to act now. You even have people like Pan Gongsheng, PBOC governor saying like, “This is what we’re trying to do.” But what’s your view in terms of the consequences of all this in terms of short-term economic growth?

Because we have seen, of course, before policymakers enact deleveraging, that was actually the one of the unintended consequences of the last deleveraging campaign was private sector credit growth, private sector borrowing cratered in a way that policymakers didn’t expect. And then they spent years trying to get more credit resources to the SMEs in the small private sector. What do you see as the outcomes, the major outcomes of this effort that you’re arguing is starting to take shape now?

Dinny: Well, I think it’s more of what we’ve already got. So, so far, we’ve got Chinese economists increasingly calling it a K-shaped economy. The up leg of that K is incredibly strong exports. The down leg is domestic demand. And I think domestic demand will stay weak and it will potentially get weaker. It also means export growth is going to become even more important, right? Because the only way for deleveraging to be successful is if China can maintain robust economic growth through robust expansion of net exports.

Now, I think what that means is that that’s going to make it even harder for the EU to get the trade deal they want out of China. I mean, China is going to be even less willing to make concessions. The question is, though, is whether China blinks. So, it might want deleveraging and it might think it’s kind of got this perfect environment in which to do it. But what it means is that weakness in the domestic economy is going to be with us for longer, potentially for quite some time, maybe even forever. And the question is whether ordinary people are willing to tolerate it.

Whether the Chinese public can take the pain, is willing to accept this chronically weak domestic demand environment. So, I think that’s really the question. I think what we’re going to have is weak ongoing domestic demand. Net exports are going to become more and more important. The question is just how long is Beijing willing to endure it?

Andrew: Yeah, that is the key question. And I think that will be the determining factor in terms of how long they’re willing to stick with the policy. And, you know, whether the policy of trying to maintain some level of what we’ll call balance sheet integrity for the economy is worth weaker demand, right? So that you can maintain some headroom for borrowing to support demand in the future. It’s all kind of trying to figure out when you want to kind of play that card to support growth because you’re going to have to do it at some point.

But, Dinny, we got to wrap up. This is all super fascinating. We will see kind of the effects on the economy. We will see how this nascent deleveraging effort plays out. I think you’re early on this. You know, you’ve been kind of looking at this for a while, testing it out. I think hearing people again, like Pan Gongsheng say it, doing our own analysis of various policy tools that have been sort of being deployed. Again, you make the strong case. So, I think it’s a good call. We’ll see kind of how it plays out, whether it’s a good early call.

And I think, like I said, you’ve convinced me. So, I appreciate you walking us through this today. Really fascinating stuff as always, man.

Dinny: No worries, mate. It’s a pleasure as always.

Andrew: All right. Yeah. Good to see you. Good to be back on the pod. And thanks, everybody, for listening. We’ll see you next time, everybody. Bye.

Our phrase of the week is: “mass appeal” (路人缘 lù rén yuán)

Context

On July 2, a court in Suzhou ruled that Molly Tea (茉莉奶白), one of China’s rising bubble tea brands, had infringed seven registered trademarks belonging to Louis Vuitton. The Shenzhen-based tea company’s logo, a black-and-white four-petal flower, closely resembles LV’s iconic monogram flower.

The court ordered Molly Tea to pay 10 million yuan ($1.4 million) in damages plus 300,000 yuan in legal costs, apologise publicly to LV, and remove all infringing designs.

Shortly after the court decision was announced, the story began trending on social media. People expressed sympathy for the tea brand, shared images of their Molly Tea receipts in support, and noted the unfairness of a fight between a 20,000-yuan handbag and an 18-yuan cup of tea.

Some even suggested that LV’s celebrated monogram is itself borrowed from Chinese culture, pointing to the “treasure flower” pattern (宝相花), a stylised floral motif found in Buddhist art from the Tang Dynasty (618-907).

So although LV won in the courtroom, its victory in the court of public opinion is less clear:

“It wasn’t a total win for LV.

They got the money, but some say it cost them their mass appeal.”

LV这边似乎并没有全然胜利——拿到了钱，却被一些声音形容为”丢了路人缘”。

And with that, we have our Sinica Phrase of the Week.

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What it means

“Mass appeal” (路人缘 lù rén yuán) is an internet slang term which comes from fan culture. The characters translate as: “passer-by” (路人 lùrén), “affinity” (缘 yuán).

In Chinese, a “passer-by” (路人) is a stranger, or a neutral onlooker. In fan circles, it specifically means people outside the fans of a particular celebrity: they are not fans, not haters, just the general public.

The second part, “affinity” (缘), is the same character used in “fateful connection” (缘分), the bond that draws people together. So “passer-by affinity” is the appeal a celebrity enjoys among people who aren’t their core fans.

