Differentiated Understanding

Grace Shao

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TECHNOLOGY
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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

1D AGO

Nathan Lambert Reflects on China’s AI Labs: DeepSeek, Open Models, and the 'Race' with the U.S.

Joining me today is Nathan Lambert, author of Interconnects AI and a post-training lead at the Allen Institute for AI. Nathan recently returned from a major tour of China’s leading AI labs, where he met with researchers and teams building some of the most impressive open models in the world. In this conversation, we discuss what Nathan saw on the ground: how Chinese AI labs differ from their U.S. counterparts, why open models have become such an important part of China’s AI strategy, and how labs like DeepSeek, Alibaba, ByteDance, Kimi, Z.ai, MiniMax, and others are navigating compute constraints, data access, and commercialization. We also dig into some of the most debated questions in AI today: Are Chinese labs really 6-9 months behind U.S. frontier labs? How meaningful are distillation accusations? Can domestic chips like Huawei’s make up for restricted access to Nvidia GPUs? And is China’s AI ecosystem actually government-directed, or is the reality more fragmented and commercially driven? Ultimately, this episode is a more nuanced look at China’s AI ecosystem that looks beyond simplistic narratives about subsidies, copying, or geopolitics, and instead examines the technical, cultural, and economic forces shaping the future of open models. Check out his two recent articles here: * Notes from inside China’s AI labs * How open model ecosystems compound 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. Chapters00:00 Insights from the China Trip11:51 Cultural Differences in AI Research18:15 The Role of DeepSeek in China’s AI Ecosystem25:26 Overview of Major Chinese AI Labs30:56 The Future of Open Source in AI37:50 Market Dynamics and Consolidation in AI42:28 Distillation and Model Convergence Controversies51:58 The Gap in AI Performance: US vs China61:09 Monetization Strategies in AI: A Comparative Analysis62:32 Government Influence and Misconceptions in AI Transcript (AI-generated for reference only) Grace Shao (00:00) Nathan, thank you so much for joining us today. Yeah, really, really excited to finally hear your thoughts on your big China trip, on what’s happening between the Chinese AI labs and the U.S. AI labs, what you think the potential compute constraints might mean for these labs and their performance in the future, and obviously the open-source ecosystem. So before we get into all of that, could you... Nathan Lambert (00:02) Yeah, thanks for having me. Grace Shao (00:23) Briefly tell us about how you ended up actually working on post-training and open language models. Just a bit about yourself. Nathan Lambert (00:29) Yeah. So I actually started my PhD at Berkeley in 2017, not working on AI things. I was an electrical engineer by training in undergrad, which is funny looking back, because that’s the same year that the Transformer paper came out. And I was like, I think I should do this AI thing, and tried to get the famous advisors to mentor me. And they’re like, we can’t take you. So I had my PhD as this wandering path to become an AI researcher. And then I ended up at Hugging Face after that, which was, realistically, the only industry research job that I had, but also a very hot startup and very fun to learn kind of at the intersection of these tools that people use a lot for AI and research, which is what I was doing. And then when ChatGPT hit, the kind of RLHF thing blew up as the hot word on the technical side of things. My PhD had ended up being in reinforcement learning, which is just the first half of reinforcement learning from human feedback. So it was kind of a natural pivot to be like, well, I might just do that. And Hugging Face was a good place for doing that, because the whole company is kind of all for that, which is like: figure out how to support the community on the hot thing and build platforms there. So they were very happy about that. And I helped build a team at Hugging Face. And then I was kind of burnt out on the remote-work time-zone thing and found out that the Allen Institute was doing such similar stuff. And I was like, wow, I have people that could be in-person friends and do similar things. I was like, quality of life — I need to do this. And a few years later, I ended up building a bunch of models. And I think being at a nonprofit opened me to this ecosystem vacuum of information, where there aren’t many people who can talk about what they’re doing. So then, with some luck and committing to write every week, I just feel like my influence filled the vacuum of nobody saying reasonable things. And it is this nice synergy between what I write about and what I work on in my day job, and it just kind of got bigger and bigger in a very fun way. I think that, generally, at the highest level, I’m motivated by wanting AI to go well on this trajectory. And I worry about a lot of near-term things, whether it’s social unrest in the U.S. and just kind of the massive hatred for AI — I think is a very big near-term problem — and then, medium term, concentration of power, because I think AI will be super powerful in ways that people don’t expect. So generally, open models are a nice way to curb both of them by being a bit more transparent to people, and it naturally is a hedge against concentration of power. There have been different reasons throughout that, but that’s kind of a recurring theme in my life in the last few years. Grace Shao (02:50) Definitely. I love your work because I think you help non-technical people like myself really understand what’s behind what’s happening in these labs a lot better. And then I actually just spoke to your former colleague, Tiejin Wang, and he was with APAC Hugging Face just last week. He was saying the same thing. Open source, in many ways, is kind of the best way to go forward as we know that this technology will not stop evolving, but it’s the best way to kind of put up guardrails and checks and balances for the monopolies. Okay, I don’t want to take up too much time on that side of things today because our focus really is about your China trip. Before we get into the weeds of all that, I want to hear about the trip itself. Most people who are writing about Chinese AI are getting their information secondhand. You really went there, you spent time with the researchers, you met with people who are building the models. Tell us about what you meant when you said you came back with great humility, right? Your eyes are a bit more open, whether it’s the good or the bad. Tell us about your trip. Nathan Lambert (03:50) I feel like I kind of went in — I mean, I had this horrible English phrase in my writing, which was like, “I knew I knew nothing about China,” which kind of tried to indicate that I knew going into the trip that I knew nothing. And it was still the fact in my current writing. This is a horribly written sentence that I had in there. And I only talk about it because somebody called me out on it. It’s like, what is this? And it’s like, leaving, which is knowing that it’s such a big country, there are just such vast amounts of talent working on these problems, and how unpredictable it is as a human to model people with very different worldviews and upbringings and training systems. Realistically, the way that people are trained in China is very different. And I just think that even being there, you can’t fully grasp: what are the pockets of three to six researchers doing that is actually a bit different than in the West, even if they’re working on the same goal? I think you could get down to that level of granularity and a sociological study and actually see differences in what they’re working on, and that’ll always change the output. I didn’t get to that level of granularity, but it’s just to start having real experiences and understanding how people explain how they work on these problems. And for me, realistically, a lot of it is coalition building, which is just like: I want there to not be vitriol at the level of the technical companies doing things in international bodies. So just meeting all the labs on both sides is really nice, because you need to do that for them to talk to you about more sensitive issues in the future. I got some criticism on the piece, which is like, this is how you shouldn’t visit China. And it’s like, well, what are you going to do if you’re going on an official visit to a bunch of companies? How do you expect to get in the door without being nice? You have to start somewhere, and I think it’s important to be respectful. Grace Shao (05:31) I think the piece was, frankly — I don’t think the criticism was fair, to be honest, because I think you were really transparent with the fact that you’re not a China person, right? It’s not like you’re going there and exoticizing everything. And if anything, a lot of people, even with China backgrounds, like to use certain dragons and tigers to describe things. I feel like you actually were really humble going and being like, I’m just a technical dude meeting with these labs, talking about their technical research, right? And then because you were physically there, you had observations of the culture and the people. So yeah, I actually thought your piece was quite good. And yeah, sorry. Nathan Lambert (06:05) I agree. I was willing to let that sail past, but I think it’s important for people who listen to realize how actively these companies are trying to court Western audiences, which is why we could get in the door. I mean, we had some prominent people on this trip, but that’s why we got all of them in

1h 3m
2D AGO

AI x education, a contentious but unavoidable future. Designing tech for children with Dex's Reni Cao

