AI Proem Podcast

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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Show Notes
Hi all,
Christian Hu from Alibaba’s Qoder joins the podcast again to talk about the fierce domestic workplace agent competition. For context, Anthropic and OpenAI have decided not to allow access to their models and products in Greater China; thus, the competition for models and agents is largely driven by domestic players. For workplace agents — coworker-style products — the most popular ones at the moment are Tencent’s WorkBuddy, Alibaba’s Qoder, and ByteDance’s Trae. The other agents we see coming from the labs are mostly coding agents. With that, I’ll hand over the floor to Christian, who graciously found ~40 minutes while on a business trip to talk to us about the landscape and Qoder’s own strategic shifts.
This episode was recorded on the day Qwen3.8 Max launched.
For more, check out the podcast lineup here and explore the episodes!
Chapters
* 00:00 — Southeast Asia Expansion and Market Dynamics
* 02:43 — The Shift Towards Business Logic in Coding Agents
* 05:29 — Industry-Specific Applications and Vertical Agents
* 11:22 — Global Strategy: Lessons from Market Differences
* 14:00 — Partnerships as a Key Element in Market Strategy
* 16:41 — The Future of Coding Agents and Market Trends
* 19:22 — Pricing Models and Inference Economics
* 22:08 — Open Source Trends in AI and Market Competition
* 27:34 — Building vs. Buying AI Models
* 29:38 — Future Directions for Coding Agents
* 32:14 — Final Thoughts on Market Opportunities
Transcript
(AI-generated, for reference only)
Grace Shao (00:00)
Hi Christian, friend of the pod, you’re back on. Really excited to have you here. Where are you these days?
Christian (00:07)
Yes. I just landed in Singapore last night.
Grace Shao (00:12)
Okay, perfect. Cause we’re gonna talk about your Southeast Asia expansion. But since we last spoke, competition among coding agents has intensified quite a lot in China, particularly — the market has changed a little bit. Where are you seeing the whole market going, and how do you think Qoder fits into all of it?
Christian (00:32)
Yes, it is a war, you know, between every major tech company in China. Because it’s a war, nobody wants to lose. I think the logic behind this coding agent war is that most companies believe that a coding agent shall be the fundamental path to AGI — artificial general intelligence. So nobody wants to be behind in this race.
But the most interesting thing behind the war, or this race, is that something is changing. When we look back to the last twelve months, everyone is talking about the models, everyone talking about what kind of model will be the most competitive advantage for your coding agent. But now, for most leading agents, they are trying to be more focused on the business logic and workflows. Just like what Qoder is doing — we want to be more into the business logic of our customers. And even for some individual users, they are trying to do something for business. They have one-person companies or one-person workflows. So they need the coding agent to do more about the business workflow, not just code generation.
So that’s the shift behind the war. For at least most of the companies, they are trying to raise not just for a coding assistant, but want to be dominant as a desktop assistant for employees or maybe some individual users. And maybe in the future, they want to be the digital employees for the industry. I think that’s maybe the ultimate race for the coding agent.
Grace Shao (02:28)
And you’ve got players like WorkBuddy from Tencent that’s been doing really well, right? Exactly to your point—
Christian (02:33)
Yes.
Grace Shao (02:33)
I think they’ve been plugging in, kind of operating as an assistant on desktop, very intuitive and user-friendly. You’ve got ByteDance with Trae pushing in a similar direction. So how do you feel about that? Where is Qoder’s differentiating point or offering here?
Christian (02:51)
I have a few things to share with you. I think for the past year, Qoder has accomplished very remarkable business performance. In terms of ARR or revenue, we are the leading one — we are number one. And maybe we are more than the combination of number two to number four. That’s a huge achievement for Qoder in business growth.
And as I just said, Qoder from day one is more focused on building an agentic platform for developers and AI builders. It’s not just a coding tool or coding assistant — we want to be a platform for the next generation of agentic power. So the skill marketplace, the plugins, the connectors, the MCP connectors — all of these features are now shining. All of these features are bringing advantage to our business. That’s the truth of what’s happening in China and even in overseas markets.
Grace Shao (04:04)
That’s really interesting. And so when we caught up in Hangzhou recently, you were saying that Qoder appears to be broadening from just coding into industry-specific agents. That was something quite fascinating, because from the start of the conversation, you say coding capability is the fundamental foundation for all of these agents, but people are moving towards these vertical agent use cases. So tell us a bit more about that. What kind of industries are you guys targeting? What is your thinking behind this new strategy?
Christian (04:34)
Okay, I can share an example. We got a very important customer case with Xiaopeng. Xiaopeng is one of the leading electric vehicle producers in China, maybe one of the best. For Xiaopeng, for the company now, Qoder is not just a coding assistant or code generation tool. Qoder has become an agentic driver for the restructuring of their workflows and business logic. They’re trying to educate their developers and their engineers, maybe even the HR department, to use Qoder to renew their business logic and their workflows.
For example, for the legal department, they’re trying to use Qoder to reduce the legal review cycle from six days to one day, or even less than one day. And it’s not just Xiaopeng — I think there are a lot of similar cases. So for Qoder, it’s not a shift to pivoting to vertical applications or specific industries. From day one, Qoder wanted to be a platform. The platform means we want to be a tool — we don’t want to be just a coding assistant competing with other coding tools. We want to be more embeddable and compatible with the customers’ business logic and workflows. So it should be more vertical. But it’s not for us to do the vertical things — it should be let the developers, the customers, and even some individual power users develop domain-specific agents on top of Qoder. That’s the philosophy for Qoder.
Grace Shao (06:51)
Okay. So the thinking is you guys are offering the tool, but really it’s still on the user to build out the tailor-made agent for themselves. Not that you’re specifically pushing—
Christian (07:01)
Yes, that’s the truth. Actually, we got some organic creation of skills by the power users. They are creating some skills, some creating some plugins on our marketplace. We are going to open the marketplace to all the business users so that the users can deploy different kinds of skills or plugins from the marketplace. That will be the market for Qoder. Maybe in the near future, we could be a marketplace for agents, a marketplace for skills.
Grace Shao (07:49)
Interesting. Okay. Well, this leads to something else you talked about. You said that you guys were building an ecosystem around Qoder. So tell us a bit more about what that means, and how should we understand or expect how Qoder might change as a product in the next few months.
Christian (08:05)
Okay. I think we can change the view of Qoder — from a coding assistant to an agentic platform. I can give an example: we are doing something together with Microsoft. That may be an example. We want to collaborate with Microsoft to make Office more usable and more accessible on an agentic system just like Qoder.
You know, in the past, Microsoft Office is Microsoft Office, ChatGPT is ChatGPT — they’re quite different products. For the users, they have quite different experiences on two different kinds of products. And now we want to be one. For Qoder, for the agents, they need the agent to know how to use Office, how to deploy Office, how to use the best features from the Office suite — the toolkit — to help the users do presentations, do documents, process data. So that’s maybe the very interesting thing for the users in the near future.
This kind of collaboration is happening everywhere. Qoder is trying to partner with partners from collaboration software, finance software, even legal software, HR SaaS providers and vendors. We are doing things like that to be more compatible and more useful in the near future.
Grace Shao (09:55)
Very cool. Let’s take a step back. I want to talk about your business expansion, because I think when we talked about a year ago, you guys were really gung-ho about going to the US, going to Japan. I know you’ve been spending a lot of time in Japan yourself. But recently it seems like you are pivoting, or at least putting more priority and focus on Southeast Asia, and now you’re in Singapore yourself. Tell us a bit about the thinking behind your global strategy, and which markets you’re currently focusing on, given that you’re the head of GTM on the international expansion side.
Christian (10:26)
Yeah, you know, I actually got a lot of lessons from the last maybe twelve months — about ten months for my global journey with Qoder. Everything is different in different markets. I just came back from Paris. I think Europe is quite different from Japan. Most people think Japan and Europe share something in common — they are pretty slow in AI adoption, they care more about compliance, they care more about trust. But things are also — you can still find something different between Japan and Europe.
In Japan, the buying cycle is pretty long. Maybe six months or maybe even twelve months is very common. But in Europe, the cycle maybe is not too long — maybe one month or maybe one week. Because they are trying to catch up with the AI wave in Europe. But there’s still some obstacles in Europe. They care more about the law, the compliance, the GDPR, the AI Act. That’s the truth.
So for every AI marketer, or anyone doing good marketing in Europe, you need to care very much about the legal — the law, the act, the things changing and happening in Europe.
But for the US — I spent almost the first quarter this year in the United States. I met a lot of AI developers, AI startups, AI founders. The story is quite different from China, from Japan, from Europe. In the United States, speed is the most important thing. Speed means you are innovating. Speed means you are upgrading your product. Speed means you are accountable. You’re telling the users that you are accountable because you are innovating, because you are upgrading your product day by day, conversation by conversation. You need to upgrade your product.
Now I come back to Southeast Asia. Singapore is the first stop for Qoder to be a global product. And now I came back to Singapore, I will go to Vietnam and Thailand in the near future. For me, Southeast Asia may be the mixture for my go-to-market strategy, because in this place, in this region, you’ll find you have competition against some American AI vendors or AI producers. You will also find some Chinese competitors. That’s the mixture. This is a very competitive market, but it also has very high potential in this region.
Grace Shao (14:00)
Really fascinating, because you’ve done a world tour and given the high-level vibes of each area. And I can totally see that it’s also quite fascinating you say, like, ending up in Singapore. In some ways, it’s almost the most competitive for a sales role, because you know you have all the options in the world and nothing is actually off limits, and it’s really a price war as well. It’s quite fascinating compared to maybe other areas of the world that will be leaning towards certain companies or certain countries’ technology, given maybe geopolitical concerns or compliance reasons, regulatory reasons, whatnot. And other areas might be purely driven by price sensitivity. So anyway, fascinating. Thank you so much for that. But why then? Why are you guys now doubling down on Southeast Asia after your big global world tour?
Christian (14:51)
Yeah. Okay. I guess I forgot one of the most important things in our last conversation. Partnership is the key element and the key part of our go-to-market strategy. Because we believe that local partners, a local ecosystem, is the best way to get a connection with local community and local industries. Around the world, we have different kinds of partners. For the past twelve months, the most important thing I did was to find partners as many as possible. That’s the strategy for our global markets.
Okay, let’s go to Southeast Asia. Actually, I don’t think Southeast Asia is the most important one. Maybe I think for now it’s too soon to nominate which region will be the most important. But Southeast Asia should be a very important part, because Southeast Asia is a fast-growing market. It’s not too much affected by geopolitical concerns, and it’s fast-growing, so for every major AI company, there are huge opportunities, huge market potentials.
And for Qoder, we are growing very fast. We are evolving every day, every conversation. People in Southeast Asia — the developers really find that it’s very interesting and they can get much from Qoder’s evolution. Because for most users in Southeast Asia, to use closed-source agents from the United States or some other place is maybe too expensive. Maybe it costs too much for most users or most developers in Southeast Asia. But if they want to try Qoder, they maybe have the best position and best time to catch up with the evolution of AI and coding platforms. That’s a good starting point for most developers in Southeast Asia. And Qoder is evolving, so they will be evolving every day. I think that’s a good point for both Qoder and the developers and industry in this region.
Grace Shao (17:30)
Interesting. Let’s take a step back and look at just the overall industry at this point and the trends that are taking off. So the underlying coding models are improving quickly, and we’re seeing that it’s increasingly becoming commoditized, or at least the price is coming down, right? As all of that is happening in the background, how does that actually affect the tools that are built on top of these models, such as your product Qoder?
Christian (17:56)
Yeah. I believe the token, or maybe some of the large language models — I think the cost of token may be zero in the near future. Most of them. Maybe in twelve months.
Grace Shao (18:13)
Wait, how would it become zero though? What’s the thinking behind that?
Christian (18:17)
It should be. It should be. Maybe it’s science — it’s about science and engineering, but it should be zero cost. Looking back to the last twelve months, if you want to buy one million tokens, it may cost you about twenty dollars. But now it’s about half a dollar, just in twelve months. Maybe in the next twelve months, the cost may be quite near zero.
But I think in the future, there’ll still be some very expensive and exclusive models — some frontier models. There may still be expensive and exclusive for some users, for some industries. But most models will be very inexpensive, and even cost zero for most developers. And I think maybe ninety percent of the daily tasks can be accomplished by these kinds of models — the public models or maybe some cost-effective models. I think that’s the future.
Grace Shao (19:22)
Okay.
Christian (19:23)
So on this kind of zero cost, the agent layer — I mean the context layer, the agent layer, and the business layer — will be the most important one for the builders, for the users. So that’s actually what Qoder wants to do in the future.
Grace Shao (19:43)
I see what you mean. But coding agents can be very token-costly, right? So how are you then thinking about the inference economics, usage limits, or even your own pricing models when you are selling your products to users?
Christian (19:57)
Yes. I just said we want to charge our users — we won’t charge by token consumption. We will charge [differently].
Grace Shao (20:03)
Mm.
Christian (20:04)
We won’t charge our users by token consumption. We will charge.
Grace Shao (20:10)
Okay. So it’s not directly token consumption. If token costs come down, your cost of your product also comes down with it. Okay.
Christian (20:17)
Yes, actually, the shift is happening. We want most of the profit of Qoder to come from the agency layer — the workflow layer, the agentic layer, the context layer, the memory layer. That’s close to your daily work and operations, not just token consumption.
Grace Shao (20:42)
How do you view the subscription model? We still see that dominate coding agents today.
Christian (20:47)
I think for today, if you are just a coding assistant, you will be dominated by the large language model. That’s the truth. If you’re just a coding assistant, you should be dominated by the large language model. For now, the best model determines the best coding tool, because you’re too close to the large language model. If a lab has a very powerful logic model, the large language model can easily change to be a coding assistant. You just need an interface — you just need to type your language, you can be a coding assistant for every user. So if you are just a coding assistant or just a coding chatbot, you will be dominated by large language models.
But if you are an agent — an agentic workmate, an agentic layer that empowers users to regenerate their workflows, to connect with their collaboration layers, connect with their ecosystems — you will not be dominated by large language models.
Grace Shao (22:08)
I see. Okay. I want to pivot and kind of look at the model layer right now. I know you don’t work in the model layer, but you’re very familiar with the ecosystem. So help me understand. Chinese labs right now have actually used open source very effectively, right? And I think they’ve built up a global reputation and taken over a lot of the market share. And obviously, there’s a lot of discussion on whether they will continue to open source some of their best models, or maybe become more and more similar to US frontier labs, what we’re seeing with what they’re doing. What do you think of this trend? Will they remain open, or do you think they’ll gradually start closing up some of their best models?
Christian (22:51)
I think I can give you two angles. For the angle of the regulators — for the government — they want the open source strategy. They want the AI wave to be a new engine of growth for the national economy. That’s actually the national strategy for the regulators. So open source should be the trend, should be the future.
And from the angle of the market side — the market players. Actually, for most players in this industry, in the AI industry, open source is maybe the only strategy, or maybe the only path, for Chinese players to surpass their counterparts in the United States. I think maybe the only path.
Grace Shao (23:53)
Why is it the only path?
Christian (23:54)
For now, we can’t see the advantage from chips. We have no advantage about chips. We have no advantage about human resources, even some capital. We have no advantage. So maybe open source is the only path for Chinese companies to surpass United States companies, to be dominant in the industry — in the applications. We have some figures to prove that — Xiaomi’s language model and Zhipu’s language model are the most used around the world, right? They are the most used models, not a Claude, not an Anthropic model, not a Google model, in terms of usage. So actually, open source is maybe the only path, or maybe the only way, to compete against the counterparts in the United States.
Grace Shao (24:57)
Question on Qwen then, just because you guys just released another very large model — it’s not getting as much attention. I feel like the headlines are being taken all by K3 right now. But Qwen3.8 Max is claiming to be just as good as Claude. What’s your view on that? And it’s available on Qoder right now, right? So how do you view the model?
Christian (25:22)
I’m not a model expert. I can’t give you an expert view about which one is better, which one is not the best. For developers, for users, you can try Qwen3.8 Max — the preview — on Qoder. I think maybe positive. It should be positive. And we believe that, as I just mentioned, the model gap is closing. In the near future, the model gap is closing. So for Qwen3—
Grace Shao (25:53)
Actually, why is that? Why do you think model gaps are closing, closer and closer?
Christian (25:58)
That’s my belief. I’ll give you an analogy about this. I think the best analogy is education. You know, for education — university education — we’ve still got some top universities, right? The Ivy League, the best in the world. And maybe some of the best two or three universities in China, they are at the top. But most universities and most education in university, I think they’re universal. I don’t see too much difference in the education in most universities around the world. People can get educated, people can learn the skills, people can learn the knowledge in most — I mean ninety percent of universities around the world. That’s not the biggest difference.
There should be some frontier models, there should be some ecosystem models in the future. But mostly — more than ninety percent — will be the same. Almost the same.
Grace Shao (27:05)
So you’re saying basically it comes down to talent. Talent is strong. So therefore, more and more talent going into the industry, thus it’s catching up. But why wasn’t it catching up a couple months ago? Why was the gap, say, six to nine months prior to that — it was claiming to be nine to twelve months. All of a sudden now people are saying maybe it’s only one to two months. Where does this number come from? I’m always fascinated by these claims.
Christian (27:28)
I’m not a scientist, I’m not an engineer. I just have the philosophy, I have the belief.
Grace Shao (27:34)
Yeah. Okay. Well, let me ask you another question that’s kind of taking over the broader industry right now. The broader debate right now is about whether enterprises should be fine-tuning privately and deploying their own models, or even — you’re seeing more and more so-called tier two, tier three smaller internet companies in China leading the way in already building up their own models. Obviously, some people are saying they have the edge of having a very strong open source ecosystem in China, so the barrier to entry is easier. Anyway, from your point of view, do you believe this trend where companies are actually going to start building their own models or hosting their own models and fine-tuning on top of it? Does that make sense? Or should companies continue to do what they were doing, which is, you know, paying for the most frontier models from the frontier labs?