The phrase emerged in the early days of the Chinese internet as “fan-circle slang” (饭圈流行语). It describes stars who are liked even by people with no interest in celebrity culture.

Over time, the phrase became a measure of a star’s mainstream popularity. And it’s now evolved beyond celebrities and can apply to brands and companies too.

Which brings us back to LV. In its legal battle with Molly Tea, the luxury brand won in legal terms. But whether it can win back its “mass appeal” is another matter.

Andrew Methven is the author of RealTime Mandarin, a resource which helps you bridge the gap to real-world fluency in Mandarin, stay informed about China, and communicate with confidence—all through weekly immersion in real news. Subscribe for free here.

Read more about how this story is being discussed in the Chinese media in this week’s RealTime Mandarin:

RealTime Mandarin

#284: Molly Tea loses brand infringement court battle againts LV

Welcome to RealTime Mandarin, a free weekly newsletter that helps you improve your Mandarin in 10 minutes a week…

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16 days ago · 3 likes · Andrew Methven

This week on Sinica, in a special episode recorded at the Davos On Air booth at the World Economic Forum’s Annual Meeting of the New Champions in Dalian, I sat down with Angela Huyue Zhang, professor of law at USC’s Gould School of Law and author of High Wire: How China Regulates Big Tech and Governs Its Economy, and S. Alex Yang, professor of management science and operations at London Business School. Angela and Alex — who are also married, and who arrived at this collaboration from opposite ends of the academy — have developed what I think is one of the most useful new mental models for understanding China’s political economy: the platform state. Their argument is that we should think of the Chinese state less as a central planner or owner and more as a platform company like NVIDIA or Apple, one that builds architecture, sets standards, and governs an ecosystem within which fiercely competitive private firms fight it out. Value accrues at the system level rather than as firm profit, and the payoff may decide who wins the race to put AI to work across an entire economy.

5:37 – Three puzzles the framework is built to solve: profitless dominance in solar, EVs, and batteries; why the “grabbing hand” hasn’t strangled Chinese innovation; and how China is attempting both zero-to-one invention and one-to-hundred scaling at once

7:57 – The platform state thesis: why the Chinese government behaves like a platform company, how nurturing an ecosystem of private firms solves the information deficit that cripples command-and-control, and why over-entry, involution, and consolidation are a repeated pattern — from EVs to the 140-plus humanoid robot companies operating today

16:13 – The aha moment: how a paper on the legal infrastructure of physical AI became the platform state idea over the Zhang-Yang dinner table, and whether this is a new species of political economy or the East Asian developmental state in new clothes

20:44 – State conditions: why state capacity and domestic scale are the two preconditions for the model, and why an ambitious Vietnam — which has the top-down capacity — may still find the Chinese playbook impossible to replicate

23:39 – Profitless dominance by design: harvesting versus extracting, the Uber analogy, overshooting as a control-theory strategy for nudging sectors, and how the anti-involution campaign and the 60-day supplier payment mandate show the state moderating the very competition it engineered

30:33 – Organized chaos: from the bike-sharing graveyards of the O2O wars to today’s disciplined market, the exit of more than 400 EV makers since 2018, and why the survivors of China’s “Premier League” of competition are now turning profitable

32:59 – The 3Gs playbook: growing markets by solving the cold-start problem, from Liuzhou’s EV test drives to Beijing’s green license plates, and how subsidy is only one lever among many

38:36 – Governing the ecosystem like Apple runs its App Store: why Beijing regulates generative AI with a light touch but physical AI is a different species entirely, law as the sixth layer of the AI stack, why robotaxis scale faster in China than in the U.S., and the state-convened standard-setting that’s driving down humanoid robot costs

46:54 – Two flywheels: the familiar data-and-cost flywheel and the deeper state capacity flywheel, and how the National AI Fund’s small but voting stake in DeepSeek aligns a complementor with the domestic stack — tilting the ecosystem toward Chinese chips

53:28 – Guarding the moat: automotive data rules and Tesla’s stalled FSD ambitions, the unwound Manus sale, China’s own small yard and high fence, and the closing provocation — that America could build the smartest frontier models and still lose the diffusion race to “artificial good-enough intelligence.” Plus: the case for coopetition, and what policymakers should (and shouldn’t) borrow from the platform state

Links from the episode

Angela’s paper on law as the sixth layer of China’s AI stack:

• The Sixth Layer: The Legal Infrastructure for Physical Artificial Intelligence in China (SSRN)

Three Project Syndicate op-eds on the platform state idea:

• The Rise of the Chinese Platform State by S. Alex Yang & Angela Huyue Zhang

• Are Government Stakes the Key to AI Sovereignty? by Angela Huyue Zhang

• Overcapacity Is China’s Biggest AI Advantage by Angela Huyue Zhang

Two Management Science papers on commercial platforms:

• Crowd-Judging on Two-Sided Platforms: An Analysis of In-Group Bias

• Improving Dispute Resolution in Two-Sided Platforms: The Case of Review Blackmail

Transcript (courtesy of the fantastic CadreScripts) further down the page. Image by Keya Zhou. Listen in the embedded player above!