I spoke with Reni Cao, the CEO and co-founder of Dex. Dex Camera is a language-learning camera for kids. Reni is a dad, a former product lead at YouTube, and on a mission to build technology that does good for kids and gives digital autonomy back to parents. We dive into his personal story from his high school days that drives his passion for AI, and why he believes the current education system is a “cookie-cutter” that fails curious kids. We get really into the nitty-gritty of what makes “good” tech versus “bad” tech for kids and why the category of ‘children-first tech’ is very overlooked. Reni explains why most children’s apps are built on an “attention economy” model that forces them to compete with addictive content, and why his team needed to build physical hardware to break that cycle. We tackle the hard questions, including the pushback from parents who believe in “no tech” childhoods. And he shared his most non-consensus view: that the era of standardized, industrial education is over. He believes we are entering a golden age of “scaled homeschooling” where AI meets kids where they are. Whether you’re a tech investor or an anxious parent, this conversation about nature versus nurture, “nei juan” (involution), and raising resilient humans in an AI world is a must-listen. 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, founders, and product managers. For more information on the podcast series, see here. To find the previous episodes of Differentiated Understanding, see here. Chapters 00:00 Reni’s Journey to Dex Camera 03:48 Designing for Children: Principles and Insights 08:05 Technology’s Impact on Child Development 12:09 Bridging the Gap: Business and Product Design 15:36 The Role of Parents in Tech Development 25:20 Leveraging AI and Language Models 29:48 Value-Driven Pricing Strategy 32:05 Defining the Product Category 34:33 Subscription Models and Content Delivery 37:58 AI and Parenting: Balancing Technology and Safety 43:29 Unexpected Use Cases and Impact 47:29 Personalized Education and Parenting Philosophy AI-generated Transcript Grace Shao (00:00) Reni let’s start with your personal story. Who are you and who are your team members? Because when I met you in SF, I was so enamored by the product and I thought your story was so interesting. So please share that. Reni Cao (00:11) Hi everyone, my name is Renny, CEO and co-founder of Dex. We’re a technology company in San Francisco, almost all parent company, which is pretty special in a startup setting. We’re a bunch of parents that having trouble with the same kind of like a reality where like our education system is a sort of like cookie cutter and our entertainment is also cookie cutter for children. So we’re like, can we harness technology, especially the latest development of the AI, in different way for families that really gives children a chance to become the best version of themselves and ⁓ give the digital autonomy back to parents themselves rather than accepting the fact that they have to struggle between technology versus no technology. So yeah, we’re the parents, of like a bunch of missionaries in this journey together to explore how can we make the best use. of the AI and our first product is called Dexta Language Learning Camera where kids can take pictures and turn the whole world into language immersions. And it’s a product targeting young children three to eight. And we’ve sold 10,000 pieces so far and ⁓ ratings has been high and we’re pretty excited about this. But yeah, this is pretty much about us. Grace Shao (01:24) But Reni, tell us a bit about what you did before Dex actually. What kind of led you to this path? I know becoming a parent really did inspire you. You have a young daughter, I think similar age to mine, around three years old. But before that, what really led you to this path? Were you always passionate about children’s tech or education? Reni Cao (01:41) I actually have been a product management guy for the last decade in Silicon Valley, some big companies like YouTube and LinkedIn, some smaller s***, ZFS, Wish. But I have been a builder since the beginning. I would actually say that my passion for decks actually originated much earlier than I started my career. It actually started right when I was at school, but happy to say more if you’re interested. Grace Shao (02:07) Yeah, no, do tell us a personal story there. Reni Cao (02:09) So I was always this random kid with tons of questions back in high school. And very unfortunately, I think the education system, especially in East Asian countries, is not designed for meet kids where they are. So every time when I come up with a random question, my teachers are usually a little bit impatient and will be like, can you just go back and finish your quiz, et cetera, et cetera. So the moment I saw when GPT-4 comes out, I was thrilled and I posted a long like blurb on LinkedIn. Basically saying like, you know, if I had, have this as a kid, I would have grown into a more complete human. So this kind of like, I feel like this like generative AI’s capability to meet kids where they are, especially meets your needs for curiosity. It’s game changing. So. I feel like I’m building this product first and foremost for a younger me that could have benefited so much from this. That’s pretty much the story about me. yeah, I know we see it and of course our parents right now we see there is a tectonic shift in terms of the skill landscape and what the future of workforce is going to be and even the existential challenge of what does human mean in a future society. So we do want to build something that’s centered around children, centered around the family to help them find what they love and build agencies around it at the end of the day. So yeah, that’s the two main driving force of me coming to Dex. But I would be honest about it. It’s like very random. When I want to start a company, a lot of my colleagues are very surprised, being like, oh my god, Renny, you’re getting into this field. But yeah, I guess I finally find the work of my life. Grace Shao (03:48) I love it. think you need to understand the passion and the personal reason behind the businesses to really understand why the design was frankly so intuitive and why you’re so passionate about building this and leaving such a comfy, know, like cushy corporate role. I think that’s the one thing that stuck out to me. The product itself is actually so natural to how children behave to your point, like my three year old. from morning to night, know, morning she wakes up, it’s like, mommy, what’s this? What’s this? What’s this? What’s this? How do say this? Why do you know that? Sometimes she gets angry at me. If I don’t know something, she’d be like, but you’re an adult, you should know everything. But the reality, especially with languages, it’s really difficult. So for example, yesterday she was coming back from her Mandarin class and she said, liu shu, she was pointing at random tree. And I was like, that’s not liu shu. All I know is not liu shu, but I actually don’t know what liu shu is in English because I think it’s only really common in mainland. I’ve never seen that kind of tree. Well, I guess it’s a willow tree. You don’t see it very commonly elsewhere. And then she kept on pointing at trees, but in Hong Kong, you clearly don’t have liu shu because Hong Kong is like tropical. And then she got really, really mad at me. And that moment I was like, wow, if we had a Dex camera, that would have been perfect. But I was literally trying to take a picture of it while we’re moving car and try to upload it to GBTB, like what tree is this? What’s the name of it? So anyway, I think it’s really great product design. And I want to kind of get into that a little bit. When you were designing it, what was the thinking? Like, what does it mean to be children first? Reni Cao (05:10) I think there are three layers of children first as a principle. The first layer we already touched upon that. So young children, their hand anxiety is very different from adults. they tend to use one hand to operate a device and another hand they want to use for sensory explorations, like they want to touch. Sometimes they want to just move things around. So this requires a different form factor that one handed use, very tactile, very intuitive for young children such that they can explore a world while harnessing the power of AI in this case. So this is kind of like the user, the special things about the user. And it’s a different design. think that’s layer number one. I think the layer number two is also that the device itself is a metaphor for the market as well. And in the market, we want to build something that’s drastically from the so-called adult-centric smart devices, namely the phones and tablets, to send the market a message that there could be a different option. There could be a good technology. There could be a family-centric technology. And we’ve picked this form factor utilizing the metaphor of magnifying glass. It is something you use to see some hidden wonders, otherwise you cannot see. I do think that’s the ⁓ second layer of the things, which is like metaphor and category creation. And at the end of the day, I do think we intentionally make the device kind of worth finding this fine balance between engagement and learning or kind of like a healthy aspect of the technology, meaning like we add a assistive screen, but we make it really kind of like limited and not the center of the whole kind of like a user journey. And we want to kind of like find a new way to put all the components in our consumer electronic

