AI Proem Podcast

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. aiproem.substack.com

  1. Aug 11

    Qoder joins again to talk about China's workplace agent war. Redirected focus on EMEA & APAC

    Show Notes Hi all, Christian Hu from Alibaba’s Qoder joins the podcast again to talk about the fierce domestic workplace agent competition. For context, Anthropic and OpenAI have decided not to allow access to their models and products in Greater China; thus, the competition for models and agents is largely driven by domestic players. For workplace agents — coworker-style products — the most popular ones at the moment are Tencent’s WorkBuddy, Alibaba’s Qoder, and ByteDance’s Trae. The other agents we see coming from the labs are mostly coding agents. With that, I’ll hand over the floor to Christian, who graciously found ~40 minutes while on a business trip to talk to us about the landscape and Qoder’s own strategic shifts. This episode was recorded on the day Qwen3.8 Max launched. For more, check out the podcast lineup here and explore the episodes! Chapters * 00:00 — Southeast Asia Expansion and Market Dynamics * 02:43 — The Shift Towards Business Logic in Coding Agents * 05:29 — Industry-Specific Applications and Vertical Agents * 11:22 — Global Strategy: Lessons from Market Differences * 14:00 — Partnerships as a Key Element in Market Strategy * 16:41 — The Future of Coding Agents and Market Trends * 19:22 — Pricing Models and Inference Economics * 22:08 — Open Source Trends in AI and Market Competition * 27:34 — Building vs. Buying AI Models * 29:38 — Future Directions for Coding Agents * 32:14 — Final Thoughts on Market Opportunities Transcript (AI-generated, for reference only) Grace Shao (00:00) Hi Christian, friend of the pod, you’re back on. Really excited to have you here. Where are you these days? Christian (00:07) Yes. I just landed in Singapore last night. Grace Shao (00:12) Okay, perfect. Cause we’re gonna talk about your Southeast Asia expansion. But since we last spoke, competition among coding agents has intensified quite a lot in China, particularly — the market has changed a little bit. Where are you seeing the whole market going, and how do you think Qoder fits into all of it? Christian (00:32) Yes, it is a war, you know, between every major tech company in China. Because it’s a war, nobody wants to lose. I think the logic behind this coding agent war is that most companies believe that a coding agent shall be the fundamental path to AGI — artificial general intelligence. So nobody wants to be behind in this race. But the most interesting thing behind the war, or this race, is that something is changing. When we look back to the last twelve months, everyone is talking about the models, everyone talking about what kind of model will be the most competitive advantage for your coding agent. But now, for most leading agents, they are trying to be more focused on the business logic and workflows. Just like what Qoder is doing — we want to be more into the business logic of our customers. And even for some individual users, they are trying to do something for business. They have one-person companies or one-person workflows. So they need the coding agent to do more about the business workflow, not just code generation. So that’s the shift behind the war. For at least most of the companies, they are trying to raise not just for a coding assistant, but want to be dominant as a desktop assistant for employees or maybe some individual users. And maybe in the future, they want to be the digital employees for the industry. I think that’s maybe the ultimate race for the coding agent. Grace Shao (02:28) And you’ve got players like WorkBuddy from Tencent that’s been doing really well, right? Exactly to your point— Christian (02:33) Yes. Grace Shao (02:33) I think they’ve been plugging in, kind of operating as an assistant on desktop, very intuitive and user-friendly. You’ve got ByteDance with Trae pushing in a similar direction. So how do you feel about that? Where is Qoder’s differentiating point or offering here? Christian (02:51) I have a few things to share with you. I think for the past year, Qoder has accomplished very remarkable business performance. In terms of ARR or revenue, we are the leading one — we are number one. And maybe we are more than the combination of number two to number four. That’s a huge achievement for Qoder in business growth. And as I just said, Qoder from day one is more focused on building an agentic platform for developers and AI builders. It’s not just a coding tool or coding assistant — we want to be a platform for the next generation of agentic power. So the skill marketplace, the plugins, the connectors, the MCP connectors — all of these features are now shining. All of these features are bringing advantage to our business. That’s the truth of what’s happening in China and even in overseas markets. Grace Shao (04:04) That’s really interesting. And so when we caught up in Hangzhou recently, you were saying that Qoder appears to be broadening from just coding into industry-specific agents. That was something quite fascinating, because from the start of the conversation, you say coding capability is the fundamental foundation for all of these agents, but people are moving towards these vertical agent use cases. So tell us a bit more about that. What kind of industries are you guys targeting? What is your thinking behind this new strategy? Christian (04:34) Okay, I can share an example. We got a very important customer case with Xiaopeng. Xiaopeng is one of the leading electric vehicle producers in China, maybe one of the best. For Xiaopeng, for the company now, Qoder is not just a coding assistant or code generation tool. Qoder has become an agentic driver for the restructuring of their workflows and business logic. They’re trying to educate their developers and their engineers, maybe even the HR department, to use Qoder to renew their business logic and their workflows. For example, for the legal department, they’re trying to use Qoder to reduce the legal review cycle from six days to one day, or even less than one day. And it’s not just Xiaopeng — I think there are a lot of similar cases. So for Qoder, it’s not a shift to pivoting to vertical applications or specific industries. From day one, Qoder wanted to be a platform. The platform means we want to be a tool — we don’t want to be just a coding assistant competing with other coding tools. We want to be more embeddable and compatible with the customers’ business logic and workflows. So it should be more vertical. But it’s not for us to do the vertical things — it should be let the developers, the customers, and even some individual power users develop domain-specific agents on top of Qoder. That’s the philosophy for Qoder. Grace Shao (06:51) Okay. So the thinking is you guys are offering the tool, but really it’s still on the user to build out the tailor-made agent for themselves. Not that you’re specifically pushing— Christian (07:01) Yes, that’s the truth. Actually, we got some organic creation of skills by the power users. They are creating some skills, some creating some plugins on our marketplace. We are going to open the marketplace to all the business users so that the users can deploy different kinds of skills or plugins from the marketplace. That will be the market for Qoder. Maybe in the near future, we could be a marketplace for agents, a marketplace for skills. Grace Shao (07:49) Interesting. Okay. Well, this leads to something else you talked about. You said that you guys were building an ecosystem around Qoder. So tell us a bit more about what that means, and how should we understand or expect how Qoder might change as a product in the next few months. Christian (08:05) Okay. I think we can change the view of Qoder — from a coding assistant to an agentic platform. I can give an example: we are doing something together with Microsoft. That may be an example. We want to collaborate with Microsoft to make Office more usable and more accessible on an agentic system just like Qoder. You know, in the past, Microsoft Office is Microsoft Office, ChatGPT is ChatGPT — they’re quite different products. For the users, they have quite different experiences on two different kinds of products. And now we want to be one. For Qoder, for the agents, they need the agent to know how to use Office, how to deploy Office, how to use the best features from the Office suite — the toolkit — to help the users do presentations, do documents, process data. So that’s maybe the very interesting thing for the users in the near future. This kind of collaboration is happening everywhere. Qoder is trying to partner with partners from collaboration software, finance software, even legal software, HR SaaS providers and vendors. We are doing things like that to be more compatible and more useful in the near future. Grace Shao (09:55) Very cool. Let’s take a step back. I want to talk about your business expansion, because I think when we talked about a year ago, you guys were really gung-ho about going to the US, going to Japan. I know you’ve been spending a lot of time in Japan yourself. But recently it seems like you are pivoting, or at least putting more priority and focus on Southeast Asia, and now you’re in Singapore yourself. Tell us a bit about the thinking behind your global strategy, and which markets you’re currently focusing on, given that you’re the head of GTM on the international expansion side. Christian (10:26) Yeah, you know, I actually got a lot of lessons from the last maybe twelve months — about ten months for my global journey with Qoder. Everything is different in different markets. I just came back from Paris. I think Europe is quite different from Japan. Most people think Japan and Europe share something in common — they are pretty slow in AI adoption, they care more about compliance, they care more about trust. But things are also — you can still find something different between

