China’s pragmatism, state-market hybrid, and how that shapes AI and capital with Baiguan’s Robert Wu

In this episode, I speak with Robert Wu, the founder and CEO of Baiguan . Our conversation focuses on two questions that increasingly overlap: how AI is reshaping the business of information, and how China’s distinctive mix of pragmatism, markets and state involvement shapes the way new technologies get adopted and financed.

We start with the professional data industry. As AI agents become a new orchestration layer above terminals, APIs, and research products, Robert argues that the biggest disruption may come not to the production of proprietary data itself, but to its distribution. For niche data providers like BigOne Lab, the opportunity is to make differentiated real-time data accessible at inference time. The unresolved problem is economics: licensing, access control and who ultimately captures the value when an AI agent becomes the interface.

From there, we widen the conversation to culture and political economy. Robert explains why debates about AI in China tend to focus less on existential or metaphysical questions and more on what the technology can actually do. We discuss whether that pragmatism comes from China’s history of technological catch-up, whether similar attitudes extend across East Asia, and the potential trade-off between being exceptionally good at applying technology and creating the conditions for more fundamental scientific discovery.

We then turn to the role of the state. Robert rejects the simple idea that China’s technology industries are created through top-down planning. Instead, he describes a hybrid system in which entrepreneurs often discover the opportunity first, while the state later supplies policy support, capital and the resources needed to scale. We use EVs and DeepSeek to explore that model, before moving into state subsidies, local-government incentives, private capital, Beijing’s evolving approach to public markets and why so many young AI and technology companies are choosing Hong Kong for their IPOs.

We close with two of Robert’s more differentiated views: that China could be entering a multi-decade equity bull market, and that outsiders often misunderstand China by assuming it has the same impulse to export its own political or cultural model. And for a slightly lighter ending, Robert explains one of the Chinese stock market’s most vivid metaphors: why generations of retail investors are compared with chives that get cut, grow back, and get cut again.

btw sorry for the weird glitch in the video around 12-13 min of the recording.

The AI Proem Podcast is under the AI Proem newsletter which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.

Chapters

00:00 Robert Wu, BigOne Lab and Baiguan03:09 From alternative data to the AI era06:02 Why AI disrupts data distribution12:11 Inference-time data, licensing and economics15:17 Why China feels more pragmatic about AI21:47 East Asia, belief systems and scientific discovery30:32 China’s hybrid model of state and private innovation45:24 Funding AI: state capital, private capital and IPOs50:30 Beijing’s market-stabilization playbook and policy risk01:07:29 Robert’s non-consensus views and the meaning of “cutting chives”

Transcript (AI-generated, for reference only)

Grace Shao (00:00)

Hey Robert. Good morning. So good to have you join us today.

Robert (00:05)

Good morning, Grace. Hello, everyone.

Grace Shao (00:08)

Robert doesn’t need much of an introduction. If you spend as much time in the Substack world as I do, you’ll know he’s a prolific writer covering everything from capital markets and property to technology and culture. My favorite niche is when he calls out Noah Smith’s articles for being wrong. Those are pure entertainment for me.For today, though, Robert is a student of history, politics and business, and I think it will be interesting to have him walk us through some of the bigger questions people have about China. I’m also curious, because he runs a data company, about how he sees the future of data providers as AI changes that relationship.So I’m handing the mic over to you, Robert. Tell us about yourself, your journey with BigOne Lab and Baiguan, and how you’re seeing the business evolve.

Robert (01:12)

Yeah, hi. So this is Robert. As Grace mentioned, we run a newsletter. But that newsletter is really our kind of side business. The actual BigOne Lab is a team of over forty people, which exclusively most of us work on data products and research products for institutional investors and corporates. Both in China and outside of China. But we have we’ve been very China focused. All of our data and research are about China, Chinese companies, Chinese industries, businesses. The Baiguan to me was partly accidental, but partly also kind of fateful. Actually in the very beginning during college I actually wanted to be a journalist. But I didn’t find a way. So I kind of dabbled in capital markets in investing, corporate finance for a few years. But eventually it kind of hit me that, there’in this new world there’actually other ways to do journalism. Data tracking, data analysis is actually could be a new form of that. And even with data you can do more powerful storytelling and that was the genesis of our newsletters as well. Right. So here we are. We are backed by S&P Global as well, which is I would say one of the most ris backed respectable, respected companies in our industry. And yeah, so it’a brief intro about ourselves.

Grace Shao (03:09)

Yeah, so tell us like what is unique about your data then in that sense.

Robert (03:14)

Right. So we started as a so-called alternative data company. Alternative is alternative to the traditional financial data, macro data, market trading data. It’no longer alternative now. All alternative data is mainstream data now. But it was first happening, it was because the explosion of data and information in the internet and especially the mobile internet age. There are just so many data being tracked. There’payment data, there’online com commerce and social media data, so vast number of data and multiplying exponentially every year. And some investment firms they realize that by harnessing all these data and aggregate them together and put them in the right context, you could actually generate a lot of alpha that is previously not available. Right? So that’how we started the business. It was a very investment firm hedge fund driven business. So that you know kick us to look at a lot of the industry verticals, a lot of the different kind of industries where there’data and we try to find the most granular, the most high frequency data we can find. Perhaps the you know we can have massive amount of data about mobile transactions in China, for example, every day, even every minute, all the transactions that we can have access to and analyze on. So that’different from many of these you know mainstream data providers, which we try to be very granular. We try to be very frequent. Yes.

Grace Shao (05:18)

Yeah, so that’really interesting. I think p one thing that really stood out to me and relating it back to AI is that when we were having our catch-up conversation, we were saying, okay, data plays obviously a huge role in AI. But what you distinctly said, there is the people that are involved in the pre-training data bit, there’like the Mercores of the world. There is the people who are more pivoting towards kind of the post-training data provider, which is what you guys are doing. Just tell us about that relationship and how you think the whole data vendor ecosystem is adopting to AI and or evolving with AI, especially and how like AI is now affecting, say, Bloomberg, Factiva, those mega data platforms that we traditionally know of.