1h 1m
MAY 11

There's more to Korea than just chips. TheVentures CIO on the country's AI stack

In this episode, I spoke to a leading South Korea VC, TheVentures’ CIO Ethan Cho. He argues that South Korea’s low fertility rate and aging population put pressure on Korea to be one of the world’s fastest adopters of AI technology, similar to its rapid embrace of high-speed internet in the early 2000s. While not a leader in foundational LLMs like the US or China, Korea’s strength lies in application and adaptation, particularly in B2C areas like personalized agents and commerce, where cultural familiarity with chatbots and digital transactions lowers resistance. The Korean startup capital funding landscape is shaped by three forces: Chaebols (Samsung, SK, Hyundai), the government, and VC firms. CVCs from Chaebols tend to reinforce existing semiconductor and hardware value chains rather than explore tangential innovation. To counter this, the Korean government has become a dominant LP through initiatives like “Everybody’s Entrepreneurship,” injecting capital to encourage novice founders. On sovereign AI, he believes the government’s push is less about global dominance and more about securing sensitive areas like finance and defense, though he warns that domestically-built software has historically struggled to scale beyond Korea. Ethan is shifting focus from purely domestic champions to founders with global ambition but local execution, often Koreans educated abroad who return dissatisfied with traditional jobs. He wants to back ventures that change the world, not just build another food delivery app. He also recognizes key opportunity areas, including defense tech, K-beauty, fashion, and mental health, as society adopts AI at scale. 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, founders, and product managers. For more information on the podcast series, see here. To find the previous episodes of Differentiated Understanding, see here. Chapters 00:00 Introduction to Ethan Cho and His Journey 02:47 Korea’s Role in the Global AI Supply Chain 05:24 Cultural Attitudes Towards AI in South Korea 11:06 Government Initiatives and Sovereign AI 16:37 The Future of Commerce and AI Integration 28:03 Consumer Behavior and AI Adoption 28:43 Enterprise AI Solutions in Banking and Manufacturing 33:39 Investing in Founders: The New Generation of Entrepreneurs 39:39 Korea’s Future Exports: AI and Beyond 41:41 K-Beauty and K-Fashion: Cultural Exports 45:15 The Future of Mental Health in the AI Era 49:53 The Limitations of AI and Human Experience AI- generated Transcript Grace Shao (00:00) As mentioned, our guest today is Ethan Cho. He has been active in the Korean VC space for over a decade with experience in the venture investing arms at Qualcomm, Google, Samsung and more. Now as a partner at the ventures, he leads a team focused on finding and nurturing the next generation of AI native startups. Ethan, thank you so much for joining us. So good to have you. Ethan Cho (00:18) Thank you, Grace. I’m very excited to be on the show and I would love to discuss with you more in detail. Grace Shao (00:24) Yeah, to start with, tell us about yourself. Tell us about venture investing in South Korea and the firm’s background. Ethan Cho (00:31) Sure. So I was born in Korea. I kind of moved internationally quite a bit. I moved to England when I was kid, when I was four years old. That was where I first learned my English, lived in England about four years, came back to Korea, then moved to Hungary, lived in Budapest for a year, came back to Korea again, spent the next 20 years in Korea, moved to the States, lived in New York for ⁓ my business school years and worked there for another year, came back to Korea after then. So I’ve been in and out of the country quite a bit. I loved startup investment very early in my career, so I wanted to move towards startup investment. I actually started out as a hedge fund analyst right after business school, but I quickly found out that I’m more interested in finding good companies and good stocks. So then I moved towards the private side, started with Samsung, and moved to Qualcomm, et cetera, et cetera. What fascinates me about Korean startups and startups in general is that everybody’s trying to change the world. I’m just such an honor to be a part of that and talking to entrepreneurs on a daily basis really excites me. Grace Shao (01:34) Awesome, I think it’s really interesting because you’ll definitely bring a very international perspective and not only just the Korean perspective and also kind of understand, you know, where a lot of our listeners are coming from as well. You have, you know, you have exposure to UK exposure to Europe, exposure to US. I think I want to ask you quickly, because you’ve actually worked in the public sector as in public investing, it’s kind of interesting because right now, obviously, the frenzy and the, you know, the global interest right now. Ethan Cho (01:57) Mm. Grace Shao (01:59) is in a lot of the big semi providers in South Korea, you know, focused on infrastructure layer that are public listed. At a high level, how should we think about the Korea’s role in the global AI supply chain? And then of course, we’ll shift gears into talking about the startup scene that you’re passionate about. Ethan Cho (02:02) Yep. I think that’s a great question. think one of the very obvious factors of AI is memory because you can only use AI based on what you or the agent knows about you or any company. So because of that, the demand for memory is exponentially increasing. I think that’s definitely a blessing for the career semi-players and also the current industry as a whole. But at the same time, think nature has always found a way to become more efficient. So the capacity or the demand constraint will not be existing forever. There’s going to be significant improvements such as Moore’s Law. There’s always an innovative solution that comes out every other year. So I think there’s definitely going to be a lot of exciting opportunities down the road, but it always evolves. So it’s going to be very different next year. It’s not going to be HBMs anymore. I think it’s going to be something else going down the road. We’ll have to see, but I think the trend is here, but the trend itself will also keep evolving down the road. Grace Shao (03:15) Awesome. So, you know, if we have a really candid assessment of what’s happening in Korea right now, what’s genuinely really strong do you think? Like the chips are strong. ⁓ What do you think that weaker maybe compared to, you know, China and the US, you know, from outsider lens, is it really the LLM labs kind of or is it diffusion? What’s happening? Ethan Cho (03:24) Hmm. Yeah, I mean, it’s a complex situation. I think one thing that a lot of people quote and one kind of fate that we cannot deny is that we have a very low fertility rate. So the birth rate is decreasing very fast. We have a very rapidly aging population. A lot of people think of this just as a curse. I think there is an aspect to it that it’s a blessing in disguise because we are one of the countries that most desperately needs AI and robotics. And because of that, I think we will be one of the more adapting or welcoming countries for AI and robotics. If we go back to the early 2000s, we were one of the countries that adapted most rapidly to high speed internet as well as mobile technology. because we had to, we talk a lot on the phone, obviously. So because of that kind of demographic instinct, we were one of the much faster adapters to that technology. I think that’s gonna repeat in AI and robotics. As you mentioned, I think that AI and robotics is definitely not, we’re not the strongest when it comes to AI robotics in the world. But in terms of adapting and using it for actual use cases, we may be one of the very strong countries. So I think there’s a lot of challenges and opportunities ahead of us. Grace Shao (04:50) That’s really interesting. think you hit something that like, you know, people are starting to pick up in the West, which is like in East Asia in general, the embrace of technology is a lot more optimistic. Some come from very realistic reasons. Like you mentioned, whether it’s in China or Japan or Korea, there is a potential labor shortage that’s coming to the next generation, right? But not only so, I think just in terms of culture and social sentiment also feels that way. So if you have to give like a high level kind of assessment on You know, just the cultural attitude and political attitude towards AI, what does it feel like on the ground in South Korea? Ethan Cho (05:24) I think AI itself, I think people think of AI in different forms, obviously. I think as far as I know, China thinks of AI closer to robotics. The US thinks of it as, I don’t know, maybe a chatbot or something that they use for the industrial usage. In Korea, as far as I’ve experienced, I think it’s more about becoming a personalized kind of agent, not agent. per se in terms of doing purchasing or actual daily tasks. We always had kind of chat bots, especially for instance, for our financial system or the banking system, we’ve always used CS bots very frequently. So we’re very used to it. So I guess there’s less resistance when it comes to adopting or adapting bots or AI featured functionality, especially in the B2C area. So although... When we say AI just by the name, it could sound creepy. I think it’s already very well embedded in the Korean startup ecosystem and the overall society as well. Grace Shao (06:27) That’s interesting. So you did kind of touch on one thing, you know, the Chinese view this way, the Americans view that way, you know, definitely there’s a bit of a dif

54 min
MAY 4

Assembled co-founder John Wang on building a AI native support system for enterprises