    Qoder joins again to talk about China's workplace agent war. Redirected focus on EMEA & APAC

  2. Aug 4

    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 podcast lineup here and explore the episodes! Chapters 00:00 Introducing Accio Work01:01 From Alibaba.com to agentic commerce05:18 What an “agentic business team” does06:27 The commerce workflow and human control11:11 “Do it for me” versus “tell me what to do”18:32 Connecting fragmented commerce tools22:36 Alibaba’s sourcing-data advantage26:03 Supplier quality, matching and verification31:58 Accio versus general-purpose AI tools33:36 How agents communicate with suppliers37:09 What Accio offers factories and suppliers39:29 Designing AI for non-technical SMEs42:42 Business model and monetization43:54 The future of agentic commerce48:57 Why not every problem needs AI AI-generated transcript for reference only Grace Shao (00:00) Hi, Ziwei. Thank you so much for joining us today. Ziwei Chen (00:02) Hi Grace, so good to see you. Grace Shao (00:07) I’m excited to talk about Accio So I just came back from Hangzhou like a month ago and I met some with some of your colleagues on the ground. Was very impressed by the product and thought was just really intuitive. So let’s get started. Tell us a bit about yourself and your role at Accio and what Accio is all about. Ziwei Chen (00:24) Well, hi everyone. my name is Ziwei Chen. I work at Alibaba.com as the product marketing lead for Accio Work. prior to Alibaba.com, I actually spent a few years in the world of developer marketing where I was kind of driving good market plans for software tools that are meant for developers who are building AI products. So now it’s kind of completing the whole picture for me to now move on to the other side about actually growing the products. built by those developers to the end users. So that’s kind of my kind of AI journey coming from the development side now to the end user side, driving the growth for Accio Work among our small and medium-sized businesses around the world. Grace Shao (01:01) And tell us, what does Accio do actually? So I think a lot of people are not familiar with it and just how it fits into the whole bigger, I guess, Alibaba ecosystem as well. Ziwei Chen (01:09) Yes, absolutely. So maybe I’ll take a quick pause and take a step back just to talk about the overall broader picture. So Alibaba grew as a large enterprise. we were founded in 1999, and our kind of main North Star was to make make it easy to do business everywhere. So that has always been kind of our focus of focusing on B2B and focusing on small and medium-sized businesses who typically don’t have that resources that large enterprises have. So throughout the years since 1999, we have been really just focusing on what we can do, either there’s a new product. And new services to make it easier for them to start a business, to launch a business, and to grow a business, maybe something to exit a business. So along that line, Alibaba.com is kind of the bread and butter where we first started, focusing on B2B sourcing. So actually, I will kind of almost break down our development or our growth journey into three eras and then show you where Accio fit. I will call it the digitization era, the AI co-pilot era, and then the agentic team era. So the first digitization is when we are first founded with that platform of Alibaba.com where on this digital platform we are connecting the sellers or what we call the suppliers, the factories, with the buyers, our buyers, maybe sellers on the other side, into one platform. So now they’re not limited by time zone, they’re not limited by the location. And then in that process, we what we have learned over the years is that things can get overwhelming. There just so many suppliers, so many products, right? So then when all of these foundational AI capabilities came about, we were like, okay, this is the moment. This is the moment where we can introduce AI capabilities to make it easier for our buyers to find the right products and the right suppliers. So the name Accio actually comes from Latin, which means summon. So the idea is we help you summon the right products, summon the right suppliers, summon the right information. So that’s when we first launched Accio as a sourcing engine back in November of 2024. And that went really well because a lot of our users now, even without any experience in sourcing, without any experience in physical products, and find it really quickly with confidence. But then what we had learned over time is that First, sourcing is only one small part of a broader business journey, right, for a lot of our users in the US, Europe, and around the world. So now we’re thinking, okay, what else can we do to expand on that? So that led to kind of the last phase for Accio Work. So when I compare Accio Work with Accio, a few things that stood out. The first is the focus from sourcing only to sourcing plus. The other thing is about the agentic part. So earlier when I talked about the three phases of digitization as a platform, right? The website. And then the second era is called the AI co-pilot. So that means is that you are still on the driver’s seat, AI is in the passenger seat. It’s giving you advice, it’s doing some little stuff for you, but you are still making all the decisions. You’re still wearing all the hats. And that’s what Accio did back then. For example, it can find suppliers, it can recommend messages for you, but that’s kind of it. So now we’re moving into what we call the agentic team era. where actually we’re gonna get things done for you and get more types of work done for you. so that’s kind of where we are really sort of moving into this phase, where truly kind of it’s like the spirit of the agentic commerce world, where you’re not only using AI as a passenger seat, but you’re actually arranging a team of agents to get things done and maybe let the teams work among themselves, what we call A2A. So that’s kind of the overall context of where we have been starting from Alibaba.com. To Accio the sourcing engine and now to Accio Work as the Agentic business team. Grace Shao (04:45) That’s very comprehensive over you. I appreciate that. So just to help listeners understand, 1688 and Alibaba.com are the sourcing kind of platform within Alibaba. And then obviously everyone knows Alibaba for the Taobao and commerce side of things, but that’s actually a merchant-facing product versus the Taobao and T-mall that are consumer-facing. with that kind of context, okay, so if I had to ask you to describe it in a very short sentence or like say two sentences. How would you actually describe the product today? It’s just like, who is it for? What does it do? Ziwei Chen (05:18) Absolutely. I think I would Start with three words that summarize what it is we will call it the agentic business team. and I’ll break it down each but one of them that eventually lead to the who and the how. So the agentic part means these are agents that get things done for you. It doesn’t just give you the recommendations, but it can write emails for you, send messages for you, publish things for you, right? And then the second part is business, right? So we are dedicated to the world business, specifically e-commerce as a really strong emphasis. an all all Aspects of business starting from the front development side to the growth side to the operations, everything. And then team part kind of aligning up to that as well is that you can build multiple agents to work among each other. So tip from a format perspective, we have a desktop app that you can download, also a web app as well. You can access the same thing on your mobile devices, on your web as well. So all these things are connected where you are creating agents on this platform. telling the agents what to do and then you can go to sleep, you can go to work, you can go to your events, right? All of these things, go to your shop, right? Do all these things while the agents are in the background running all these tasks assigned by you. Grace Shao (06:27) So exactly your point, like you can do a lot of things, and that’s what I found the most fascinating thing. So, you know, we know a lot of products on the market these days tha

