AI Proem Podcast

Grace Shao

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. 9h ago

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

    The Future of Agentic Payments with Clink Founder Patrick Wu

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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?

  7. Jun 1

    China’s internet ecosystem, manufacturing base, batteries, EVs, robotics, and semiconductor becoming an AI-enabled industrial system

    In this episode of Differentiated Understanding, I spoke with THE TP Huang, an independent China tech analyst known for his work on fintech, EVs, batteries, AI, semiconductors, and the broader China industrial ecosystem. The conversation traces China’s technology evolution from the early internet era to the present. TP argues that China’s internet ecosystem was shaped by a combination of censorship, protectionism, local engineering talent, and intense competition. That created powerful domestic champions such as Tencent, Alibaba, Huawei, Baidu, and ByteDance, which later became the foundation for super apps, payments, e-commerce, cloud infrastructure, and AI. The discussion then moves into China’s shift from software and internet platforms into hard tech: EVs, batteries, robotics, drones, semiconductor supply chains, and AI-enabled industrial systems. TP emphasizes that China’s technology companies are unusually willing to enter each other’s markets. Xiaomi moved from phones to chips and EVs; Huawei moved from telecom to semiconductors, AI chips, and autos; BYD moved from batteries to cars, solar, transit, chips, and potentially robotics. A major theme of the episode is that China’s AI story is not only about large language models. It is also about the physical stack around AI: batteries, sensors, motors, chips, power systems, critical minerals, factories, and real-world deployment. TP argues that this manufacturing and supply-chain density may become a major advantage in embodied AI and robotics, especially as real-world robot data becomes more valuable. Follow TP Huang here on X or Substack here 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 The Evolution of China’s Tech Landscape 05:58 China’s Internet and Tech Sovereignty 09:01 Investment Trends in China’s Tech Sector 11:04 The Role of Government in AI Development 20:00 The Intersection of EVs and Robotics 26:07 China’s Competitive Edge in EVs and Robotics 36:18 Global Strategies of Chinese EV Companies 42:31 Advancements in AI and Robotics in China 48:31 China’s Digital Infrastructure and AI Adoption 57:38 Underappreciated Developments in China’s Tech Landscape 01:00:00 Non-Consensus Views on China’s Economic Health AI Generated Transcript (for reference only) Grace Shao (00:00) Hello everyone, welcome back to another episode of Differentiated Understanding. I am your host, Grace Shao. As many of you know, I also write the newsletter AI Proem, which is AI PROEM on Substack, so do give that a follow. Today we’re doing something special. We’re doing an audio-only version. I’m joined by TP Huang, an independent China tech analyst who writes about the intersection of fintech, EVs, batteries, AI, and broader China industrial policy. He has built a large following on X and Substack by combining data, supply-chain detail, and geopolitics to explain where China tech is actually heading. In this conversation, I want to use TP’s lens to understand the bigger China tech landscape: how China moved from internet platforms and payments into EVs, batteries, robotics, and now AI-enabled industrial systems. And since he quite literally said, “I can talk about anything China tech,” when I reached out, this conversation may follow the themes that I prepared, or really just go anywhere it naturally takes us. Very excited to have him on. Welcome, TP. Grace Shao (00:02) Hi, TP. Thank you so much for joining us today. I just did your intro before talking to you. And I told everyone that when I emailed you and reached out, I said, here are some topics I want to talk about. Is that okay? And you quite literally said, “We can talk about anything China tech.” So the conversation today could cover quite a lot of bases. I’m so excited to hear from you and have you kind of dissect a lot of your knowledge for us. And, you know, I’ve been a big fan of following your Twitter, your X, for a long time. Anyhow, thank you so much for joining us today. TP (00:33) I’m just really glad to be here, Grace. Grace Shao (00:37) Yeah. So you’re a mysterious man. Give us some color on your background and why you are so knowledgeable about China’s tech ecosystem, because you’ve really been covering everything from robotics to LLMs to the internet era. You cover them all, including hardware and chips and everything. TP (00:56) Yeah, so it’s kind of interesting that my actual background is not very technical in that area because I’ve been working mostly in the finance sector, or fintech sector slash crypto, for