In this episode, I sit down with John Wang, the co-founder of Assembled, to explore how AI is revolutionizing customer support. Having transitioned from a Stripe engineer to an AI startup founder, John shares his unique insights into the evolution of support tools. We delve into how these tools have shifted from being mere cost centers to becoming strategic assets that enhance customer experiences. John and I discuss the impact of AI on support volumes and staffing, highlighting how integration is reshaping the landscape. He emphasizes the importance of talent density and assembling high-caliber teams to drive success in the tech industry. Through his experiences, John provides practical insights into AI's current capabilities and limitations in support operations. We also explore the strategic considerations for future AI support ecosystems. John shares his thoughts on the role of support in driving revenue and customer satisfaction, and how AI can orchestrate with human support agents to create a seamless experience. His perspective on building high-performing support organizations offers valuable lessons for anyone looking to innovate in this space. 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, founders, and product managers. For more information on the podcast series, see here. To find the previous episodes of Differentiated Understanding, see here. Chapters 00:00 The Journey from Stripe to Assembled 02:25 Understanding the Importance of Customer Support 05:29 Lessons Learned from Stripe 10:25 AI in Customer Support: Current State and Future 16:04 The Economic Impact of Support Operations 18:25 The Role of AI in Transforming Support Jobs 24:30 The Future of Support Organizations 26:58 Guardrails Against Fraud in AI Support 32:42 Navigating the AI Ecosystem 38:00 The Value of Long-Term Commitment in Careers AI-generated transcript Grace Shao (00:00) Hey, John, thank you so much for joining us. I just recorded your bio already. It’s extremely impressive. And you’ve done quite a, you’ve had quite a few different roles now as the co-founder of assembled, right? To start, can you just tell us about your story? Like what inspired you to leave Stripe, you know, go into, you know, right now what you guys are doing, which is a software for people who run customer service support operations. You know, now you guys are pivoting into AI as well, or at least leaning into AI. Tell us about all of this. John Wang (00:28) Yeah, great question. When we, well, when my co-founders and I started, we were all at Stripe. We worked on a bunch of different things at Stripe. And one of the last things that my two other co-founders worked on was a support tool, an internal support tool. And I remember pretty clearly that they were making a bunch of headway. It was really, really cool. And... They had gone to this really, really high up person and product. And this person was basically like, why are you guys wasting your time on this? Like you guys are kind of like, you’ve been at Stripe for so long, you know all these things and you’re doing support. Like I’ve got this really cool Bitcoin project that I would love for you to work on instead. And I remember my co-founder coming to me and being like, hey, like pretty bummed this is what happened. And then I was like, wait, you just saved Stripe, you know, quite a few million dollars, increased customer satisfaction by 40%. And still they don’t understand the value of this. And that’s when we were like, hey, ⁓ there’s something here where there’s a market opportunity. So that’s what got us really, really excited about support. We were doing it at Stripe. We knew it was an undervalued place. We didn’t see any very good tools out there to do support well. And so we decided to go build something really, really great in the support space and just like make transform and elevate support is our mission. Yeah. Grace Shao (01:50) Do you think it was just that stripe was too rich? They were just, and they just didn’t care about saving a couple million dollars? Or do you think it was actually a blind spot for people? John Wang (01:59) I think Stripe was definitely very rich at the time. think it was also a blind, it was a combination, right? Because most people, you think of support, you think of it as just a cost center. And I think recently that started to change in the sense that like, hey, this is actually a really important part of your business. But for a lot of companies, like if you look at FinTech, if you look at like a lot of health tech companies, their entire product is their relationship with their customers. And so support’s actually really, really important for that. And I think a lot of people underappreciated that for quite a while. And now I think people are starting to understand again, hey, if you piss off your customers every time they come and talk to you, that’s not going to be a very good thing. You better be a monopoly. Otherwise, you know, they might not be coming back. Grace Shao (02:46) Yeah, definitely. I think I want to kind of lean into that later in our conversation as well. It’s like people are trying to replace support and customer service AI first. But if anything, it’s not the best experience when you’re frustrated with a product and you keep on getting a robot, right? But I want to kind of talk more about your experience at Stripe. You were there quite early. What do you think it taught you, you know, as a very early employee at such a successful startup now? if even considered still startup and then like what were things that you think you learned there lessons even if soft skills that you kind of took away to to your current role like as a founder. John Wang (03:21) Yeah, it’s a great question. You know, it’s really funny actually. I just met up with someone where, so when I was starting out of college, I had applied for all these jobs. I was able to get a lot of them, except for this one company that I really, really wanted to go to. It was called Meteor Development Group. They built open source software. In college, I had built open source software at Ruby on Rails. I was really big in that community. was like, wow, it’d be awesome to go and make this something I do day to day. And I didn’t get the job. I was really bummed about it. And then I was like, I’ll just fall back on my second here, which is Stripe. And Stripe was the obvious second choice because just the people were really, good. And now like 10 years later, I like think about that and I’m like, the business model is really important. because Meteor was not a good business. Like open source frameworks is not a good business, but Stripe, really boring. Honestly, it’s just like payments. You process payments, you go talk to Visa. You literally have to like, we had a server in the server room that would send like a specific file with specific tabs and spaces in order to get it out to Visa. Really boring. Really, really core infrastructure too. And so like the big overarching thing that I learned was like one, business model is unbelievably important because if you can just make a good product when the kind of like market is there and when there’s a really big need, then this can scale like unbelievably fast. Two was the people. I remember talking actually to a few people, Greg Brockman was maybe the second or third person I talked to. who’s now the co-founder of OpenAI. And I remember just talking to him and being like, wow, this person is so, so smart. This is awesome. And I would talk to kind of like, I would go to the lunchroom and be talking to people at Stripe. that was just, people were talking about all sorts of things. And I think like talent density was a really, really big part of like what made Stripe successful. And It wasn’t any one thing over time. was one, Stripe was in a great market. And then two, it iterated really, really fast on a lot of little things over and over and over again. So I thought that was a really good place to learn a lot about like what makes a company great. Grace Shao (05:49) Yeah, I think it’s interesting you’re talking about talent density and a lot of the AI labs I speak to actually also talk about that. But I’m curious, what does it mean when you have really strong talent? Is it like that they are technologically superior, like they can code better? Or does it mean actually that they can think outside the box, they’re more creative, they can pivot faster? Like what does it really mean to have really high caliber talent on your team? John Wang (06:11) I think it depends on what company or like what you’re trying to solve, right? Like talent density for Los Alamos national, like Los Alamos, like building the atomic bomb is like very different than talent density for like Bell Labs, which is very different than talent density at early Stripe, which is very different also than talent density at OpenAI Research. Like I think for Stripe in particular, the type of talent density that was there was really high curiosity. Grace Shao (06:31) Right. John Wang (06:38) really high product thinking, really technical people, and people that could dive deep on certain problems and weren’t afraid to go talk to a bunch of customers. You saw so many conversations about like, how do we make this particular API parameter better for everyone? And like hours and hours and hours of like making sure it was a really, really good product. And people who weren’t afraid to like, you know, take a week of work and just like dump it away because it wasn’t quite there. So it was like, This combination of like, they worked really hard, they’re really smart, and they care a lot about the end result and have

43 min
APR 27

Matt Sheehan on China’s AI Policies: Employment, Anxiety, Safety, and State Priorities