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

  3. Jul 28

    The Future of Agentic Payments with Clink Founder Patrick Wu

    In this episode, I spoke with Patrick Wu, Founder and CEO of Clink, about what payments need to look like in an agentic commerce world. Patrick previously worked on payments infrastructure at Amazon and AWS Payments, and later led global payments at Temu as the company expanded market by market. His view is that payments today are largely built for humans at checkout, but not yet for agents acting under delegated authority. The conversation started with a basic but important question: if Stripe, PayPal, Airwallex, Visa and Mastercard already exist, why do we need another payments company? Patrick’s answer is that the problem is not just processing a payment. For agentic commerce to work, the system needs to understand who the agent represents, what permission it has, whether the purchase fits the user’s original intent, and whether that authorization can still be verified later. Clink is positioning itself less as a replacement for payment processors and more as a connector across agents, merchants, payment providers, and card networks. We also spent a lot of time on why payments remain so fragmented globally. Payment habits are local: cards, wallets, bank transfers, convenience-store payments in Japan, and different payment methods across emerging markets. Patrick argues that no single provider is best in every market, which is why AI builders and global merchants may need orchestration across multiple PSPs, local payment methods, and eventually multiple agent platforms. The most interesting part of the conversation was around trust and liability. If an agent buys the wrong thing, who is responsible? Patrick’s view is that fully autonomous shopping is possible eventually, but the trust layer is not there yet. In the near term, agentic payments will likely work through delegated mandates: user-defined instructions, spending limits, merchant controls, passkeys, audit trails, and chargeback mechanisms. His differentiated view is that the industry may be too focused on creating brand new crypto or blockchain rails, when the more immediate challenge is making existing fiat rails work safely for agents — and getting the ecosystem to align around clearer standards. Get in touch with Patrick via LinkedIn. And company website here. Chapters 00:00 Introduction 00:13 What Clink does and why payments need to evolve for AI agents 01:18 Why today’s payment stack is not ready for agentic commerce 03:07 Why payments remain fragmented across geographies, regulations and user habits 05:48 Clink’s role as a connector across agents, merchants, processors and networks 07:06 Why AI builders may need more than Stripe, Airwallex or PayPal 09:34 Visa partnership and what it means to be an “agent enabler” 15:23 What happens when agents spend a user’s money 21:02 Trust, liability, chargebacks and why China’s wallet model is different 23:26 What agentic commerce actually means today 43:54 Patrick’s differentiated view on fiat rails, stablecoins and protocol fragmentation Transcript (AI-generated, for reference only) Grace Shao (00:00) Hey Patrick, so good to have you on today on AI Proem. Thanks so much for joining. Patrick (00:05) Hey Grace, thank you for having me here. Grace Shao (00:06) Yeah. To start with, give us the high level. Who are you and what is Clink in a few sentences? Patrick (00:13) Sure. so at simplest level, I’ll introduce Clink first. Clink is building the the payment and the bill infrastructure for AI builders and for the agent that they will serve. So payments today we see at design around human being on a checkout page and but we’re building the layer that let authorized agents complete the same transaction safely while helping merchants accept the global payments through the system they already use. And for myself, I started my payment career path at Amazon and AWS. I learned how to build a payment infrastructure that has to perform at a very high level of reliability and the security. And at Temu, I led a team to solve the problem with payments and the global market expansion. So we expand country by country, market by market, and solving the payment issue locally. And Clink bring those lessons to a new problem that when we see agent becoming the new commercial actor, but we see the payment stack is not fully ready for that, so we won’t solve that problem. Grace Shao (01:18) So what do you mean by the payment stack is not ready for that? what do you mean by the world’s not ready for agentic payments? And where do you see the gap from existing offerings? Because obviously, when we talked offline, I did challenge you on this. I said, you know, there’s a lot of major fintech players already and payment solutions, Stripe Airwallex PayPal, right? But where does Clink come in and how does Clink solve this issue, Patrick (01:44) Yeah, I think payments are largely solved for human who is present at checkout. so the industry had made it much easier for developer to accept payments like except from card, from our wallet and from like so many payment that local customers prefer. and also the developer can easily launch a product to launch a subscription, send a payment link, right? Those those are like a real progress already made the word. So but what is not solved on the payment side we see is agents acting under the delegated authority. Right. Processing the charges only one part of that transaction, but the system also needs to know who the agent represents and the what permission that it has and whether the purchase is within the policy, within the intent, like matches the original mandate that the user has given. And then the how the evidence being being persist, right? And if we come back in six months later. is that transaction still like able to be verified at the point that the agent made the decision. So I think like global merchants still face another unsolved problems like a single provider isn’t no single provider that best at the every market and solve all that agent issue. And then we know worldwide that the agent can can buy product from anywhere. So we see that’s a big gap that the agent e commerce need to have a both the authentication layer authorization layer and also a practical way to across with all the providers in the market today. Grace Shao (03:07) Yeah, actually on that, you did say, you know, payments are very fragmented, right? Like, and but is that fragmentation mostly based on geo geography, regulatory reasons? Is it about payment habits? Like how do we understand that? Why is it so fragmented and it’s not a one solution fits all? Patrick (03:24) Yeah, I think that’s a great question and a great observation, right? And to be honest, I think it’s all about like all about that because payment really reflects a local financial system, right? Like you spend twenty dollars in Singapore that you may use a card, you may use your wallet, your bank transfer. But if you know in Japan that many e-commerce orders actually being paid over konbini payment. Konbini is the Japanese word for convention store. If you would like to place an order online and getting to a company store and the pay when they check out some some goodies. So it’s just a habits and also the regulation and also how the payments being developed in that particular marketplace. So we see that it’s like all different across the world. That that’s a lesson we learned from from building Tammu payment stack that we actually adopted 70 payment method when in the first year. So every country has their own top three payment method. And every time you add a new pay method, you got some new customers. So it’s kind of very interesting. And then but then the fragmentation is not simply like I I guess you can solve that by building an API and building more like a convenient experience, but we see that as a reflection of local, right? Like habits and migration, all that just as we said. And the the things that we cannot force the users, right? Use got their own votes for what they want to pay. And that will always exist because that’s how the world works here. Grace Shao (04:48) And so thus like where do you fit in then? Like do you have a certain geo gro geography you’re targeting or a certain kind of I guess use case that you’re targeting? Patrick (04:58) you mean the agent e commerce or like in general? I think Clink today we try to solve two problems. Yeah. Yeah, we’re trying to solve two problems. Yeah. So one one problem is for today, like the global monetization for the digital servers and AI builders, right? They they’re selling their product, their great application to worldwide, and then the users from worldwide that like to pay them with the local payment. Grace Shao (05:02) Just in general because Mm. Yeah, yeah. Sorry, go on. Patrick (05:23) So that’s the monetization issue we we try to solve. And also also we’re trying to solve a forward looking issue that when we see the trend that the the actor, the consumer is moving from human to agents, and how agents can leverage the user’s assets, users’ local pay methods, right, and to make that payment, make that purchase, and to make all the information flow and the funds flow fat like try to flow fluently as it is today. Grace Shao (05:48) I see, I see. Let’s double click on the business. Help me understand your business a bit more. So one thing that really stood out to me from our earlier conversations that Clink acts more like a connector. how do you describe your role in the whole like payment stat? Patrick (06:01) sure, yeah. So I would describe Clink as a connector, as you said, right? And a controller or an aux trader. So we do not want to replace the bank, the car network, a processor, all the commerce platforms, the merchants or other agents, right? So we translate the agent’s intent into a transaction, or it could be the human’s intent and delegate to the agent, right, into a transaction. And the merchan