Christian (28:20)
Okay. I think for most companies, even some large enterprises, it doesn’t make sense for them to fine-tune their private models. I can’t see the sense, I can’t see the business logic behind this. Just like the analogy of education — I don’t think you need a private school for your own children. I don’t think most people need a private school for your own children, just for your two or three children.
Maybe ten percent of enterprises have very complicated, very sensitive applications, sensitive data. They need to deploy their own private models, fine-tune their own models. But I think most companies don’t need to do that. Instead, I think they should invest more in the context layer, just as I mentioned. They must invest more in the business logic, in the business, in the workflow, in employee management. That’s where they should invest. Because that will benefit their business growth, benefit their management, benefit their governance. I don’t think it’s necessary to invest in fine-tuning their own large language models.
Grace Shao (29:54)
Makes sense. Not every company should be working on the R&D of this, but really should be putting more energy into managing their own data, context, memory, expertise. Okay, well, I have a last question for you, which is, stepping back and looking at the whole industry right now, where do you expect the coding agent market to evolve over the next year? Because you alluded to a little bit throughout our conversation, but where do we see this whole industry going? Are we going to continue to see standard products that are going to be the main driver in coding agents, or like you said, model labs will swallow everyone’s lunch, and then it’s really all just putting up an interface on top of them? Are we going to see more enterprise-grade, specific use cases and systems built? How do you see this direction?
Christian (30:48)
Yes. I don’t think there will be too many coding agents in the near future. I think some of the coding agents, even some existing coding agents, will be swallowed by large language models. I believe that. Because language models can easily build a coding agent. So there should be some — maybe two, maybe three, or some number of coding agents still in the market. But most of them will be swallowed by large language models. And there should be some new agents — business agents, workflow agents, or some domain-specific agents in the future. We’ll have more domain-specific agents in the near future. Legal agents, human resource agents, even some agents for you — just like podcasters or bloggers, we’ll have some new agents for you.
Actually, agents will be just like digital employees. That’s the future. You will have more digital employees. You can ask your digital employees, just like you can ask your teammates, you can ask your friends — but it’s digital friends — to help you do anything you want them to do. That’s not just a coding agent, that’s your working agent.
Grace Shao (32:14)
Very interesting. All right, Christian, I have one last question actually for you, which is a question I ask everyone that comes on the podcast. You’re not unfamiliar with this. What is one non-consensus view you hold now? Or has anything changed since we last spoke?
Christian (32:28)
You mean from last time? What’s changed?
Grace Shao (32:30)
No, just any non-consensus view. Even what you said earlier was against what a lot of the industry is saying already, but do you have any view you think is very non-consensus or against consensus right now?
Christian (32:48)
Okay. I have something, maybe something shocking for many AI builders. I don’t see too much potential in the United States market. I mean for Chinese AI—
Grace Shao (33:06)
You mean for Chinese companies going global, whose first stop is often the US? Okay, very interesting. Go on.
Christian (33:09)
Yes. Actually, I want them to quit. You can invest in the US market, but I don’t think they can benefit, or get real margin and profit from the US market. But it’s a different story between consumer AI and enterprise AI. For consumer AI, there may be some possibilities for Chinese companies, because we still have some advantage — we have cheaper manufacturing, cheaper human resources. That’s the advantage for consumer AI companies to do business in the United States. But for enterprise AI, I think it’s quite difficult. Quite tricky and quite — it’s just not a friendly strategy to do business in the United States.
Grace Shao (34:11)
Where should they be going? Southeast Asia right now, Singapore, Japan, like you guys?
Christian (34:16)
Southeast Asia is much easier for them to do business, compared to the United States.
Grace Shao (34:24)
But the thinking from a lot of these founders is often that Southeast Asia, frankly, doesn’t have as much purchasing power either, or willingness to pay, especially on software.
Christian (34:32)
But they are growing. They are growing. We must invest in the future. And one thing I want to note — there’s much more potential in Europe.
Grace Shao (34:41)
Interesting. Okay. I think Europe has been a bit overlooked. People have kind of — some people have written it off a little bit, frankly, just given the recent years of slower innovation. How do you view the European market?
Christian (34:54)
I think Europeans are catching up. They are awake, they are working to catch up.
Grace Shao (34:59)
Mindfully.
Christian (35:01)
Yes, they’re working to catch up. That’s what I got from the Paris Summit, very intensively. And I believe — I think from the economy side, you must look at the market from the economy side, I mean the macro side. The economy in Europe — they are struggling. That’s my personal opinion. The economy in Europe, for many countries in Europe, they are struggling. So they need a new power, they need a new engine to restart the economy. I think AI should be the best engine for new growth in Europe.
Grace Shao (35:40)
Very interesting.
Christian (35:41)
So what kind of technology? Whose technology do they want? I don’t think they have the time to catch up just building their own AI labs, their own AI infrastructure.
Grace Shao (35:54)
Their whole stack, yeah.
Christian (35:55)
Yeah. They need help from China, even some other places. So I think that’s a huge potential for Chinese companies to do business in Europe.
Grace Shao (36:05)
Interesting. Okay. Very differentiated. Very cool. Okay. Thank you, Christian. Thank you for your time.
Christian (36:11)
Thank you.
Grace Shao (36:12)
Enjoy your trip and travels.
Christian (36:15)
Thank you so much. I enjoy it. Bye bye.
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In this episode, I speak with Ziwei Chen, product marketing lead for Accio Work at Alibaba.com, about Alibaba’s effort to turn decades of sourcing data and commerce know-how into an agentic business platform for small and medium-sized businesses. Accio began as an AI sourcing engine, but Accio Work is designed to support a broader workflow, from product strategy and supplier selection to store operations, marketing and growth.
The most interesting distinction is between AI that tells a business owner what to do and AI that actually does the work. Ziwei explains how agents can research products, compare and vet suppliers, draft inquiries, follow up on missing answers, update Shopify listings, generate structured data and coordinate campaigns across existing tools. Alibaba’s advantage is not only its supplier network, but the category knowledge, transaction context and direct communication layer built around it.
We also discuss where human judgment remains essential. Accio Work can narrow a supplier list, negotiate across variables such as MOQ, lead time and materials, and prepare an order, but the user still approves purchases and typically takes over the final supplier relationship. That balance matters because commerce is not only a workflow problem: branding, product taste, trust and long-term supplier relationships remain difficult to automate.
Finally, we explore why Alibaba built Accio as a separate, more open product; how it works with third-party platforms rather than replacing them; its subscription and usage-based model; and the future of agentic commerce across B2B and B2C. Ziwei’s non-consensus view is a useful one: not every problem needs AI, and domain expertise becomes more valuable, not less, when powerful tools are widely available.
For more, check out the podcast lineup here and explore the episodes!
Chapters
00:00 Introducing Accio Work01:01 From Alibaba.com to agentic commerce05:18 What an “agentic business team” does06:27 The commerce workflow and human control11:11 “Do it for me” versus “tell me what to do”18:32 Connecting fragmented commerce tools22:36 Alibaba’s sourcing-data advantage26:03 Supplier quality, matching and verification31:58 Accio versus general-purpose AI tools33:36 How agents communicate with suppliers37:09 What Accio offers factories and suppliers39:29 Designing AI for non-technical SMEs42:42 Business model and monetization43:54 The future of agentic commerce48:57 Why not every problem needs AI
AI-generated transcript for reference only
Grace Shao (00:00)
Hi, Ziwei. Thank you so much for joining us today.
Ziwei Chen (00:02)
Hi Grace, so good to see you.
Grace Shao (00:07)
I’m excited to talk about Accio So I just came back from Hangzhou like a month ago and I met some with some of your colleagues on the ground. Was very impressed by the product and thought was just really intuitive. So let’s get started. Tell us a bit about yourself and your role at Accio and what Accio is all about.
Ziwei Chen (00:24)
Well, hi everyone. my name is Ziwei Chen. I work at Alibaba.com as the product marketing lead for Accio Work. prior to Alibaba.com, I actually spent a few years in the world of developer marketing where I was kind of driving good market plans for software tools that are meant for developers who are building AI products. So now it’s kind of completing the whole picture for me to now move on to the other side about actually growing the products. built by those developers to the end users. So that’s kind of my kind of AI journey coming from the development side now to the end user side, driving the growth for Accio Work among our small and medium-sized businesses around the world.
Grace Shao (01:01)
And tell us, what does Accio do actually? So I think a lot of people are not familiar with it and just how it fits into the whole bigger, I guess, Alibaba ecosystem as well.
Ziwei Chen (01:09)
Yes, absolutely. So maybe I’ll take a quick pause and take a step back just to talk about the overall broader picture. So Alibaba grew as a large enterprise. we were founded in 1999, and our kind of main North Star was to make make it easy to do business everywhere. So that has always been kind of our focus of focusing on B2B and focusing on small and medium-sized businesses who typically don’t have that resources that large enterprises have. So throughout the years since 1999, we have been really just focusing on what we can do, either there’s a new product. And new services to make it easier for them to start a business, to launch a business, and to grow a business, maybe something to exit a business. So along that line, Alibaba.com is kind of the bread and butter where we first started, focusing on B2B sourcing. So actually, I will kind of almost break down our development or our growth journey into three eras and then show you where Accio fit. I will call it the digitization era, the AI co-pilot era, and then the agentic team era. So the first digitization is when we are first founded with that platform of Alibaba.com where on this digital platform we are connecting the sellers or what we call the suppliers, the factories, with the buyers, our buyers, maybe sellers on the other side, into one platform. So now they’re not limited by time zone, they’re not limited by the location. And then in that process, we what we have learned over the years is that things can get overwhelming. There just so many suppliers, so many products, right? So then when all of these foundational AI capabilities came about, we were like, okay, this is the moment. This is the moment where we can introduce AI capabilities to make it easier for our buyers to find the right products and the right suppliers. So the name Accio actually comes from Latin, which means summon. So the idea is we help you summon the right products, summon the right suppliers, summon the right information. So that’s when we first launched Accio as a sourcing engine back in November of 2024. And that went really well because a lot of our users now, even without any experience in sourcing, without any experience in physical products, and find it really quickly with confidence. But then what we had learned over time is that First, sourcing is only one small part of a broader business journey, right, for a lot of our users in the US, Europe, and around the world. So now we’re thinking, okay, what else can we do to expand on that? So that led to kind of the last phase for Accio Work. So when I compare Accio Work with Accio, a few things that stood out. The first is the focus from sourcing only to sourcing plus. The other thing is about the agentic part. So earlier when I talked about the three phases of digitization as a platform, right? The website. And then the second era is called the AI co-pilot. So that means is that you are still on the driver’s seat, AI is in the passenger seat. It’s giving you advice, it’s doing some little stuff for you, but you are still making all the decisions. You’re still wearing all the hats. And that’s what Accio did back then. For example, it can find suppliers, it can recommend messages for you, but that’s kind of it. So now we’re moving into what we call the agentic team era. where actually we’re gonna get things done for you and get more types of work done for you. so that’s kind of where we are really sort of moving into this phase, where truly kind of it’s like the spirit of the agentic commerce world, where you’re not only using AI as a passenger seat, but you’re actually arranging a team of agents to get things done and maybe let the teams work among themselves, what we call A2A. So that’s kind of the overall context of where we have been starting from Alibaba.com. To Accio the sourcing engine and now to Accio Work as the Agentic business team.
Grace Shao (04:45)
That’s very comprehensive over you. I appreciate that. So just to help listeners understand, 1688 and Alibaba.com are the sourcing kind of platform within Alibaba. And then obviously everyone knows Alibaba for the Taobao and commerce side of things, but that’s actually a merchant-facing product versus the Taobao and T-mall that are consumer-facing. with that kind of context, okay, so if I had to ask you to describe it in a very short sentence or like say two sentences. How would you actually describe the product today? It’s just like, who is it for? What does it do?
Ziwei Chen (05:18)
Absolutely. I think I would Start with three words that summarize what it is we will call it the agentic business team. and I’ll break it down each but one of them that eventually lead to the who and the how. So the agentic part means these are agents that get things done for you. It doesn’t just give you the recommendations, but it can write emails for you, send messages for you, publish things for you, right? And then the second part is business, right? So we are dedicated to the world business, specifically e-commerce as a really strong emphasis. an all all Aspects of business starting from the front development side to the growth side to the operations, everything. And then team part kind of aligning up to that as well is that you can build multiple agents to work among each other. So tip from a format perspective, we have a desktop app that you can download, also a web app as well. You can access the same thing on your mobile devices, on your web as well. So all these things are connected where you are creating agents on this platform. telling the agents what to do and then you can go to sleep, you can go to work, you can go to your events, right? All of these things, go to your shop, right? Do all these things while the agents are in the background running all these tasks assigned by you.
Grace Shao (06:27)
So exactly your point, like you can do a lot of things, and that’s what I found the most fascinating thing. So, you know, we know a lot of products on the market these days that can do a lot of the, I guess, step three, step four in selling. So what I mean by that is they can help you manage this SEO, the content management, the marketing, and the like, you know, plugging into Mailchimp and sending out your emails. But your edge really isn’t just the agentic AI part, what I thought is really your supply chain network, right? so of course Across the entire workflow right now. Like walk us through like the different steps. Cause I remember there was like a slide your colleague showed me. It was like there’s four different phases of commerce building a business. you guys can go across it and how much of it is actually truly a genetically run right now and how much of it is still, I guess, needing direction from a human to really verify and process in the pro yeah.
Ziwei Chen (07:17)
Break it down to the two parts. I’ll talk a little bit about the overall flow first. I think it can kind of follow a typical business like life cycle where you’re starting from the strategy and the planning part. Let’s say what product, what’s the portfolio, what’s your branding, right? What’s your competitor, what’s your positioning or pricing, all of that. And then into the actual product development and sourcing part of that. And then you’re going into your store management, especially your e-commerce and different marketplaces. And then eventually the last one will be for marketing and growth. it can be for your B2C. Your traffic, right? Your conversion. Also, some of our users are not only doing B2C, but also B2B to wholesale. So, how do you deal with that B2B, like management or relationship, all of that? And then, so that’s across the different core stages of run launching and running a business. And then Accio Work supports all of that, but not individually, but it connects to that all together. So that entire ecosystem play, right? It’s really where it stands out, where we call the sourcing plus. So it’s not just about SEO, right? It’s not just about how to improve, let’s say, the images on your store, but how do you do that starting from the moment you have an idea, bring it to life, bring it to your store, and then drive it to more users, and then maybe iterate over time all the way back to what other products you can do, or how can you make your current product better. And along this way, I think when it comes to which areas that are truly agentic, or kind of where does that relationship between human and agent do? I think that there are a few ways to go about it. So I think for example, all of the critical decisions or area still remains a human to be to approve. For example, you can develop or create an order. The agent can create an order on behalf of you. So you can just click and then go into the checkout page, but it will not check out for you. So you have to go in and confirm that or from a security perspective. That’s one example. The other example is about your own vision as a founder or as a brand owner. So let’s say it’s the position of your brand. Some of them can be subjective. Maybe it’s your personal story, your personal style of the brand. It can also be kind of that. More objective or critical thinking that after learning about different perspectives, this is where we decide to do. I think running a business is it is a lot about the science part, but it’s also the personal, the entrepreneurship, right? So a lot of these kind of personal decisions still plays a role. So for example, maybe the agent can develop all different kinds of images and videos on behalf of you, but how do you d determine what’s Good, it can be through A/B testing, right? But it can also just be this is kind of the aesthetic you are going for that you think what matters the most to your audience. So that’s a second example. I think the last one, taking another pause, I know that when we talk about human, it’s not just about the users, it can also be about the developers, right? It could be the people in the Accio Work team. And then so where we are seeing is that we put we will love for Accio Work, we are specifically targeting small and medium-sized business operators who are not. The strongest in AI tools. They’re not developers by training, right? That’s not where they want to dedicate their time to. So what our product team and engineering team and algorithm team have been really focusing on is what are the things that we can do it for you. So take the burden off of you. For example, in this broader ecosystem of everything that Accio Work can do, it does require a lot of connect connections to different channels and platforms right can be about marketplaces having different tools i think that ecosystem is where it really plays a really great job but then who is creating those connection points you know you can have your account but then from our perspective where our team spend a lot of our time is to build those connections so you can do a one-click activation to connect axial word to your email to your CRM to your Shopify store so that is one of the strategic areas where we decided to take more of a burden onto ourselves so our audience can just click and then log in and then start to connect the dots for themselves.
Grace Shao (11:11)
Very cool. I want to double click on the partnership and like how you guys built the plugins definitely a bit later. I do want to ask about the use cases because so here my brand. So when I went to visit you guys, there was like really it was really interesting. So on one hand, it was very obvious on how SMEs would use this, like you said, like you know, if you already have a very strong intent on how to what you want to purchase, what you want to sell, so then that whole process is very clear. But one Example one of your colleagues showed me was so fascinating. Basically, like they were running an event, someone was running an event using Accio. They went on the Accio like webpage and then basically uploaded a picture of like the banner, the physical banner like size of like an outdoor space and said, I don’t know how large this space is actually. I don’t know what looks the best with this background, but I need a banner to run this event. And then Accio became like a thought partner. And it was really interesting because it was not like just a general like AI like thought partner. It was actually someone that had a lot of know-how and industry know-how from creating these things, from manufacturing. So they would literally say, it seems like blah, blah, blah, the weather is like this and that. You might need this material because that kind of texture is more durable in the sun or durable in the rain. And I just thought that was so fascinating. and I just want to hear from you what use cases are most common and then beyond that, what are some like I guess not so mainstream use cases that you’ve seen that are quite fun and interesting.