most of my working life. And I did spend a year recently working in an AI firm, so that was something different. But now I’m back to doing more crypto kind of stuff. So my background, I guess now, is a lot more AI-related. But a lot of the interest I had back in the day was in the renewable space and climate change and things like that. So that really got me started following solar panels, wind turbines, and then EVs. I first read about BYD back in 2008, like a lot of other people. And then as EVs were really taking off in China, that’s when I thought, okay, I really need to understand the full tech stack behind it. So that kind of got me into the entire battery supply chain, a lot of the upstream stuff, and then chips. The chips part became such a big deal because of AI. So then we had the October surprise back in 2022. That’s when I decided, okay, I’m really going to try to understand how the semiconductor manufacturing part of it works also. And thankfully, I was able to be connected to a lot of people. That allowed me to really understand a lot more. So I don’t profess to be an industry insider or anything like that. I’m just talking to other people who are working in the industry for some knowledge and writing about it. And then with AI, I actually worked on my own, no, not on my own. I worked with an AI startup, and one of the projects we did was actually for an AI toy. So I had experience running what I would consider to be AI robotics efforts. So I have a lot of real-time experience with embodied AI and also just using large language models. That’s kind of how I got into all this stuff in the first place. Grace Shao (03:26) It’s really cool because you have experience across the whole array. One personal question is: what drives you to really continue writing? Because you do write prolifically on Twitter. You have these hot takes, you put things together, and I think you’re quite widely followed by anyone who covers China tech. So what makes you want to share things publicly? TP (03:49) Yeah, I guess it’s more like a personality kind of thing, where I really just enjoy writing. And I think there’s something missing in the information space about what is going on in China. Last summer I was in China for a month, and I plan to be in China again for a month this summer, and I just saw a lot of really cool stuff. I think it’s good for the world as a whole to understand what’s going on in China, for Americans and for all Westerners to understand what’s going on in China, so that we are better informed in understanding how people can work with China and what kind of things people who want to compete against China need to know. But as a whole, I think it’s better to get proper information out there. And because China is a different language, and most people in China post in their own internet ecosystem on Weibo or WeChat, people don’t really read this stuff. So they get their sources from very bad sources on the English internet. A lot of them are just missing the nuance of what’s actually going on inside China. So because there is this vacuum, I just felt I’m obligated to actually do something about it, to help everyone understand better. Grace Shao (05:31) That’s awesome. It’s part of why I write AI Proem too. Well, okay, let’s get into the real stuff today. You’ve been following China’s tech for a while, like you said. Help us understand, just with the sentiment shift, how you view the early internet era to today’s success in hard tech and AI. What really has propelled China’s success in the tech sector in the last 10 to 20 years? TP (05:58) Yeah, so I think if we look back on things, China made a pretty big bet on developing its tech sovereignty back in the early 2000s and 2010s. It put a lot of policy in there under censorship reasons. It said, we’re blocking, we don’t want Google or whoever wants to enter China to actually censor the search results so that it fits our local law. And then what actually ended up happening was it became more of a protectionism kind of thing. So China was protecting the local tech champions at the same time that it was pouring a lot of money into these firms. So it allowed firms like Tencent and obviously Huawei and Alibaba to grow up. Later on, China also developed ByteDance. And if you look at how things are around the world, most countries, most leading Western countries that could have possibly developed their own tech ecosystem, like European countries or Japan, didn’t do it. The only other country that has a pretty robust local tech ecosystem or tech champion is Korea with Naver. And if you go to Korea, you notice that if you’re using Google Maps, it’s almost unusable. You kind of have to use Naver. So I think there’s a clear correlation between blocking US tech and some level of protectionism to having a local tech ecosystem being developed. And obviously it requires good local engineers also, so that they can take advantage of that. But China ha