Robert (06:02)

Yeah. So the term data company is really problematic for us. It’really a kind of a name that covers very different kind of businesses serving different needs, entirely different kind of businesses. So you mentioned that there are data companies that are serving the large language model training right now, the Mercor, the Surge AI. So they are they are they are good at massively labeling data, connecting the you know different type of data and help the help build up the data sets that are used for the training. Well for us, we are more on the on the on the more on the real time data end. And it’so for the industry that we operate in, we have Also, we have not a consensus on the name for our industry, to be honest. I call it professional data industry. Some people call it market data, some people call it market intelligence data. But at the end it’it’about tracking and understanding of the real world on a real time basis, if we have to define it. So it’much more about what is happening rather than the logical connections between different pieces of information, which I think is what the pre-training data i is mostly about. And so in our industry, AI is placing is playing a huge kind of disruptive role for our industry. So in the professional market data industry, there are main several main stages, maybe three. There is the production the original production of the data, there’a distribution, and there is the what we call activation. I won’t maybe go to details of each one of them, but if you understand production, production is really where the data is originated, right? For example, if you are Nasdaq, all the trading data on your Nasdaq platform is originated at Nasdaq. That’called production. The second is distribution. Is how you combine all this data into products, right? Companies like SP’marketing intelligence, like Bloomberg, like you know FacSet are in the distribution part. They don’t generate data on their own or mostly don’t not on their own, but they provide the interface for users to interact. Right. And activation is really how data is used. I won’t go to detail for that part. But right now the one the stage that is facing the biggest disruption is not the production side, right? You still need to generate data. You still have to have some kind of source of data. AI won’t help that. But on the distribution side, there’a there’a huge, I would say, change that is undergoing. Imagine if you are an analyst twenty years ago. It’required for you to have either a Bloomberg terminal or you know a FactSet terminal, or if you’trying to a wind terminal, right? It’it’a terminal kind of portal driven business. A go-to portal or source for information. AI is fundamentally going to change that by adding a new what we call orchestration layer above all the data types. You’not going to go to any terminal in the traditional software sense, but you’going to have this advisor to you that is going to massively, quickly, rapidly going through all the data, find the data you need, give you the conclusions, do the comparisons of and all that. Right. So there’a big tension right now between this trend of increasingly more people is rel relying on their AI agents to do research and the existing incumbents. Of the of the market which and then if you look at the incumbents the different companies are adapting differently so you have companies like SP and Faxet they are embracing AI and they signed big contracts with large language models they allow you know clause users or open AI users to access their data through these their AI products and they are they are embracing it. But then you also have company like Bloomberg, which is really at the core, at the at the at the apex of traditional financial and market data industry. I think they’still trying to figure out what to do with this. And I think their natural tendency is to build their you know in-house AI. They still want people to, go into their universe. And to make to like people still go to their universe to check the data. Right. So there is some debates right now and it’going to be interesting, who is going to prosper, who is going to stay. Yeah.

Grace Shao (11:39)

Yeah, Bloomberg definitely still wants you within their terminal. Everything is within their terminal. And once you exit terminal, that’majority of their revenue. They don’t want you to jeopardize that. But for someone like you, it’quite interesting. I said something I made a mistake earlier. What you told me was that you guys are a data provider on the inference end now. How do we understand that? And how do we understand how you are going to work with whether it’the model companies directly? Or how you will provide your institutional clients your data differently.

Robert (12:11)

Right. So at this moment we are still observing. We are actually niche provider of some really high value data, but not needed by most other people. So we’not like the mainstream data sets, but we are observing and we believe that in the end we’ll have no choice but to kind of open us up to the large language models. To us, this will be a new form of access to use our data. Apart from, right now we provide our data to our clients through API, through Excel spreadsheets, through even research reports, this is our current method. But in the end, I think as more and more clients rely on AI to pull data, we will we will we will kind of open us up. And that’the That’in the inference part. That’when people actually are using data to do analysis, to do research. And so we are firmly in that part. And I think it’just inevitable that we will be connected to these AI at some point. It’just the problem right now is about the economics. How do the economics work? How do we kind of get us exposed to it, but also make sure that there’strong enough licensing and you know strong enough gate that we can put on our more exclusive, more differentiated data sets. That’a question that there also hasn’t been a consensus yet in the industry. Yeah. So that’why we are taking this kind of stacking back and observing kind of view of it.

Grace Shao (13:55)

I see, very interesting. Okay. So enough about the dry stuff. The most interesting stuff I read from you are actually your takes, because I think your takes are very nuanced. They’you’a deep thinker. You bring together cultural sentiment, history, political reality, and then the business together. To start with, I think one of the questions I get the most from people right now is just that this general attitude around why Chinese people feel more normal about AI. I wouldn’t even use optimistic. I mean as a as a society as a whole, it does feel more optimistic. But it just seems like whether it’how the government and the regulators are looking at how to regulate this new technology or how people are adopting it, like a trial error kind of feel, there’less of a philosophical push up a pushback towards AI, but more maybe, obvious concerns of what disruption or change might may mean for job displacement, whatnot. But in general, quite optimistic, quite normal. How do you view this right now? If I just kind of bringing together all the different aspects and it because I can’t, I don’t believe people just saying, just because people are more pragmatic. That’that I mean, I made that argument slightly, but I even think it should be deeper, more nuanced than that.