Ziwei Chen (12:34)
I think when it comes to use cases, you know, we can break we can slice the pie in a lot of different ways. One way is about the specific type of like actions for a business owner to do, but I’ll take it a different approach to talk about I think just in general when it comes to agentic tools in general, I think there are two types of use cases where air comes into play. The first is like do it for me. And then the other is tell me what to do. So the do-it for me part is that I know exactly what to do. I know the specific steps. Either I’m just busy or I’m just one person, there’s not enough hands. So this is where AI does a really great job as a follower, but it can really scale for you, right? It can be, for example, When it comes to assessing different suppliers. If I’m just up one person, I can only s assess so many, but now the tools can do it for you. So what we are seeing in similar cases, it can be using Accio to find, evaluate, vet, and compare suppliers or products. It can be you up creating and updating listings, especially for those sellers. with categories that have multiple SKUs, multiple kind of products where I want to make an update to all of my marketplaces, all different colors, all different sizes, right? It’s really doing taking that repetitive work off their shoulder, but making sure things are consistent across different channels. can be creating content, right, on either social media, it can be about blogs, all of that. That’s one area. The other area that kind of really aligns well with what you’re saying is that tell me more. Like I don’t know what I don’t know. Sometimes I don’t even know what’s the right question to ask, or maybe I do know the right question, but maybe the LM itself did not have that training to start with. And this is where we are seeing a really strong aha moment from our users. And when I say users, it’s not only just people who are new to e-commerce or new to physicals or like products. That’s definitely the area where we have seen the most appreciation. But it can also be for even successful sellers or operators entering into a new category. And it happens a lot, right? Maybe extending your product line, maybe you successful exit once one brand entering into a new one. They all they they truly understand. the cost or the costly mistakes and how important that could be. So in both cases, what we are seeing is open-ended questions on this is kind of what I’m thinking, this is what I need, or just this is my store. Tell me what’s wrong, tell me what’s missing. And then lear first learning from actual work based on what we have seen in the space about key areas, key specs, key steps or things to flag. And also, I’ll tell you what’s wrong, or I’ll tell you what you should pay attention to, and then let me fix that for you. And I’ll show you a few examples. The first example about banner for sure makes a lot of sense. we had one, a similar case, but with a different flavor to that. We had a almost like a con like a product consultant. So he would take out new projects all the time. Very common, even as someone who’s experienced in the field, to enter into new categories. And he was helping a client with the bronze. plaque outside the building where you know there’s a building name or the history and all of that. And then so he it was a retro case where he’s already spent the time and the cost to pay for a designer to kind of put his client’s need into this kind of sizing and whatever. so he took the time, spent the money, sent it to the factory, and then realized that actually the font size did not work for readability purposes. So while he actually replicated that need into axial work. One of the first things that actually were told him is that depending on the technique for the for the font, is it going in or out from that plaque? here are his recommended size or the height of each each of these tags to make sure that you are following the best practices. And then he was like, Wow, I wish I had that when he was working on that project. So he can save not only the cost, because he had to redo the thing, repay the designer to fix it, but also lost weeks in between. So there’s lots of examples around that. They’re small, but they can be costly, right? Especially you’re busy, it’s your own money, you’re bootstrapping into your business. So that’s one example. The other one, it can be we recently had a had a user who was just who has a Shopify store and was just trying to see like what what can I do? What can I do with Accio Award? So he’s not someone who knows exactly this is a tool I’m gonna use for X, Y, and Z. He just pasted his Shopify link. To Accio Work and tell me what’s the low-hanging fruit. And Accio Work actually dig out a bunch of like very specific areas for improvement, especially when it comes to SEOs and GEO. For example, for all his images on his store, he was lacking the image alt tags, which is a really important thing for SEO purposes. There were thousands of images. And he was like, he knew that was kind of important, but he never really paid attention. But Axe Work was like, if with with this, now you can improve your ranking. And then in about a few hours, Accio Work did all of these tagging for him in the back end, on Shopify. And then the other thing was that his product was baby Like baby beddings or a baby clothes. So there’s a size guide. But then what actually work called out is that his size guide was just an image. It does not have structured data. So LLMs cannot read it, which means that he’s not getting any traffic when people, when new parents are searching about sizing for their babies, right? If it were baby clothes. So then he actually told me that he he started this task right before a drive, and it was an hour drive on the road, and then actually worked basically using that and One hour, recreated his size guide to make sure there’s structured data behind it. And then so far, he’s already seeing like new traffic from like LLMs or like AI, like what we call now the AEO, right? for the first time. So these are all really great examples of what I call like teach me what to do, or teach me kind of what I’m missing out on, where we are seeing lots of like aha moment from our users as well.
Grace Shao (18:32)
That’s incredible. I think I’m like absolutely in shock because I think a couple of years ago during COVID, I was like playing around with the idea of drop shipping things. I wanted to build this like pet shop store. But just like not having that kind of guidance is actually really daunting to start something new when you’re not in this space. And if you just had that kind of thought partner, that would be incredibly helpful. Actually, I want to ask you about the partnership thing you mentioned earlier. You talk it did kind of touch on, you know, there’s these plugins that are built in. at SEO will help the vendors actually distribute across different distribution channels. So walk us through that. Does that not kind of cannibalize your own business? Or are you is that not like, you know, you guys are not in the same category, you don’t see that? Like, how does that relationship work?
Ziwei Chen (19:16)
A few thoughts here. I think first thing first just putting Putting down our Alibaba.com hat and just think from the user’s perspective. I think one of the biggest learnings that we have had by looking at our e-commerce sellers in this space is fragmentation. It’s truly, and when I say fragmentation, it’s not just about multi-marketplace. Cause you know, some people have like a Shopify store or Etsy store, that’s one type, but also think about you have your storefront, you have your B2B sourcing side, you’re speaking with manufacturers and different messaging channels, and then maybe you have your own like CRM. System, you have your own, let’s say, analytics system, right? And the all of each of these might have its own very professional SaaS solutions already, or maybe they’re hiring a consultant for that. So I think a lot of their time is really spread thin by just connecting these dots. So I do think that’s even from a North Star perspective, connecting a dot, putting them into an ecosystem is ultimately what’s gonna drive benefits or impact to our users, which is what we want, right? So I think that has always been we feel confident in that direction. And on the other hand, I think just like how there are so many different startups or even statute companies focus on each one of these, just like how Alibaba.com, our bread and butter started from sourcing. So I think yes, we can choose to do everything on our own or which is gonna spread thin. Odd, right? Or we can choose to collaborate with them as well. Especially a lot of times maybe our users are already so familiar with an existing tool. So making them give up on and then transfer is one option, or maybe in the transitioning phase, right? Or maybe just as a collaboration to say, bring your tools into Accio Work with just one click and then you’re login, and now you can connect all the dots together. We are definitely seeing that, for example, we have a user who has their own kind of email, like like solution system where he was struggling partially because of setting up campaigns and take lot of time but also you know he’s using Accio Work for research for messaging and now he has to translate all of these learnings right into a different tool. So he just integrated that into Accio Work and use Accio War as a central hub of say I have a new product now establish a new email campaign establish a new SMS campaign and then this is my key message you go To those sites on behalf of me. So I think that consistency of I have a decision, I want to apply it across different platforms, is where it’s gonna bring the benefit to our users, while without making them transfer or give them everything in their ecosystem that they have built out over time. And I think in that process, what we are also benefiting or learning through those partnerships with those channels are also just like learning opportunities on what’s what’s the future of AI when it comes to different kinds of areas. I think a lot of those traditional SaaS solutions still have are also experimenting. So I think that also creates a really win-win collaboration like experimentational type of like collaboration of let’s see how what kind of new agentic behavior that maybe actual work introduced through our our plugin to the new platforms and now they are learning what else they can improve or enhance or ex or or in integrate into their own ecosystem as well. But ultimately, because the our sellers or users are using those tools back and forth, it’s a shared type of traffic that has that synergy in general.
Grace Shao (22:34)
Very fascinating. So so it’s.
Grace Shao (22:36)
It’s really the user experience first, but like Alibaba second in that sense. So let’s talk about Alibaba then. so it’s no secret ATT sits on top of like Alibaba supply network, you know, guys got transaction history, sourcing know-how, all the data in the world from e-commerce over the last two, three decades. how do we understand that? Like is that like huge edge for you guys? do you think you guys have leveraged Alibaba’s ecosystem to build your things out? How do you see that relationship?
Ziwei Chen (23:02)
I think in general, I would say that having our own like our own supply chain and our own learnings and the industry know-house is definitely like the biggest part about our remote. or the starting point of our remote. I don’t think it’s the only thing, but I think that sourcing plus storyline is something that we definitely want to keep in mind to differentiate from the rest. obviously at the same time, we also understand that Alibaba.com is only one of the marketplaces that our users might be sourcing from. So we’re also trying to make it more open in the sense that it actually where you can also search for suppliers and like products even outside of Alibaba.com’s ecosystem as well. but I think how that looks like into how that translates into kind of our own product experiences. So far we have briefly touched upon that industry know-how because we know all of these Category best practices. So no matter what kind of categories essentially we have experience in, you’re gonna get that intro that recommendation or that guidance along the way. That can also include negotiation and supplier communication. So for example, because right now through Accio Work, we have our AI Auto Chat function, meaning that now you can train the agent to conduct or multiple agents to conduct that sourcing process. Task on behalf of you. So instead of you having to stay up late, depending on time zone, asking or answering questions, you have 20 agents, right? Each speaking with the supplier, negotiating. And then when I say negotiation, it’s not just about the bulk order price. It can be about the sample cost. It can be about the lead time. It can be about your MOQ. It can be about different material flexibility. So all of these kind of How do you approach negotiation? How do you approach establishing credibility as someone new in the space? How do you nurture that relationship with the supplier? All of these longer term things. So it’s not just like you find a supplier, you place an order and you’re done. So that the overall growth journey is something that we see continues as our users’ business grow or kind of evolve over time. That’s one thing. I think the other thing again is kind of that. synergy or consistency across platforms where you made a change to your product because it happens quite often where you are updating your products, maybe because of your learning and the competitive space, sometimes learning from Accio Work, or it can be your suppliers are recommending the latest techniques to the production, like you know, capabilities that you’re incorporating that. So how do you make sure your communication on the production end is reflected to your marketplace? is reflected to your social media, is reflected to your community, to your newsletter. So I think that sourcing plus, kind of that type of like experience or that consistency is also where we see we take pride in as kind of that holistic experience or evolution for our users as well. and then so that’s something that we’re definitely taking pride in, keeping keeping it as our mode while expanding on How do we translate that know-how into every aspect of our users or our operators business lifecycle?
Grace Shao (26:03)
Yeah, actually I’m gonna double click on that. So Alibaba itself has cited one point five million verified suppliers, you know, more than something like four hundred million SKUs behind Accio. these are wild numbers. this is not even including other suppliers, right? Like you just mentioned. How do you maintain that data quality and actually how do you ensure there’s fairness in terms of like how you actually provide your users the right supplier network or the supplier contact because I don’t understand how the back end ends. you know, when I’m looking for say a hair clip, what comes up? How do I understand what goes behind the interface?
Ziwei Chen (26:40)
I think I’ll answer it in two different layers. So I’ll separate Alibaba.com where our supply chain network as one layer and then talk about how where actual work fits in. but starting from the Alibaba.com as our own network, it has we have established our own very comprehensive system for that verification. for example, for all of our verified suppliers, that verified batch means it’s being verified by a third party where they have submitted their like certificates, all of the requirements and the registries and all of that. That’s one example. And the second example is that for example for all of those like Trade Assurance or all of these things that we’re constantly evolving to make sure all of these things are available for you to make sure you know you can choose and also including a lot of so that’s kind of on our own network side that we have our own system. And then where Accio Word comes in because it’s a it’s a different name, right? It’s not called Alibaba.com So it offers a few different flavors. The first thing first is that in Accio Work, there isn’t any sponsorship or ads in place. Well in Alibaba.com, yes, there are those different placements, but Accio Work purposely decided not to do that to make sure you are getting recommendations based on the best matches of your requirements. So let’s say depending on where you are, sometimes you might be in an exploration phase. Let’s say, I’m interested in in building what was the other thing I looked for the other day? It was like Wooden bits for like board games. And I was like, I don’t even know like where to start. And then Accio can tell you. So here are the five key areas you should consider. Let me walk you through how to make each of these decisions first. And then let me match it with the right supplier. So don’t rush, let’s take it slow. Or it might be, I don’t know exactly what’s the model, what’s my location, what’s my MOQ, what’s my price training, etc., and then match it for me. So you’re what you’re gonna see in Accio Work is let’s say you have a list of five things, and then for each of these suppliers, are they three out of five, are they four out of five? And what else might be a recommendation? And this can be hard requirements. Let’s say it must be this model. it must be they must have expert experience in certain places. It can also be about preferences. Let’s say maybe you already have lots of factories in a certain region, and then you just prefer that this new factory for this new component is in the same region. So it’s easier for consolidation for shipping. So you can be a preferences. And those are areas where a regular label, right, doesn’t or tag doesn’t cover as well compared to an AI for LLM to process that for you. So that’s one that’s one area. And then the other area is that so that’s the first thing that actually baked in is that no ads just based on what fits the best, based on your matches. The other thing obviously is kind of having that third triangulation of data. So we do have this ability for you to vet a supplier on Accio Work, which means that what it’s gonna do, it’s it’s not only gonna search for in the back end for alibawa.com, like what kind of certificates they have, but let me go on a custom public records. Let me go in, let me go on social media, double check, do they have a presence? do they have they attended trade shows? So all of these additional data you don’t get from the platform, actually was pulling in. Compare notes to make sure you are making a confident decision on that end. so that’s the second thing. And the third layer again is that even with all of this fixed data, or sometimes could be outdated, or you just want to verify whether triple or triple check it, and that’s where that automatic communication comes in. Where now let let the agent directly speak to the suppliers on behalf of you, ask for photos of certificates, right? As for images of the factory if they don’t have any, triple check on all of these areas. So all of these three layers are how Accio Work is building a different experience to make sure you are making a more confident decision.
Grace Shao (30:28)
Okay, so why then was it so necessary to build a separate agent on top of Alibaba.com and 1688 instead of you know just adding a chat interface or conversation interface on top of I believe a lot of the Alibaba products that right now all have an AI chat bot it embedded in their product interface, but why was it so necessary to build a separate Accio?
Ziwei Chen (30:50)
Yeah, good question. I think A few things. the first is that maybe back to our North Star when it comes to that open ecosystem. I think having a separate brand or even of an identity is a must-have to make sure our supply chain is not limited to Alibaba.com and also our use cases are not just limited to sourcing. So that explains everything that Accio Work is doing by incorporating new supply chain options, partnerships, incorporating new connectors or services or like solutions together. I think that’s kind of the biggest thing itself. And I think the other side of the things is that when it comes to branding, to some extent, although we do have lots of existing Alibaba.com users adopting Accio Work for that new value add as well. But we are, Accio Work is really reaching a brand new group of audiences around the world. A lot of them are like net new, aspiring entrepreneurs. Who now realize that opening your business on your own is possible. So I think there’s a brand new type of like audiences that we’re reaching, with its own kind of the AI flavor of like a branding as well. So we’re building a community that is also beyond Alibaba.com.
Grace Shao (31:58)
That’s interesting. I have a bit of a a spicy question for you. Then what’s the difference really if I were a solo entrepreneur using ATSIO versus maybe I just get Claude then? I get Cowork.
Ziwei Chen (32:08)
That’s a great idea. I think a few thoughts here. The first thing first, the simple answer is that Accio Work is the only agentic platform now that has the official connection to Alibaba.com. So all the other AI tools, maybe they can search for some of these suppliers by using the browser extension and all of that, but they’re not gonna get all the back-end data about those suppliers and not gonna be able to directly communicate with the agents, cannot chat directly with them and also cannot connect the dots all across the board. I think that’s a the simple, the simple way out. But I think also because all of our team’s energy is super focused on this field. And I think in this world where everything is nothing, right? So by us putting all of our resources and energy into just supporting e-commerce, small and medium-sized businesses, we are making more progress, more in-depth progresses on just that ecosystem specifically. And I think in that phase, the connectors with the plugins are just one of the areas. The other one is the just the general industry best practices. And what I’m referring to is not just kind of what the platform has learned, but also the people in the ecosystem. we’re building a community, we’re sharing skills, very like or even sometimes building different agents and now you can learn from each other as well. So I think it’s not just about The data is about a community, it’s about a best practices as well. So people are learning from each other. all of those solopreneurs are aspiring entrepreneurs of how to start something and then grow from there.
Grace Shao (33:36)
So then I wanna ask you about something you mentioned, I think, in passing a couple of times, which is like the agents can help you speak to the suppliers. It was quite fascinating. how does that work in the back end? Like our are at this point are just like agents talking to agents and then making deals happen and then you know, you have a product that’s purchased for you and the next thing you know it’s already out there on a website. What does the human need to do still? So tell us about that.
Ziwei Chen (34:01)
Yeah, so maybe let’s we can walk through a pretty classic source and experience. Let’s assume that you kind of already have a pretty good idea about what you’re looking for. So you’re gonna go into actually we’re gonna talk about let’s say maybe this is a category, this is my specs, the color, the material, etc. give me recommendations and then you can it will give you some top recommendations you can select and then actually were the agent will develop a pretty professional inquiry for you to make sure it’s speaking. The industry language to set you up for success as someone who’s an experienced series buyer. And then you can say, select these five, what’s like top five, and send this inquiry directly to them. So then the back end, it’s connected through the Alibaba.com. So the agents are sending those information. That’s the first thing. But what else that’s gonna turn on is what we call the AI Auto Chat, meaning for each chat conversation, the agent is actively engaging in conversation during that time. And then what we have seen As the most common workflow, which actually aligns very well even in the pre-AI era, is that people start broad, they shortlist, and when it comes to the final one or two, they actually go in for the full-on investigation, right? So that’s what we’re seeing for Accio Work for our AI Auto Chat as well, is that the agents does the best, brings the most value in that early short listing phase. So let’s say for example, I have a really highly customized product. It’s really hard for me to tell or some really unique needs. It’s not enough for me just to go on a website to evaluate that. And I have a list of five questions for every single supplier. In the past, I have to ask each of these five questions, maybe two five suppliers max, right? But then not all of them are gonna answer the question in the right order, or maybe some are missing something. So you are going you’re the you’re the Excel sheet, right? You go into this chat and say, okay, question number one, number Number three, number four is done, but not number two. The other one is only as another whatever, right? So the agent, what it’s gonna do is say, okay, you gave me a list of five questions. I will make sure every single supplier responds to each one of them. If they’re missing one, I’m gonna chase them and then I’m gonna save those answers into a table so you can make apples-to-apples comparison. And usually halfway, this supplier says they cannot accept this, that supplier says they cannot do that, or they’re out of order for something. You end up with one or two, and then Typically now I can jump in to say, okay, now I feel good about it. auto-chat. You can take a pause, let me take over the communication just to make sure the final collaborations are on are I feel good about that. and then I can place an order, etc. And in that process, I do want to highlight that obviously AI can automate a lot of things for you, but in many cases, just like in other industries, it’s a lot about relationship, right? Unless You’re paying for what we call like ready-to-ship, like standardized product. Let’s say I’m just buying some balloons for a party or buying some bracelets for an event. Otherwise, for highly customized product, you’re building a relationship. So at the end of the day, it is recommended for you to have some interaction with the suppliers because you might be working with them on your next iteration and new product as well.
