“So our philosophy here is to integrate the globally optimal models and give users the best results.” — Hang Yu, Head of Product at Qoder, Alibaba

This is the first episode in a series of founder and builder dispatches, featuring interviews with the people creating the future. If you are a founder, builder, or investor in this space and would like to share your story, please reach out.

Today, I am joined by two guests from the Qoder team at Alibaba: Hang Yu, Head of Product, and Christian Hu, Head of Global Marketing and Operations. The Qoder team launched just over two months ago, joining the likes of Cursor, Warp, and Copilot to make coding more agentic, so today we get to learn from them directly about their unique positioning being part of the Alibaba ecosystem.

Hang discusses the thinking behind designing Qoder, how it differentiates itself from peers currently available on the market, the future of agentic work, his fears and excitement about the pursuit of AGI, and finally, challenges the notion that the future of AI may not be based on Transformers.

Christian walks us through Qoder’s business positioning, global ambitions, how it fits into the Alibaba ecosystem, and the reasons for routing between models, beyond just Qwen.

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.

Topics we covered:

Product

* Introduction to Qoder and AI Coding Agents

* The Transition from Copilot to Agentic AI

* The Future of Developer Productivity with AI

* Addressing Developer Bottlenecks

* Multi-Model Strategy and (Qwen) Integration

* Differentiated Views on AGI and AI’s Future

Business

* Understanding Qoder’s Positioning in the Market

* The Competitive Landscape of Coding Tools

* Qoder’s Role in Alibaba’s AI Strategy

* International Ambitions and Challenges

Transcript (AI-generated)

A. Hang Yu, Head of Product at Qoder

Grace Shao

And again, I just want to say thank you so much for joining me today, Hang and Christian. So today, the first half of our conversation will really focus on the product design of Qoder and the transition that we’re seeing from Copilot to Agentic. And then we’ll move into the second half of the conversation, which will really focus on the business strategy of Qoder, international expansion goals, objectives, and then how it really fits into the bigger Alibaba AI plan and the bigger AI playbook. So with that, I just want to bring in Hang. Hang, it’s lovely to meet you and thank you so much for joining us today. Let’s start with the very, very basics. What is Qoder in plain language and what does it actually do for developers day to day?

Hang

Hey, great. Thanks for having me. So yeah, so in one sentence, Qoder is an AI coding assistant that helps developers maintain and improve existing software system, not just build some new stuff from scratch. And I think that distinction is actually really important. So when we look at what developers actually do every day, we found that 95 % of professional developers spend their time maintaining what we call real software.

So commercially valuable, long-lived systems that are not building new projects every day from zero. So that’s why we design code around that reality. We are optimizing for the messy, important work of understanding existing code, making targeted improvements, and maintaining production systems.

Grace Shao

That’s really interesting. you know, there’s for me again, not a technical person. The hype we hear about is all this vibe coding. And then there’s even these marketing phrases calling it like ⁓ AI coding, build your app from a single prompt. That’s not what’s really happening, right? How does it actually really work?

Hang

Yeah, exactly. actually, that’s the flashy use case everyone talks about, like building one app from a single prompt. But if you’re working at a real company maintaining a five-year-old code base with 500,000 lines of code and 20 different developers who have touched it over time, you need help understanding what’s actually here. So you need help making careful modifications without breaking things.

That’s where code delivers value. So in terms of how we think about the AI developer relationship, we see it evolving through three stages. So the first is assistive programming. In this stage, AI helps the developer while human leads. So like code completion, fixing syntax errors. And the second stage is collaborative programming, like co-pilot. Like the human and AI works together like pair programming.

And the final stage is autonomous programming. In this stage, AI takes on complete tasks independently. So the developer can delegate work, and the AI runs in the background and comes back with results. That’s what our Qoder Quest mode does.