This week on Sinica, in a special episode recorded at the Davos On Air booth at the World Economic Forum’s Annual Meeting of the New Champions in Dalian, I sat down with Angela Huyue Zhang, professor of law at USC’s Gould School of Law and author of High Wire: How China Regulates Big Tech and Governs Its Economy, and S. Alex Yang, professor of management science and operations at London Business School. Angela and Alex — who are also married, and who arrived at this collaboration from opposite ends of the academy — have developed what I think is one of the most useful new mental models for understanding China’s political economy: the platform state. Their argument is that we should think of the Chinese state less as a central planner or owner and more as a platform company like NVIDIA or Apple, one that builds architecture, sets standards, and governs an ecosystem within which fiercely competitive private firms fight it out. Value accrues at the system level rather than as firm profit, and the payoff may decide who wins the race to put AI to work across an entire economy.

5:37 – Three puzzles the framework is built to solve: profitless dominance in solar, EVs, and batteries; why the “grabbing hand” hasn’t strangled Chinese innovation; and how China is attempting both zero-to-one invention and one-to-hundred scaling at once

7:57 – The platform state thesis: why the Chinese government behaves like a platform company, how nurturing an ecosystem of private firms solves the information deficit that cripples command-and-control, and why over-entry, involution, and consolidation are a repeated pattern — from EVs to the 140-plus humanoid robot companies operating today

16:13 – The aha moment: how a paper on the legal infrastructure of physical AI became the platform state idea over the Zhang-Yang dinner table, and whether this is a new species of political economy or the East Asian developmental state in new clothes

20:44 – State conditions: why state capacity and domestic scale are the two preconditions for the model, and why an ambitious Vietnam — which has the top-down capacity — may still find the Chinese playbook impossible to replicate

23:39 – Profitless dominance by design: harvesting versus extracting, the Uber analogy, overshooting as a control-theory strategy for nudging sectors, and how the anti-involution campaign and the 60-day supplier payment mandate show the state moderating the very competition it engineered

30:33 – Organized chaos: from the bike-sharing graveyards of the O2O wars to today’s disciplined market, the exit of more than 400 EV makers since 2018, and why the survivors of China’s “Premier League” of competition are now turning profitable

32:59 – The 3Gs playbook: growing markets by solving the cold-start problem, from Liuzhou’s EV test drives to Beijing’s green license plates, and how subsidy is only one lever among many

38:36 – Governing the ecosystem like Apple runs its App Store: why Beijing regulates generative AI with a light touch but physical AI is a different species entirely, law as the sixth layer of the AI stack, why robotaxis scale faster in China than in the U.S., and the state-convened standard-setting that’s driving down humanoid robot costs

46:54 – Two flywheels: the familiar data-and-cost flywheel and the deeper state capacity flywheel, and how the National AI Fund’s small but voting stake in DeepSeek aligns a complementor with the domestic stack — tilting the ecosystem toward Chinese chips

53:28 – Guarding the moat: automotive data rules and Tesla’s stalled FSD ambitions, the unwound Manus sale, China’s own small yard and high fence, and the closing provocation — that America could build the smartest frontier models and still lose the diffusion race to “artificial good-enough intelligence.” Plus: the case for coopetition, and what policymakers should (and shouldn’t) borrow from the platform state

Links from the episode

Angela’s paper on law as the sixth layer of China’s AI stack:

• The Sixth Layer: The Legal Infrastructure for Physical Artificial Intelligence in China (SSRN)

Three Project Syndicate op-eds on the platform state idea:

• The Rise of the Chinese Platform State by S. Alex Yang & Angela Huyue Zhang

• Are Government Stakes the Key to AI Sovereignty? by Angela Huyue Zhang

• Overcapacity Is China’s Biggest AI Advantage by Angela Huyue Zhang

Two Management Science papers on commercial platforms:

• Crowd-Judging on Two-Sided Platforms: An Analysis of In-Group Bias

• Improving Dispute Resolution in Two-Sided Platforms: The Case of Review Blackmail

Transcript

Kaiser Kuo: Welcome to this special edition of the Sinica Podcast, a weekly discussion of current affairs in China. In this program, we look at books, ideas, new research, intellectual currents, and cultural trends that can help us better understand what’s happening in China’s politics, foreign relations, economics, and society. Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China.