Grace Shao (37:09)
Very interesting. So it’s really just helping improve the lead quality and then shortening the negotiation time in many sense. And then but the ultimate actually decision making is still like in the hands of the human. that makes a lot of sense. So I want to turn the conversation around. We talked a lot about how it’s helping businesses, right? Helping the vendor, the seller. but the other end of it is the suppliers. And I believe that at a lot of suppliers are actually on at SEO and also turning on auto chat and whatnot. So what basically support do you provide suppliers and help them in selling their products to the vendor in the between or like the brand owner.
Ziwei Chen (37:41)
Yeah, absolutely. so we do have a version or specific plugins that are meant for the factories and the suppliers who are who can also choose to be on Accio Work for all of their for their side of the sales as well. and I think even pre-Axial World, a lot of the more AI tech savvy factories are already developing their own chatbot for services as well. So I don’t think that’s new to some extent. But I what I think a few of the really key areas that we are seeing success, the first One is actually a little bit more about background research on potential buyers because a lot of them maybe they’re overseas, they don’t have all the all the awareness or the ability to search across the world about learning about who is this buyer, are they serious, what’s the scale, right? All of these things. So we’re seeing a lot of our factories or the sellers, our sellers on the platform using Accio Work to To understand their potential buyers. Because a lot of times, again, maybe in the maybe there are buyers who are not as experienced but are very serious. That wouldn’t come across in their messaging, especially maybe English and other first language either, right? We have users in Europe, in in Latin America as well, so and also in the US. So I think by giving more data to the suppliers to get a better picture about the potential buyers, it also helps them. capture opportunities or avoid losing or missing out on opportunity. I think that’s a really critical part for sure. And I think the other part is about that general selling optimization as well. It can be most of the times it will be on the Alibaba.com platform. How do you stand out as a seller by learning about the data, learning about the user behavior as well? So I think these are a few areas that we are seeing a lot of usage from the supplier side.
Grace Shao (39:27)
I see, I see. It makes sense.
Ziwei Chen (39:28)
Excuse me.
Grace Shao (39:29)
Something we talked about offline before recording this is, you know, that a lot of the users, whether they’re the s your sellers or, you know, the actual brand owners, is that AI is still relatively new, right? They’re not the people in Silicon Valley. they’re not working for big tech. And that’s quite different from a lot of the other genetic tools being sold or marketed to these more so called sophisticated AI users. Now, how are you helping them, I guess, whether it’s upskill or understand or not be so intimidated by AI?
Ziwei Chen (39:57)
Yeah, great question. I think a few thoughts here. maybe the different a few different perspectives when it comes to the product development side, on the go-to-market side, on the educational side, et cetera. I think to start from the product side, I think what we have been really emphasizing or de-emphasizing is the is a is a focus. It’s too much focus on the features themselves. So what we have learned is that when we’re packaging them, we’re not like It’s almost like sometimes our users they don’t need a toolbox, they just need like an almost built up tool that can run itself. So what our product team has been really focusing on is not just to build the building blocks, but build the framework of those building blocks. So we’re not making our users to learn to become a Lego expertise like expert right away. so I think from a product end, it’s more about how can we take off the burden of the configuration as as much as possible. That has always been an emphasis, but even more importantly, as we’re getting more newer users in the space. I think that’s one thing. The other thing when it comes to just overall marketing side, what we are also experimenting, exploring now is a lot more when it comes to live events, either it’s in person or online, where we’re taking things slow. And then in this process, what we are trying to emphasize is more the workflow, the use cases as opposed to the feature. Like let me show you how this thing works from what’s the input and what’s the output. And this is all you need to do is to copy and paste and whatever. And then I can explain to you what happens on the back end. Because ultimately what we call the job to be done, right? They’re not adopting a nail, right? They’re adopting a hole like on the wall, things like that. I think that kind of stays consistent. I think the very last thing in this process From an educational perspective, that it’s also learning from my perspective, is that a lot of times the users are not only learning about AI, but they’re learning about just how to launch and run a business. That’s almost even more important than a tool. The tool is an enabler to help you achieve the goals in business as well. So what we are trying to do is to resurface the business know-how. It can be sourcing, it can be about product design, it can be about how do you do SEO or like how do you do like B2B, like pipeline or lead gen? And then let me bake in those automation in the back end. But what you’re taking away is how to do this thing, even in the pre-AI era, but now just making it easier to do. So I think that has been more of an emphasis from a go-to market or like a community building perspective as well, to make sure we’re not overwhelming our audiences. because maybe they’re already overwhelmed by all the AI tools out there in the market as well. So how can we shorten that and let them get to the aha moment faster and easier?
Grace Shao (42:42)
So how do you plan to monetize that SEO then? Are you guys charging subscription? Are you guys gonna start plugging in advertisement?
Ziwei Chen (42:49)
Yeah, so Accio Work is on currently on a subscription plan model, both for personal subscription plan and also for business plan as well. and then all of these typically by a monthly plan or an annual plan. It’s primarily based on token usage. So depending on how much how much work. How many types of things was the frequency of the task that you anticipate on the platform and then you’re upgrading yourself based on the amount of work that token consumes. So typically that depends again on the volume, on the complexity, and also on different models you choose to activate for each of these tasks. And that kind of r it aligns with our vision since the beginning, at least starting from Accio. the sourcing engine to now this the agented platform as well. So it’s mostly focused on usage. we do not currently have a plan again because of that kind of fairness or the quality of results, we’re not dealing with like advertisement from suppliers. most other things is more about how do we empower our our like sellers, like or buyers on our platform, the sellers to the consumers to get their business running using the agentic capability.
Grace Shao (43:54)
Now throwing it forward, where do you see agentic commerce going? Because it’s really interesting. I just interviewed agentic payment solution company last week as well. And then now obviously talking to you guys about agentic supplying, supply network support. where does agentic commerce kind of take us? Like in the future, are we just gonna be like telling the agents to do this whole transaction, this whole activity loop? Or Do you think that human is still needed for a lot of the verification and and and quality control in between?
Ziwei Chen (44:22)
Yeah, I think a few thoughts here. I do think that human in the loop is critical at different stages of a development. So for example, there are when we talk about agentic or agentic automation, right? We’re we’re automating an existing workflow. Yes, there are a lot of best practices, but it really varies, right? So even having your you’re co-developing an automation workflow with the AI tools in the beginning, and that will vary all the time. The more you The more you invest in that co-creation with the agents in the beginning, the better the automation or the workflow will work well for you. And then along the way, what we’re also seeing from our uses is that they’re learning while doing. You’re building a plane while you’re flying it. So there’s always gonna be learnings along the way. Just like in coding, there are ways for you to build the agents to do that automation by themselves. But I think the more complex the considerations are, the better. Better impact a human can bring to that loop as well. So that’s in the middle part, right? And also in the end, I think there’s still we’re still building that trust along the way, like between the human and the agent or the agentic loop as well. So I do think that having the option to or for an agency, for the human to have that agency, is always critical. That I think it’s it needs to stay. but then on the other hand, what I also want to add is that I think the word of agentic commerce. Can vary quite a bit between B2B and B2C. for example, in the B2C world, there’s always that shopping experience. It’s not gonna get taken away, right? Just sh going to it’s browsing that emotional experience is not gonna be replaced by AI. While the B2B world is a little bit more calculated, is more, right? But still it has that you have that relationship part. So I think in both end, I think there’s still human touch. In addition to human decision, needs to go in. But where I’m the most excited about is that because our target audience or our target user sits literally in between. They’re in this loop for B2B agentic commerce, and also they’re in this loop of B2C agentic commerce. So what I’m excited to see is how these two worlds are merging, or our our core audiences sit in this overlap in between. And then I’m I’m excited to see more of that. synergy of what works in the B2C authentic commerce world can get better integrated with the B2B world as well. Because again, like our sellers or our buyers are sitting between the factories, right? And them as a brand owner and then the consumers as well. So I think this is where I think the next iteration of innovation or maybe redefined like r the new definition of workflows of tools that might happen as well.
Grace Shao (47:04)
That’s super fascinating. Cause I think when I’ve been speaking.
Ziwei Chen (47:05)
Yeah, but.
Grace Shao (47:06)
To people who are working in the agentic commerce space, it’s often so very compartmentized, like what you said, like you there’s a B2C world but there’s B2B world, and these two worlds don’t really, you know, co like collide at all because whoever’s talking to a factory is not really telling what’s happening to the consumer and there’s no really feedback. Factories don’t even know where the product’s going and how it’s being branded. But now there’s that kind of ecosystem, I g like communication or the world colliding between on Accio. It’s very interesting. what do you think the world’s still getting wrong about agentic commerce? Because I think there’s still some people who are very reluctant or resistant towards agentic commerce or the idea of it. What do you think is people are having misunderstanding about it?
Ziwei Chen (47:47)
I think one thing that we briefly already touched upon is whether a gender commerce is taking away the agency of human. so I think that’s the part that causes the most concern or hesitance is like is the agent gonna replace me? But I think all a lot of these judgment calls, even that branding, right? that personal story about you being as an operator, you having that guidance on the branding on the story and all of that, it’s not gonna go away. But also it’s not gonna go away, but it’s also critical for you to inform where the agent should go. I think that’s one thing. But the other thing that I feel like maybe where I think it’s also like A of times when just as you said, when people think about commerce, they’re th thinking it still tend to go more siloed or go into just on specific steps. So I think it’s still that orchestration layer when things start to get connected and st start to flow together. So it’s not just about how well each of a step is done well, but how well all of these steps are connected. And then that’s also where that human in the loop or your agency is bringing that flow together. So I think that’s an overall area where I think it’s it’s People might be evaluating the wrong thing as opposed to their making the wrong judgment call about a one specific thing.
Grace Shao (48:57)
Thank you. And then my last question for you is a question I ask every single guest that comes on, as the podcast name is called Differing Understanding. what is one differentiated view you hold or something you think that’s very non-consensus?
Ziwei Chen (49:08)
Yeah, so I think my the first thing that really stood out to me is that not every problem for not not every problem needs AI to be solved, or AI is not a solution for everything. and I think that has implications both on the development side and also on the user side. So on development side, what we mean is that there are solutions or there are things that we can build that is more efficient or more accurate even without AI. So we should not shy away from that. And that should that will impact how people build products. But on the other hand, even for the users, that also means that skills in AI is important, but that’s not enough, or that’s not the only thing that I will focus on in this era AI era phase. Instead, I will still spend time in building your domain knowledge as well, because without that. You’re not gonna be able to learn how to use AI the best because maybe sometimes AI cannot solve all the problems as well. So I think be having a balanced view about where AI sits in the product or in your tool stack is something that will allow you to get the best out of all the AI tools available and drive results for yourself.
Grace Shao (50:21)
I love that it’s like keeping the human side of things in check. But I think it’s only non-consensus of where you sit because sit in the Bay Area. Only people in the Bay Area believe AI is taking over the world right now.
Ziwei Chen (50:31)
Okay.
Grace Shao (50:32)
But thank you so much for your time, Switzue. really appreciated your insights, and you’re very you know, deep understanding of the agentic commerce world.
Ziwei Chen (50:39)
Yeah, thank you. I had fun. Appreciate it.
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In this episode, I spoke with Patrick Wu, Founder and CEO of Clink, about what payments need to look like in an agentic commerce world. Patrick previously worked on payments infrastructure at Amazon and AWS Payments, and later led global payments at Temu as the company expanded market by market. His view is that payments today are largely built for humans at checkout, but not yet for agents acting under delegated authority.
The conversation started with a basic but important question: if Stripe, PayPal, Airwallex, Visa and Mastercard already exist, why do we need another payments company? Patrick’s answer is that the problem is not just processing a payment. For agentic commerce to work, the system needs to understand who the agent represents, what permission it has, whether the purchase fits the user’s original intent, and whether that authorization can still be verified later. Clink is positioning itself less as a replacement for payment processors and more as a connector across agents, merchants, payment providers, and card networks.
We also spent a lot of time on why payments remain so fragmented globally. Payment habits are local: cards, wallets, bank transfers, convenience-store payments in Japan, and different payment methods across emerging markets. Patrick argues that no single provider is best in every market, which is why AI builders and global merchants may need orchestration across multiple PSPs, local payment methods, and eventually multiple agent platforms.
The most interesting part of the conversation was around trust and liability. If an agent buys the wrong thing, who is responsible? Patrick’s view is that fully autonomous shopping is possible eventually, but the trust layer is not there yet. In the near term, agentic payments will likely work through delegated mandates: user-defined instructions, spending limits, merchant controls, passkeys, audit trails, and chargeback mechanisms. His differentiated view is that the industry may be too focused on creating brand new crypto or blockchain rails, when the more immediate challenge is making existing fiat rails work safely for agents — and getting the ecosystem to align around clearer standards.
Get in touch with Patrick via LinkedIn.
And company website here.
Chapters
00:00 Introduction
00:13 What Clink does and why payments need to evolve for AI agents
01:18 Why today’s payment stack is not ready for agentic commerce
03:07 Why payments remain fragmented across geographies, regulations and user habits
05:48 Clink’s role as a connector across agents, merchants, processors and networks
07:06 Why AI builders may need more than Stripe, Airwallex or PayPal
09:34 Visa partnership and what it means to be an “agent enabler”
15:23 What happens when agents spend a user’s money
21:02 Trust, liability, chargebacks and why China’s wallet model is different
23:26 What agentic commerce actually means today
43:54 Patrick’s differentiated view on fiat rails, stablecoins and protocol fragmentation
Transcript (AI-generated, for reference only)
Grace Shao (00:00)
Hey Patrick, so good to have you on today on AI Proem. Thanks so much for joining.
Patrick (00:05)
Hey Grace, thank you for having me here.
Grace Shao (00:06)
Yeah. To start with, give us the high level. Who are you and what is Clink in a few sentences?
Patrick (00:13)
Sure. so at simplest level, I’ll introduce Clink first. Clink is building the the payment and the bill infrastructure for AI builders and for the agent that they will serve. So payments today we see at design around human being on a checkout page and but we’re building the layer that let authorized agents complete the same transaction safely while helping merchants accept the global payments through the system they already use.
And for myself, I started my payment career path at Amazon and AWS. I learned how to build a payment infrastructure that has to perform at a very high level of reliability and the security. And at Temu, I led a team to solve the problem with payments and the global market expansion. So we expand country by country, market by market, and solving the payment issue locally.
And Clink bring those lessons to a new problem that when we see agent becoming the new commercial actor, but we see the payment stack is not fully ready for that, so we won’t solve that problem.
Grace Shao (01:18)
So what do you mean by the payment stack is not ready for that?
what do you mean by the world’s not ready for agentic payments? And where do you see the gap from existing offerings? Because obviously, when we talked offline, I did challenge you on this. I said, you know, there’s a lot of major fintech players already and payment solutions, Stripe Airwallex PayPal,
right? But where does Clink come in and how does Clink solve this issue,
Patrick (01:44)
Yeah, I think payments are largely solved for human who is present at checkout. so the industry had made it much easier for developer to accept payments like except from card, from our wallet and from like so many payment that local customers prefer. and also the developer can easily launch a product to launch a subscription, send a payment link, right? Those those are like a real progress already made the word.
So but what is not solved on the payment side we see is agents acting under the delegated authority. Right. Processing the charges only one part of that transaction, but the system also needs to know who the agent represents and the what permission that it has and whether the purchase is within the policy, within the intent, like matches the original mandate that the user has given. And then the how the evidence being being persist, right? And if we come back in six months later.
is that transaction still like able to be verified at the point that the agent made the decision. So I think like global merchants still face another unsolved problems like a single provider isn’t no single provider that best at the every market and solve all that agent issue. And then we know worldwide that the agent can can buy product from anywhere. So we see that’s a big gap that the agent e commerce need to
have a both the authentication layer authorization layer and also a practical way to across with all the providers in the market today.
Grace Shao (03:07)
Yeah, actually on that, you did say, you know, payments are very fragmented, right? Like, and but is that fragmentation mostly based on geo geography, regulatory reasons? Is it about payment habits? Like how do we understand that? Why is it so fragmented and it’s not a one solution fits all?
Patrick (03:24)
Yeah, I think that’s a great question and a great observation, right? And to be honest, I think it’s all about like all about that because payment really reflects a local financial system, right? Like you spend twenty dollars in Singapore that you may use a card, you may use your wallet, your bank transfer. But if you know in Japan that many e-commerce orders actually being paid over konbini payment. Konbini is the Japanese word for convention store. If you would like to place an order online and
getting to a company store and the pay when they check out some some goodies. So it’s just a habits and also the regulation and also how the payments being developed in that particular marketplace. So we see that it’s like all different across the world. That that’s a lesson we learned from from building Tammu payment stack that we actually adopted 70 payment method when in the first year. So every country has their own top three payment method.