    China’s internet ecosystem, manufacturing base, batteries, EVs, robotics, and semiconductor becoming an AI-enabled industrial system

  8. May 25

    China's open-source ecosytem and the future of AI bootstrapping with ex-Hugging Face APAC head

    Joining me today is Tiezhen Wang (Tom), formerly of Hugging Face, where he worked with researchers in China, Australia, South Korea, Japan and across APAC, to help make open-source models more discoverable, usable, and visible to the global developer community. In this conversation, Tiezhen explains why Hugging Face became the GitHub for models and why open source is not just a distribution mechanism but a different way of coordinating research. We discuss why Chinese AI labs have leaned so aggressively into open models, how DeepSeek changed the commercial logic of open source, and why Qwen, Kimi, GLM, MiniMax, and others are using openness as a way to win attention, recruit talent, and accelerate the whole ecosystem. His core argument is that China’s open-source AI push has three layers. At the researcher level, open source preserves attribution and career mobility. At the company level, open models can become benchmark-led marketing, developer distribution, and a recruiting advantage. At the ecosystem level, government and university incentives are beginning to cultivate open-source culture among younger engineers. We also discuss why US frontier labs have pulled back from openness as research and business have become more tightly coupled, why distillation is much murkier than the public debate suggests, and how DeepSeek’s releases increasingly function as shared R&D for the broader AI ecosystem. The conversation then turns to monetization: why open-weight labs can still make money through API tokens, base-model access, post-training services, and inference optimization. Finally, he lays out his current thinking on AI bootstrapping: the idea that agents may eventually help improve their own harnesses, generate training data, and even improve the models they rely on. We close on a more philosophical question: if a handful of closed labs control access to frontier capability, open source becomes more than a technical preference. It becomes a check on the concentration of power. Tiezhen/ Tom is based in Sydney, Australia. Feel free to reach out to him on X to chat. 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 04:07 The Philosophy of Open Source at Hugging Face 12:51 Challenges and Opportunities in Open Source 17:12 The Role of Collaboration in Research 21:50 The Future of Open Source and AI 33:58 What Constitutes Distillation in AI 37:18 Navigating Copyright and AI Distillation 37:43 The APAC AI Landscape: Insights Beyond China 43:08 Understanding the Ecosystem: Labs vs. Hyperscalers 46:21 Monetizing Open Source AI Models 52:02 The Future of AI: Bootstrapping and Self-Evolution Transcript (AI- generated for reference only) Grace Shao (00:00) Tie Zhen thank you so much for joining us today. I’m really excited to have you on. We’ve been trying to make this happen for a while and just so glad the timing’s finally worked out. To start, can you tell us a bit about yourself, your journey, and where you’re at right now in your career and how you see the whole ecosystem? And also, just help us understand Hugging Face a little bit as well. Tiezhen Wang (00:19) Yeah, thanks, Grace, for inviting me. I know, sorry for the long delay. It has been a while, but I’m recently in transition because I just left Hugging Face. So to give you a quick information about very high-level overview, you can think of Hugging Face as the GitHub for AI. If you are not familiar with GitHub, you can think of Hugging Face as Amazon, where you can find all kinds of models in one store. And we are helping, so my job is to help researchers to get their models, which is the open source models on Hugging Face. And they can use the best, like all the tools, all the services on Hugging Face to make their models more discoverable and available to everyone. We also offer all kinds of technologies. For example, we allow them to create demos so that developers do not need to download the whole models. and they were able to try it out and see how it goes. And we also offer services so you can create your own agent using open source models. We do all kinds of scaffolding on top of open source models. another part of work that we do is to help them get more traction. We use LinkedIn. I use Twitter mostly to help them getting well known by the public. And we write analysis on their models and letting people know what are the new inventions from the model, et cetera. we work with researchers across the world. Like myself, it’s focused on APAC, especially Chinese researchers. Yeah, that’s pretty much the goal. quick overview of what I do. If you have any questions, just let me know. Grace Shao (02:03) And how did you get to this role? Because I understand you were with Google for quite a while as well. Tiezhen Wang (02:07) Yes, I was with Google as an engineer. work on ML frameworks. But then we had a bunch of reorg. And I