I’m Kaiser Kuo, coming to you this week — and that’s what makes this a “special edition” — from Dalian, from the Davos On Air booth at the World Economic Forum’s Annual Meeting of the New Champions 2026.

For over 20 years now, I have had the privilege of working as an official writer for the World Economic Forum. And this year, they’ve asked some podcasters to team up with them to bring shows to you under both the Davos on Air banner and under the banner of our own podcast. So, I am delighted to be able to do this with Sinica.

Sinica, of course, is supported this year by the Center for East Asian Studies at the University of Wisconsin-Madison, a national resource center for the study of East Asia.

Listeners, you can support my work by becoming a paying subscriber at sinicapodcast.com. Really, I do need your help to keep doing this work to be able to continue to bring you these conversations.

When this year’s two sessions wrapped up in Beijing and the 15th five-year plan was formalized, to the surprise of basically no one, the Chinese leadership had placed at the center of China’s economic future the familiar “new quality productive forces.” Things like, of course, AI, quantum computing, biotech, clean energy, drones, and other components of the so-called low-altitude economy, 6G telephony.

The easy read was one that we so often hear from the punditry in the West especially. This is a command economy picking champions and winners and directing investment and other resources from the top down. But that read keeps colliding with an awkward fact. In most of the sectors where China now leads — EVs, batteries, solar, increasingly in AI — the dominant firms are actually private.

Margins, despite all the help from the government, are razor-thin. Competition is so brutal that the government often has to step in to cool things down. So, what exactly is the state doing if it’s not central planning as we once understood it? My guests today have a really striking answer. They tell us we should think of the Chinese state less as a planner and more as a platform company like NVIDIA or Apple that builds the architecture, that sets the standards and governs the ecosystem, within which fiercely competitive firms are then allowed to fight it out.

Value doesn’t show up as firm profit. It accrues instead at the system level in lower costs, integrated supply chains, and faster learning. They call it the “platform state,” and they break its playbook into a handful of deliberate moves, growing markets, governing them, and guarding them. So growing, governing, and guarding. The payoff, they argue, could decide who wins, not the race to build the smartest model necessarily, but the race to put AI to work across an entire economy.

I find it a really compelling mental model that I suspect is going to catch on. You’ll remember you heard about it here first. It’s sure to generate a lot of debates and some pushback too, but it’s a terrific idea.

I am joined today in Dalian by the two people behind this argument. Angela Zhang is professor of law at the University of Southern California, previously seven years at University of Hong Kong, and she’s the author of High Wire: How China Regulates Big Tech and Governs Its Economy, and Chinese Antitrust Exceptionalism.

Her recent work includes a paper arguing that law is a sixth layer of China’s AI stack. I’ve been following her work for years, but only just met her actually earlier this year in Hong Kong, where she, until recently, lived at an AI governance conference there that we both attended. Angela, great to see you here in Dalian, and a warm welcome at last to Sinica.

Angela Zhang: I’m so happy and delighted to be here. I’m a big fan of your show, Kaiser.

Kaiser: Thank you so much. That’s very kind of you. Also here in Dalian with us is Alex Yang, Professor of Management Science and Operations at London Business School, where he studies platforms, not surprisingly, supply chains, and operations strategy. What I find irresistible about this collaboration is that they arrive at the same thesis kind of from opposite ends of the economy, or the of the academy, really. One from law and regulation and the other from operations and platform economics.

They are also, I should mention, not meeting here for the first time. They’re husband and wife, a household that apparently argues about Chinese industrial policy over dinner. Maybe we can get them to talk about that a little bit. But Alex, first, a very warm welcome to Sinica.

Alex Yang: Thank you. Thank you for having me, Kaiser.

Kaiser: It’s a real pleasure to have you both on. And let’s just jump right in and talk about how you frame this in talks that you’ve given about the paper in terms of puzzles. And I’ll lay out some of these. I want to start thinking about this because these are things that your framework is really built to solve, to really give us an understanding into why these conundrums exist.

I mean, these are things that I’m sure many of the people listening will have thought about, will have scratched their heads over. These are seeming contradictions. So, three of them I’ll just talk about very quickly. The first is what we might call profitless dominance. Right? China controls over 80 percent, as everybody knows, of the global solar supply chain.

BYD has passed Tesla now as the world’s largest EV maker. Battery makers like CATL are enormous. The firms, though, they find themselves, not in all cases, but many of them find themselves bleeding cash in these vicious price wars. We’ve all talked about neijuan, you know, about involution. How do you win an industry while your own champions are going broke? So that’s one puzzle.