Robert (15:17)

Right. Yes, I mean this is a good question. Without if you don’t ask me that, I wouldn’t even realise it’a question. Because sitting in China, it’true that it is well not you know most people don’t talk about that. Some people do, but definitely not a mainstream discussion on the kind of existential kind of risk of crisis that AI is posing to the humanity. That type of question is not that asked is not you know asked that often in China. For the good and bad, right? I personally I don’t know w which part which approach is better, the more pragmatic one or the more philosophical one. But that’the phenomenon. It’true. Most people don’t think i in that way. The exactly why, you know Probably I would if you are looking for a more subtle, more nuanced answer, probably you won’t be able to find here. Because I was I was also thinking that it’it’really because of the pragmatic and down to earth nature of most things in China. People tend to ask more, what can this be used for? Other than why we have to do this or w what’the bigger contact what’the bigger picture? People tend to focus on the productivity side of things. I mean that’just prevalent in all industries or new industries. And especially China attached a premium to new industries. I think that’the kind of cultural reflex of the last few hundred years, after China kind of fell behind the West. In terms of technology and suffered all the consequences from that. So there was now a kind of reflex to emulate the world, to catch up to the world in all kinds of new technologies out there. Every time Silicon Valley coined some new term, some new idea, there would be some at least some kind of reflection and discussion about that. If you remember a few years ago there was this concept called metaverse. Right. Now nobody talk about that anymore. But back then it was also a very hot topic in China because you know people might think this is maybe the future because the Silicon Valley, the US chose that, and maybe we should think about whether that’the future. When crypto came out first, China was at the very beginning also embracing it, right? My very first Bitcoin was bought in China with RMB, while when there was like many RMB exchanges there. Well, then it hit some problems, it got banned and all that, but that’what happened later. But China ha always had this at this contemporary China had the tendency to learn the new things and to try to, to grow their our own knowledge and strength along these new verticals. So that’the I would say the big context, the big framework that people use, kind of equipped themselves with when they look at these new things. And less so about the philosoph philosophical and maybe not a philosophical but metaphysical, right? The ones that are hard to prove or disprove at this point and just kind of descend into discussion about contact concepts, on the abstract side. That kind of discussion, that kind of discourses really doesn’t have a big market in China. Small circles, yes, but most people Just don’t like to engage in that kind of discussions. Platonic, Aristotle level discussions. Yeah.

Grace Shao (19:23)

Is it just because it’kinda I don’t know, it’just like what’there to gain from that for the average Lao Bai Xing the average Joe? When they think about it, it

Grace Shao (19:32)

Seems like it’kinda Okay, if I embrace it, I win. I don’t embrace it, I lose. It’a bit of FOMO. Especially for the next generation, when I talk to parents, there’less of a concern about what this technology might mean in terms of safety for the kids, but there’more about how do I embrace this technology and teach my kids so my t kids can go basically go ahead of everyone else and come on top? I don’t know. Is that kind of what

Robert (20:01)

Yes and I think big part of that the these better philosophos philosophical questions don’t tend to produce results. Right? It’not a question that w if we debate and discuss we’ll have some kind of agreement. They just tend to stay philosophical. But most people I would say that most people I know here don’t tend to keep going. Keep debating on this type of questions. And maybe that’right, maybe that’wrong. I don’t know. But that’just the phenomenon that we are seeing here. Yeah.

Grace Shao (20:39)

Just how it is. Less of a chatter class, if you must put it, or at least less prominent in.

Robert (20:44)

Yeah, good way to put it. Yeah. Yeah.

Grace Shao (20:49)

I think another thing we kind of touched on briefly when we were catching up for this recording was that we said, look, this Chinese pragmatic approach to technology and like how to even day to day life is not really just limited to China, right? Like it feels like it’a phenomenon across maybe even East Asia. Other markets like South Korea, Singapore, a lot of, studies, whether it’by Stanford or by local communities, have shown that people are also embracing, Singapore’own ministers are coming out talking about how they’claud coding or vibe coding out there. Why do you think I know that you write sometimes with a bit of a this versus that or like a bit of a historical and a eth ethnic and history kind of tied to your analyses? Why do you think that maybe East Asia feels more pragmatic towards AI or contemporary East Asia, like you put it just now?

Robert (21:47)

It’a it’a very deep question. So I think if I have to attribute, and I’m just throwing out ideas right now, religion is definitely huge part. The whole tradition, the tr of the history of thoughts, the history of religion, the history of philosophical discourses, has to play plays a huge role. So we don’t have a tradition of seeing something abstract, either as a natural law or a god in this part of the world, historically, right? So people the w the reason why the people are more pragmatic about things is just like all the things that happen in your life are pragmatic. There’a there’a flood and then we have to fix it. We have to you know do some work on around that to fix it. All the laws, I mean all the folklores, all the all the lessons of history surround about how to deal with these you know disasters, wars in human life with a human way. Right? So it’it’more there’a concrete problem, there’concrete solution. And very seldom you you see like people in China turns to God, for example. For a solution. Maybe

Grace Shao (23:19)

That’really interesting, but I will push back. They are like the Buddhas and the temples that still exist where people like pray for money, which is hilarious. Again, it’a very pramatic result. Or you like you pray for a child. You literally, you’re

Robert (23:31)

Exactly. Right.

Grace Shao (23:33)

Not you’just give me a child. Like you pray for fertility, give me money. You pray for money. But there’praying in that, but it’not omnipresent. It’like each god has a or like each Buddha has a very clear ask and reward almost. I don’t know, like

Robert (23:51)