    The Future of Agentic Payments with Clink Founder Patrick Wu

  4. Jul 21

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

    When James Peng founded Pony.ai in 2016, many in Silicon Valley believed autonomous driving was only three to five years away. But he expected it would take at least a decade, because the challenge was never just teaching a car to drive. Commercialization also required regulatory approval, public trust, reliable fleet operations, and a cost structure that could support large-scale deployment. Ten years later, his vision is becoming reality. In this conversation, we start with his founding journey, the milestones and how Pony.ai became a leader in the autonomous driving space. We also discuss the gap between assisted driving, Level 4 autonomy, and the longer-term goal of Level 5, as well as how Pony.ai uses simulation, real-world driving data, and increasingly capable AI models to improve safety. James explains that the hardest problems are often not the obvious ones but interpreting unpredictable human behavior and handling rare edge cases consistently. The conversation also explores China’s cost advantage in robotaxis. A mature automotive and electronics supply chain, close collaboration with automakers, and faster iteration can materially lower vehicle and system costs. But moving into new markets still requires Pony.ai to adapt to different road conditions, regulations and driving cultures, from trams and roundabouts to local pickup behavior. James’s broader point is that the industry has focused too heavily on the initial technological breakthrough. Getting a car to drive itself is only the beginning. The next phase is about deployment density, utilization, maintenance, charging, remote support, and economics. At the end of the conversation, I asked what he believes is underrated. James, an experienced operator, replied - scaling. Pony.ai may have crossed the zero-to-one threshold, but the harder task is scaling from one to ten, and eventually from ten to one hundred. Check out this insightful conversation. For more interesting conversations with people who are charting the way of the future of AI, check out the podcast tab or follow us on Spotify! Chapters 02:36 Why James Peng founded Pony.ai06:36 The milestone that proved robotaxis could work09:27 How passengers learned to trust driverless cars11:41 Level 2, Level 4 and Level 5 autonomy18:59 How AI and simulation improve self-driving24:13 Teaching cars to understand human behavior29:11 China’s cost advantage and global competition35:03 Expanding robotaxis into international markets41:13 Why Pony.ai is also building autonomous trucks48:40 Adapting to new cities, roads and driving cultures53:45 Why scaling is often harder than reaching zero to one Transcript Grace Shao: Hi everyone, welcome back to another episode of AI Proem Differentiated Understanding. This is your host, Grace Shao. Look where I am, the back seat of a car. Doesn’t look that exciting, does it? Let me flip this around. Look at that. There is no driver. I’m in the back seat of a Pony.ai robotaxi. Today joining me is James Peng, co-founder and CEO of the leading robotaxi company. It’s expanded its footprint across the globe, in Asia, in Europe, in the Middle East. But obviously, today we’re in its leading home market, China and Shenzhen, where it has a fleet of a couple hundred vehicles deployed on the streets already. Hi James, thank you so much for sitting down with me. I’m really excited to be having this conversation with you. So you left Baidu in 2016 to found Pony.ai when many in Silicon Valley were saying self-driving cars are only three years away. But obviously that wasn’t the case. Grace Shao: So, what did you believe then that this consensus was getting wrong? Tell us about your journey from 2016 until now. James Peng: Yeah, sure. We were founded in 2016, about 10 years ago. But even at that time, I didn’t believe that autonomous driving can be solved in three to five years. Just from a technical point of view, because even back then, 10 years ago, even a demo for autonomous driving was already very hard. Later on, there’s complexity involved in the autonomous driving industry that involves regulation, user acceptance, the readiness of the ecosystem. So because of the sheer complexity, even then, my prediction was it’s going to take at least a decade for this to be a real application. It turned out to be that my prediction was about right. Now, 10 years down the road, we actually have fully driverless commercial applications in many cities. Of course, it’s just the beginning of the long journey for autonomous driving. But at least now we have real commercial applications. James Peng: So I think people, like any new industry, people were super optimistic for the short term, but they were underestimating the potential for the long term. So I think autonomous driving is definitely one of those industries. Grace Shao: What really drove you to want to actually work on this, work on this technology and the future mobility? James Peng: I think the motivation was twofold. One is that the potential, both commercially and also societal benefits for the autonomous driving is so huge. Think about like everyone needs to have some sort of mobility. Autonomous driving is much safer than a human driver. So it has huge societal benefit of saving people’s lives. So essentially, it’s just such a great industry to work on. Although back then, 10 years ago, it was very unclear when this can be done. The other reason is, of course, because the sheer technical challenge of autonomous driving involves because I was actually in my previous jobs. I worked on different areas, software, hardware, large scale distributing systems, AI and whatnot. But none of the things I worked on is as complex as autonomous driving, which is a field that involves hardware, software, hardware and software integration and Many other things. There’s AI, there’s real time system, there’s also large scale AI training and all that. James Peng: So just from a sheer technical point of view, it’s such an amazing and challenging thing to work on. So I think those two reasons propelled me to start the company. Grace Shao: There’s definitely a lot to unpack there. I think later on we can definitely double click on the hardware, software integration, as well as the safety concern there. You say that autonomous driving is much safer than humans. For sure, it’s safer than me driving. I know that. But some may argue otherwise. So let’s talk about that later. But first, I want to ask you about something that was quite interesting. During 2020-23, there was a bit of a public reckoning, I think, within the industry. A lot of peers folded during that time. People decided to pull out of this sector. Some people worried that autonomous driving would really become a reality. But you guys charged ahead and you really believed in your vision. Tell us about that period and how maybe that changed your vision or your growth mentality. James Peng: I think 2020-23 was a period of time where the autonomous driving industry has evolved for roughly 10 years. I think that was the time of reckoning. That’s the time where the haves and have-nots have really diverged. So I think that’s actually exactly the time. As a company, we have seen tremendous progress. At the end of 2022, beginning of 2023, that was the time we actually finally had the first fully driverless commercial applications operations on the road. So because we made such progress, both from a technical and also from a regulatory point of view, that, of course, we made the progress. We finally see the glimpse of hope. Then, of course, we charge ahead. I think a lot of the other companies who weren’t able to, either from a technical point of view, or from a pure capital-raising point of view, or from a regulatory approval point of view, that weren’t able to have fully driverless applications. Then they were faded away. James Peng: So it’s sort of like, well, everyone is in school. There’s no big difference. But after graduation, then there’s haves and have-nots. So I think that was the time of division. Grace Shao: Yeah. So speaking of milestones, I want to kind of go back into history a little bit. So in 2021, Pony.ai had the third highest number of miles driven behind Waymo, Cruise. In 2022, Pony.ai became the first autonomous driving company to get a taxi license in China. In 2023, Pony.ai was licensed to operate robotaxis in Guangzhou, etc. And expansion continued. So kind of following what you just said, there was good momentum behind you guys. Now, today marks Pony.ai’s 10th year officially. You kind of talked about how you guys have grown. But what was one or two of the biggest milestones that you’re really proud of looking back now and that you think have really set the tone for your company Now as you are really expanding globally? James Peng: Yeah, I think in my view, the biggest milestone, actually, I have already mentioned, is the end of 2022, beginning of 2023, where we were granted the fully driverless commercial license in both Beijing and Guangzhou. We start to have the operation to the general public. Actually, it was in mid-January in 2023 that I was the first road in our commercial robotaxis operations in Beijing. Surprisingly, it was exactly on that day, it was snowing in Beijing, and I was in the vehicle by myself. That was the moment where I actually saw our vehicles were able to drive by itself. Anyone besides me in the vehicle. Because of the snowing, it was also a very challenging scenario. We were actually not being suspended for operation. We continued to operate, and I was in there. That was the moment. Finally, it felt like a dream come true, right? Finally, it’s not just because our technology is ready. James Peng: Also, because we actually got the approval from the government to have the license to operate. So it’s like all the seven plus years of efforts finally pays off. To me, that was felt like