Today, I’m joined by Matt Sheehan who writes this insightful newsletter. Matt is a senior fellow in the Asia Program at the Carnegie Endowment for International Peace. He researches China’s AI ecosystem, Chinese tech policy, and how technology shapes the country’s political economy. Matt lived and worked in China from 2010 to 2016 and later led China tech research at the Paulson Institute’s MacroPolo. He’s the author of The Transpacific Experiment. He speaks Mandarin, and he turns complex policy into plain English. In this episode, he helps us understand China’s AI governance, about how Beijing is thinking through the social and political consequences of rapid AI adoption. We focus especially on a shift that became more visible in early 2025: rising concern inside China’s policy community about AI’s impact on jobs, worker anxiety, and social stability. Matt explains why China’s AI labor question is different from the Western debate. We also discuss how the Chinese government is trying to balance support for technological progress with the need to manage public anxiety, clarify labor rules, and avoid social instability as AI becomes more deeply embedded in the economy. He broke down the myths, explained the jargon, and the regulatory bodies in China. Our conversation started slow, but it became very, very heavy, what they call 干货满满 substance heavy. Also, a shoutout to Nathan Lambert’s work in helping us better understand the open-source ecosystem and Rui Ma’s for helping us understand investing in China AI! 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, founders, and product managers. For more information on the podcast series, see here. To find the previous episodes of Differentiated Understanding, see here. Chapters 00:00 Introduction to AI Policy in China 03:10 Matt Sheehan’s Journey into Chinese Tech Policy 05:55 Shifting Perspectives on AI and Labor 09:02 Public Concerns Over Job Security and Government Responses 15:09 Education and AI: Preparing for the Future 17:50 Regulatory Landscape of AI in China 34:00 Navigating China’s AI Regulatory Landscape 40:58 Misconceptions About Chinese AI and Government Funding 43:57 Understanding AI Safety and Security in China 52:03 Global AI Governance: Cooperation or Parallel Paths? AI-generated transcript Grace Shao (00:01) Hi Matt, thank you so much for joining us today. I’m so, so happy to finally have you on the pod for people who are listening. We’ve been trying to make this happen for like six months, but between us, there are like three little children running around with a bunch of viruses and have just not been able to make this happen. I’m really excited. ⁓ A few months ago, what really caught my attention about your work again is that you shared something on WeChat saying you were dissecting the new Chinese AI safety paper, like the big national one. ⁓like verbatim in Chinese. And I was like, wow, this is extremely impressive. It’s not an easy task. I commend you for doing that. So I really wanted you to help us understand the nuances of the AI policy world, especially how people are perceiving AI in China. I think there’s more more interest in how China’s governing AI ⁓ while we were hearing the backdrop of how the Chinese government is trying to push on AI diffusion, right? And then on top of all of this, like where areas where China’s AI governance seem to be leading, because in many ways it seems like China’s AI regulators are much faster to respond to how fast technology is evolving. But to start, we would love to hear about your personal story. Tell us about how you ended up studying China, studying Chinese tech policy. We met in Beijing years ago, maybe a decade ago. ⁓ Yeah, so tell us about that. Matt Sheehan (01:11) Sure. Yeah. Yeah, sure. Sort of stumbled into China stuff. I hadn’t taken Chinese or really knew anything about China until about halfway through college when I ended up getting a summer job in Beijing. I was just kind of like instantly fascinated and knew I wanted to move back there after I graduated. So took a little bit of Chinese my senior year, moved to Xi’an, taught English, kind of followed what at the time was a very like typical know, trajectory of like, go there, teach English and then go study Chinese at university and then get a job and get a slightly better job. And eventually I was able to kind of wiggle my way into journalism. And so I was a China correspondent for a publication called The World Post at the time. And that took me up. was there from 2010 to 2016. So kind of like the hinge period before and after she came to power. Pretty interesting thing to see. And When I moved back to California in 2016, I started working on a book about China-California ties. I’m from California and this was like the period of kind of explosion in cross-border investment and Chinese students come into California in the Silicon Valley-China relationship getting even more like twisted and complicated. China-Hollywood. So I wrote a book about that and as I was doing it, the kind of the tech section, the China-Silicon Valley, China-U.S. tech connections kept growing bigger and bigger and I ended up working a little bit with Kai-Fu Lee on his book, AI Superpowers, which was kind of my turn from like, it was like all things China, China, California, China, Silicon Valley, China AI. And since 2017, I’ve been working almost exclusively on AI issues in China. Maybe the first three years of that, like 2017 to 2020, was very focused on comparative capabilities. This was kind of a period right after the National AI Plan in China when there’s a big explosion in activity. And I think... This is kind of was like the first time America kind of got freaked out about Chinese AI capabilities. And so I spent a few years being like, okay, let’s try to like ground these assessments in some data. Let’s get like an actual grounded sense of where the countries are with each other. ⁓ And then starting in 2021, I sort of turned into focusing on Chinese AI governance, Chinese AI regulations. That’s when they first started rolling out their regulations or recommendation algorithms and sort of deep fakes. And I was kind of making a bet that I think If China continues to be at or near the frontier of AI, then how they choose to regulate it domestically is going to have huge implications for China’s own ecosystem. And then it’s going to really ripple out internationally on safety, security, growth, all this stuff. So I spent the last, now it’s like five years, ⁓ just deep in the weeds of Chinese AI policy and regulation. Grace Shao (04:04) Oh, great. I think I definitely want to double click on all the algorithm security and the kind of, you coined the answer versus what’s called security and versus what’s the other Chinese word? Yes, yes. Matt Sheehan (04:17) Anshun safety security. ⁓ Jeff Ding was talking about this very early on, but yeah, it’s a, it’s a constant thing that we have to negotiate for people who don’t know it’s the word Chinese, the Chinese word Anshun ⁓ means both safety and security. whenever you’re kind of translating documents on this front, you have to know, you talking about AI safety, which is kind of a different thing versus AI security, right. I think both you and Jeff definitely are some of the more nuanced scholars I follow. And I do want to kind of double click on that later on. But to start, I think I want to talk about something that’s top of mind for a lot of people. You just wrote a piece that you said was not super serious. It was just your scattered thinking put together on subset. I thought it was very well written about the growing anxiety around potential job losses. ⁓ your perspective is that you know there are more and more people voicing this kind of concern and I wanted to hear a perspective on that and I kind of wanted to share a bit of my different share my different perspective on this and what I’m hearing on the ground and kind of have a conversation around that as well. Yeah why don’t you start with sharing like what you found yeah Matt Sheehan (05:22) Yeah, sounds great. Yeah. So, I’m not an AI and labor person. That’s not been my focus for a long time, but I’ve been monitoring it for a long time and just lightly. And starting around, I guess it was early 2025, I just started to hear a lot more out of the Chinese policy community about worries about AI’s impact on labor and jobs. And this was kind of a surprise to me because ⁓ just a little bit prior to this, say early 2024, I had, I sometimes ⁓ in my job, I run these kind of like informal surveys or almost like a, what do call it, focus group of American and Chinese AI policy people and asking them like, you how would you rank these different risks? How concerned are you about ⁓ job risks versus privacy versus military AI? And we have both sides that like rank the risks and then talk about the results. And when I ran one of these in early 2024, it was very striking that the Chinese side I think it was at the time we had seven different risks and the Chinese side ranked labor impacts a second to last as six out of seven. And so my sort of baseline was like, OK, for a variety of reasons, this isn’t really too on the radar of China’s ⁓ policy community or wider policy community. And then starting around early 2025, some of those same people who I had been talking to about this before had really changed their thinking. They were saying that there was a big change in thinking within China, maybe especially within China’s of policy and government circles, but then I think also a little bit wider. And so that sort of sparked my curiosity. An

1h 1m
APR 21

Tencent's QClaw goes global, aims to serve the average consumer user, with PM Shuyu Zhang