Yeah, exactly, exactly. So I mean there are like symbols of belief or faith or whatever, but exactly as you said, people use these very pragmatically, practically. They’the you know when Buddha said that you know w we w you know Buddha doesn’t want it the original Buddha doesn’t want him to be worshipped as a god. Right. It’really a teaching about how to position how to think of oneself and how to position yourself to the universe, to the world. Right. That’the whole teach but then it lost that favor in China. It became this, inf fused with all these other very down to earth beliefs and to become this, now you assign this Buddha. To ask for kids, that Buddha to ask for money. It’definitely not what Buddha originally taught. So it’just it’just it has been like that for not just decades, centuries, even millennia. So I mean that’also that’a very big part about China, which I’m yet to write about, but I kind of touch on it at several points. Is that you know a big part of China is China really I mean Chinese people and maybe East Asian in general, because we don’t have such a strong kind of belief in some abstract things, we also tend not to want to convert other people into our kind of belief, right? So we tend to focus on the practical. That’why we have a lot of business people, for example, that are focused on making deals, right? Trading, benefit you, benefit me, and so it’all very down to earth, but very practical things. And that does definitely have limitations. I would argue that in terms of fundamental pure science discoveries, that kind of mindset creates a disadvantage. You really have to, when you do groundbreaking scientific discoveries, you really have to forget about all these worldly stuff. You really have to forget about things well what’this mathematical formula have to do with my life? You have to forget about that. You have to just focus on this the purity of sciences, of mathematics to have to have some great discovery. Then that’why you know like the people were debating recently you had these metal this mathematicalist, Chinese ethnic ones, but getting their awards not in China, not while they are in China, but because they have further studies in the West. Right now in China there’a big debate about you know whether Chinese college graduates can do you know achieve that kind of level of achievement in sciences if they stay in China. I think right now I’m not that optimistic because overall people are still very focused on the use cases, the pragmatic use cases. But most of the time when some big scientific truth is discovered, they don’t have a direct use cases. And that’definitely not how they start a discovery exploration. Right. So that’well, the thing is, the reason I want to sometimes compare the China and the West is not I want to say which one is better. I actually my main point is we are many societies can be different. But in our world, different societies, different economies, different kind of people can play different roles. You so you need thinkers, you need the people who think about the abstract, but then you also need the people who actually can put things into use and create productivity and make people’lives better. And it’it’it’great that you know you have different kind of people serving their different kind of purposes. And I and I think I love I actually think that you know China being different and the West being different in their own ways, a net benefit for the whole world. And that’you know my actual overarching key point in writing about all of these.

Grace Shao (28:39)

That’really interesting. I think, like offline I wanna dig more into the religious aspect. It was just really interesting. I never thought about it that way. And we might get some heat and pushback on this because obviously South Korea nowadays is like a very Christian country and you know it kinda goes against what you earlier said. But I do think what you meant by East Asian worshipping in general is not so much omnipresent, but it’I don’t want to say it’opportunities, but people often go to these Like Buddhas when they need something, when that thing happens, or when something bad happens. But it’not like you’not taught to be thinking about it day in, day out. So that godlike attitude is very, very different. And I think it does translate to how people are perceiving AI these days, because in the West, right now, AI is seen as like a kind of or a lot of cult like figures are coming forward. And, a positioning AI like a new magic or something that will fundamentally change society as we know it. Anyway, so we can talk more about that maybe offline, but I want to bring it back to then things that you write about a lot, which is how should we understand then China’unique state planning and how it drives economy? Because I think it all relates to what you just said. China itself, in a way, you can say a lot of people are taught to be very, very strong execution and execution people doers, but they

Robert (30:01)

Mm-hmm.

Grace Shao (30:01)

Are maybe less of these creative, wild thinkers. Obviously that’not. All true, but in a general sense, yes. So then when it comes to then how the state interacts with the private sector in terms of innovation and state planning, we see that again, there’a top-down vision or priority. And then companies or sectors as a whole will start executing and create abundance. How does that all work and how do you view that kind of relationship?

Robert (30:32)

Yeah, I think, when we talk about relationship between state and business and innovation, again, people frequently fall into the traps of big concepts, right? People the classic question is China socialist or is China capitalist? And these are also tend to be kind of the Western preference in terms of discussing things. While again in China, People tend to be not so focused on the on these conceptuals, on these, black or white. So when Deng Xiaoping said, black w black cat, white cat, whoever catches the mice is a good cat, it’not just his opinion. He’only a manifestation of the average most of the people in China. Whatever works, whatever can solve the problem of the day. We will use them. Right. So that’the bigger contact context here. And when we look at specifically industrial policy, innovation and all that, I think people, both the government and business people, tend also adopt this view. Whatever works. So if we look at the EVs, for example. When EV became a thing in China, it’a confluence of forces. It’not just like the state said, we want to develop an EV industry, and then it happens. If you look at say BYD, the Wang Chuanfu, when he started to have the idea that we should start an EV business, it was actually earlier than Tesla. And Wang Cheng Fu when he did that. It’not because he think that the state should do it or the state tells him to do it, right? He did it on his own. He has his own vision, his own dream. And it’just how so happens that the priorities, the goals of these business people and the state converged. And really for say something as massive, as important as the EV industry to happen, you have to have all these factors line up. You have to have entrepreneurs who are really willing to take the risk. BYD at the time took enormous amount of risks. But then you also have to have government that have the policies that are friendly to EVs. You know the consumer rebase for EVs, for the infrastructure build out and all that. All these forces are important. Fast forward to today, AI, for example, DeepSeek is a very great example. Of this dynamic. When Liang Wen Feng started the DeepSeek venture, he never thought about Beijing. I mean, Beijing even didn’t realize that a quant fund could you know incubate such a you know important AI company. You know back in 2023, 2024, there was even a crackdown on quant funds, causing some kind of market crash. Back then. We call it the quant crash. That was only two years ago. You know

Grace Shao (33:56)

Why were they being cracked out? Why were they being kind of scrutinized?

Robert (34:02)