  5. Jul 14

    From Beauty Apps to AI Agents: Meitu’s CFO Gary Ngan on the Future of Visual AI

    In this episode, I spoke with Gary Ngan, CFO of Meitu, about how the company is evolving from its roots in consumer photo editing into a broader AI-native visual creation platform across photo, video, design, and agents. For many investors, Meitu is still associated with beauty editing and selfie apps, but Gary frames the company today as an AI application company serving both leisure use cases and productivity workflows. We spent a lot of time on Meitu’s business edge: why visual AI is not just a foundation-model race, and why aesthetic judgment, controllability, and vertical context matter. Gary argues that visual creation is highly subjective. The same prompt can mean very different things across countries, cultures, product categories, and commercial goals. That is why Meitu is building verticalized products such as Picchi, DesignKit, Kaipai, Vmake, and RoboNeo, instead of relying only on one general-purpose AI model. We also discussed the business model. Consumer subscriptions have become Meitu’s main revenue engine, while advertising is no longer the strategic growth driver it once was. Gary explained the shift in Chinese consumer willingness to pay for apps, the higher ARPU potential in overseas markets, and how new AI-native products like Picchi could introduce additional monetization through personalized models and AI credits. He also addressed AI compute cost, why more than 90% of Meitu’s AI outputs come from its own models, and why the company sees AI as a TAM-expanding opportunity rather than simply a margin risk. Finally, we covered competition and globalization. Gary explained how Meitu thinks about competing with ByteDance, Kuaishou, Canva, Adobe, Shopify, Alibaba, and other AI-native visual tools, and why Meitu’s approach is more vertical-driven than general design-platform driven. Lastly, we touched localization, from different beauty preferences across markets to why true globalization requires understanding culture at a much deeper level than translation or marketing campaigns. CHECK OUT THIS CONVERSATION. Gary’s so cool. To find the previous episodes of Differentiated Understanding, see here. 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. Season two will host a series of guests from analysts, VC investors, builders, researchers, founders, and product managers. For more information on the podcast series, see here. Chapters: 00:00 What is Meitu today? Mapping Meitu’s product portfolio04:14 Why vertical focus still matters in the age of AI agents06:36 Aesthetic standards, subjective prompts, and visual AI nuance11:31 How AI changes art and creative expression15:02 MeituHub and MiracleVision as visual AI infrastructure17:01 Why Meitu needs its own models20:55 How Meitu chooses models and the role of designers25:09 Meitu’s AI legacy and generative AI strategy28:27 AI compute cost, ROI, and gross margin38:22 Subscription growth and advertising dependence40:47 Partnerships with consumer chatbots and platforms43:27 Competition with ByteDance, Kuaishou, Canva, Adobe, and others49:49 Deepfakes, misuse, and AI safety safeguards52:42 Globalization, localization, and cultural differences59:16 The biggest investor misconception about Meitu Transcript (AI-generated, for reference only) Grace Shao:Gary, thank you so much for joining us today. Gary Ngan:Hi Grace. Good to be here. Grace Shao:I’m really excited to have this conversation. To start, tell us what Meitu is up to these days. For a lot of investors and users, when they think of Meitu, they still think of the selfie and beauty-editing app. How would you define Meitu today? Is it still simply a consumer AI company, or is it much more than that now? Gary Ngan:Meitu is no longer just a selfie or beauty-editing company. I would define Meitu today as an AI application company specializing in photo, video, and design. We focus on very high-value verticals where we can leverage AI to deliver high-quality results to users. We often refer to these users as prosumers: people who have strong design needs, but no prior formal design training. So that is how I would define Meitu today. Grace Shao:That makes sense. Tell us about the products, because you have quite an array of them. Some are more consumer-facing, some are more prosumer-facing, and some may even be a bit more enterprise-facing. There is Meitu, BeautyCam, Wink, Picchi, DesignKit, Kaipai, Vmake, RoboNeo. Help us map out the ecosystem. Gary Ngan:We think about Meitu’s product portfolio in two main categories: applications for leisure and applications for productivity. Applications for leisure include the Meitu app, BeautyCam, Wink, and Picchi. They serve use cases such as photo-taking, photo editing, and video editing, usually for sharing on social media. The Meitu app and BeautyCam are our core consumer applications. Wink extends our capability from photo to video editing. Picchi is our latest portrait-retouching agent, focused on personalizing editing styles. The second bucket is applications for productivity, which includes DesignKit, Kaipai, Vmake, and RoboNeo. These products serve professional and commercial content creation needs. DesignKit focuses on e-commerce product-listing design. It helps merchants and creators produce product images, model images, and marketing materials much more efficiently. Kaipai and Vmake focus on talking-video and marketing-video creation. Kaipai is more focused on the domestic Chinese market in verticals such as insurance and real estate, while Vmake is seeing strong traction in the U.S. fitness and wellness market. To give you a sense, as of May this year, Kaipai had about three million monthly active creators, and cumulative content creation exceeded 400 million pieces. For Vmake, ARR in the first quarter of 2026 was about US$4 million. Then there is RoboNeo, our AI-native agent product launched in July 2025. It is currently targeting the AI short-drama vertical. Its agent workflows can support scriptwriting, characters, storyboards, visual generation, and asset management. So that gives you a rough idea of the different vertical products. But the ecosystem logic is very important, because many new products come from user insights we observe in existing products. For example, DesignKit came from the poster-design function within the Meitu app. Kaipai came from the AI teleprompter feature in BeautyCam. Picchi came from new user behaviors we observed in the Meitu app. So our portfolio is not a random collection of apps. It is a structured expansion from consumer imaging into AI-native workflows across photo, video, and design. Grace Shao:That makes a lot of sense. But in the age of AI agents, would it make sense for Meitu to consolidate a lot of these apps? Or do you still think it is better to keep them separate for different types of users and workflows? Gary Ngan:In the age of agents, we still believe we should focus on high-value verticals, because different verticals have many differences. First of all, aesthetic standards are very different. I’ll give you an example. The phrase “handsome guy” would be interpreted very differently in an application serving the U.S. market versus an Asian market. Even within the Asian market, if you are addressing e-commerce merchants selling gym products versus formal apparel, the word “handsome” will also be interpreted very differently across those verticals. So being able to separate these different verticals gives you a very good head start in focusing on the aesthetic standards that each vertical needs. Also, users in different verticals have very different behaviors and workflows. It is very important to build those workflows and that know-how into each vertical in order to create the right products. With agents, you can cover a slightly bigger boundary. But I still think you want to focus on different verticals to maximize the output for the user, and also make it more efficient and easier to market within each vertical. Grace Shao:That is really interesting. You touched on something that a lot of people discuss when they think about visual AI, which is how to ensure consistency and accuracy when translating language into visuals, especially when text can be in different languages and words can be subjective. As you said, if you say “handsome” and I say “handsome,” that could mean very different things in our heads. How do you ensure that identity, description, and nuance are not lost? You mentioned vertical focus, but what is the technical side of that? Gary Ngan:Instead of calling it fragmentation, I would say vertical focus is very important. That sets the tone. Behind that, we also have a large team of designers who control different points in the model fine-tuning process. They help set the right direction for the aesthetic standards within each vertical. That is something differentiated in our product offerings. Then, if you move one step forward, the data flywheel is also very important. Users within a vertical give us data through their behavior: which photos they use, which photos they edit, which ones they throw away. That is very important for us to improve image creation. We are also in the camp that believes controllability in visual applications should not just come from AI. You still need manual touch-ups at the end for users to make last-mile improvements, because aesthetic judgment is very subjective. Even if an AI model works with you every day, you will always have subjective comments and small edits you want to make. One other interesting point is that when we talk about aesthetic standards, in the case of leisure products, the face is usually yours. So you have a strong say and a strong sense of what is good for you. The way we learn that is by studying the trend in your geographic location, giving recommendations, letting you try them, and th

    From Beauty Apps to AI Agents: Meitu’s CFO Gary Ngan on the Future of Visual AI

  6. Jul 13

    Paul Triolo on Chinese labs makings chips, SMIC, Huawei and the importance of AI governance