And every time you add a new pay method, you got some new customers. So it’s kind of very interesting. And then but then the fragmentation is not simply like I I guess you can solve that by building an API and building more like a convenient experience, but we see that as a reflection of local, right? Like habits and migration, all that just as we said. And the the things that we cannot force the users, right? Use got their own votes for what they want to pay.
And that will always exist because that’s how the world works here.
Grace Shao (04:48)
And so thus like where do you fit in then? Like do you have a certain geo gro geography you’re targeting or a certain kind of I guess use case that you’re targeting?
Patrick (04:58)
you mean the agent e commerce or like in general? I think Clink today we try to solve two problems. Yeah. Yeah, we’re trying to solve two problems. Yeah. So one one problem is for today, like the global monetization for the digital servers and AI builders, right? They they’re selling their product, their great application to worldwide, and then the users from worldwide that like to pay them with the local payment.
Grace Shao (05:02)
Just in general because Mm. Yeah, yeah. Sorry, go on.
Patrick (05:23)
So that’s the monetization issue we we try to solve. And also also we’re trying to solve a forward looking issue that when we see the trend that the the actor, the consumer is moving from human to agents, and how agents can leverage the user’s assets, users’ local pay methods, right, and to make that payment, make that purchase, and to make all the information flow and the funds flow fat like try to flow fluently as it is today.
Grace Shao (05:48)
I see, I see. Let’s double click on the business. Help me understand your business a bit more. So one thing that really stood out to me from our earlier conversations that Clink acts more like a connector. how do you describe your role in the whole like payment stat?
Patrick (06:01)
sure, yeah. So I would describe Clink as a connector, as you said, right? And a controller or an aux trader. So we do not want to replace the bank, the car network, a processor, all the commerce platforms, the merchants or other agents, right? So we translate the agent’s intent into a transaction, or it could be the human’s intent and delegate to the agent, right, into a transaction. And the merchant existing system can accept.
apply the rules that the user has set earlier and route the payment as the user preferred payment and also preserve the record for the future validation or whatever verification that comes from. So that will let merchant connect once rather than building a customer payment pass that for every agent, for every processor. So that’s the important part that we won’t be that connector anywhere alive.
And it carries contacts, permissions, all that and solving the issue to connect all different kind of agents, different kinda agent platforms, and also the different kind of mer merchants and all the PSPs and different kind of networks as well.
Grace Shao (07:06)
I see. So I’m I’m gonna challenge you again on this way. I brought it up already earlier, but why would clients then choose Clink instead of an established platform, given that it’s so important for trust, reliability, proven processes in in payment, right? Why would they choose you over another major platform?
Patrick (07:24)
Yeah, we we mentioned some names earlier like Stripe, Airwx, PayPal, right? And there’s so many of them. Actually, I think probably thousands of processors all over the world. So I think sometimes when when you just want to sell one product in a particular market, then a single PSP might satisfy what you want. but I think the trend here today is all the AI builders, we are all selling products. what do I?
So that means like if you do want to of course trip and water can cover majority of the market, but we see in emerging markets and all different places, like different processes have different advantages, right? And if you as a merchant want to maximize that, then you may have to connect with multiple PSPs. Also pricing is another concern that if you want to lower your cost on payment acceptance, then you may have to have multiple PSPs to lower your cost.
So Clink will be like helpful as a connector that we saw the problem cross bond trees that you have multiple markets by default and you have all different kind of local payment methods, especially for example, we have some solutions in India, in Latin America. So like we are beyond one processor, right? So that’s on the payment acceptance side. But also we see that the AI application today requires, well, not that complicated, but does require some sort of bidding.
Right, top ups and subscriptions. And if you want to build from scratch, that’s something extra. And if you stay with existing PSPs, that’s a solution. But the expansion may not be that flexible. So we see that clinks serve as a connector layer, but that’s for the human business, the co like the product side of monetization. But when agents come in, that the problem becomes bigger, right? Because the you you don’t know where the agent platforms from.
It could be Kodas, could be cloud, it could be Gemini or any vertical agent platform that you never heard of. It could be own, like Ermers or Open Cloud. Right. And on the other side, the merchant may, as we we just said, the merchant may have only a single processor, but they may have many. But for that particular issue, then if there are many, who’s doing the routing? And who is connecting the agent to a particular merchant and their processor?
Right, and then we see that a single processor may not be capable of solving all that problem at once.
Grace Shao (09:34)
I see, I see. so tell us like how you’re working with existing platforms too, because this is another interesting point when we talked offline, you were telling me you have actually a lot of partnerships with traditional payment platforms like such as Visa, whatnot. how do you fit into this stack here?
Patrick (09:50)
you mentioned Visa, right? So I wanna bring an interesting fact that Visa described Clink’s role as an agent enabler. So I think that captures very well. So like there’s so many agents out there today, right? The big names we just mentioned, and also lots of self deployed or vertical agent platforms, right? And then we see that all those agents have the need the the requirement or the demand to make payments.
to do transactional for the user that trusts them. So but however that agents may not be able to integrate with our like Visa system. Right? Visa requires you to have a PCI because agent may not be able to touch the credential information. And if for self-deployed then like we don’t know like what is being uploaded and all that, all the security reasons and privacy concerns are there. So as the agent enabler
We process all that credentials, all that sensitive information. So we connect with the visa for all the like pass key verification, the mandate creation, intent verification, all that. So we perform that on behalf on the agents. And also so that that means we turn on or we enable the existing merchants to be able to connect with visa’s network securely or in a certified way. So that’s how we play with it.
existing I mean like schemes like a visa building a network also similar for MASCAR. And you mentioned some other big commerce platforms like Shopify and so I think Shopify is highly complementary for to to to zero, right, for sure. And there it’s already giving the merchants a catalog, a card, a great system. And they’re like a great pro it provides great solution for the merchant to easily
set up your own site and start selling goods. And we’re not going to rebuild all that. It’s it’s just simply not feasible and we see that the ecosystem is super strong. And we want to be add on to the ecosystem. Right? We see that added to like being a plugin for example, then we will turn on Shopify’s merchant to be able to like agent ready. Right. We build that agent readiness into the merchants. doesn’t matter it’s Shopify or WooCommerce or Mikado or so so many like open source
building platforms and commerce systems. We see all of them are being like like have their own adoption in different marketplaces and then the role of Clink is not going to replace all like any of them. And we want to be like add on to them. We solve the problem that the merchants at this point may not be aging ready and we want to make them aging ready. So that comes back to the earlier question that the role of Clink as a connector
Grace Shao (12:23)
Really interesting because basically, actually, you’re not trying to take the payment companies pie, you’re not trying to take the merchants business either. Then who are you charging in between how are you making money? Are you taking a cut from say visa and the transaction fee, or are you taking are are you making the merchants pay you for your service?
Patrick (12:42)
in in general the goal will be having the merchant pay us. But we are seeing different like from our own business models, right? Different monetization paths that if the merchant in our network that we have the influence on the consumer agent side, that we have the potential of charging them the billing management. So the the billing is not just to human billing but also to agent billing. That agent billing includes like
help the agent to check out and process that information, process the aging side of credentials that to allow the merchant to have the order and we capture all the credentials and the intent of all that. So that’s a potential of charging the merchants on that. And also we see the card schemes having some solutions to treat the agent and the everybody on the path being a like
the the the search changing or GEO side of thing that you have the potential of charging a fee for for the influencing capability. So I think in general today we want to build the ecosystem and having more and more users, agents and merchants on board and the monetization path will be clear and across all the actors over the ecosystem.
Grace Shao (13:46)
I see. Okay. Well, there’s often a disconnect between the merchant brand people and how they see, you know, payment versus what the infrastructure is behind the payment. ex explain to us like why there’s that mis disconnect, I guess.
Patrick (13:59)
Well, the the customer sees the merchant becomes the merchant they own the product, right? The price, a promise, and for e commerce is a fulfillment as well. And underneath that the connect the process actually connects the payment and the network and the process the transaction. So and there’s the issuer that decides whether to approve it, to decline it, and there might be fraud, credentials tax and settlement, all that. So those roles matter and when something fails.
So who made the promise, who improved the payment, right? Who processed the pre the payment, who protects the credentials, right? Who should handle the fraud, who’s responsible for that, who owns the liability. So I think the agent e commerce adds another actor and the chain becomes even more important. I think the the biggest difference in agent e commerce is when we look at from physical store to e commerce, right? like the ultimate decision makers do human.
But with Gene Commerce, with the autonomy that the agent is performing, does a potential the decision making is from the agent. Then is that decision making legitimate? So those questions are still remains and not being solved clearly. So all that we see that the clinic’s role is to try to carry that authority and the transaction context and all that across the trend. So again, come back to the definition of connector.
So we connect all the parties and try to pass through the information, the authority, across the chain, across the rails.
Grace Shao (15:23)
I see. We can double click on the safety and liability side of things later. I do find that quite fascinating. But it one thing you just brought up that was quite fascinating is that it agents will have autonomy. However, even though they have autonomy, the money, the feat of money is not theirs, right? So how do we understand that relationship if your agent is out there spending your money? do you always have to give them consent like before they go out? Or
Potentially what we’re gonna see in the future is agents literally just like going out there buying things without you even knowing they’re purchasing things.
Patrick (15:55)
Well that that’s a good question, all right. And I think the step by step would would just describe that agent in the future that like shopping fully autonomously it’s possible, right? But I I just think it’s not there because the trust is not there yet. So all the we we see all the demand is being delegated. So that means you as a human that you have some requirement, you have some demand and you want to make some purchases that essentially the demand is from human.
And the agents help you discover, help you to make some decisions, but maybe not for for the final decision. Right. So we see that as like involving pass. So but but as you just said, after all, the agents spend your own money, right? Spend your asset, see, spend your fiat. So we see that for today, in some case by case, like just one-time purchases.
That the user or the human will set up intent, give enough contact to the to the agent and the sign off on that. The sign off could be like a passkey, like we with a signature. Or it could be like A P two kind of a protocols, right? So there’s many actors in the industry try to solve that issue. But after that I see
Like the agent later on will do the the sourcing and do the patriotization as long as the falling fall under that mandate or instruction that was given, then we approve it. But essentially if we come back, see like where the orange and the context from, that’s from the user. So that’s for like a one-time instruction. But for sometimes if the merchant is trusted, like a digital service you always buy or digital you always top up frequently, then you can set up a recurring mandate.
Saying, okay, for this particular product or this particular merchant. as long as like over two hundred dollars a week or like every top up is a five dollar, that that’s fine. Then the agent does not have to receive or like get your approval every single time. But rather it’s just like a one time setup in the very early. But the setup or the mandate or instruction had to be super clear saying, okay, this is a trusted merchant. And we like it’s it’s fine for the agent to keep top up on that.
Grace Shao (17:53)
It’s like auto renewal but the without like resubscription.
Patrick (17:57)
Yeah. Well
I think the this is a little bit different, right? People are saying like if from merchant side if you give the merchant’s car and then merchant tell the merchant to do auto reload, yeah that that sometimes will achieve the same outcome. But I think the role is different. So on that side is merchant W and on this way it’s like the agent top up, like the actor is different. Right. And I think in general it’s like you trust the agent more or a tr tr or trust the merchant more.
Grace Shao (18:19)
Mm-hmm.
Patrick (18:24)
Right, that we see a lot of issues like disputes or chargebacks from the merchants actually deduct money, debit money from the user without that without the the the approval. And then with the agent being the actor that and also clean in the middle, then like we we actually protect the user. Right when the when the agent yeah, got
Grace Shao (18:28)
Mm mm.
I see what you mean. The ball
the ball’s back in the buyer’s court, kind of like the actual intent comes from that. It’s very interesting while we’re talking. I was thinking about a conversation I had with Ali Baba and Tencent recently. Both of them obviously are exploring agentic commerce. And then for them, you know, the biggest hurdle is how to manage this liability issue with who approves the payment. And obviously they both have payment systems embedded. So on the Baba side with Quinn.
Patrick (18:46)
Exactly. Right.
Grace Shao (19:09)
It’s really interesting. They’re saying that even though they’re doing agentic commerce, actually every time the money is gonna go out of your account, they’re still gonna push out a human verification kind of notification. You still have to like manually take that responsibility and press that button. On the Tencent side, I think they’re exploring the idea of creating a separate ReChat payment account, like kind of like two separate accountings, like two separate accounts.
And then one account will just potentially have very limited amount of money for the agent to play with. So you kind of just gave the agent like you know access to that one account, but not your full account. I don’t know. It’s just very interesting. I I’m kind of going on a rant, but it is funny because, like, on the other hand, it could potentially become like a scenario where essentially you give a credit card to your like unhinged teenage daughter and they would just go rogue and buy anything. yes. but let’s talk about agentic commerce.
Patrick (20:07)
No, no, I was just saying that which just is right, that just like giving a teenager your your child a card, right? And then you in in you don’t know what they’re gonna s spend and but after all you know them, right? But for Asian, I think the issue is like they may not have a trust yet, like fully trusted. So that’s what you mentioned, the Tencent has a weChat card and also the the Q and they they want the user to prove that every single time. And we see that that’s because the
the the different level of or different like I mean the path of payment being developed in in the country is different. So China has moved to the e-wallet and has very few dispute and chargebacks. So that means when comes to this particular situation, I think it’s just we we skip the credit card time. Like we skip the credit card UA and then move into the bank based or U wallet based directly. But if you look at the the
Grace Shao (20:49)
Why is that? What what
Sorry, my question is more like, why are there less dispute because it’s a U wallet? Wouldn’t like cat merchants still charge you if they want to charge you? Like if you’d like subscribe to certain things?
Patrick (21:02)
the the
because yeah.
yeah, so there’s two side of like if the the the payment is one side from the buyer to the seller, right, and then that’s real time transfer. And then we all know that today you have to scan your face in China, right? Or use a fingerprint to authorize the payment. And in those cases like the the dispute or charge back it become nearly impossible or or not necessary. And owning with a card card yeah, yeah, it’s so hard.
Grace Shao (21:23)
I see.
Like fraud is harder.
Patrick (21:37)
And then for for the auto debit thing then indeed and Alipay and WeChat has a compliance check and they revoke a lot of merchants auto debit. And now if you go want to receive the auto debit, it has to be super critical and then describe your business and the models clearly to the payment team and they will approve that. And it’s merchant by merchant of approval today. Unlike previously, you may either two turn that on. So but come back like when we
we see that issue particularly in card system, right? And then because like we we all know the disputes and chargebacks are all fraud, right? And like it’s just like so popular. Well not popular, it’s a common to see, right, in in the United States and in Europe as well. And that’s that’s why Visa has the entire chargeback management system to protect the the rights of the consumers. Right. so when they come to a genetic word that that create the the convenience that
because it is card based or the credential based that the user may not necessarily to approve every single transaction. So that brings convenience. But again that brings the risk, right? But however, thanks to the the chargeback system that has been developed over decades, the the risk can be like manipulated or to be like managed properly. so that’s why we we need the mandate system, we need the instruction to
to validate afterwards where when something really is bad then we come back to see if whether the transaction was being authorized properly by the human. And if it is, then well, probably the chargeback or dispute will not go through. Right. And so that mech mechanism does not e exist in Chinese payment industry today. So that that’s why that’s what I’m saying thinking is like may maybe the reason that they have to use a small amount of like a sub account, subcar.
Or a ask you as a human to approve every single time. And but honestly I think that actually yeah. So I actually Yeah.
Grace Shao (23:26)
I see, okay. That’s so nuance. Yeah, yeah, interesting.
okay, so but let’s talk about agentic commerce. So I think so many people have confusions around what really agentic commerce means. it’s you know, is it just that like basically like you said, you go out there, your agents are out there like going rogue and buying you things, or agentic commerce more understood? Like there is a process right now being built out. Like, help us understand what gender commerce means. What is the use case for agentic commerce right now, and are we actually seeing
it being proliferated in the real like real life right now.
Patrick (23:59)
yeah, yeah. So I think agent cameras is a very big word, right? And then it has many aspects and the dedicated execution. like I think that’s the most important happening right now ‘cause the the as I said earlier, the the the demand is still from the human and then the the product could be recommended by the chatbot or the agent. But after all that the the final decision may still come in from
the the human at most of time at today, right? But moving forward it could be the agent, with your authorization or understand you better, like has more context of you from the lifetime conversations, right? And then make a decision for you as long as the decision actually sits fitting the policy that you have set for the agent, right? So that’s another sort of like a next level of agent e commerce. so I think today
We see more and more like agent being an actor, but the actor could be on the sourcing side, it could be on the queuing side. And it really depends on how you as a user trust the agent. So in general I think agent commerce is very big but in general, like e commerce is a long chain, like doing all the actions from from the very beginning where the intent started and where to to the final the others play placed or the fulfilled. Right. So
in that long pass, any part that agent take kicks in, right, I think it’s agent commerce. But most of the time right now the agent is in the consumer side to provide help sourcing because the efficiency that they search products, right? And they may discover some very rare product that you may never be able to find. Right. There was a joke back at the time that probably as a human you read only first page of the Google search results, right? Super clear.
Right. The best place to hide their body is a second page of Google Search Results. But agent can easily search through twenty pages and get all different products or they they just have more context of you. They know your taste, for example, moving forward. And then they may be able to find like from a very real merchant, right? You you never know. Right. So that that’s a great potential that we see agent can unlock. And I think today that’s
Some of the aha moments I’ve talked to many people on Gene Commerce is the product search or recommendation site that the the the agent gave them some such suggestion or recommendation they never thought of as themselves.
Grace Shao (26:14)
That’s interesting. But I also think wouldn’t it make more sense for enterprise use case given the nature of like you said, sometimes it’s repeat purchases, it’s not always evolving. would would would that make more sense? Like if you’re a construction company, you’re buying like cement every year or whatever for this project, you know exactly the quality, the the the price you want to pay. your lumber company, whatever. You know what I mean? Like, wouldn’t that be much easier for agentic commerce to be in
Patrick (26:40)
Yeah.
Grace Shao (26:42)
integrated, implemented.