So two years ago there was this moment where the market was like sliding down and the quant was like kind of magnifying that sliding down. And the reflexes of regulator was really to kind of hold it, hold them back. There was one episode where the regulator kind of stopped a quant fund to from trading, basically plucked out the cables. So they were, because the mechanics the mechanism of quant trading is usually to kind of magnifying could help the market trend to get even you know more pronounced than it is so there’always some kind of controversy about our industry. So it’hard to imagine that Beijing actually found a quant fund and say, we are going to place a huge amount of money or huge amount of resources and ping our hope on you. Right? It just didn’t happen like that. Now had no state backing. He had his own dream for AI and he had money, he doesn’t have to rely on anyone else. And but after he became successful, after DeepSeek became a an international sensation, then you know a few ye a few days after last year, DeepSeek’moment, he was received by Premier Li Qiang. And then you know they become kind of a national priority. And in the this year’fundraise. There’also very top level state fund from Beijing that invested in DeepSeek alongside with Tencent and all these other companies. Right. So I think that’dynamic is interesting. It’at the same time there is a strong hand from Beijing, but also there’at the same time a huge tolerance for the natural growth of you know companies, industries, people on their own. And Beijing is less a planner, but more a kind of a picking picker of the winner. Right? So they set the long term goal. They say that we want to develop new productive, I mean new quality productive forces, but they never define specifically what are they. They kind of leave that open for the for the markets to explore. To for our own talents to explore. And once there is some clear winner, they come in and back them up with the more resources and help them scale. So that’I would say that’a hybrid. That’really a hybrid model. No single side of it can define this whole model. And this hybrid nature rests on the fact that people again we are flexible We don’t we don’t stick to any single type of ideology or ways of doing things. Whatever it works, right? Some industry needs creativity, then it cannot be top down. It has to rely on these spontaneous ventures and people. But also some industry if they want to scale, they need to have massive allocation of capital to them. And in China, if you want to really get massive amount of capital, you have to have the backing from the state. And that’how it happens. And so I think it’just natural. And it’also it’it’a hybrid model that is proving to be working and maybe for the new other industries it will also prove to be working as well. Yeah.

Grace Shao (37:48)

It’really interesting the way you put it. It’almost like they’a parent. So you get enabled and you get resources when they like something that you’doing, but you get beaten down

Robert (37:54)

Yeah. Yeah.

Grace Shao (37:57)

Or you get scolded and grounded if you’doing something they don’t like you’doing. And that brings me to the next point, which I want to ask you about. And you kinda alluded to this already, you touched on it. It’like the relationship between the state and the prime, it’something I think a lot of people find hard to understand. State as SOEs, state owned enterprises. State subsidies into industries, and then like obviously favorable policy making. It’very interesting because from your point of view, you’saying this is natural. Like you said, it is what it is. You need that kind of parental help or you need that parental guardrail, whatever, or safekeeping in one hand. On the other hand, from obviously a very American perspective or a Western perspective, is why are you involved? Is there for state subsidy than unfair, which I find kind of interesting of an argument. But there’obviously accusations from the West saying these Chinese AI companies are state subsidized, therefore they’not really competitive. I’m but they’still competitive from an innovative perspective. But anyway, and then you obviously have a lot of these AI companies now worried about taking state capital because if they want to go global or even go l like go get listed publicly somewhere non-mainland China. Then there’also concerns about shareholder setup if there is like clear state backing. Anyway, this is a again a bit of a big open question, broad commentary, but I’m gonna throw it back at you. How do you view all these different agents or different stakeholders and their relationship? And how do you view whether it is fair for certain companies to get state subsidy or not? And how to view their then independent competition and innovation.

Robert (39:47)

Right. So this whole kind of debates or controversy about subsidies in China, there’just so many I mean so many ways that I don’t I don’t feel okay with. I mean, like for example the in the West, it’not as if the Western government don’t have subsidies and don’t even have huge subsidies, right? I mean in EU many industries are being subsidized. In the US, if you look at say Tesla in the early days, I mean SpaceX even, all these companies rely a lot on policy support. So I mean maybe the difference between the US and China or EU and China is the I would say the role of the local governments. There is a huge tendency for local governments to go out of their way to support new businesses, which is really part of their own incentive arrangement. It actually helps them to grow the local GDP and help them promote it. So and it also creates some kind of over competition between the local governments. But it’not by design almost. It’just naturally happen that all these government sector support they just come in and out of their own interest They support these businesses. However, I would always argue that all these controversy or debates about subsidy tend to make people believe that it’because of the subsidies that Chinese companies become competitive. I think any basic student of economics would understand this cannot be true. I mean no businesses can be subsidized to be competitive. It’just It doesn’t work like that. Not in China, not in the US, not in EU, in not in Latin America, not in any history, in any human history. No competitive businesses become competitive because they have state subsidies. And usually it’the opposite. Subsidies only create uncompetitive businesses. Because whatever you do, if you are profitable or not profitable, you still have the state backing and which will make you artificially profitable. Who will do that? Who will be competitive? It just doesn’t make sense. And the reason that subsidies or state support or whatever support policy work in China is because every actor in this industry are working towards the same goal. Businesses, owners, the state, central government, local governments, all other stakeholders. It’really about everyone pushing, everyone going, and all the talents engineers in these companies. Everyone agree on something and push for walk forward to it. So it’definitely not just the subsidies. It’it’a whole spectrum of this converted uniform action of every party that make Chinese businesses competitive. And if the West just comes in and says, it’a subsidy that’responsible for that, i it’just not a very effective criticism. I mean and then reflex will be the West will have more subsidies to support their businesses, which, in fact, the wrong kind of prognosis will lead to a wrong prescription, which will be interesting as well. Yeah, I mean I’m pretty kind of I would say it’it’kind of kind of emotionally bit charged topic for me, but I really want

Grace Shao (43:37)

You’passionate about this topic.

Robert (43:38)

Yeah. So I really want to speak it out about this, yeah.

Grace Shao (43:44)

Yeah, so it’interesting then, how do you view this generation of AI companies and kind of the I guess how they’overlapping these space? Because like you said, and we know here at AI Prome where a lot of these labs actually even struggled to get capital in the beginning, before the GPT moment, like your point, Silicon Valley can set the tone. Once ChatGPT took off, Chinese labs. Were able to kind of rally up and garner attention and interest domestically. They got their first kind of pot of gold, set the labs up a bit further, more like bit more sophisticated ways. Clearly they’still struggling to, or not struggling, I would say they still need a capital. So then two of them rushed to go public. Now more thinking about that. All of this indicates, first of all, obviously training models is extremely expensive. But they’still not really getting the funding they need. And some of them are choosing to not get the state backing or state kind of related capital they, that’out there. I guess this question is a bit long windy, but I guess just how do you see the relationship of the AI companies right now with all the different stakeholders and capital players in China? Because the state has the money, some of them don’t want take it. The state clearly is have favoring AI right now and rolling out a lot of strong policies and helping them with compute and energy and whatnot. How are they interacting with SOEs? In fact, how are they interacting with the big tech? How are these different stakeholders now I guess involved with each other?