    Hi all, we’re back with the podcast. This is the perfect time for Paul Triolo to join us as he gives us a preview of the upcoming WAIC and walks us through some of the top-of-mind questions we have about China’s AI space right now. I do want to apologize for a bit of the echoing in the background; lesson learned to always use headphones going forward. In this episode, I speak with Paul Triolo, partner at DGA-Albright Stonebridge Group, about how to think clearly about China’s semiconductor ecosystem, Huawei’s role in the domestic AI stack, and whether U.S. export controls are actually working as intended. We start with the latest debate around restricting foreign access to advanced Chinese AI models, before moving into the semiconductor stack itself: SMIC’s role, capacity bottlenecks, domestic GPU startups, hyperscaler chip efforts, Huawei’s vertical integration, and why software ecosystems like CUDA, CANN, and MindSpore matter just as much as hardware. Paul argues that the usual framing- whether China can “catch up” to Nvidia or TSMC is simply too narrow. The more important story is that export controls have pushed China toward a broader systems-engineering response across chips, tools, packaging, memory, software, and cloud deployment. We also discuss HBM, rare earths, remote-access loopholes, the logic behind Huawei’s roadmap, and why the collateral effects of controls may be larger than policymakers expected. We close on the bigger strategic question: whether the U.S. and China are drifting into an AI race dynamic that raises risks for everyone, and why more direct dialogue — not just more restrictions — may matter most from here. [Paul co-authored a piece here discussing how to navigate the complexities of the U.S.-China AI safety dialog] This is an extremely insight-dense episode, and I hope you enjoy it as much as I did. Thanks again Paul Triolo. Btw, coming up next are a few episodes featuring founders and execs from hot-listed AI, autonomous driving, and spatial intelligence companies. To find the previous episodes of Differentiated Understanding, see here. 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. Season two will host a series of guests from analysts, VC investors, builders, researchers, founders, and product managers. For more information on the podcast series, see here. Chapters 00:00 Beijing, Chinese AI models, and why regulators are paying attention04:43 Why AI labs are moving into chip design08:12 SMIC’s role and the fight for domestic chip capacity17:56 Huawei’s capabilities and why it became the center of the conversation26:53 CUDA, CANN, and whether export controls really worked38:24 Can Huawei’s software stack win developer mindshare?51:10 Why China can still progress despite compute constraints57:07 The AI race narrative and why Paul is skeptical of it1:07:46 Why governments still lack the technical capacity to respond1:08:42 Will frontier AI labs eventually be nationalized?1:13:59 The risks of zero-sum U.S.-China AI policy1:20:46 Why more direct U.S.-China dialogue matters Transcript (AI-generated for reference only) Grace Shao (00:00) Hi, Paul. Thank you so much for joining. Really excited to have you on. And I feel like you’re the perfect person for a few of the questions I have prepared in the beginning of the podcast before we get into the actual topic. There are so many things happening, and I can’t keep up with like Twitter these days. So no, so supposedly Reuters reporting saying Beijing is restricting foreign users in accessing Chinese models. Like, what is that all about? What’s your view on that? Paul Triolo (00:15) It’s hard to keep up. Yeah, I think in the wake of the fable mythos fiasco, if you will, in the US and the capabilities of these advanced models getting really good and getting into areas like cybersecurity and biosecurity, I think it’s not surprising that the Chinese government is at least considering what to do about open-weight more properly models that are that are that are reaching sort of frontier level capabilities, particularly DeepSeek and Zhipu and then even more recently, you know, Meituan and others. I think my sense is this is just preliminary discussions with the labs about this issue because, you know, even in the US, there’s lots of confusion about what the government’s role should be here in determining when models are released and under what circumstances and how do you measure capabilities. even though the US has been thinking about this for a while, and I’m sure that’s to some degree that’s happening in China, there’s no general agreement on how do you do this. And these companies in China, as you know, are all commercial companies that just like in the US are under a lot of pressure to continue to put out models. and so I think I would not read too much into that. I think that’s it’s clear that the Chinese government is Trying to figure out what to do with about this, but I don’t think they’ve reached any conclusion about which models to control and how to control them. they’re they’re learning from the companies. They’re probably going out and saying, you know, how do companies themselves evaluate these models internally, in terms of capabilities that could be of concern? and what should the Chinese government eventually do? I mean, they’ll they’ll they’ll do something eventually, but I think we’re in the early stages still. as a result of the Grace Shao (01:57) Yeah, for sure. I think you know, Zhipu, Minimax, these companies are public listed, like they actually face, you know, just shareholder pressure as well. So it but the one thing, the nuance is Chinese companies usually are a bit more prepared or aware of potential regulatory, I guess, involvement. so yeah, let’s let’s wait and see. Because I read this and I was like, this seems bit counterintuitive, frankly, to the model’s going forward. I think like the deadline was a little out in front here. I think it’s Paul Triolo (02:49) Right. I think that the headline was a little out in front here. I think it’s clear that there’s concern as these models become more about what to do about tiered releasing. but this is a more general discussion I think that’s happening. it doesn’t surpr it to people who’ve been following this sector for a long time, the idea that we would be here at this moment, you know, was not surprising. The problem government gov the ability of governments to keep up with the pace of development of the technology is just clearly here, it’s it’s woefully inadequate to the moment because you know within these large AI labs, and I’m now calling DeepSeek and Zhipu, along with Anthropic and OpenAI, you know, the top four global frontier AI labs. you know, th researchers n understand these issues and they’re they’re really concerned about this because P particularly things like recursive self improvement, which is which means models are basically training the not training themselves, but they’re they’re optimizing some of the orchestration or the harnessing that the sort of platforms that these things operate on. You know, that’s been a that’s a growing concern because they’re you know, the models themselves now are able to improve the overall ecosystem without human intervention. Right. and that people miss that, I think, in the in the US with all the fable mythos kerfluffle. The Anthropic released a blog that talked about this, that recursive self-improvement is now sort of part of the landscape. And so that’s also I think part of the concern including in within the Chinese government about okay, you know, Chinese labs are getting pretty good. what should the government how should the government think about Grace Shao (04:43) Yeah, definitely. And I think, you know, in China, usually the regulators and the industry actually work pretty close together. so hopefully, you know, people can kind of regulators can keep up, will hopefully catch up on understanding technology a bit faster and better. Okay, so another quick commentary on what is happening in the news. Supposedly DeepSeek and Kai AI are going out and making their own chips. What is happening? What is your high level view on this? Paul Triolo (05:08) Everybody’s doing it. Now, you know, in it this is not surprising. US, of course, some of the hyperscalers and more of the hyperscalers like Google and AWS have long d determined that it would be useful have specially designed ASICs, application-specific integrated circuits that are optimized for running certain workloads in their cloud. and you know and optimi na and now optimized for models specif specifically for you know large advanced models for which general purpose GPUs, which is what NVIDIA and AMD produce, may be, you know, may be suboptimal. but this is a complicated issue because to do semiconductor design, you know, this is a whole nother thing than building models. And so for both Zhipu and DeepSeek, you know, this requires building a team of design so some semiconductor design engineers, right? Who now they’re not they’re not a lot of these guys laying around that are not gainfully employed, particularly for designing really sophisticated chips here. So I think it’s not surprising that they want to do this, but I would be again sort of a little bit skeptical that they’re gonna do be able to do this in the you within the next year even. You have to build a team. It’s expensive. use you have to get advanced semiconductor design tools, electronic design automation tools. and then you have to begin figuring out where you’re gonna manufacture these, right? And in China, of course, as we know, because of export controls, these companies are likely gonna have to use SMIC, the domestic foundry. So they’ll be competing wi

    Paul Triolo on Chinese labs makings chips, SMIC, Huawei and the importance of AI governance

  7. Jun 17

    Future of mobility, a deep dive into the forces driving the Chinese EV revolution with Tu Le