Patrick (26:43)
I think that’s a great point. but I I I do want to call that those cases are easier because they’re repeat repeatable, right? They’re bonded and you need to verify. it doesn’t matter the the buyer is enterprise or a pioneer of consumers, right? So it’s rather the behavior or the product itself define that the that use cases is more trustworthy or like you are more comfortable because to r like rebuy laundry stuff for example, right? And then
Grace Shao (27:00)
I see, yeah.
Patrick (27:09)
You don’t like that you always use a single brand and you you probably most of the time don’t want to try something new then then just a repeatable ask agent to to keep doing that. I think that makes it total total sense, right? To have the agent do that delegation, right? And like to to help you execute. And but like if it’s enterprise or like the pioneers of consumers, I don’t I’m not sure. It’s always like a some group, a small group of people.
They’re they’re interested in trying everything new, right? I like all the new stuff they they on board. I think those people are really push the word forward and help us to do the early adoptions and explore all the issues and helping improve the systems. It could be some pioneer of the enterprise, we don’t know, right? Small teams like startup teams, they are always willing to try stuff new. It’s all possible, but we don’t we don’t limit there. But I think you’re right that we use the
a small amount, repeatable and you need to verify purchases, to build the trust between the consumer and the agent. But the consumer could be human, it could be enterprise.
Grace Shao (28:11)
walk us through that case study ‘cause you were telling about it beforehand called Hello Minds. That was quite fascinating.
Patrick (28:16)
yeah, so it was a it was a short story. Actually, it was quick. when we were at Supreme in Singapore, they came to our booth saying, okay, they they see a clear need from their customer, they want to do transactional stuff. But as an agent platform they are not able to do that because they talk to Visa and then it’s just simply difficult for for them to to receive a PCI and all that in a short time. Right. So
then visa recommended us because we as aging enabler we’re designed to to turn on the capability of the aging platforms to do transactional. So and then we we just work with them super easily. There they have our skill pre-installed for their platform and then the the user be able to tell the HelloMind I want to buy this and that and then the system will actually get the user into our aging portal.
and add the card and we will go through the entire visa process for the verification, for device registration, and then it just happened automatically. So I think that’s a great example seeing like out of like all those vertical agents, they see the clear customer requirement, but for the role that they are today, they see a longer path to achieve that by themselves. And they see the they are seeking for for the help and for the experience partnership as Clink.
from the ecosystem to help them. So and then I think it it’s super clear for for Clink as well. We like to help them, right? And help other agents because our goal is being a connector. We’re now building our own consumer side agent. Of course we can do it, but we see it’s just like a payment method, right? Different people, different location, different markets have different preferences. Hello Minds is a great Asian platform over Hong Kong and and APAC area. So that’s kind of
like a possibly a capability that Clink provides actually help all their agent platforms to build their own and to satisfy or serve their customer better.
Grace Shao (30:12)
I see. Okay. Yeah. Cause you did say you’re agent agnostic. So that makes sense what we’re saying here now. but if the feature is, you know, many different agents acting on behalf of users, what does the payment layer need to do especially well then? Is is it in the identity, authorization, routing, settlement, security? Like how how I guess for you guys in the middle layer, what is the core, core offering that you have for everyone?
Patrick (30:36)
yeah, I think you’re right that like a one model may not fit all, right? Like a one base model or one agent may not fit all and the one payment solution may not fit all, right? Payment methods and the acquires, carnet was so many things. So all you mentioned, like identity, association, routing settlement, security, all of them actually matters. Right. So for us like we
We’re not going to like just build a single rear or like being a connector, we want to optimize every single layer, right? To be a connector that brought like try to bring every parties all together and closer and then try to solve the friction along the path for the agent to perform transactions. So in general that’s Klink’s goal at this point. We want to create a smooth word for the agent.
from talking to users and to the placing order on the merchant side and also have moving help moving the the fiat funds from from consumer to the merchant as it is today to have ‘cause we we see that as most smooth because after decades of development, the merchant solution, their commerce systems, their payment stack are being mature.
And there’s no reason to rebuild all of that just for the agent, right? And the return on that investment could be super low. So that’s why we see that as an evolving pass but rather a revolution pass.
Grace Shao (31:59)
look, I think let’s talk about the the most sensitive bit now. I want to talk about the trust and safety bit. So, you know, you do emphasize, you know, you’re building reliability, you’re building safety into the product and everything. But as the consumer, it still seems like really, really unsafe or scary to try out a new payment system. You know, as the average consumer, we will still default to big names like Visa and MasterCard, even if we know we’re paying them much more, right?
so help us understand that. Like what what is what is a trust barrier here and how do you build that up? and I guess who bears that liability? So hypothetically, I’m using Clink to buy something on whatever, let’s say Amazon. Okay. If something goes wrong, am I supposed to go to Amazon? I’m or am I supposed to go to you? Or is Amazon Amazon gonna come chase you down? What what is the relationship here?
Patrick (32:51)
Yeah, yeah, good question. I think so we are being agent enabled there or the connector here. So that means we are not the merchant of the record. So you still own your order on Amazon, right? So that’s our relationship with the merchant as well. We are not going to replace a merchant, repla take over their customer relationship. That’s not what we’re going to do. So what we’re doing is here, like it’s being a connector. That means we pass along the information.
like the payment credentials and all that, then we also capture your intent instruction that the the user talk to the agent and then send that over to Visa. So after all you’re still seeing your Visa card paid Amazon. I’m just using Apple as example. You after all you still see that you have an order with Amazon. And then you still pay with your regular car, right? Just the the in the middle the two actors, one is the agent. So agent performs the excursion.
as you authorized. And the clin perform as another set of actors and take over the agent’s direct execution, but help you and your agent to place the order on Amazon because we have been certified by Visa. And we have certified by the payment industry because we are PCI compliant. PCI but by the way, PCI means that we can securely manage all the payment credentials, right? Being authorized by the certified agents that we can
persist and the process user’s kind numbers, right? And given that we have capability to pass that over to Amazon. But after all, still you place an order. We’re just solving the problem that agent may not be able to directly touch the credentials or you don’t trust the agent too, right? And I mean at this point. So that’s why the we as a connector in the middle, we persist your critical information and then pass along to the merchants. And also we
Help you validate the policy that you set for the agents and the the initial instruction. For example, you authorize the agent to buy a shoe, right, and under $100. And somehow the agent got mad and bought you a t-shirt for $200. Then clearly it does not match the initial instruction. And we will block you before we send that over to the particular merchant that the agent wanted. Right. So that provides additional layer of protection. And also because like all that is being also certified by the visa.
Like because we are the first agent enabler and the agent side of partnership with Visa in APAC in in the Visa intelligent commerce program. So all that is being like even though it’s still pilot, we see the potential that we can add additional layer of protection and also additional layer of privacy.
Grace Shao (35:23)
Really interesting because I actually have two questions. That means number one, I mean, this is not unique to you guys, like, but you essentially have a database everyone’s credit card. There’s definitely a risk there, right? And then the second part of the question is that, like you said, when you don’t trust your agents to touch your credit cards now. So, say one day the education has been done, the market has been educated, agents have been normalized. Five years down the line, everyone is using agents to do shopping.
Does that just completely skip over Clink then if we’re all just gonna give our agents our credit card numbers, if we even still have credit card numbers, whatever payment look like back in the future? Would that just actually frankly o omit the the the role of Clink then?
Patrick (36:03)
that that’s a good great question, but I don’t think that will happen because for like even though the agent development all that then the the the concern to like you still need additional layers of protection because it’s like we are the neutral layer and give you the additional protection, right? Even though you trust the agent much but you still want the third party. It’s just like at the very beginning
of the e-commerce in China, you have Taobao, you have the consumer, like you have the buyer and the seller. But you still need to about just LP in the middle, right? To prov provide you that layer of security and you you want another layer. Yeah, so a a neutral standpoint to to to also right to to monitor, to audit the agent behavior. And also from the car scheme or the merchant side, they want some additional layer of protection as well. Right. No one can simply trust a single agent can perform all of that.
Grace Shao (36:38)
See what I mean?
Patrick (36:54)
Right. It’s just like additional yeah.
Grace Shao (36:55)
I see what mean. So there’s guardrails
built into you as like the guardian almost of all this.
Patrick (37:01)
Yeah. Yeah, exactly. the an an a layer of protection and I think Clink by the time it will build the trust across the consumer side and also build the trust across the merchant side. Because we see that the potential of having more a merchants being aging readiness and into the aging ready merchant network will bring them the advantage across the five years development that you just mentioned.
Grace Shao (37:24)
So I have a question. I I have two three questions to wrap this up. first one is what is your vision of the future of agentic commerce then? Like where do you see this as going? You can start with this, and then I want to throw you another one first just for you to think about in the back of your mind, What is the smartest question you’ve heard someone else ask you about the agentic economy that I have not not asked? So
Patrick (37:44)
Sure.
I think yeah, I again I guess we can get started with agent commerce, how that will go and aging payment. I see that has a long way to go. That as you said, like you have a child and like if you have a child that they grow up, they become adults, they become mature. And actually at this point we see that agents are growing fast, but are they close to the AGI?
Right, are they close to a particular point that that they’re being smart enough enough to do all that? So I’m not sure about that. And on the scare side, if they are really that smart and as another adult, do you really trust them to manage all your assets? Right. So those are like I I see those questions are tough to understand, are tough to answer, but at this point we see that agents are being super good or super useful as a tool, right, as a helper.
to help you do the excursion to do all the repeatable work or like all that you want to skip. And the sum of part of the commerce is being part of that. But I also see that shopping is a great journey, right? I think you as a lady understand what I’m saying. So I I mean as I mean I I typically don’t do enjoy that process, but rather just get what I want, right? But my wife and they do enjoy like the journey of exploring products. I
I think those are entertainments, not just commerce or shopping. So those will never be replaced by agent. But it could be like you I I know there’s some services in in China I’ve heard of that. like you you got a guy or like a girl that accompany you or walk through the the big malls and do shopping all together, like a company, right? It could be like in the future those accompanying role replaced by agent. It could be. But still provides a a great journey.
help you sourcing and find a great product that fits you. But I think that also would be a very pleasant experience. But I I think in general like for now we see that it’s a delegation for e-commerce for most of that valuable part. it’s not a new demand. But we also see for the digital service side and also for the like a
Mostly on digital and the model requirements or all the model f model side of features, those are new requirements. But it it it replacing a lot of the previous like data sourcing work or like investing work investigation work that you’ve done as a worker. But this may be replaced by agents as well, and along that path will generate a different requirement or different demand for genetic payment. So those are new, I think.
And in general along the past, hopefully we see that agents and humans in a word that can both like pay and get paid. Right. So that like today the the we’re we’re talking about AI applications, but in the future the the the product may be served by agent. But you never know. So it’s like a human to human, human to agent, agent to human. We’ve seen an interesting case that Waymo plants order online.
get a human to close the door. Right. If s a passenger got off the the the ride and forgot to close the door, and then you you place an order and get a human to do that. Right. So it’s like aging to human, human to aging. We never know. It’s that’s what you said, agen agent economy, right? Just not not commerce. So I I think like that word will eventually come and along the way that we have the base models to develop being smarter and also a lot of
Issues like around identity, around trust in general to solve. And then that’ll that’ll be the case. And then to a second question, like the smarter question, I think is like really come to that when when intelligence or labor or execution becomes abundant, right? what remains valuable? Right, or die, right? And do we still need transactions?
Because whatever you want, like the goodies or the services is being so abandoned and probably at no cost at all. So where is the transaction? Where is the value? Right? I think then that I think that’s a smarter question or tough question could always come to me saying, like really like if we as a society or the entire world develop who develop to that level of then what what’s what what are you p people paying for? We never know.
Right. And maybe that that’s a time that come back to okay, the the taste and the the people that creating the value that I I don’t know, I just don’t know. I think that’s a very broad question, open question. And we may not have an answer when really that comes. Yeah. What becomes valuable and what are people paying for?
Grace Shao (42:07)
First, I wanted to say it’s really scary to think that we potentially have machine overlords telling us what to do and working for them. second of all, I think the second half is something that’s really interesting. It’s it’s not just in the whole abundance of a gentic era. It’s just like you’re seeing it play out already. Like even people are saying, you know, luxury is a structural short because why who is still buying luxury? When you have more money, are we really spending money on putting
Patrick (42:15)
Well, that’s a bit scary.
Grace Shao (42:35)
you know, brands and logos on our bodies anymore or is society moving towards like paying for experiences, praying for health, paying for, you know, things that actually cannot be so easily replicated because, you know, logos can be replicated easily these days. It it’s kinda interesting. So it it will be interesting once intelligence is abundant, what will happen. But then I do think that question is also overgeneralizing humanity because we do forget that, you know, not everyone
Is working in a career that actually requires intelligence. I’m not saying people are not intelligent. I’m just saying intelligence as a currency to for, you know, salary or, you know, whatever or or capital gain is actually not for everyone. And if anything, then do we say the value is more in craftsmanship, laborist work, you know, you know.
Patrick (43:08)
Yeah.
Grace Shao (43:28)
experience and different things. It’s just very interesting, but for sure it’s already moving away from objects, like tangible objects, like where people really want to pay, right?
Patrick (43:36)
Yeah. Right. And you th you mentioned labor go go ahead, no, that’s a sign. No.
No, I was just saying like a labor as well. Like the the labor may even the work may not require intelligence, but that work and like the labor side of work may still be replaced by robotics. And with a small robotic, that that’s even more scary, right? Like as a move yeah. Yeah, yeah, yeah. So but but hopefully we we humans still aren’t in in control when that comes.
Grace Shao (43:54)
I s I see where you stand in all of this, Patrick. I see where you stand.
I don’t know, Patrick, the way you’ve pro you painted the futures, we have machine overlords telling us to close the door. Why are we even leaving the house then? okay, jokes aside, I have my last question for you, which is the question I ask every single guest that comes on. What is one differentiative you you hold, something that you think is maybe against consensus or people still you think get wrong or underestimate?
Patrick (44:12)
Yeah.
I I think the two two sides I want to answer that. One is that I think we’re we’re spending too much attention or like on the itching payment side require a new brand new rail like broad chain a crypto rail. So I’m not saying that’s wrong and we see the potential of the stablecoin or the capability of blockchain that for cross border settlement, machine too much micropayments, all that, but like a a reel that
Like the the real exist because we move value, right? And most of the time today the buyers and the merchant that they use VAT and we see we may not spend enough attention to solving that side of the rails issue. Right. So but I I see that on the long run, of course, the blockchain aspect stable coin will have a great value and being super useful. But at this point if we want the agent to be capable of handling payment.
instruction doing all that payments and we probably want to solve more issues around the the fiat rail and to to make that smooth. So that’s one thing. A second thing is I I think like even you’re not a payment in the payment industry you may hear of a lot of protocol names, right? A P two, UCP, ACP, SPT, and it’s just so many of them, right? And I’m not saying like
like every single protocol as the has their values and standards and so try to solve a particular issue or a like a vertical scenario, right? Or some of them being try trying to be generic and some of them well like we’ll want to solve the issue of product discovery and all that. And but I feel just too many of them. Like just way too many of them. And that creates a barrier or creates a a like confusion for the merchant.
And for other actors in the ecosystems, like which one should I adopt? Which one should I integrate? And the more and more are coming out, right? Every single player has their own standard. And as a merchant, I may feel like so confused, like which one I should adopt. I think really that the entire ecosystem will want to align or I mean to get some agreement.
Well, I mean not necessarily a single one or two protocols, but to have some fundamental rules in place that people understand okay, that’s how we want move and that’s the direction we go and people start investing on that. And eventually some generic or universal protocol that’s been agreed by everybody, like by the majority of the actor in the market, they’ll come out. And that’s a time where I see that
agents or the merchants will be more comfortable. Otherwise it’ll be too many of them for for the ecosystem to advance to next step to to next stage that agents and merchants are like feel so like worry free to onboard. Otherwise they feel like okay if I bought I onboard A C P last year, right? And they’re really like like not all agents are supporting them. Right. And then if we are on board U C P this year, we see other agents not support them. So like those kind of issues are being
like a making just like a too too fragmented. And as a generic like a pass forward, I I I really want that we we somehow get aligned in one particular vision, the entire ecosystem and try to push that new outcome.
Grace Shao (47:38)
There needs to be some standardization consistency to help agentic commerce go forward. All right, Patrick. Thank you so much for your time today. Really appreciate all your insights. I’ve learned a ton. if anyone’s interested, feel free to reach out to Patrick or I’ll put the link in the podcast show notes as well as on Substack notes. thanks again, Patrick. Speak soon again.
Patrick (47:56)
thank you. Thank you, Grace, for having me here.
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When James Peng founded Pony.ai in 2016, many in Silicon Valley believed autonomous driving was only three to five years away. But he expected it would take at least a decade, because the challenge was never just teaching a car to drive. Commercialization also required regulatory approval, public trust, reliable fleet operations, and a cost structure that could support large-scale deployment. Ten years later, his vision is becoming reality.
In this conversation, we start with his founding journey, the milestones and how Pony.ai became a leader in the autonomous driving space. We also discuss the gap between assisted driving, Level 4 autonomy, and the longer-term goal of Level 5, as well as how Pony.ai uses simulation, real-world driving data, and increasingly capable AI models to improve safety. James explains that the hardest problems are often not the obvious ones but interpreting unpredictable human behavior and handling rare edge cases consistently.
The conversation also explores China’s cost advantage in robotaxis. A mature automotive and electronics supply chain, close collaboration with automakers, and faster iteration can materially lower vehicle and system costs. But moving into new markets still requires Pony.ai to adapt to different road conditions, regulations and driving cultures, from trams and roundabouts to local pickup behavior.
James’s broader point is that the industry has focused too heavily on the initial technological breakthrough. Getting a car to drive itself is only the beginning. The next phase is about deployment density, utilization, maintenance, charging, remote support, and economics. At the end of the conversation, I asked what he believes is underrated. James, an experienced operator, replied - scaling. Pony.ai may have crossed the zero-to-one threshold, but the harder task is scaling from one to ten, and eventually from ten to one hundred. Check out this insightful conversation.