A second is older, maybe more theoretical. There’s a common view that I’m sure many, probably people hold to that somehow, without secure property rights, without constraints on state power, authoritarian systems just can’t sustain innovation. The state becomes a grabbing hand, not a helping hand. And in the Chinese tech world, it’s pretty easy to find examples that seem to support this idea of the state as a grabbing hand. If you think the Manus sale to Meta, which had to be completely unwound, exit bans that they’ve placed on leading Chinese AI researchers.

So I guess the puzzle, if you wanted to put it this way, is why hasn’t this grabbing hand strangled Chinese innovation? Because quite obviously China continues to innovate. And third, a puzzle again rooted in a common idea that innovation and discovery tend to under decentralization while scaling is perhaps easier under central control. So, looks like, you know, America, very decentralized, very, very good at zero to one innovation. And then China, the great scaler, the one that goes one to 100 really well because of its tight central control.

But no system is supposed to be able to do both. Here we have though China really now trying very hard to do both. So, let me put it simply into Angela. Is there a single key that unlocks all three of these puzzles at once, and what is it?

Angela: Yeah, and the idea that we propose in the paper that crucial key to understanding these puzzles is to see the Chinese government as a platform company. I mean, I want to first clarify that I want to see the government as a platform company rather than just a platform. So, think about what platform companies do, right? I mean, why platform company represents such a revolutionary idea for organization in the past two decades, right?

I mean, companies like Google, Amazon, NVIDIA, they become like dominant forces in economy. It’s because they unlock values through facilitating transactions among platform participants from different sides. So they don’t directly participate in either providing the services or producing the goods, right? Just like Uber does not own the car or don’t drive the car themselves, but they’re trying to facilitate those transactions and that’s how they create values, right? I mean, at the end of the day, they want to nurture an ecosystem.

So that allows them to take a very asset-like approach to scale very quickly. And what we see is the Chinese government is adopting a very similar approach right now. When you look at the fastest growing companies, the fastest growing industry in China, whether it’s renewable energy or EV, right, and now humanoid robots, as well as other future industries that China want to invest in, they’re almost all predominantly privately owned, right?

I mean, it doesn’t mean that the state does not invest in anything in those companies, but those states are very small, right? And for the most part, the government is trying to nurture an ecosystem, trying to get everybody on board. And whether from different stages of supply chain to get everyone there, and then trying to get them to compete, right? And that’s the way for the state to accrue its capability. And that’s why, you know, you see these industries, China, have dominated one sector after

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Our phrase of the week is: “gaming the system ” (卡bug kǎ bug)

Context

As June temperatures have soared past 40°C in Germany, France, the UK and beyond, Europeans are rushing to buy air conditioners.

Only around 20% of European households are air conditioned. Older properties were built for cooler weather, and strict heritage rules limit changes to their exteriors, such as fitting external air conditioning units. So installing one is often either prohibited altogether, or prohibitively expensive.

Yet orders have surged at big Asian manufacturers including Midea (美的), a Chinese white-goods maker headquartered in Foshan in Guangdong.

Midea has designed a product specifically for the European market. It’s called the PortaSplit, which is a portable “split-unit” air conditioner with one part which clips onto the outside windowsill, and a cooling unit which sits on the floor inside.

The PortaSplit’s popularity is because it fits just within strict and varied building rules across different European countries. Its outdoor unit clips onto a windowsill with a simple bracket which means no tools or drilling are required and it counts as an internal appliance on a shelf. Its refrigerant capacity is 1.99kg, just under the French 2kg threshold. In silent mode it runs at 35 decibels, right on the German limit. And its efficiency rating of 6.1 lands just inside the bottom end of Switzerland’s A++ band.

This ingenious design has caught attention in China, and is described with this confusing internet slang phrase:

This is product design at its finest, which has been jokingly called “gaming the system”.

Midea’s global, legal and design teams all deserve a hefty year-end bonus.

这种极致的产品定义被戏称为”卡bug”，美的的外贸、法务和设计团队都该集体加年终奖。

And with that, we have our Sinica Phrase of the Week.

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What it means

“Gaming the system” is our translation of this slang phrase which starts with the Chinese verb “to jam” or “get stuck” (卡 kǎ) followed by the English word “bug”.

“Bug” is used here in the context of computing. The source of that word dates back to the late 1940s, when Grace Hopper, a computer scientist working on the Mark II machine at Harvard, traced a fault to a moth trapped inside one of its relays. She taped the moth into her logbook and recorded it as the first real “bug” found in a computer. The word stuck, and “bug” became the standard term for a fault or flaw in a computer, program, or application.

The Chinese phrase “jamming the bug” (卡bug) comes from the early days of first-person shooter (FPS) games, which spread through Chinese internet cafés from the early 2000s. These games inevitably had flaws in their map design, and players learned to exploit those faults to their advantage like breaking through a wall, or reaching a spot where no opponent could hit them.

