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Differentiated Understanding

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by Grace Shao

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Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently. <a href="https://aiproem.substack.com?utm_medium=podcast">aiproem.substack.com</a>

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Recent Episodes

August 4, 2026

From Sourcing Engine to Agentic Commerce with Alibaba's Accio Agent

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 <a target="_blank" href="https://aiproem.substack.com/podcast">podcast lineup here</a> and explore the episodes!Chapters00:00 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 AIAI-generated transcript for reference onlyGrace 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.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at <a href="https://aiproem.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4">aiproem.substack.com/subscribe</a>

July 28, 2026

The Future of Agentic Payments with Clink Founder Patrick Wu

Host Grace Shao interviews Clink Founder and CEO Patrick Wu about evolving payments for agentic commerce and navigating global fragmentation.

July 21, 2026

Pony.ai’s Founder and CEO James Peng on What It Takes to Scale Robotaxis

When<a target="_blank" href="https://time.com/7320769/pony-ai-ceo-james-peng-interview/"> James Peng</a> 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, <a target="_blank" href="https://aiproem.substack.com/podcast">check out the podcast tab </a>or follow us on<a target="_blank" href="https://open.spotify.com/show/5FHayFPSNBHsVKHP9OQ7xe?si=56109385e4dd4557"> Spotify!</a>Chapters02:36 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 oneTranscriptGrace 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.AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to AI Proem at <a href="https://aiproem.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4">aiproem.substack.com/subscribe</a>

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