For more interesting conversations with people who are charting the way of the future of AI, check out the podcast tab or follow us on Spotify!
Chapters
02:36 Why James Peng founded Pony.ai06:36 The milestone that proved robotaxis could work09:27 How passengers learned to trust driverless cars11:41 Level 2, Level 4 and Level 5 autonomy18:59 How AI and simulation improve self-driving24:13 Teaching cars to understand human behavior29:11 China’s cost advantage and global competition35:03 Expanding robotaxis into international markets41:13 Why Pony.ai is also building autonomous trucks48:40 Adapting to new cities, roads and driving cultures53:45 Why scaling is often harder than reaching zero to one
Transcript
Grace Shao: Hi everyone, welcome back to another episode of AI Proem Differentiated Understanding. This is your host, Grace Shao. Look where I am, the back seat of a car. Doesn’t look that exciting, does it? Let me flip this around. Look at that. There is no driver. I’m in the back seat of a Pony.ai robotaxi. Today joining me is James Peng, co-founder and CEO of the leading robotaxi company. It’s expanded its footprint across the globe, in Asia, in Europe, in the Middle East. But obviously, today we’re in its leading home market, China and Shenzhen, where it has a fleet of a couple hundred vehicles deployed on the streets already. Hi James, thank you so much for sitting down with me. I’m really excited to be having this conversation with you. So you left Baidu in 2016 to found Pony.ai when many in Silicon Valley were saying self-driving cars are only three years away. But obviously that wasn’t the case.
Grace Shao: So, what did you believe then that this consensus was getting wrong? Tell us about your journey from 2016 until now.
James Peng: Yeah, sure. We were founded in 2016, about 10 years ago. But even at that time, I didn’t believe that autonomous driving can be solved in three to five years. Just from a technical point of view, because even back then, 10 years ago, even a demo for autonomous driving was already very hard. Later on, there’s complexity involved in the autonomous driving industry that involves regulation, user acceptance, the readiness of the ecosystem. So because of the sheer complexity, even then, my prediction was it’s going to take at least a decade for this to be a real application. It turned out to be that my prediction was about right. Now, 10 years down the road, we actually have fully driverless commercial applications in many cities. Of course, it’s just the beginning of the long journey for autonomous driving. But at least now we have real commercial applications.
James Peng: So I think people, like any new industry, people were super optimistic for the short term, but they were underestimating the potential for the long term. So I think autonomous driving is definitely one of those industries.
Grace Shao: What really drove you to want to actually work on this, work on this technology and the future mobility?
James Peng: I think the motivation was twofold. One is that the potential, both commercially and also societal benefits for the autonomous driving is so huge. Think about like everyone needs to have some sort of mobility. Autonomous driving is much safer than a human driver. So it has huge societal benefit of saving people’s lives. So essentially, it’s just such a great industry to work on. Although back then, 10 years ago, it was very unclear when this can be done. The other reason is, of course, because the sheer technical challenge of autonomous driving involves because I was actually in my previous jobs. I worked on different areas, software, hardware, large scale distributing systems, AI and whatnot. But none of the things I worked on is as complex as autonomous driving, which is a field that involves hardware, software, hardware and software integration and Many other things. There’s AI, there’s real time system, there’s also large scale AI training and all that.
James Peng: So just from a sheer technical point of view, it’s such an amazing and challenging thing to work on. So I think those two reasons propelled me to start the company.
Grace Shao: There’s definitely a lot to unpack there. I think later on we can definitely double click on the hardware, software integration, as well as the safety concern there. You say that autonomous driving is much safer than humans. For sure, it’s safer than me driving. I know that. But some may argue otherwise. So let’s talk about that later. But first, I want to ask you about something that was quite interesting. During 2020-23, there was a bit of a public reckoning, I think, within the industry. A lot of peers folded during that time. People decided to pull out of this sector. Some people worried that autonomous driving would really become a reality. But you guys charged ahead and you really believed in your vision. Tell us about that period and how maybe that changed your vision or your growth mentality.
James Peng: I think 2020-23 was a period of time where the autonomous driving industry has evolved for roughly 10 years. I think that was the time of reckoning. That’s the time where the haves and have-nots have really diverged. So I think that’s actually exactly the time. As a company, we have seen tremendous progress. At the end of 2022, beginning of 2023, that was the time we actually finally had the first fully driverless commercial applications operations on the road. So because we made such progress, both from a technical and also from a regulatory point of view, that, of course, we made the progress. We finally see the glimpse of hope. Then, of course, we charge ahead. I think a lot of the other companies who weren’t able to, either from a technical point of view, or from a pure capital-raising point of view, or from a regulatory approval point of view, that weren’t able to have fully driverless applications. Then they were faded away.
James Peng: So it’s sort of like, well, everyone is in school. There’s no big difference. But after graduation, then there’s haves and have-nots. So I think that was the time of division.
Grace Shao: Yeah. So speaking of milestones, I want to kind of go back into history a little bit. So in 2021, Pony.ai had the third highest number of miles driven behind Waymo, Cruise. In 2022, Pony.ai became the first autonomous driving company to get a taxi license in China. In 2023, Pony.ai was licensed to operate robotaxis in Guangzhou, etc. And expansion continued. So kind of following what you just said, there was good momentum behind you guys. Now, today marks Pony.ai’s 10th year officially. You kind of talked about how you guys have grown. But what was one or two of the biggest milestones that you’re really proud of looking back now and that you think have really set the tone for your company Now as you are really expanding globally?
James Peng: Yeah, I think in my view, the biggest milestone, actually, I have already mentioned, is the end of 2022, beginning of 2023, where we were granted the fully driverless commercial license in both Beijing and Guangzhou. We start to have the operation to the general public. Actually, it was in mid-January in 2023 that I was the first road in our commercial robotaxis operations in Beijing. Surprisingly, it was exactly on that day, it was snowing in Beijing, and I was in the vehicle by myself. That was the moment where I actually saw our vehicles were able to drive by itself. Anyone besides me in the vehicle. Because of the snowing, it was also a very challenging scenario. We were actually not being suspended for operation. We continued to operate, and I was in there. That was the moment. Finally, it felt like a dream come true, right? Finally, it’s not just because our technology is ready.
James Peng: Also, because we actually got the approval from the government to have the license to operate. So it’s like all the seven plus years of efforts finally pays off. To me, that was felt like, as Lyndon Johnson said, the small steps for a person, but a giant leap for the human race. Although I wouldn’t call it as big as the Apollo, but to me, it felt like it’s finally from zero to one. So I think that was a deciding moment or defining moment for Pony.ai.
Grace Shao: That’s a personal Apollo moment. I love how you visualize it because I could just imagine how chaotic the roads were in Beijing. Also to be quite romantic when Beijing is snowing because it’s such a beautiful city. Okay, but let’s talk about what is a robotaxi and how the public actually even felt about it when it first rolled out. Before we started recording, Ivy was even telling me, I was like, hey, look, I get a bit scared when I see Waymos on the roads or Pony.ai vehicles when there’s no one Driving behind the wheel. Now, that’s because I’m not used to it. You said, oh, yeah, it’s okay. People get used to it eventually, right? But let’s look back at 2022 when it first was deployed to the public. What’s the public’s reaction?
James Peng: I think because it was a gradual process in the operational domains, in the operational zone that we had. We used to have a safety operator behind the wheel, although the driver actually didn’t touch the wheel or push the pedal. But people gradually get used to it. Actually, at the very beginning, when we were just deployed in Guangzhou, in those days, if you look at the picture of our first and second generation of Autonomous driving vehicles, you still see those spinning LIDARs on the top, and they were very much visible. People were curious. But gradually, people are just getting, this is like business as usual. As a rider’s point of view, the experience of a robot taxi is exactly like a typical taxi. The only difference is there’s no driver inside the vehicle, right? So the way you get the vehicle, the way you get in and get out is exactly the same.
James Peng: Also the other traffic participants, like the pedestrians and cyclists, they get used to it. So I think it just takes time. It’s just like the first cell phone comes out, the first real smartphone comes out. People were very curious. Now it’s just, nobody cares about it. So I think it just takes time.
Grace Shao: It normalizes eventually, right? Absolutely. I think we’ve really had a few years of consumer education done by quite a few of the players, including yourselves. Okay, well, let’s talk about the technical side of things. For outsiders, people might not understand the nuances between L2 and L4. Increasingly, we’re getting closer to L5 supposedly, are we? So help us understand your thinking on there. How do you structure your own teams, your products, working on different technology? Who gets held accountable for the actions in an L2 vehicle versus an L4 vehicle? Then finally, are we getting a glimpse into the future of L5? Are we going to be able to complete the road anywhere we want with autonomous vehicles? It’s a big, broad question, but I’ll throw it to you.
James Peng: Yeah, sure. So the definition of the level of automation for vehicles was actually defined about 20 years ago. So, of course, the industry evolved quite a bit. I don’t think that the levels from L0 to L5 might be the right way of defining what the level of automation is. So in my opinion, actually, there are two different products. One is what we call the driver assist systems, ADAS. The other is fully driverless. So in a broad sense, I think there are two categories. There are definitely two different products. The biggest difference is not on the technical side, but rather, as you mentioned, probably on the regulatory side, is who is first in line for the responsibility if there is ever an accident. I think for any ADAS system, any driver assistance system, it’s always the driver behind the wheel that’s responsible. Regardless if he or she is looking at the road or has their hands on the wheel.
James Peng: Whereas for the fully driverless systems, it’s the system that’s first in line. Because of that requirement, right? Think about if there’s a driver behind the wheel, it sort of serves as a safety net. So the system does not need to be bulletproof. It’s probably, well, as long as it can handle 99%, the case is probably good enough. Whereas for fully driverless, it has to be dealing with all the edge cases, all the extreme cases, and have a fallback system. We can get into those details later. But essentially, in my opinion, there are two different products. Of course, for the driver assistance systems, there are different levels, right? You can be, say, only highway or there’s only keeping in lane. Or they will actually even be able to handle some of the automations in the urban environment. For the fully driverless, of course, as you mentioned, there’s L4, L5 in a traditional definition. L4 means in certain areas. It can be fully driverless. L5 is everywhere.
James Peng: But I think it’s never a clear division. Essentially, you can think of it as how we drive, right? We start with the area, then we gradually improve. Eventually, it will be everywhere. So I think that will be a gradual process instead of a clear division.
Grace Shao: So actually, I want to double click on what you just said. So then help me understand, what is the gap between L4 and L5? Right now, Pony.ai is at L4, right? They’re robotaxis. Is that correct? How am I understanding this?
James Peng: No, I wouldn’t call them a gap. I think it’s a different product definition. Because they serve different purposes. I think most people view this as a process of evolution, right? From L0 to L2, L3, L4. But it’s actually a wrong way of looking at it. As I already mentioned, because the clear difference is that who’s first in line with responsibility. That’s decided by regulatory, actually. By product definition. By regulatory as well. So because of that, it’s essentially two different products. As the product is getting more and more mature, getting more powerful, in my personal view, the division of two different products is getting wider instead of narrower.
Grace Shao: Okay, then I’ll push on this. Then what is the real bottleneck right now for companies like you to deploy at a faster scale? Or to go into more cities quicker?
James Peng: I think that’s the reason that I wouldn’t say it’s one single blocker or one single bottleneck that prevented us to grow faster. I think it’s because the sheer complexity of the autonomous driving and what entails to ensure safety. There’s regulatory, there’s technical things. We also, because this is such a brand new system, that we need a manufacturing capacity. We need deployment. We need to get all the operational things ready, like all the garage space and whatnot. Also user acceptance, user education, as we already mentioned. I think all those take time.
Grace Shao: I believe also we have different partnerships with different managers of your local fleets. That kind of know-how also takes time for them to understand, to transfer over, right? For them to manage robotaxi fleets versus human fleets.
James Peng: Absolutely, absolutely. All those takes time.
Grace Shao: So I want to bring it back to technical. You have said publicly that you use the least compute footprint to reach L4. I thought that was quite interesting. Help us understand how you achieve that. How the model on the vehicle versus the large model you train in the labs actually work together.
James Peng: I think all the AI systems more or less take the same approach, is that you have data on the backend, on the data center side. You train a large model where you essentially try to get all the cases to be learned. In our case, we use the word model, where you can think of it as a simulated city, where we train the virtual driver and let us drive on all different kinds of roads and learn the driving ability. So that’s what’s condensed as a model from all the learning that we deploy on the vehicle. In the traditional AI sense, that’s called edge computing. You put it on the edge, put it on the devices, and put it on the car, where it’s a much smaller model. In a human sense, it’s like we learn everything. Then when we go to a test, we don’t need everything. We just need to be able to have the ability to handle the test.
James Peng: So that’s typically the training, where the backend system needs a lot of computing, but on the actual usage side, you don’t need that much computing power. So when the car is running on its own, it’s actually only using the model on edge, essentially.
Grace Shao: Absolutely. I see. Okay, so let’s talk about AI systems, because AI systems for language, images, code have improved dramatically. There’s also obviously a lot of hype right now around world models, but what you’ve been describing actually has been something that’s not been coined world models for a decade, over a decade. What has generative AI done for you guys? How has it changed how you view your own AI system? Do you think, I guess, the word world models do your system justice in that sense?
James Peng: Yes, it’s a little bit different, and they’re also related. Again, use human as an analogy. It’s actually quite similar to how we think, right? Because think about the large language model, how we process image, how we process knowledge. It’s sort of related to our memory and our logical areas of the brain. Whereas when we drive, it’s not just the memory and our knowledge. It’s also how we react, how we action, and all that. So the example is, one is related to how we learn a new skill. That’s the language model. Whereas for driving, it’s like how we learn to ride a bike. It’s actually different types of brain, different types of skill sets. That’s why it’s different. It’s not the same AI, because that’s how humans deal with different skills for knowledge.
Grace Shao: So Gen-AI has not affected you, but how do you view the idea of now calling, I guess, the physical AI world, world models? Because you guys have been doing this for more than a decade. That’s kind of my question, I guess.
James Peng: Yeah, that’s why I’m trying to get to it. For language model, it’s related with knowledge, with language, with logic. That means you need to be a very large model. Because think about it, if you don’t know a historical event, there’s no way you know it. So you have to have all the knowledge of a human ever created in your model for it to be powerful. So that’s why a large language model requires a lot of computing power and memory and everything. Whereas for driving, that’s how we learn riding a bike. We don’t need to have a PhD degree to learn how to ride a bike. But rather, it requires a lot of practice and training. That’s what world model is related to, or is assembled like. It essentially is a model where the virtual driver can start learning by itself, to learn how to interact with other cars, cyclists, pedestrians, and then learn the driving skill out of that.
James Peng: So it’s a bit related with the large language model, but it’s quite different. Because it’s related with action, related with manipulation, related with collision avoidance. So that’s the key for the world model.
Grace Shao: So tell us about how you simulate these systems. How do you leverage simulation systems for these edge cases?
James Peng: So essentially, that’s how we learn how to drive. There are several key factors for the world model. One is it needs to be very real. So we call the fidelity. It needs to have high fidelity, means it resembles the real world. Second is everything that moves in the world model, meaning cars, pedestrians, needs to be smart. Meaning that because the thing that’s driving is the interactive process. Our action, because we constantly make decisions in there, our action will affect everybody else around us. So they need to react accordingly. So it’s interactive. It’s more like interactive gaming, where we react with everything else. So that’s the second challenge is all the interaction needs to be smart, needs to be intelligent. The third challenge is how do we evaluate what is a good driving? You can interact and everything. You avoid collision. Is that a great driving? No. Not enough, right? Because there’s a passenger inside. Comfort is important. Efficiency is important.
James Peng: From A to B, we want to use the minimum amount of time. So essentially, it’s a multi-metric evaluation system in there to see what is a good driving. So there are three key challenges for the world model. We certainly, all our effort developing the world model related with that three areas.
Grace Shao: But human drivers can be so emotional, right? Either you can be scared or you can be rage driving. Or we can be communicating sometimes without obvious signs, right? We’re looking at each other. We communicate with eye contact, hand gestures. How do you train your fleets to understand human behavior right now? Because obviously, human drivers are still the majority of drivers on the road today. In your case, I actually think you’re right. At one point, maybe removing all the human drivers will make it even safer, right? Especially removing drivers like myself, if I say it again. But yeah, how do you actually help these cars understand all these non-obvious signals? Not someone quite directly clashing onto you. Someone forgetting to turn on the turn sign. Someone stop sign looking at you, waving to go.
James Peng: Like all the nuances. Absolutely. See, that’s why the first thing is how we become a better driver. Essentially, it’s a continuous learning process. The first thing, that’s how we learn how to drive, right? The first thing is you avoid collision. You were a cautious driver. Then gradually, you start learning a bit of everything else. All the signs, all the nonverbal cues, and the hand gestures. So that’s exactly the case for us as well. The earlier model of our system is just driving and try to avoid collision. Then gradually, we put a lot of new things, new recognitions, new perception models into our system where we start recognizing, for example, the hand gestures, especially all the policemen, all the typical police gestures, stop, Go, and all those things. Then we start recognizing, for example, potholes on the road, small obstacles on the road. So it’s sort of how we learn. We start getting all the big pictures first.
James Peng: Then we start learning all the nitty-gritty details down the road and put them to enhance our system. Regarding the second point, you’ll see, when all the cars are autonomous driving by themselves, it will be easier to drive. Yes or no? Because the thing that the big, especially in China, in the roads in China, the biggest challenge is not the other vehicles. In a lot of cases, it’s cyclists and pedestrians. While we can’t make them to be autonomous driving, so I think having the ability to recognize pedestrians, recognize the intention, their sign, and give You a specific example on a crosswalk, the way pedestrians look at and how they pay attention. For example, if they want to directly cross, they typically look straight. But if they were looking back, that means they will more likely not to cross the crosswalk. So we actually take those cues to decide whether we let them cross or proceed straight ahead.