Amid Anthropic’s success with coding products, many AI labs and companies have also tried to lean into that vertical. OpenAI has stepped back from courting consumers and shut down its video model division, Sora. Alibaba, meanwhile, has more recently begun releasing closed-weight proprietary models and is reportedly pushing the Qwen team to find clearer paths to monetization. The Chinese tech giant has also launched Qoder, a Cursor-like product under the Alibaba umbrella, which we interviewed last year. But despite all this, Tencent remains notably committed to the mass consumer market. The OpenClaw frenzy has already led to five different Clawbot-style products emerging across its ecosystem. Joining me today is Shuyu Zhang, Senior Product Manager of QClaw, to break down the thinking behind that frenzy, from the cultural logic to the business rationale to the product design choices shaping it all. QClaw is to be accessible to everyone on April 21. It is the first consumer-grade AI agent built on OpenClaw. No technical setup, scan a QR Code, and the agent will be live in 3 minutes. Product link: qclawsg.qq.com Waist list: https://docs.google.com/forms/d/e/1FAIpQLSeIfEzlOV8jq_tGMbV5mqTSALyufE0kZ933XqE3Fnha1_CRfA/viewform?usp=publish-editor (Founding Claw — limited 20,000 slots) 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, founders, and product managers. For more information on the podcast series, see here. To find the previous episodes of Differentiated Understanding, see here. Chapters 00:00 Introduction to OpenClaw Frenzy in China 02:30 Shuyu Zhang’s Journey and Insights on AI Accessibility 05:27 Cultural and Societal Factors Driving AI Adoption in China 07:48 Understanding OpenClaw’s Popularity and Usage 10:36 Exploring Tencent’s AI Product Ecosystem 13:33 QClaw’s Integration and User Experience 15:45 The Philosophy Behind QClaw’s Design 18:14 Raising the Claw: The Concept of Personal AI 22:48 Incremental Value in AI Products 23:33 User Experience as a Priority 27:55 Understanding User Needs and Safety Concerns 33:07 Business Model and Global Expansion Transcript (AI-generated) Grace Shao (00:00) Thank you so much for joining us today, Shuyu. I’m so excited to have you on the first day. I understand it’s such a pleasure to have you here. I’m really excited for today’s topic because it’s something that’s kind of been at the top of mind for a lot of people. Why was there a massive open call frenzy in China? Why did it take off? The thinking behind it all. How did WeChat go about opening up to this whole new era of agentic AI within WeChat development? And there’s no better person to talk about this topic than you. So first of all, for the audience, please start with telling us about yourself, your role. How did you end up here? Shuyu Zhang (00:35) OK. Hi, everyone. I’m Shuyu, product lead of Qclaw. Also, the architect behind it’s overnight growth story in China. I achieved breakout success with zero marketing spend. I got a master’s degree of finance from Washington University in San Luis, then worked for Alibaba Group as head of AI product in Sanyo for four years. We mostly crafted AI product for business there. Almost everything we did was about AI for work. But one day, I decided to do something different. I want to get more exposed to consumer side. to experiment with the chemistry of AI for common people. Tencent is famous for its consumer side products I want to work with and learn from consumer product experts here. So I came here last September. Yeah. Grace Shao (01:15) Really cool. And I think one thing that really kind of resonated with me is when we’re talking about AI agents right now, how a lot of times, you know, the products still don’t feel that intuitive for non-technical people. And you yourself joked and said, you know, look, you’re like a social science and liberal arts student. You’re not a technical person yourself, but you’re able to lead the product design for this. And your mission is really to make QClaw more accessible to non-technical people. Tell us about that and the thinking behind it all. Shuyu Zhang (01:43) Okay, okay. Actually, the story starts when I was working for Alibaba. Initially, we worked for the engineers to help them improve their working efficiency with AI. But later I found out that this group is overly served. They only account for like 1 % of all of the people, but Every day there are lot of products designed exactly for them. And I think this is still the major theme of AI revolution since 2023, where people think the way to AGI lies. But these groups also are easily unsatisfied. They raise a lot of questions about the services that the AI provided, and at the same time, they’re afraid to be replaced. So I think the vibe is strange. So when I’m moving to a new environment to Shenzhen, when I was hanging around, I found a lot of interesting common people are using AI. And they’re getting a lot of happiness and convenience, even though the product capabilities for them are not the most advanced and designated for their needs. They’re still satisfied. There are two interesting stories. The first story is when I was getting home on the plane during spring festival, I met a 12 year old girl. This girl looks smart. She was playing with some toys. And after that, she was playing with a chatbot during waiting for the plane to take off. I was shocked. Because in the past, when I am younger, we usually play games during while we’re waiting for the plane to take off. But the youngest generations are playing with chatbots. This is interesting. And she’s using it to either cut the photos and even make phone calls with the chatbot. And when I talked to her, he also told me that when their friends are hanging out, the 12 years old girls are hanging out. They’re playing with the chatbot too. And I was really shocked by that story. And the second story is during the early January, Yang Liping was doing opera in Shenzhen. And I went there too. And a 50 year old lady sitting next to me, I cleared at her phone screen. The phone storage was actually out of use. But she didn’t delete the chat box applications in her phone as well. She only reserved WeChat, TikTok and the chat box applications in her phone, even though it’s already full because she might not use the very advanced phones. Yeah. And then I found out that for common people, the requirements or the needs for AI exists as well. And problems widespread for the problems, not widespread, for the problems widespread, but not complicated. But the supplies for them are far from enough. And I know there are a lot of people are chasing higher and stronger AI, but the gap between the bottom and the ceilings, 90 % of the people are between them. I want to make a product that can feel and satisfy these people’s needs. Yeah, that’s my story. Grace Shao (04:30) That’s really interesting. think it really ties into a theme that we’ve been writing a lot about AI-prone, which is also about how China is really embracing it as a full on mass market product versus I think in the West right now, AI is still really used by a select group of knowledge workers or certain kind of demographic. I do want to double click on that, which is what do you mean by the young girl is playing with AI? What was she doing actually? Was it interactive with their friends or were they trying to build products or what were they doing? Shuyu Zhang (05:00) Okay. The young girl, when she was playing with the chat bot applications, she actually sent a photo of her roughly taken, not in a very good light or in a very good background. And she just asked that, asked that application to curve it for me to make me look prettier or make me look funnier. She’s not actually a, because I asked, I also asked her a very interesting question. I asked, do you post TikTok shorts or Instagram? She said, I don’t because I don’t like to show enough my life to the public, but I just like to see my photos in the funny way. don’t even though I use AI to, you know, process my photos, I’m just enjoying it by myself. I don’t want to it to other people. And the happiness of the AI processing of the photos is already enough for her. And this is the first scenery she’s using. And the second scenery is that she actually stays at school all the time. She didn’t go home during the from Monday to Friday. So she told me when she missed her mother, because her mother worked in Shenzhen and is a working mother, her mother doesn’t have a lot of time to, you know, FaceTime with her or the teacher doesn’t allow that as well. Because when she go back to the dormitory, the roommates are silent. They can’t do that, but she can always talk to that chatbot. It’s like a companion. And that also makes me feel warm actually, but also little bit sad for her. And the third scenery for her is that she told me she would call the chatbot because the chatbot never blames her and the chatbot always holds her words because sometimes for I don’t know, for the young generations, a lot of their topics are hard to get for the friends, but She said, chatbot is always a good friend because the young generations, they don’t actually care about the or they don’t know about the appearance of people or the words behind the words. But the chatbot is always blunt and sincere and always happy to chat with. Yeah, this is the three scenarios she’s using it. Grace Shao (00:00) Following up on our previous conversation about why Chinese people seem to have a much more optimistic approach to AI and why did the open claw, why did open claw take off in China, like such like wildfire. Shuyu Zhang (00:15) Okay, so I think Chinese p