This could be done through certain moves like repeatedly nudging a particular prop in the game, switching weapons, or hitting keys in a certain rhythm to freeze the character in mid-air. This kind of manoeuvre became known as “jamming a bug” (卡bug).

Within gaming, the phrase broadened to cover any trick that exploits a loophole in a game’s settings such as grabbing high-value equipment, skipping past a task, or winning an unfair advantage over other players.

At some point in the last 10 years or so the phrase moved beyond gaming into mainstream use. It’s now used for anything that “games the system” or rules without technically breaking them by finding gaps in a system and slipping through.

Which is why this phrase is the ideal descriptor for Midea’s PortaSplit. The air conditioner doesn’t break any European regulations. Instead it sits deliberately right on the edge of all of them, creating a hugely popular product which has come at the right time for many people overheating in their homes.

Andrew Methven is the author of RealTime Mandarin, a resource which helps you bridge the gap to real-world fluency in Mandarin, stay informed about China, and communicate with confidence—all through weekly immersion in real news. Subscribe for free here.

Read more about how this story is being discussed in the Chinese media in this week’s RealTime Mandarin:

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#283: Europe’s heat wave is an opportunity for Chinese manufacturers

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Transcript (courtesy of the fantastic CadreScripts) further down the page. Image by Keya Zhou. Listen in the embedded player above!

Recorded live from the Davos on Air booth at the World Economic Forum’s Annual Meeting of the New Champions in Dalian, this special episode of Sinica tackles the “pacing problem”: the widening gap between how fast AI moves and how slowly regulation can catch up. I sit down with Xue Lan, dean of Schwarzman College at Tsinghua University and one of the architects of China’s concept of “agile governance,” to unpack what that term means in practice. He traces China’s regulatory evolution from the 2017 AI plan through the generative AI rules and the 2021 tech crackdown, compare Chinese, American, and European approaches, and ask whether Beijing’s adaptive style can travel to other political systems — including liberal democracies.

• 00:09 – Live from Dalian: the “pacing problem” and why AI has turned it into a chasm

• 03:18 – Introducing Xue Lan, dean of Schwarzman College and architect of “agile governance”

• 04:34 – Why AI’s pace makes it uniquely hard to regulate

• 06:01 – Defining agile governance: mindset, partnership over adversary, and light-touch tools

• 11:54 – From the 2017 AI plan to today: China’s two-track approach to tech and governance

• 20:07 – Balancing development and security amid the US-China AI race

• 23:24 – Revisiting the 2021 tech crackdown: failure of the model, or agility of a different kind?

• 26:14 – The “DeepSeek moment,” open-weight models, and regulatory uncertainty by design

• 37:10 – EU comprehensiveness vs. US patchwork vs. China’s modular, adaptive approach

• 46:59 – Can agile governance travel to liberal democracies? Finding common ground on global AI risk

Transcript

Kaiser Kuo: Welcome to this special edition of the Sinica Podcast, a weekly discussion of current affairs in China, coming to you this week from Dalian, from the Davos on Air booth at the World Economic Forum’s annual meeting of the New Champions, also known as Summer Davos.

In this program, we look at books, ideas, new research, intellectual currents, and cultural trends that can help us better understand what’s happening in China’s politics, foreign relations, economics, and society. Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China.

I’m Kaiser Kuo. For over 20 years now, I’ve had the distinct privilege of working with the World Economic Forum as an official writer. And this year, they’ve asked some podcasters to team up with them to bring you shows both under the World Economic Forum banner and under the banner of their own podcast. So, I’m delighted to be able to do this this year with Sinica.

Listeners, please support my work by becoming a paying subscriber at sinicapodcast.com. I do need your help to keep doing this work and to keep bringing you these conversations.

Technology has always run ahead of the rules meant to govern it. Scholars call this the pacing problem, the chronic lag between what innovators can do and what regulators have figured out how to actually handle. We’ve seen this with nearly every major new wave of technological innovation. Recent examples would include Uber and Airbnb, for example, or how regulation really had to catch up much earlier than that. You could go back to the advent of the automobile.

Cars hit the roads before anyone had invented things like speed limits or driver’s licenses or traffic lights or even the concept of jaywalking. I was lucky enough to be in China and to have a front row seat to watch this whole thing unfold when the Internet really took off in China in the late 1990s. In recent years, artificial intelligence has turned that lag into a real chasm, and nowhere is the dilemma more vivid than in China, a country trying to do two things at once that often can pull in opposite directions to, on the one hand, unleash technological development at extraordinary speed, and on the other, to keep that development firmly within bounds that the state can manage.