James Peng: So a lot of those details need to be put into the system to make it safer and at the same time efficient.
Grace Shao: Is the judgment made on the spot using the cameras?
James Peng: Yes, using all the sensors. Cameras and LiDAR provide the sensor input.
Grace Shao: We take it all and then we make the comprehensive decision based on the input. Definitely China has more complex and less predictable driving conditions, especially given the number of pedestrians, cyclists, motorcycles we just talked about. So if you can drive safely there, I bet you can drive safely anywhere. But jokes aside, it’s really interesting because, we talk about as your fleet grows, you accumulate more and more world data, real-world data. Is that kind of data eventually becoming an advantage and a serious edge for incumbent fleets and a structural barrier that makes it very hard for new entrants to compete then?
James Peng: Data is important, but data is not everything. So how we understand that is this. Probably give you an example. Think about how we learn. Let’s say we learn math, right? You can think of the data is like the practice sets that we have. Of course, you need to do enough of practice to be a good knowledge about the subject. But doesn’t mean that you have the problem sets of the whole world that you become math experts. So that’s exactly the same case. We need enough of data sets in order to know what the real world driving condition looks like. But we don’t need everything because once we know enough, we can always generate enough knowledge about the driving. So in a way, I think the driving data is important, but it’s not everything. So that’s exactly how you view this.
Grace Shao: So as we speak of this, how do you view the whole landscape right now? Who would you say are your biggest competitors globally? How do you view the different markets playing out?
James Peng: Yeah, that’s a very complex problem. I think a question to answer because I think that I think the first and foremost, I think maybe I got some premises on this. First, the whole mobility industry, especially related with autonomous driving, is very large. They certainly have enough room for several players. Second, it’s still at a fairly early stage for the fully driverless. I don’t think the landscape is already divided in the set. So giving that two promises, I think currently, when I look at the players, I have to judge their current deployment. Although everybody can say, oh, they will have, they will, they will have thousands, hundreds of thousands of vehicles on the road. Actually, giving the current situation, I use the metric as having fully driverless commercial operation as a baseline. Giving that as a factor, I think in the US, Waymo is definitely leading the way. Because Waymo already have 4,000 or 5,000 vehicles on the road, 4,000 plus.
James Peng: Then, of course, there are some other players trying to play a catch up. Zoox, Cruise, maybe Tesla as well. So there’s, of course, some. So I would say in the US, Waymo is leading the way. There’s three to five players trying to play a catch up. In a global sense, I think from a technical point of view, China’s player is certainly on par with the US players. But from the total cost or the economical sense of a vehicle, for example, our vehicle is four or five times cheaper than Waymo’s vehicle. So in the global markets, such as Europe, such as Middle East, I think we will play a huge edge compared with the US players. Certainly, the whole landscape is still evolving.
Grace Shao: But especially in the global markets, you’ll see we will definitely not play a catch up, but taking a leading position. You’ve been an advocate for hardware optimization, software optimization, battery solution optimization. Is that the strategy behind being able to have a vehicle that’s four to five times cheaper than Waymo’s? Or where’s the edge? Or how are you building these comparable vehicles at a relatively cheaper cost?
James Peng: Yeah, I think as you mentioned, you definitely mentioned the most important factor to have the much cheaper price on the vehicles, which is optimization on Hardware, software, and everything else. I think another reason, of course, is because the whole ecosystem related with autonomous driving in China is relatively mature, and the scale is larger. So that price is cheaper. For example, the vehicle itself, the sensors, they’re relatively cheaper in China than anywhere else. Because of the ecosystem, because of the scale. So that plays an important role as well. That touches on something. A lot of physical AI, a lot of robotics companies are also now leaning into the Chinese supply chain. A lot of your peers, actually, autonomous driving, or even the EV players are now looking to expand into physical AI, whether that’s robots, humanoids, Quadrupeds, whatnot.
Grace Shao: So you’ve stayed really focused. You’ve not launched any robots out there or anything. What’s your thinking behind this?
James Peng: Yeah, absolutely true. I think autonomous driving definitely is probably the first large application of physical AI. All the others, humanoids, robots, and everything else, probably will have real applications down the road. For us, we view the autonomous driving as our brand and partner. Of course, as I mentioned, this is still early stage. I think we still have a lot of mileage to go. For all the other physical AI applications, we don’t have any specific plans yet. But I think they’re definitely interrelated. We may enter them down the road, depending on whether we need it or not. Because my judgment is that for the physical AI, it probably will follow the similar trend as autonomous driving. It might take another decade for it to mature. I think for us, it’s more like whether we have real applications for it. Give you a specific example.
James Peng: Even for our fleet, once we go to hundreds of thousands, millions of vehicles, how we maintain those vehicles, how we do the charging, cleaning, servicing, They may use robotic applications. So I guess my view is that we will not probably do robotic actions just for the sake of doing it. But we may do the related applications when we see the real applications.
Grace Shao: So it’s fair to say you’re cautiously optimistic that there is a potential use case further down. But it’s nowhere close to where it’s been hyped in the three to five years kind of use case.
James Peng: Yeah, I think it’s the same thing as autonomous driving 10 years ago.
Grace Shao: All right, well, let’s talk about your international footprint. You mentioned earlier, you have a global strategy. You’re in Europe and Luxembourg was your first launch, right? You’re in Southeast Asia, parts of East Asia, you’re in the Middle East growing really fast over there. Tell us about how you think of your next steps in your global expansion.
James Peng: Yeah, I think the mobility demand everywhere is the same, right? There’s a strong demand across the globe. But we have to focus on the most important markets first. I think eventually we’ll go everywhere, because that’s our motto is we have autonomous mobility everywhere. That’s our ambition when we started 10 years ago. But our first launch, we have several criteria. One is related with regulatory, right? It needs to have relatively accommodating regulatory environment. Second is it needs to be a relatively mature mobility market. In a more obvious sense is that the local taxi fares needs to be relatively high, because I think that our pricing anchor point is always a human driving Taxi. So that price needs to be relatively okay. The third criteria are that we need a good local player to partner with, because a lot of other things like regulatory, like the back end services needs to be Handled by the local partners.
James Peng: So judging from that three categories, I think Europe, Middle East, Southeast Asia, Japan, South Korea, Australia maybe. Those will be probably the potential markets for the initial launch. Of course, those are already big enough of number of countries. So we’ll pick and choose some to start with.
Grace Shao: How do I understand your partnership models? Because I believe you’ve quite a few different kind of models depending on the location, the regulatory environment, potential partnerships, know-how, etc. Tell us about that.
James Peng: Maybe I’ll take one step back first. Think about what is a robotaxi industry. The type of players, I’ll divide them into four categories. One category is for the user acquisition. Those are ride-hailing applications. Those are the Ubers and the Lyfts and the DDs alike. The second is a vehicle. You need a car, how you manufacture a car. The third is a driver. The fourth is all the back end services, cleaning, charging, servicing, insurance, and everything else. For us, our main job is creating a virtual driver, is making a really safe, efficient driver. So that’s definitely what we do. All the three other categories, we might have partners, we might do ourselves. So that, depending on the market, depending on what’s the strong local players, we might pick and choose players who’s handling one or two or three of the Other things. For example, we work with the ride-hailing platforms for the user acquisition.
James Peng: We work with some of the back end services who’s providing the parking space, who’s cleaning, charging our cars. We also have OEM partners that work on the cars. So that’s how we view the partnerships landscape.
Grace Shao: So after you deploy, say you send these out to Australia, what happens walk us through that. Because once these cars actually get off the boat and ships and they land in Australia, are they your responsibility or your partner’s responsibility? Do you send an engineer? Do you send your own management? Or do you transfer that know-how and maintenance know-how to the local partners to handle?
James Peng: Great question. That really depends on the different partnerships and different regulatory environment. In some markets, it’s the local player who’s first in line with managing the fleet. That means in those cases, we manufacture the cars with OEM. But then once we ship the vehicles to the local country, Australia, giving you an example, or Singapore, let’s say, then we actually, in those cases, we sell The vehicle to the local partner. It’s like selling hardware. It’s like selling hardware. But we will, of course, have engineers handling the driving because we are in charge of the driving. So all the driving related work will be done by us. But then the user acquisition, the cleaning, servicing, charging will be done by the local partner. So those are one case. But in some markets, we actually ship the vehicle and we apply licenses by ourselves. The vehicle is still on our own book. But those are rare cases.
James Peng: We actually, our preferred model is to have the local partner that handles most of the logistics and we will be the tech providers. We’ll essentially have the virtual drivers handling the driving and everything else is done by the local partner.
Grace Shao: I see. Would you ever view OEMs as competitors in any way? Because right now you’re partnering with them. You’re giving them the software enablement, right? Would they produce their own robotaxis?
James Peng: I think in most cases, in my view, that they probably will be partners instead of competitors. It’s very different because they were mostly on the hardware business. Very few of them will be in the robotaxis business because they’re quite different.
Grace Shao: I see. I see. Something a bit niche is, I know you run robotaxis as well as trucks. Walk us through how you think about that. Why do you guys also have a truck business? What kind of scenarios are they already being deployed in? I believe they’re the heavy trucks and then the light trucks. How do I understand this?
James Peng: Yes. As I already mentioned, think about our business is that all our technology is that we are creating a safe virtual driver. Virtual driver is our core. As a driver, you should be able to drive all different types of vehicles. The two biggest applications for driver is one is for the transportation of human beings and the other is for goods. That’s related with robotaxis and then for all the trucks. Within the logistic industry, there are actually three categories. One is for the long haul, which is typically done by the heavy trucks, the 18 wheelers and whatnot. Then there’s also in-city network, which is the light duty truck. Then there were also the last mile, typically is handled by much smaller vehicles. Our main focus, of course, is on the long haul and the intra-city transportation. On the last mile, we are the providers of the domain controllers, but that’s not the areas we’re working on. So think about we’re creating driver.
James Peng: Driver should be drive different types of vehicles. That’s how we view the trucks versus the robotaxis.
Grace Shao: Usually, I would assume these are like ports, airports, maybe?
James Peng: They will eventually be everywhere as well. We start with ports. We start with some dedicated routes, for example, like a minefield to the local distribution center, those 30-50 mile routes. The reason we start with those applications is because typically it’s mostly because of regulatory reasons. Because the ports and the dedicated routes and those are typically a semi -private road. It’s much easier to get the regulatory approval. Of course, we are working on long haul trucks as well. We already actually have a fleet of heavy-duty trucks doing the real goods transfers on highways, but still with a safety driver, of course. Eventually, we’ll be fully driverless as well.
Grace Shao: You’ve said you have a target of running fleets commercially across more than 20 cities by the end of this year. What do you know today that you could not have learned without actually operating at scale already on the streets? What makes you have the confidence to do that now, I think, compared to maybe a few years ago?
James Peng: Again, I think for robotaxis commercial business to be a reality, there are three important factors. One is technology. Second is regulatory approval. The third is user acceptance. I think within the last three to four years, we have gained a lot of experience on all three categories. The reason we were confident to deploy in 20 cities is because clear vision on the regulatory approval. There’s a lot of cities globally, both in China and in some global cities, they actually start coming out with regulations for supporting fully driverless commercial applications. Also we have planners. Planners want them. So I think all the important factors are falling into place. That gives us confidence.
Grace Shao: I’m going to play devil’s advocate a little bit here. With the rise of AI right now, there’s a bit of a fear of replacement of people’s jobs. The rise of autonomous driving obviously lead to job loss in people who are currently drivers. How do you view that? Because just now we talked about robotaxi drivers. We talked about people driving heavy-duty trucks that could potentially be replaced. Frankly, I’m in a camp that people could be maybe freed up to do more things that they can do otherwise. People will find alternative careers. But are regulators becoming more cautious. How do you feel about the current public pushback a little bit on AI, autonomous driving, autonomous everything at the moment?
James Peng: Yeah. Actually, driving is a hard job. Driving is a lot of cases in a stop vehicle for 10, 12 hours a day. It’s a really tough job. The thing that because autonomous driving itself is a highly regulated industry, the pace of our roll up is determined by the number of licenses. The thing about also a lot of the drivers were not young. The young generation, younger generations actually don’t want to be drivers. So I think, especially a lot of the global markets, we actually come in to fill the gap for the labor shortage for the driver. We’ll not change the human driving vehicles overnight. It will be a gradual process. So that’s sort of the development of the cities and the human society. It takes time. It becomes gradually a norm. Then, as you just mentioned, then the drivers can find other jobs.
James Peng: Even we actually absorb a lot of jobs, for example, for the remote assistance, maintenance, which are much safer and much less strenuous job conditions. So I think society as a whole has always a way to absorb jobs. To adopt, adapt, and then evolve.
Grace Shao: The current pay for a lot of times for these heavy truckload drivers are like 200 to 300k USD. They’re considered very high-earning jobs. But at the same time, people forget they’re extremely dangerous. There’s life lost constantly on the roads. So I can see that could be very valuable if people can actually replace those routes with robo-drivers.
James Peng: It’s not just replacing. Look at the truckers. Their average age is 45 plus. In North America right now? In North America. In China, they’re 40 plus as well. So a lot of younger generations, they don’t want that type of jobs. We’re coming not only to replace, but actually to fill the void for that job shortage.
Grace Shao: All right. So I think I want to wrap up our conversation soon about this. Is there anything I’m really missing, you think, about robotaxis and your business at this point?
James Peng: I think we’ve probably covered a lot of topics.
Grace Shao: Oh, I had one question. Another one about your business before we go into your personal thing. You mentioned Croatia just now when we were talking offline. I thought that was so fascinating. In my mind, I thought these robotaxis were being deployed mostly in futuristic cities like Silicon Valley and SF, out here in Shenzhen where we’re here today. But Croatia, help us understand the need for robotaxis in these countries where a lot of the roads are aged, are not really made for cars to start with, Are not easy to drive in, actually, even for humans. Then how does that make sense even for your economics, actually?
James Peng: Of course, there were some challenges. From a technical point of view, two challenges initially. One is there’s a lot of roundabouts. Actually, there were not many roundabouts in China. So although a lot of other very complex situations like heavy storms and whatnot, we were able to handle them really well. But roundabouts, we had some, but we haven’t trained that much. So we actually have to retrain a bit on the roundabouts. The second is the trams. There were just a lot of trams in the Zagreb. Their behavior of the trams is different from cars. So we need a little bit more training to get used to it. But it’s like how we drive. When we go to a new city, we might not drive as a perfect driver initially. But then we learn and adapt. Once we have a good learning system set up, then we can quickly learn. That’s exactly our experience in Zagreb, Croatia. Two things that we actually have to learn in Croatia.
James Peng: One is the roundabouts. The other is trams. Because those are not something that typically you will see on the roads in China. So for those new situations, it’s like how we learn. How we learn driving. When we go to a new city, we probably know 95%, 98% of the situation. Some of the scenarios probably we didn’t encounter previously. Then we learn. We adapt. So that’s exactly the case for us in Croatia. After three to four months of learning and training and retraining, we actually were able to handle those cases like roundabouts and trams really well. Because there’s a lot of roundabouts in other cities in Europe. They actually have different rules for roundabouts. Some of the roundabouts, I think the cars outside roundabouts have right-of -way. Some of the vehicles inside the roundabouts have right-of-way. But we can adapt once we have the system set up.
James Peng: So as I mentioned, the most important characteristic of our system is not how powerful it is, it’s how adaptive and how easy to learn on our system so that We were able to adapt.
Grace Shao: Brilliant. So a lot of localization as well for your vehicles. I have two last questions. One is, what is something you think people still get wrong often about your sector, in this case, autonomous vehicles, autonomous mobility? The second question is a bit of a curveball. I’ll throw it to you first, you can think about it. What is one differentiated view you hold? Something that’s a bit against consensus, maybe.
James Peng: Autonomous driving industry, I think people put too much focus on technology and probably underestimated the complexity of robotaxi as a business. Essentially, of course, technical is the most important. If you can’t drive safely, you’ll not have a business. But once you even have the most safest driving, you still have to, as a business, there’s a lot of other things involved. For example, how you deploy a fleet, how you make the pickup and drop off easy for the user, how you handle all the edge cases of the complaints of the Passengers, how you make the charging, servicing, cleaning efficient. For example, especially give you a specific example, the electricity fares during the day fluctuates. If you have the charging at the low fare, you can save a lot of cost. Then how you manage your fleet? Although you have the low fare for electricity, but the demand of the passengers is really high. How do you make a decision?
James Peng: So essentially, it’s a lot more optimization involved than just the driving itself. I think a lot of people underestimate the complexity with the management of a fleet of autonomous driving vehicles. We actually, as a company, have put a lot of emphasis and take a lot of efforts in optimizing everything. So that’s why I think those will be a very strong competitive edge down the road.
Grace Shao: Once you guys scale further, especially.
James Peng: Exactly, absolutely. Very interesting.
Grace Shao: The second one, I’ll put you on the spot again. What is one differentiative you hold?
James Peng: I think I’ll take the one related to the answer of my first question. Is that, again, people always put too much emphasis or give too much credit on zero to one and think about less for one to ten. Give a lot of examples, right? People always think an invention is so hard, but putting an invention to be a scaled application is equally hard or a lot harder. Because the scale involves cost optimization, involves user education, involves a regulatory approval, it involves making the things a lot easier to use. So many examples like this, right?
Grace Shao: Definitely. Say a rocket is put in the sky. Oh, it’s so hard. But having the rockets to always be able to safely take off and recycle, that’s extremely hard.
James Peng: So I think related with autonomous driving is we certainly crossed zero to one. I think we crossed one to five, maybe. But from five to ten, ten to a hundred, I think there will be still a lot of challenges ahead.
Grace Shao: That’s very insightful. I agree with you. When we look at the internet era and a lot of players that still stand today versus who are the actual ones that created a lot of the internet use cases we know of today. Thank you so much, James. It was a pleasure and an honor to learn more about your business, yourself, the man behind the company that is changing the future of autonomous mobility. Thank you again.
James Peng: Thank you for having me.
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