45 min
JAN 14

Sovereign AI, Open Source, and the Gulf’s Big Bet with Interconnected Kevin Xu

Every panel on AI and geopolitics seems to default to the same cliché: “the US–China race.” In this episode of Differential Understanding, I wanted to sit with someone who has actually lived inside DC, Silicon Valley, and the US–China tech corridor, and ask whether that framing still makes sense. My guest is Kevin Xu, founder of Interconnected Capital – a global hedge fund focused on the picks and shovels of AI – and author of the Interconnected newsletter, which sits at the intersection of tech, business, and geopolitics. Kevin’s path runs from Obama campaign staffer and White House / Commerce Department comms to GitHub’s international expansion lead, and now to full-time investor–writer with a very explicit geopolitical lens. We start with why he insists on “thinking in public” as an investor, and why he believes ideas soulocking in a vault. From there, we dive into his critique of the “race” narrative and his alternative concept of US–China co-opetition – a messy mix of competition, cooperation, and outright co-opting of each other’s models and research. That leads naturally into China’s open-source AI ecosystem, the Manus–Meta deal, and what he would need to see before feeling comfortable owning the upcoming MiniMax and Zhipu IPOs in Hong Kong. In the second half, we zoom out to sovereign AI: why South Korea might be one of the few countries outside the US and China with a shot at true full-stack AI sovereignty; how to read OpenAI’s Stargate initiative as an explicit American export play; and why the Gulf – particularly the UAE – is emerging as an AI “swing vote”, combining abundant energy, sovereign wealth, and a 1.5 million-strong construction workforce into a potential global compute hub. We close with Kevin’s differentiated view on China: AI diffusion is far more visible there, but the economic impact is not necessarily greater, and Beijing may end up being the first government forced to confront AI’s social implications. In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful. 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. For more information on the podcast series, see here. Timestamps (chapters): * 00:57 – From DC to GitHub to Interconnected Capital * 02:39 – Why Kevin “thinks in public” and writes Interconnected * 04:44 – US–China AI is not a “race”: co-opetition explained * 09:27 – Open-source / open-weight AI as last bastion of global cooperation * 12:14 – Capital flows, decoupling and why “capital finds a way” * 15:36 – Manus x Meta: product quality, viral growth, rationality in AI * 19:40 – MiniMax & Zhipu IPOs: revenue reality vs AI lab hype * 24:39 – Can Chinese labs win the Global South with cheaper AI? * 27:09 – Sovereign AI 101 and why South Korea looks uniquely powerful * 32:03 – Stargate as de-facto US sovereign AI and export strategy * 34:44 – Kevin’s trip to UAE: Gulf AI strategies and the “swing vote” thesis * 40:06 – Sovereign funds, MGX, and attracting talent from Hong Kong & beyond * 44:18 – Non-consensus bet: UAE Stargate as a global compute hub * 46:57 – Differentiated views on China AI diffusion and economic impact * 51:29 – Embodied AI, “aunties pressing elevator buttons” and social risk * 55:01 – Robotaxis, delivery drivers, and why China may go slower than expected AI-generated transcript Grace Shao (00:00)Hey everyone, welcome back to another episode of Differential Understanding. This is your host, Grace Shao And joining me today is Kevin Xu Kevin Xu is the founder of Interconnected Capital, a global hedge fund focused on the picks and shovels of AI. He writes the Interconnected newsletter on SubSac, which covers tech, business, and geopolitics. His insights have been frequently cited by the New York Times, Bloomberg, Economist, CNBC Information, Financial Times, Wall Street Journal, among many other media outlets. He previously worked as a senior executive at GitHub, the world’s largest developer platform, and served in the White House and Commerce Department during the Obama administration. He studied international relations at Brown University and law and computer science at Stanford. Grace Shao (00:40) Hey Kevin, thank you so much for joining us today. I already introduced you, but for listeners who may not know you that well, can you introduce yourself, the different hats you wear today, running Interconnected Capital, writing Interconnected Newsletter, and operating at the intersection of US, Asia, tech, geopolitics, and investing. Kevin Xu (00:57) Yeah, first of all, thank you for having me. So as you mentioned, what I do currently during the day is I write the interconnected newsletter on the intersections of geopolitics, technology, and business. I also run my own long only fund called Interconnected Capital, focused on the picks and shovels of AI, both hardware and software. Prior to that, I actually work as an operator inside multiple Silicon Valley tech startups. The most recent one is GitHub, which is the Microsoft-owned developer platform. I was their lead for international expansion strategy. That was my most recent real job, if you will. I also spent a bunch of time at different startups of varying sizes. And before that, actually started my career in politics. So I joined the first Obama administration’s campaign back in 2008. I was a campaign staffer. That was my first job out of college and then moved with the campaign team to DC, ⁓ worked in a few different roles. in the Commerce Department as well as the White House doing mostly press and communications work. So that is ⁓ my sort of all over the place background that led me to what I’m doing today, which is investing and writing ⁓ in technology, but with a very heavy geopolitical lens to the process. Grace Shao (02:13) I think that’s really interesting and explains to why you have a geopolitical lens, given that you actually have a DC background, right? But you run a fund and you actually keep most of your thinking, public, So instead of just keeping it mostly all private, which is what most investors do, why do you publish a very opinionated, very insightful sub stack and, share it very, I think generously with the public? What kind of conversation gap are you trying to fill when you start the interconnected newsletter? Kevin Xu (02:39) I think there are two elements to that. First is that this is more of my Silicon Valley ethos, which is that no idea is that worth keeping in secret. It’s all about the execution. Like I’m not, I didn’t come from a Wall Street finance background, right? Where a proprietary trading algorithm or some secret information you got from a meeting is this big trade secret that you want to lock into a vault inside Goldman Sachs or whatever. And that’s going to make you billions of dollars. That is not my approach to investing. I think thinking in public, sharing in public, and really getting the feedback that I get from writing is much more valuable than keeping all these thoughts in my head as if they’re the next best thing since sliced bread. When you actually write it down, when you put it out into the internet, half of them are good, half of them are actually crap. And I use writing and writing in public specifically basically as a canvas for me to think better, to hold my thoughts more clearly. I think knowing how to think is the most important skill for any investor to be able to succeed for the long term. And if any idea that I shared out there benefits somebody else, and you made some money off of it for free, so be it. Good for you that you actually understood some of the value from the writing, even perhaps more than I did as the writer. But for me, that’s not something that I keep very possessively as a trade secret. Grace Shao (04:00) I really relate to that and I think exactly to your point you’re like writing everything down is the way of thinking through your thoughts sometimes it’s all jumbled up in there and then also people ask me why do you keep AI Pro all free? I was like well if it really benefits you you know I don’t really mind I’m not trying to make money off of like selling you know just my content but really the content is my thinking and to your point sometimes I put in so much work and then the result and feedback is so bad and then some things I just kind of like throw out there And then it actually really sticks with people you never know. It’s really good to get the feedback from the public as well. Well, I think now I want to ask you about your journey into investing and really covering China and US. So you are based in the US, but you are Chinese. birth, right? ⁓ So does that play into why you cover China-U.S. related work right now? And I do want to talk about your recent article, which you said you think calling the China-U.S. relationship in tech and AI a race is quite lazy. You said instead of seeing it as pure competition, you think it’s more of a competition. So cooperation plus competition. Do walk us through that and how you kind of came out with this frame. Kevin Xu (04:44) Correct. So just to put a finer print on it, as far as the personal history is concerned, I was born in China. I moved to Canada when I was little, similar to you, think, Grace. And I moved to US later on. So obviously, I work in the US government. So I’m a US citizen. So I’m actually a card-carrying Canadian as well as an American rig

59 min
JAN 6

EVs taking on AI OS and the Delivery War. The Chinese Tech Winners Beyond BAT with Alan Zhang