Out of that tension has come an idea, “agile governance,” that has worked its way into the global policy vocabulary. The question I want to explore today is whether it’s a genuine model that others can borrow or whether it’s something so deeply rooted in China’s particular system that it can’t really be transplanted. And there’s no better place to ask it than here in Dalian, where this year’s theme is Innovating at Scale. It makes the governance question really more urgent than ever because scaling at innovation, scaling innovation itself, also means scaling the risks of technology.

So, my guest is one of the people who has thought longest and hardest about all of this. Xue Lan is dean of Schwarzman College at Tsinghua University. And because I’ve been involved with Schwartzman for many years, really since its inception, that’s how I know Dean Xue best, but he’s also director of Tsinghua’s Institute for AI International Governance. He’s a Cheung Kong Distinguished Chair Professor with a doctorate in engineering and in public policy from Carnegie Mellon University in Pittsburgh.

He chairs China’s National Expert Committee on the Governance of Next Generation AI. And some of you may recall that was one of those, from China, who spoke to Senator Bernie Sanders of Vermont on cooperation on AI governance back on April 30th of this year. Crucially, for our purposes today, he is also one of the intellectual architects of this very concept of agile governance as a concept in Chinese policymaking. So, there are few people who are better placed to tell us what it really means and whether it travels. Dean Xue, it is a real pleasure to have you at last here in Dalian, and a warm welcome to Sinica.

Xue Lan: Thank you, my pleasure.

Kaiser: So Dean, let’s start with the basic problem. Every regulator faces what’s been called this pacing problem, technology evolving faster than the rules that can keep up with it. This isn’t genuinely new, but it does seem newly acute. What makes it so? It’s not just AI either. It’s biotech. It’s robotics. It’s new materials. It’s quantum. And more, yes?

Dean Xue: I would still argue that AI is really unique. I think partially because AI has been changing so fast. If you look at the other technologies, I think that very few that can develop so fast. I mean, think about the frontier models.

Kaiser: Right.

Dean Xue: In the last few years, I think it’s maybe every half year, not every two months and every month, even just few weeks. So, the rapid evolution, I think, it’s just really unseen.

Kaiser: Yeah.

Dean Xue: I think that’s part of one aspect. The other thing is also the impact on so many domains daily life of our economic activities. So bio maybe is in certain sectors, but not in all. AI, you can’t think of it, an area that AI would not touch on.

Kaiser: Right.

Dean Xue: Yeah.

Kaiser: Yeah, absolutely. So you are very closely associated with this concept of agile governance. So maybe in just a couple of sentences for our listeners, what does that actually mean? And just as importantly, what is it a reaction against? Agile can sound like a Silicon Valley slogan, you know, move fast and break things. So, how is the governance version different from simply light touch regulation or from deregulation?

Dean Xue: Sure. Indeed, I think those two words actually are kind of a very strange combination. We think about the governance, of course, it’s very official, very formal, is going to take a long time, and so on. So you have that image there. Agile is something that’s light, quick, and then the change fast. So those two combinations, I mean, it’s kind of weird combination. But that’s exactly what I think that we’ve been trying to achieve. That is, you want to achieve the function of governance. But at the same time, the way you achieve that is not through the traditional, you know, official approach.

But rather you try to learn from the, you know, in other spheres, for example, in industry and so on. You try to accelerate. So that’s the combination that we’re trying to achieve. I think, in general, I think there core elements, what I see as really embodied in the so-called the agile governance. And the first thing is that, you know, I’m a scholar of public policy. So, when we think about governance, we think about regulations, you always try to study this thing very thoughtfully, I mean, very thoroughly.

And so, you want to make sure that you’re not leaving any gaps. You want to review all the evidence, all the problems and so on. And then you come up with a comprehensive, accurate kind of a regulations that you very thoughtfully deliberated. And then you go through, you know, very elaborative process, being reviewed by experts, by stakeholders, and going through the kind of a due process, then to get your final thing coming out.

And that’s the regular governance that we have to go through. Very unfortunately for a technology like AI, by the time you’ve gone through that process…

Kaiser: The thing you’re regulating has changed.

Dean Xue: The thing you’re regulating has already changed. It’s totally been the way. So, what do you do? And that’s so in a way, if you really want to be useful, you really have to change your mindset. You can’t be comprehensive. You can’t be accurate on everything. And you really have to move fast. That’s the, I think, the first thing is that you have to change your mindset. The second thing I that you also have to change the idea that the regulator and the regulatee, the companies being regulated, you are the enemy. You are playing the cat and mouse game.