    Hi all, I’m really scared to even share this episode because the last time I recorded an episode with Kyle Chan and mentioned cars, I got ripped online. So I just want to emphasize again that for car enthusiasts, I AM NOT A CAR person. I am here to learn. Haha, ok now that I’ve made that disclaimer… Joining me today is the ever-so-knowledgeable Tu Le. He is the founder and managing director of Sino Auto Insights, author of the SAI Weekly Substack, and co-host of the China EVs and More & At The Wheel podcasts. He has worked across Detroit, Silicon Valley, and China, so he views the industry from the inside, through the traditional auto industry, the tech industry, and the Chinese market. I wanted to do this episode almost as an educational primer, not just for you all but for myself as well. Most people now understand that Chinese EVs are competitive. But very few people understand why and how that is translating into the Physical AI space. We talked through the Chinese EV landscape, why traditional OEMs struggled to make good EVs, how autonomous driving fits in, how these carmakers are integrating AI, and why home appliance and smartphone companies like Huawei, Xiaomi, and Dreame are suddenly making cars. Follow Sino Auto Insights here: https://x.com/SinoAutoInsight For consulting inquiries, go DM Tu Le on LinkedIn! Website: https://www.sinoautoinsights.com/ Btw, I’m rebranding Differentiated Understanding to AI Proem Podcast. To find the previous episodes of Differentiated Understanding, see here. 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. Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, and product managers. For more information on the podcast series, see here. Chapters 00:00 Introduction to Tu Le and Sino Auto Insights 04:28 Mapping the Chinese EV Industry 09:19 The Rise of Xiaomi in the EV Market 14:17 Understanding BYD’s Market Position 17:54 Challenges for Traditional OEMs in EV Production 31:29 The Role of Government Subsidies and Policies 36:12 AI Integration in EVs and the Future of Mobility 45:13 The Evolution of Brand Experience in EVs 46:53 The Future of Manufacturing and Market Dynamics 50:46 Safety Concerns in Rapid Development 52:51 Current Landscape of Autonomous Driving in China 57:51 Challenges in Deploying Autonomous Vehicles 01:05:00 The Future of Mobility and Urban Planning AI-generated transcript (for reference only) Grace Shao (00:00) Hi Tu. Thank you so much for joining us today. I’m really excited to have you on. Tu Le - Sino Auto Insights (00:04) Thanks for having me on, Grace. Grace Shao (00:06) Yeah, to start, why don’t you tell us a bit about yourself? We were just having this conversation right before recording. I find your background really fascinating.You know, you can talk to a very diverse group of kind of people. You run a successful consulting gig, a consulting company. Tell us about everything that you do. Tu Le - Sino Auto Insights (00:24) So name is Tu Le I’m the managing director at Sino Auto Insights. I also create content. I run or I co-host two podcasts, China EVs and more, and at the wheel with my co-hosts that are very, very good at what they do as well. And then I write a weekly newsletter, almost weekly anyways, called Sino Auto Insights Weekly that just kind of goes over my thoughts on what’s happening in the industry now globally every week. And I’m actually not Chinese. I’m Vietnamese. And I was born in Vietnam and moved to the United States when I was a year old and grew up right outside of Detroit. My whole family, youngest of eight, whole family’s automotive. So grew up car kid and did that for a few years before going back to grad school and moving to Silicon Valley to work for seven years. So that’s where the knowledge of the tech comes in, especially the hard tech, where hardware software integration is such an important part of creating a great user experience. And then I met a girl and in San Francisco. my girlfriend, who’s now my wife, was transferred by her company over to Beijing, where she was born. And I decided to pull the ripcord and and follow her over. And what we thought was going to be a three- or four-year assignment ended up being thirteen. And during this time I worked in automotive; I worked at a few Chinese EV e-commerce startups. And so that’s when I learned and experienced nine nine six myself for about two years. And yeah, it’s not fun, super intense, but again I wouldn’t trade those experiences for the world because it gives me the perspective that I have now. about eight years ago I saw this huge disconnect because EVs were becoming a thing because of Tesla. Companies like NIO and XPeng had just been founded. And you know, in Beijing, as you know, Grace, there’s a lot of the German OEMs, and so there was a bit of arrogance about how hard or how simple they thought software was and really, really being consumer focused as opposed to product focused. So I saw this opportunity, and I started this consultancy, Sino Auto Insights, and you know we’ve been growing since we’ve done traditional work. We’ve worked with the UK government, US government on things. And then also when I moved back four years ago from Beijing, I left during COVID. So August of 2022 and then November, December timeframe, China opens its border and says, What COVID? Come on in. So we didn’t know that was going to be the case. so we decided to move back. And we opened an office here in just outside of Detroit. And we’ve been helping more on the investment side, looking for investment opportunities, what’s around the corner, but also giving our clients a better understanding of the Chinese EV players and the battery players and what they’re doing outside of China. So it’s a very, very interesting time. The mobility space, as you know, Grace, involves now AI, silicon, data centers, data privacy, data security, batteries. So it’s just, just a tremendously unique sector that I get to be a part of. Grace Shao (03:55) Thank you so much for sharing your life story. First of all, kudos to your mother. Eight kids. Like, I don’t know how she did that. Like I have two and I’m already dying. And also, I love your personal touch, you know, why you moved to Beijing and just learning about your background. I think it’s super fascinating. You pointed one thing out. Like when I was living in Beijing in Shanghai as well, I met a lot of German OEM like employees and people kind of low key don’t know this, that there’s a huge German community i it in the huge like and and French as well. A lot of Europeans are actually working in China for these, especially like luxury vehicle companies. and a lot of them did relocate out of China during COVID times. And a lot of them I’ve even heard anecdotally from two friends who say they’re dying to get back because they were born in Munich or Frankfurt. just because they’re so bored. Tu Le - Sino Auto Insights (04:23) Huge. We hear those stories a lot, don’t we, Grace? We hear those stories a lot. Grace Shao (04:48) it’s just because it’s just the fast-paced energy in China. However, okay, COVID was crazy. China’s fast paced. Let’s get to that actual topic today. I wanna talk about EVs. I wanna learn everything from you. so before we get started, when we think of Chinese EVs, most people outside of China think of BYD. think of maybe like the few other ones you mentioned, like NIO X Peng. Now Xiaomi Dreame, which is crazy; essentially, these home appliance companies are going into the space as well. they are there are state-owned companies, there are old independent automakers, there are startups that we just talked about. And then some of them also produce batteries; some of them are, like I said, home appliance and phone companies. Basically, all of these different moving parts, they’re all coming into the same arena. Help us map out the industry first. Like to start with, who are the main players? What are the buckets? what does each group bring to the table or what’s their differentiating kind of offering? I know this is a very big question, but start with a big picture. Tu Le - Sino Auto Insights (05:45) So well, let me press rewind and kind of frame it and create more context as opposed to just we’ll we’ll zoom out and then we’ll zoom into the China market. So last year, twenty twenty-five, Toyota was the number one global automaker, eleven million units, around eleven, just over eleven million units. Volkswagen was number two at eight million. To give you a sense of scale, Tesla was one point six. million units and BYD was about 4.6, which makes them a top 10 automaker. The other top 10 automaker for the Chinese was Geely. Geely and everybody else outside of BYD and Tesla build ICEs and EVs. Or in China, they call them NEVs, new energy vehicles, which means that they’re battery electric vehicles plus plug-in hybrids, E Revs, and then fuel cells. So fuel cells, for our intents and purposes, are rounding error. So when we talk NEVs, we’re talking battery electric and plug-in hybrids and extended range electric vehicles. So Toyota’s been number one for a long, long time. And you know, the China market has been the number one passenger vehicle market since 2009, overtaking the United States. And now the China market is almost twice as big as the US market. If we add the European market, which is around 12 and a half, 13 million units, and the US market, which is around 15 and a half, 16 million units, it’s almost the same as China. And so the scale of the China market is enormous. And so to talk about EV specifically, I would create different sets of buckets. And I would look at BYD, Chery, Geely, Great Wall as separate companies, SAIC because they produce in the

    Future of mobility, a deep dive into the forces driving the Chinese EV revolution with Tu Le

  8. Jun 8

    Where does Europe fit in the so-called China-US AI race?