In this episode, I sit down with Alan Zhang (Principal & Portfolio Manager at Ox Capital Management) to map China’s tech landscape through an investor’s lens. We break down how Alibaba, Tencent, and ByteDance are approaching AI, and why the “AI OS” is the real endgame. Finally, we analyze what’s changing in China’s consumer internet, EV ecosystem, and embodied AI pipeline. We also unpack China’s delivery wars (Alibaba vs Meituan vs JD), why quick commerce is structurally different from traditional e-commerce, and how markets price geopolitical risk into China tech valuations. Alan Zhang is a Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia; previously, he spent years as an investment analyst on the Asia team at Platinum Asset Management. He studied Actuarial Science and Commerce at the University of New South Wales, and he’s even taught advanced econometrics. So if you like the intersection of fundamentals, market structure, and Asia platform businesses, well then, this one’s for you. In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful. 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. For more information on the podcast series, see here. Chapters 01:34 Alan’s background: quant → Asia equities 03:11 US vs China AI: frontier vs “two-legged” approach 05:25 “Uninvestable” China and what changed 07:31 Beyond BAT: Xiaomi, Meituan, Mindray, MicroPort 09:24 BAT AI strategies and the AI OS thesis 13:45 Tencent: tools, data, distribution, and model strategy 16:33 AI-native phones: ByteDance × ZTE and what’s next 26:51 China EV landscape: BYD, Huawei, Xiaomi, Zeekr 31:28 Why phone OEMs can compete in EVs 34:16 Embodied AI: robotics parts, redundancy, and Unitree 39:38 Valuation + geopolitics: why Asia tech trades discounted 41:53 China delivery wars: subsidies, quick commerce, Meituan’s edge 50:27 12–18 month predictions + what investors miss (healthcare) AI-Generated Transcript Grace Shao (00:00)In today’s world, there’s no shortage of information. Knowledge is abundant. Perspectives are everywhere. But true insight doesn’t come from access alone. It comes from differentiated understanding — the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful. 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. So today, joining me is Alan Zhang. And I’m Grace Shao. Alan, really excited to have you. I’m excited about today’s conversation because we’re going to get into the investor’s perspective on Asia tech and emerging markets — with a proper markets-and-math backbone. Alan Zhang is Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia. Before that, he spent years as an investment analyst on the Asia team at Platinum Asset Management. He studied actuarial science and commerce at the University of New South Wales, and he’s even taught advanced econometrics. So if you like the intersection of fundamentals, market structure, and Asia platform businesses, this episode is for you. Alan, welcome. Alan Zhang (01:31)Thank you, Grace. Pleasure to be here. Grace Shao (01:34)Alan, to start, why don’t you tell us about yourself — your background — and what it is that you cover now? Alan Zhang (01:40)I grew up partly in Hong Kong, mainland China — Shenzhen particularly — and in Australia. I spent close to a decade in Australia doing my schooling and education, and worked for a firm called Platinum Asset Management, then co-founded Ox Capital with Joseph Lai. I studied actuarial science, so I’ve had a lot of experience manipulating numbers, cleaning up data — and that helped me tremendously in public equities. Nowadays there’s no shortage of financial data, and the ability to understand them — and the intent behind them — is crucial to investing. Grace Shao (02:34)Yeah, yeah. Alan Zhang (02:46)At Ox Capital, we also built a tool called the Mode Model, which distills more than a million financial data points from various sources to help us understand our coverage region a lot more. In terms of my coverage, I build quant models, I look at equities, and I also help with portfolio positioning based on macroeconomics in Asia. Grace Shao (03:11)That’s interesting because you started off in quant, but now you’re looking at equities — the fundamentals, right? You’re covering a lot of ADRs, and a lot of China’s big tech. Let’s talk about that. What is the China big tech internet ecosystem looking like right now? How does it compare to the US? Alan Zhang (03:20)In the US, they are focusing more on frontier models, while Chinese companies are taking more of a two-legged approach — tackling AI with different approaches. The US has invested a lot of resources into advancing frontier models. On one hand, we see successful cases like Gemini, Anthropic, and OpenAI, while we also see a lot of AI subscriptions cutting their prices by more than 90% in the last few years. If you remember in 2023 and 2024, many subscriptions were priced at a few hundred — some over $1,000 a month — based on investment assumptions. Now they’re cutting prices to sub-$100 a month. Some may never make their money back based on those assumptions, but it’s not being discussed today because the benefit of AI far outweighs that blip, and large-cap companies are investing enough to offset the impact. If we look at China, they haven’t gone through this episode — and I don’t think they will. Anyone who looks at Asia understands Asian users will never assume people will pay over $1,000 a month for subscriptions. China is working on frontier models, applications, and infrastructure at the same time. In summary, China is still the runner-up, but they’re developing AI in a more balanced manner. And it’s also good to see the US pivoting — in the recent 12 months, we’re seeing more US companies investing in software and applications rather than just frontier models. Grace Shao (05:25)China was deemed uninvestable, especially for Western investors. Your fund is based in Australia and Hong Kong, and your LPs are non-Chinese. For public investors who want exposure to China’s AI upside — what are they looking at? What are they thinking? Alan Zhang (05:46)Usually the big tech. China went through the property adjustment and the antitrust campaign in the internet space. It was painful — people called it uninvestable because they couldn’t see new growth drivers. And if they could, they were too insignificant compared to the two most important industries at the time: internet tech and property, which were both recalibrating. But things are different now because investors can see new growth drivers scaling up. In hindsight, these adjustments also helped innovation: talent that dreamed of landing a job at Meituan, Tencent, Alibaba went to smaller firms or startups; capital that made easy money in real estate went to new areas. Economic transformation is still a work in progress, and investing in China becomes more attractive if we see AI, consumption, and advanced manufacturing play a bigger role. We’re still in that phase. But we’re glad to see some companies bottoming out and making progress under the current setup. Grace Shao (07:19)In a pragmatic way, does that mean we’re looking at BAT? What companies should we be looking at for exposure to Chinese AI and economic transformation? Alan Zhang (07:31)Besides Alibaba and Tencent, people should look at relatively smaller cap — but still large-cap — companies like Xiaomi and Meituan. And also industries outside the internet. For example, Mindray in healthcare, or MicroPort in surgical robotics — they can implement AI into their products and make their portfolio more attractive. Grace Shao (07:41)When we chatted offline, you said a lot of companies are overlooked. Beyond BAT — what are some “1.5 tier” or “second-tier” companies that are huge by market cap but not well known in the West? Alan Zhang (08:09)People will naturally see them more over time. Tencent and Alibaba were making active efforts overseas; now as the market matures, more companies are going global. If I’m on a roadshow, people ask about Keeta, which is a subsidiary of Meituan. Xiaomi is opening more stores in Europe — even Africa and South America. People will naturally see them more. If you come to China and compare what’s here to where you live, you’ll see a clear difference. Grace Shao (09:24)Let’s double click on BAT — Alibaba, Tencent, and ByteDance. At a high level, how do you compare their AI strategies? Are they playing the same game, or different playbooks? Alan Zhang (09:52)Same, but different. They’re all investing heavily in frontier models and infrastructure. Ultimately, they all want to build the AI OS people will use. The DoorDash–OpenAI collaboration was a good example of what AI and a commerce company can do. Whether it’s an app within an app or an app within a phone — that’s st

49 min

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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

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Grace Shao
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Clean
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Updated May 12
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Episodes

Nathan Lambert Reflects on China’s AI Labs: DeepSeek, Open Models, and the 'Race' with the U.S.

AI x education, a contentious but unavoidable future. Designing tech for children with Dex's Reni Cao

There's more to Korea than just chips. TheVentures CIO on the country's AI stack

Assembled co-founder John Wang on building a AI native support system for enterprises

Matt Sheehan on China’s AI Policies: Employment, Anxiety, Safety, and State Priorities

Tencent's QClaw goes global, aims to serve the average consumer user, with PM Shuyu Zhang

Sovereign AI, Open Source, and the Gulf’s Big Bet with Interconnected Kevin Xu

EVs taking on AI OS and the Delivery War. The Chinese Tech Winners Beyond BAT with Alan Zhang

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