Robert (45:24)

I think a key variable that was not on the table a few years ago was the role of the capital market. So we have we cannot leave that out when we talk about funding for these new companies. So I think the Beijing is very proactively pushing and helping many of these AI or even right now robotics companies to go list it. Either to Hong Kong or prefer preferably even in domestic A share market. The speed of making these companies public even just a few years after they were founded, it was actually unprecedented by Chinese standard. The y the capital market used to be closed to most of the new economy companies. So that’why when Alibaba went listed they the default was go to Nasdaq. Right. So that default was no longer applicable. No company by default want to go to the US for listing. While at the same time, China Chinese regulators did make it easier for companies to go listed in at least greater China, right? Hong Kong and Shanghai, Shenzhen. And I think that’a yeah.

Grace Shao (46:46)

Jump in really quickly. I think people also don’t understand sometimes and miss the point on a lot of these new economy companies from China are not going to go list in the US is not actually like actually help us explain. Is it a China’regulation reason or is a US regulatory reason?

Robert (47:06)

So it’actually a combination, but I would say the most of the issue is on the US side. Maybe sixty percent US responsible, forty percent China responsible. But anyway, there’a pull and push that make companies think about. So at the same time it’get just getting harder to get listed in the US. There’always a risk to be delisted, for example. And while to apply to US listing now you have to go to Chinese regulator as well, which there was no such approval process before, right? So it’hard. But then at the same time, it’getting easier to list in A share and also in H-share. And also liquidity in Hong Kong is way better than before. So there’both push and the pull. There are still some companies get listed in the US, very few. Recently this year there’this company called Taso Chuo that was just got listed in US. I think they have their own reasons for that. But most companies would prefer to just stay put in this part of the world. Right. So that’a key variable. And I think that’the key leverage that Beijing is using to help these companies raise funding. Like to be honest, I think Beijing is very I would say sometimes like a very strict you mentioned parent, right? Beijing is a very stingy parrot. Actually Beijing doesn’t want to spend too much money on, all the projects. But they are ambassadors at leveraging other people’money to achieve their own goal. Right? So like if you look at deep seeks fundraise, Beijing invested only a small part of that. Most of the money is contributed by you know Tencent or other private investors. For them, it already achieves a goal. It helps the company that Beijing wants to grow raise funds while at the minimum amount of money that Beijing can actually need to chip in. That’pretty smart, you know. It’it’not like it’not like i it is smart to keep resources at your hands and try to leverage other resources other people’resources to support your goal. And capital market is exactly like that. It’not just capital from big companies and big funds, but a capital from all over the market. Everyone, every even retail investor, get to participate. The that only that way you can ensure a everlasting strong stream of support in the in the future. So that’I think a very different that’actually very different, say compared with a few years ago, where you don’t have such a as strong a capital market as we have now. And now Beijing also have a vested interest in support the market. And they have also developed their own techniques and their own muscle memories in supporting the market, which is what we don’t have even five years ago. Right.

Grace Shao (50:22)

Right. But some still argue that the Chinese government could support the stock market more. I don’t know. That’just things I hear. Well, how do you view that?

Robert (50:30)

Yeah. Actually they are now sophisticated enough to understand that you need to be balanced. So what I mean is there are actually two episodes that could remain as lessons for them. One is the twenty fifteen, twenty sixteen market crash. Second is the recent market crash in South Korea. In both episodes, there was a bull market, even a crazy bull market. And in both episodes, the governments initially played a very strong role to boost the market. Back then, in 2020 I mean 20 fif fifteen, there was a People’Daily article saying directly that the market should go above, I forgot it’five thousand or or four thousand points. Which was cited as a kind of a rally call for many people to go into the market because the Beijing says we should, buy, buy, buy. So Beijing actively kind of contributes to the building up of a big, big bubble. And then after Beijing felt it was too crazy, it cracks down on leverage. And a lot crackdown on leverage burst the bubble and it has become a really bad market for the next two years. Same thing as South Korea, right? Like they prime min president of South Korea said, I’m also buying the stocks. Every policy going to support the market. But then the government was too concerned about a leverage. So crackdown on leverage. And then boom, the market dropped. And so I think you know Beijing of today is Pretty sophisticated with that. They want to have a bull market for sure, but they also don’t want it to, turn into a crazy boo. And exactly how they do that, because this is some not something that you can say, I want this, I that so I can achieve that, right? Because it’a market. There’a lot of players. When the sentiment builds up, even Beijing cannot stop people from buying or selling. So exactly how, interestingly, they all have also developed. Dev develop their own technique, which is this so-called stabilization mechanism. So for the first time in history, in the last two years, Beijing was actively employing and deploying capital to act as a stabilization factor for the Chinese capital market. By stabilization I do not mean just a buying mechanism. It’a stabilization mechanism. Which means when the valuation was really depressed and Beijing wants it to go up, they actually now come into the market with real cash to boost the market to help reset the valuation. This is different from before. In the past, I think there’never been an episode where Beijing used real cash to support the market. There was messaging, there was this policy, that policy, this tax policy, that tax policy. But never before was Beijing deploying so much capital directly into the market. But then after the market become more hot, or hotter than what they want, they actually sold what they have. Right. So it’stabilization. It’almost like also recently in the oil market, the moment that Hormuz was closed, Beijing stopped buying oil, waiting out the episodes. Which was a contributing factor, decide a determining factor for right now the oil prices didn’t went through the roof. And same thing was you know the same thing was when in the ancient China. There was a big role of government was to be a stabilization factor in the grains. Right. So when there is a lack of there’more grains than there’needed and the prices are low, the government actually comes out and purchases the grains and store in the storage. And when there is a famine, it’government’role is to release these grains, selling them at maybe a higher price, but eventually serving a social purpose. This is just it’just Chinese regulator is now using his ancient technology to apply it to modern statecraft. And it’working. It’working. Last year the market was just about to be crazy. Last December, last November. And soon Beijing started to sell off their holdings in the ETFs. Which tempered the sentiment, right? Beijing is very smart. They actually made huge profits about after this buying and selling in their own game. And now they have more cash than before and so if the market goes down from some level they are ready to come in again. So this is actually very nuanced and I think I think it’it’it’great that there is not only a desire for market to go up, but also a desire to for the market to grow up in within a safe zone. A zone that’that’that’will not be crazy, that will not cause a lot of sentiment crash, especially for the all of the retail investors. Right. So yeah.