    Joining me today is Alex Lu, who offers a unique perspective. Alex works at the intersection of three very different AI worlds: China, Europe, and enterprise transformation. Having spent more than a decade in France and now advising European companies on AI adoption (often Chinese models), he offers a perspective that is often missing from the broader AI conversation, which is typically framed as a competition between the United States and China. In this conversation, we explore how European companies are actually approaching AI implementation. Rather than racing to deploy the latest models, many are focused on organizational design, employee adoption, process changes, and measurable returns on investment. Alex explains why European firms tend to be more cautious than their Chinese counterparts, how concerns around AI sovereignty shape technology decisions, and why companies increasingly find themselves balancing U.S. frontier models, Chinese cost-efficient models, and European alternatives such as Mistral AI. We also discuss the economics of AI adoption, including the emerging concept of “tokenmaxxing” or rather if that is even the wise path forward, whether AI is truly replacing jobs, how companies should think about ROI when AI introduces variable costs, and why the future may involve token budgets becoming as commonplace as mobile data plans. Finally, we explore Europe’s position in robotics, industrial AI, and regulation, and whether Europe’s strength may ultimately lie not in building the largest and best-performing models, but in defining how AI is deployed responsibly at scale. To find the previous episodes of Differentiated Understanding, see here. 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. Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, early adopters, and product managers. For more information on the podcast series, see here. AI-generated transcript (for reference only) Grace Shao (00:01) Hi Sheng Yun. Thank you so much for joining us today. Really excited to have you. Alex Lu (00:05) Yeah, thanks very thanks for inviting me. I’m also very excited to have this conversation with you. Grace Shao (00:11) Yeah, awesome. So tell us about your journey. I think you’re in a pretty unique position. You know, like I said in the intro, you know, a lot of the conversation about AI right now is often positioned between China versus US, But you actually work predominantly with European companies in adopting AI and their digital transformation. So tell us about your your background and how you got into this. Alex Lu (00:31) Yeah. so thanks a lot. So actually, I went to France. I spent more than 10 years in France. I went to France in 2004 and I studied in a school called Ecole Polytechnique. and then when I graduated from the school, I started my work in in Europe, mainly for automotive industry and afterwards for the consulting industry. And still when I was in the consulting industry, I worked mainly for for the auto sector. So I have a very traditional background of automotive. That’s why some of the work I’m doing currently in the in the AI, we can come back on that, is in the automotive manufacturing sector and mainly for European companies. Because I started my career in Europe, so I know I don’t I know them pretty better, pretty good. And the the the other thing point I want to mention is the school I started actually the Ecole Polytechnique was Let’s say it it was a famous school in France or in Europe, but it it’s not so famous in in the world. actually this is in France they have a different educational system. but still with the with the rising of of AI in Europe, especially the French large language model called Mistral AI, the school becomes famous because the founder of the of of of Mistral AI comes from the the same school. So basically it’s also a a little bit like Tsinghua university in China is like the the Tsinghua in in France, having the best talents for for the AI. So nowadays, when I continue my work in the AI transformation for companies or AI implementation for the companies, I work a lot with European companies. Firstly, I know that my I as I said before, and secondly, is when we look into the global competition between China, US, and Europe. In the AI landscape, it’s pretty clear. It’s like China and and US or US China being the tier one or first ranked models. And Europe is kind of lagged behind. So most of the European European companies, they have this kind of attitude of being a little bit complex, I would say. on one hand, they are kind of seeking for, of course, for the best technology in the in the world to enhance their company’s competitiveness. There comes the question, how I can define my AI strategy for next year’s between Chinese and US tech stack in AI. And the second question they raised often is while we are European companies, we want to keep keep our AI sovereignty, which is a very important topic in AI. again, we can come back on that. So their question is: okay, between this US and China tech race. Is there any place for European companies regarding the foundation model companies or application companies or even corporate clients? What could be the playground for European companies? So these are major two questions are often received from European companies and you will you can see the thinking angle is they European companies want to at the same time keep it keep the AI sovereignty and at the same time keeping their competitiveness. That makes the question a little bit complex. Yeah. Grace Shao (03:49) Actually why don’t we just double click on the unpack that a little bit? What’s your view on it? Like what what do you advise your clients to do then if if they are kind of cut caught in a pickle or unsure how to build out the next stage of their infrastructure kind of being caught in between China and the US? Alex Lu (04:08) Yeah. So the the first thing I I always shared is in in in this tag race actually China and US we are not I want to twist twist a little bit the angle saying this is a competition between China and US. Actually, if we look into details, actually China and US are taking different directions in terms of the AI development, if I can say, because let’s say if we look into the US, AI ecosystem or the AI development. I think a lot of efforts are put on the foundation model or kind of foundational research regarding how AI can be become AGI can be bring beneficial benefits to the humanity, or how we can guide Rails AI so that okay, one day we will not go into the direction of science fiction movies. So this is a little bit the the push from the US AI companies. While in China, actually the ecosystem or from the national perspective, China’s AI is more about applications and more about how we can have the s beneficial from the whole society from the AI and how I can combine AI with my traditional technologies or traditional business to to to to to grab more values. So if we think in this angle, actually it will give us two different pictures. One is we cannot say that it’s kind of from front to front front competition, because these two nations are just take different angles. The second thing is if we look into details based on these assumptions, we will say one nation is pursuing having the most advanced AI technology and one nation is pursuing most kind of most beneficial AI for the society regarding cost effectiveness, et cetera, et cetera. So then it comes to the question that you raised for European companies is We always brainstorm and conclude on the simple question is what kind of AI are we looking for for European companies? Are we looking for, let’s make it simple, I take some an analogy. Are we looking for kind of you need all the employees to be the PhD employees having the most intelligence in the world? Then that will be the US foundation models. Or if we want to say we have the most cost efficient and best performing employees, virtual employees in your company. Then we might consider Chinese models, foundation models. Then this is the trade-off. I think the companies should figure out. And the answer will not be so simple like that, saying, tomorrow I will switch to all US tech stack or Chinese tech stack. I would say the two ecosystem, as in the past in the digital area, will still continue for European companies, meaning that they need to juggle with Chinese tech stack in certain markets. maybe in Chinese market for sure, but for other markets, developing markets where the Chinese foundation model are taking influence and as well as with US models. So this is the thing. And I think the other angle answer to to to their question is I I usually take the statement from Jensen Jensen Huang saying the AI is kind of five layer cake. So what we are talking about is only one layer, which is the foundation model. And if we go deeper, then we will have infrastructure like data centers, like powers, chips, and electricities. And if we go upper, we will have the applications. So I would tell European companies or I told European companies often is I think the use cases in Europe makes a lot of sense because the cost is there and the employee was pretty much expensive than Chinese employees. So if we deploy the same model, let’s say, and it of cost the ROI return on investment, you make the business case very easily in Europe than in China because the labor cost is kind of lower. And the the advantage of Europe, one I would say one of the advantages is about power and electricity. I study in France and in France y you would see they have the most advanced nuclear nuclear power technology in the world, at least in the past. And I think the French government is also think about how we can build more power plants in the in

    Where does Europe fit in the so-called China-US AI race?
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About

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. aiproem.substack.com

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  • Creator
    Grace Shao
  • Years Active
    2025 - 2026
  • Episodes
    37
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    Clean
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    © AI Proem
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