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Recorded live from the Davos on Air booth at the World Economic Forum's Annual Meeting of the New Champions in Dalian, this special episode of Sinica tackles the "pacing problem": the widening gap between how fast AI moves and how slowly regulation can catch up. I sit down with Xue Lan, dean of Schwarzman College at Tsinghua University and one of the architects of China's concept of "agile governance," to unpack what that term means in practice. He traces China's regulatory evolution from the 2017 AI plan through the generative AI rules and the 2021 tech crackdown, compare Chinese, American, and European approaches, and ask whether Beijing's adaptive style can travel to other political systems — including liberal democracies.

• 00:09 – Live from Dalian: the “pacing problem” and why AI has turned it into a chasm

• 03:18 – Introducing Xue Lan, dean of Schwarzman College and architect of “agile governance”

• 04:34 – Why AI’s pace makes it uniquely hard to regulate

• 06:01 – Defining agile governance: mindset, partnership over adversary, and light-touch tools

• 11:54 – From the 2017 AI plan to today: China’s two-track approach to tech and governance

• 20:07 – Balancing development and security amid the US-China AI race

• 23:24 – Revisiting the 2021 tech crackdown: failure of the model, or agility of a different kind?

• 26:14 – The “DeepSeek moment,” open-weight models, and regulatory uncertainty by design

• 37:10 – EU comprehensiveness vs. US patchwork vs. China’s modular, adaptive approach

• 46:59 – Can agile governance travel to liberal democracies? Finding common ground on global AI risk

Our phrase of the week is: “The Card Master” (卡牌大师 kǎ pái dà shī)

Context

At the 2026 World Cup, which kicked off on June 11, China's national team is absent again. In fact, Team China hasn't qualified for the tournament since 2002.

There is, however, one Chinese representative on the pitch: head referee Ma Ning (马宁). His first match was Ecuador versus Curaçao on June 20, which drew over 10 million Chinese viewers on social media platform RED.

Born in 1979, Ma Ning started his career as a university PE teacher in Wuxi. He began refereeing as a hobby in his spare time. At age 26, he came top of the Chinese Football Association’s national referee programme, then worked his way up from the lower leagues to the Chinese Super League, making international referee in 2011.

He made the officials’ list for Qatar 2022 but only as fourth official and VAR assistant, never taking charge of a match. That looked like his last chance, yet he trained four more years and, against the odds, made the refereeing squad for this World Cup at 47.

Ma Ning is a hit with fans in China. But not just because he is China’s “only starter” (唯一首发) on the pitch at the World Cup. It’s his unforgiving, principled style which has won fans over and given rise to his nickname:

Within the profession, he’s known as “The Card Master.”

It’s not a jab, but a respectful acknowledgement.

His approach on the pitch is simple to the point of stubbornness: he follows the rules, not a player’s reputation.

业内人送他外号“卡牌大师”。这不是调侃，是带着敬畏的认可。他的赛场逻辑简单到近乎固执：只认规则，不认脸。

And with that, we have our Sinica Phrase of the Week.

Subscribe now

What it means

"Card Master" (卡牌大师 kǎ pái dà shī) is an internet slang phrase which came from online gaming and is now the nickname given to Ma Ning.

It’s origin is League of Legends (英雄联盟), the hugely popular online battle game, and the source of many other well-known internet slang phrases. The “Card Master” (卡牌大师) is a characters in the game. He’s a champion who fights by hurling playing cards, and is known in English as Twisted Fate.

The Card Master is a notorious yet charming gambler and con man. He is world-famous for his skill with a deck of cards and always keeps an ace up his sleeve. Everything he does revolves around cards: he flings them at enemies, draws blue, red or gold cards for different effects, and has a signature move that reveals every opponent on the map.

The nickname crossed over into football, and onto Ma Ning himself, after a notorious Shanghai derby in May 2015, between Shanghai SIPG (上海上港) and Shanghai Shenhua (上海申花). During that match he handed out 12 cards in total: nine yellow and three red. His liberal use of cards drew the comparison to the Card Master in League of Legends, and the name stuck.

Ma Ning has since become a superstar in China, and his nickname “Card Master” has followed him as an admiring nod to his adherence to the rules, and his willingness to punish any player who breaks them, no matter how big the name.

With China’s national team watching from home, Chinese football fans have turned to the Card Master for someone to support at the World Cup, calling him the most neutral official at the tournament.

Andrew Methven is the author of RealTime Mandarin, a resource which helps you bridge the gap to real-world fluency in Mandarin, stay informed about China, and communicate with confidence—all through weekly immersion in real news. Subscribe for free here.

Read more about how this story is being discussed in the Chinese media in this week’s RealTime Mandarin:

RealTime Mandarin

#282: The Card Master: China's Unlikely Face at the World Cup

Welcome to RealTime Mandarin, a free weekly newsletter that helps you improve your Mandarin in 10 minutes a week…

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a month ago · 2 likes · Andrew Methven