Grace Shao (56:20)

Yeah, I think that’really interesting to hear. I’ve obviously not heard of that like in detail. But then, the question I get a lot is then how do you view the flip side of the government had in the market? Obviously, we’ve seen, kind of internet crackdown, education, property, whatnot. Like you can name a few industries in the last few years, it’been hit pretty hard in valuation can get wiped out overnight. So, How do we view that kind of government hand in the public market? And then I do want to tie it back to then how do we then find confidence in investing in AI and a lot of these publicly listed companies right now coming out of China, like these AI wave companies beyond the model companies that we talked about? Like you mentioned, there are the robot ones, there’infra layer ones, there’even now spatial intelligence companies getting listed. But yeah, just tie it all together.

Robert (57:17)

Hm. Yeah. So my mental model, my personal mental model to understand policy risk in China, is that I think Beijing, the regulators there, are learning. They actually didn’t have as much experience about capital market say even five years ago. So you mentioned the education industry. That was a very important episode in policy making, in expectation management, a very important lesson for Beijing regulators. So when Beijing cracked down on that education industry, actually I don’t think they have realized what kind of you know problems that would cause for the wider you know sectors, especially capital markets. They are narrowly focused on the industry itself. But they actually learn from that. They actually learn that you have to think about all these other factors because all these things are interconnected. There are signs of that, there are evidence of that. Maybe I wouldn’t have time to go into detail, but maybe can go check my newsletter about my years of observations of Beijing’scale. At expectation management and also at thinking this as part of a bigger whole, not just like single policy. Right. So that’my key mental model, which is to treat it as an evolution, to treat all these necessary lessons as part of a bigger learning curve. So here in 2026, I would say today’Beijing. Has way more lessons and way more skills and way more sophisticated than Beijing five years ago. And it’it keenly understand the importance of capital market and also in understand the importance of expectation in the capital market. So they are now very they were they are they are they are way more holistic than before. And I would not think that the double reduction education episode in twenty one would repeat because they have learned. It’a lesson for them. Right. So in that in that policy risk, actually it weakened the risk weakened, lessened considerably than before. And well in terms of investments though, if you just look at these AI and robotic company as you know a pure investment from the pure investment angle. I’d say that it’really not for everyone. The valuation judging by traditional standards is really, really high. But then if you’a believer in AI, displacing ten to twenty percent of global GDP, then all this valuation doesn’t seem high at all, right? So it’really up to the taste and the style of different investors and the risk appetites. In general, I would think the Chinese market will be more and more mature, the capital market will be more and more mature. And the stronger state’hand compared with say the Western market is also I would say understandable given that China’market, especially A-share market, is a is a highly retail driven market. Seventy percent, eighty percent of the money is retail. And retail tend to fall into the traps of herding. Which means like everyone going to one direction. So someone has to come out and be the shepherd. So it’a it’a it’a shepherd to herd model that is different from the West, where people most of the market participants are more mature and more sophisticated, analyzing, researching, which is different from China. So it’just natural for Beijing to play a role, to play a balanced role. Not a like a not a like a very strong role, but a silent, invisible role. Give you one example. So this whole stabilization mechanism I mentioned, actually it’only my name for it. There the Beijing doesn’t even have a name for it. Beijing doesn’t even disclose what exactly are the mechanisms. When they purchase stocks, is they don’t purchase directly. They purchased a list of ETFs and those ETFs purchase the stocks. So they are also very Conscious of their presence and they want to lessen their co their presence. They want to be the kind of the secret shadowy force that is making it making the market stable. But they don’t want to say, we want it stable and this is our message, this is our view. It’not crude, it’actually very nuanced. Yeah. So they are learning. They are really learning really fast.

Grace Shao (1:02:35)

That’very interesting. It’like it just makes me think of like high school teenager parenting again when you influence them, but you don’t directly tell them what to do. You have to influence them in like

Robert (1:02:43)

Exactly. Yeah. Yeah.

Grace Shao (1:02:46)

But one yeah, just like I guess I want to wrap up soon, even though I feel like I can keep on asking you questions. I have another hour of questions for you, but for the sake of today,

Robert (1:02:56)

Thank you. Yeah.

Grace Shao (1:02:57)

How do we understand then, we are seeing a Crazy wave of IPOs right now in Hong Kong. Like we said, a lot of them are directly AI labs, obviously. The others are AI adjacent, or some are pegging to AI. So

Robert (1:03:15)

Mm-hmm.

Grace Shao (1:03:17)

How do we understand these companies? Like or how or why do they want to go to Hong Kong first and not maybe A Shares first?