was assigned to a project which is not open-source. But I really like talking to people in the open source world. It’s kind of very different. So when you are paid to work something versus you want to work on something yourself, Like you have very different mentality and very different feelings. So when I was working on the open source machine learning framework, I talked to people outside Google. And I can see the stars in their eyes. They do want to work on something they want. And even though they may not get paid, et cetera, I really like this feeling. So after I was assigned to the non-open-source project, I want to try something like new but also in open source and I was like talking to people in Hugging Face and I really liked them. At that time, like Hugging Face was not like part of the mainstream. It was like a niche product for researchers where researchers can upload models. But I do see there’s a huge potential for Hugging Face to grow up because first I believe in open source and the second like Hugging Face is going to be the entry point where like all people will come in and search for open source models. But the most important of all is that I feel that Hugging Face is a company who understands how open source works. Open source is a huge leverage. If you use it well, it’s going to be very powerful. And Hugging Face is like 200 people, like very small companies compared to other companies growing up from the same area. But they are able to use open source as a leverage. and called for collaborations across the world and do very impactful things. a lot of people, a lot of big companies are doing open source, but they just don’t understand this age. That’s the essence of open source. And I do feel that Hugging Face is doing really well there. That’s one of the reasons why I want to join Hugging Face. Grace Shao (04:06) Yeah, I think that’s amazing. I think that’s something we definitely will double click on later, especially when we talk about why China’s labs seem to have been embracing open source. Just kind of one last question on just the whole ecosystem and how hugging face fit into it. What was the philosophy really held by the whole company? Because I actually listened to one of the founders interviews, Clem’s interview recently. And during the interview, he talked about how Chinese scientists have always been long term contributors to open source technology. And then he said it was really like kind of a pivotal moment around 2022 where American open source contributors kind of took a step back and then there was a sentimental shift in the ecosystem. Why is that and how does Hugging Face kind of view the whole ecosystem? Tiezhen Wang (04:47) Yeah, there are several questions. Let me try to address them one by one. The first one is the philosophy behind Hugging Face. I think it’s really the mindset. so anything that we see where we can have a collaboration, like Hugging Face will just reach out and see if we can collaborate. So if you go to see a lot of work released by researchers, they will have paper on arXiv. and also their project on GitHub. And you’ll see me on all of these issue number one, which is the first issue after the repository has been released. And we just write something saying, offer blah, blah, blah. Do you want to collaborate on something? So for anything that we can collaborate on, we will just call for collaboration. And some we’ll go through, some we’ll not. But this collaborative mindset is very, very different from. like a business point of view. From a business point of view, you will first think, what is my edge and how I win the market, how I compete with others, and what are the end areas. After the competition, what’s the end game, how it will go. So that’s the way of how you can justify the investment and everything. In open source world, it’s totally different. It’s like, I want to do something. I just say it and I do it and there are developers who want to join in and we do it together and we grow the pie gradually. we do not have like, let me put it the other way. So if you see an open source model coming from one of the Chinese lab, for example, GLM 5.1 is released and you may think like Kimi or Minimax like other open source model provider. in China would compete with them. But actually not. Like you will see they are commenting on the Twitter saying, congratulations, et cetera. This is a collaborative mindset where everyone is stepping up on each other. we can do a lot of, as a group, can continue to push the frontier forward. So I think this is very, very different. Yeah, and talking about your second question, the Chinese, well, I wouldn’t

    China's open-source ecosytem and the future of AI bootstrapping with ex-Hugging Face APAC head
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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

Information

  • Creator
    Grace Shao
  • Years Active
    2025 - 2026
  • Episodes
    35
  • Rating
    Clean
  • Copyright
    © AI Proem
  • Show Website
    AI Proem Podcast

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