Robert (1:03:26)

Yeah. It’definitely easier to go it relatively easier to go to Hong Kong for listing rather than A-share. A-share is stricter and mostly because A-share in A-share there are a lot of retail you know mom and pop’investors in the A share. And as you as you frequently alluded to and I agree with is that Chinese political system or regulators, I don’t think the authoritarian is a is a good word, but I do think paternalistic is a good word. They do see themselves as parents. And people do see themselves them as parents, right? So as parents, they tend to be kind of over caring for their kids, which are the people and r retail investors. So the threshold, the bar for listed in A-share is actually very high. And even if you get listed, the pricing that you can place on yourself is also I would say much lower than it should. Like if you look at CXMT, for example, when it first got listed, the IPO price was about one fifth of what it was, t ended up trading at on the first day of trading, right? So why is that? Because they artificially kind of compressed evaluation to make sure that every mom and pop who joined the IPO earn money, make profits, had a good experience. So there’a very clear kind of kind of emphasis on retail investor protection in A-share. Well in the A-share though, not many mainland retail investors can trade in Hong Kong. Some can, but most of the retail investors are not qualified to trade in Hong Kong. Right. So it’very institutionalized. So it’really so for Beijing it’really like a pressure valve for the IPOs. So they actually encourage you to go to Hong Kong. And the Hong Kong exchange, stock exchange, they also encourage you to go listed there. So there’a confluence of interest there. And also at the same time, if you go listed in Hong Kong, you raise US dollars, and which are as which are great for, China based company because there’still capital control in China. Right. So there’a there’def defin just a confluence of interest of all stakeholders to now go to Hong Kong to list first. Unless you are CXMT,

Grace Shao (1:06:07)

Makes sense.

Robert (1:06:09)

They are really good and you qualify for A share. But then you also suffer a bit because of the valuation for the for the kind of money that you are you can raise, but you cannot. Yeah. So

Grace Shao (1:06:23)

It’it’interesting. It’like a balance between over caring and overbearing, it seems like. And yeah.

Robert (1:06:28)

Yes. Yes.

Grace Shao (1:06:30)

All right. Well, look, I wanna ask you one question that I ask every single guest, which is what is one differentiative view you hold or something you think is non consensus? It could be about anything. It could be about China, it could be about the stock market. And I know we touched about touched on quite a few different topics today. I always appreciate again your nuanced view on a lot of these things. I don’t frankly agree with everything you do say, but I do think, what I appreciate is at least you try to really string together different parts of how the world works instead of just over-generalizing China as this one unit. And I think sometimes China observers unfortunately just over-generalize China or oversimplify China. Anyway, I wanna throw this question to you. What is one different

Robert (1:07:20)

Okay.

Grace Shao (1:07:20)

Of you hold? Or maybe you think something that the world still misunderstands about this part of the world, especially when it relates to technology and capital market and everything.

Robert (1:07:29)

Right. So they’actually a lot. I’m just trying to pick through my mind which one is relevant for today’discussion, and maybe this one. I think Chi

Grace Shao (1:07:38)

Give us two then. Give us two.

Robert (1:07:41)

Yeah. Okay. So there’a the there’a small one and a big one, right? The small one is about the capital market. I think China is entering a multi decade bull market. The U A share. There is just so much kind of tailwinds that are supporting it. I’ve already mentioned some of them, like a very sophisticated Beijing. But also RB is trending up. I mean, there it’been the joke of the day that despite the tenfold, twentyfold of growth of Chinese GDP, Chinese stock market is going nowhere. I don’t think it’going to be true in for the next at least one or two decades. It’a new paradigm. So that’you know definitely a big part that all the investors should pay attention to. And I don’t think that’appreciated enough. And a bigger question that I always love to share about China, like if you ask some American or some you know Westerner what’the single most important thing. If just one thing you have to remember about China, nothing else, just one thing. I will always say that Chinese people or China are not interested in changing other people. We are not in this preaching or you know proselytizing mindset. We mind our own businesses. We don’t want to change other people’lives. So much as some Westerners will want to change other people’lives. We don’t. And the reason I want to emphasize this point is that I realize that when you have both sides who want to change the other party, that’a recipe for conflicts and wars. But if you realize that actually one big party of that is not interested in the other party, then I mean in changing other parties. Right. Then you realise maybe there’a chance for peace and prosperity. So I want to I cannot stress this point strongly enough, but I want to maybe use your platform to voice that again. Thank you.

Grace Shao (1:10:09)

No, I really appreciate ending on such a positive and somber note on that. And then I think another thing I wanna ask, which is a bit for fun, is can you explain to us what is good jot hai? Why do people go around talking about like cutting Chinese

Robert (1:10:26)

Yeah.

Grace Shao (1:10:27)

Chives? What does that mean in the capital market space?

Robert (1:10:31)

Yeah. Chives a very interesting vegetable. It tastes a little bit strange, definitely not for everyone. But the key things about chives if you are in the farming business is that chives grows really fast. So when you cut, one chive, a few days or a few weeks later it grows up again. And you cut them down and they grow up again. This is what retail investors are, right? They get cut down all the time, but they grow back all the time. This current generation of chives when they were cut down, they will leave the market. But then you also have a new generation of investors who have no experience, no knowledge of that and they want to try out themselves and they got cut down again. So again and again and again. So that’why they become a term. Each generation have their own kind of symbol for that. Like the last generation, for example, for many of them, they got cut down on Xiaomi, for example. They got a huge IPO, but then it kind of crashed for a few years. Maybe that next this generation for this generation is all these AI names. I don’t know. But every generation, I mean it’a generational thing and it’kind of it is built into the system Right? Because you will have new people coming in. And new people, by definition, don’t have knowledge of the old. So they just kept it’very I would say very figurative, very apt kind of explanation of the mechanism, yeah.

Grace Shao (1:12:14)

I love how technically you got into like people know agriculture and how farmers no, I just thought

Grace Shao (1:12:19)

It was like it’one of those Chinese internet slangs again that are just so hilariously random if you don’t understand the context. But like you said, if you actually understand the thinking behind it, it makes a lot of sense. And so it’actually a very popular internet slang people use to describe retail investors that get kind of hurt and then the joke is institutional investors will just wait for the chives to get cut.

Robert (1:12:42)

Yeah. Yeah.

Grace Shao (1:12:42)

Or chives get cut one around and after another. Anyway, thank you again, Robert, for your time. Really, really appreciate it. I also appreciate that you let me kind of take you in all kinds of directions with this conversation. Please come back again.

Robert (1:12:57)

Thank you for all the tough questions. Okay, yeah, see ya.

Grace Shao (1:12:59)

Yeah, thank you.

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