Grace Shao

So the really unique bit of Qoder Quest is the autonomous piece, right? And I really want to dig into that a bit more, a bit later. So right now I’m curious. So when you say the user experience is intuitive even for non-technical users, what design choices really led to that? Because honestly, a lot of developer tools are pretty intimidating, especially for people like myself who’ve never done any coding. But what I’m hearing on the street is people are going out of their way, even as non-technical people, building their own apps now with the help of AI. How are they able to do that?

Hang

Yeah, great question. So we have this philosophy. So don’t make users think about things they shouldn’t have to think about. So for example, if you look at some products, they have like 40 different AI models in a dropdown menu. Honestly, that creates a lot of cognitive load. So developers end up becoming model select or instead of focusing on building their own product. So our philosophy here is integrate the globally optimal models and give users the best results. So we will auto select the right model based on the task. So we believe model selection will be better than human selection. And the same thing with context management. The users shouldn’t have to manually figure out, OK, which files to include, what tokens to optimize. Our context engineering handles that automatically. So the goal here is to remove the cognitive overhead. And let developers focus on what they are trying to build, not on configuring the AI tool.

Grace Shao

I have a really dumb question, but is there a latency then between me prompting like, can you help me build this versus the machine telling me which model is optimized? Is there like a latency? It’s automatic.

Hang

No, no, there would be no latency. Yeah, it’s all automatically. The user will not feel it.

Grace Shao

that’s amazing. Okay, let’s talk about kind of the hype right now, the copilot versus the agentic transition right now in the coding space. Many tools are being called assistants or some are called copilots, You know, the cursors of the world. And cursors essentially been leading this. So where does coders autonomous capability really truly defer or is unique or different? And what’s really the big breakthrough we’ve seen here?

Hang

Yeah, this is the defining question, So Cursor has ⁓ perfected the co-pilot approach, real-time help while you’re coding. Their tab completion, tab, tab, tab, is industry-leading after two years of their custom model training. But I’ll say this. So Cursor’s tab completion compatibility is catching up fast. We have made significant progress in recent months and rapidly closing the gap.

But here’s where we see the real future, the autonomous programming. So you delegate a complete task, implement this feature, fixing this bug, et cetera, whatever. And then the AI works on it in the background. You don’t have to watch every line of code being written. You just come back and review the result. now sophisticated autonomous coding and production skill is still involved.

Hang

We think it’s about two to three years out, maybe 2027 or 2028, before it’s really mature. But that creates an opportunity window.

Grace Shao

That’s actually quite soon. So what does that really mean practically?

Hang

Yeah,so it means With autonomous coding you can actually Delicate your work and make and the the AI agentic will can work it background in the cloud so Like you can close your laptop and the AI keeps working

go to a meeting, go home, do whatever you want, the coder is still running. So in other words, you can spin up 10 parallel sessions working on different tasks, and it doesn’t slow down your own machine. As one colleague said, he said, I’m managing 10 agents. My productivity went up 10 times, and it didn’t mess up my work-life balance. So yeah.

Hang

So this cloud execution model is pretty similar to what cursor recently launched with their cloud agents feature. So both approaches let you handle your tasks to agent running remotely. The key advantage here is you are not tied to your machine. You can dedicate the work and then close your laptop and then come back to complete the result. And then when the

Hang

Yeah, and then when the agent finishes, we just need to review the results.

Grace Shao

So that’s actually my question. Like when you talk about reviewing the results, is it very obvious to kind of find the issues only in the result or do you have to go back to the process? Like how do you audit the whole process actually?

Hang

Yeah, so the agents will present to you summary of, this is what I’ve done. This is the result of the question, or this is the feature. You can see it through the browser, or the agents will submit a PR to your GitHub to your routing workflow. And you can easily check, OK, is it done good, or it needs to be modified again.

Grace Shao

So does that mean that junior developers in a way will be replaced then? Because essentially you only really need people who understand a higher level of the code and the execution can be actually outsourced so easily.

Hang

Hmm. Good question. So simple, agentic task works well today. You can already dedicate things like write a unit task for this function or add log into this module and come back get some good results.

So sophisticated, agentic coding at production scale, where AI can take a high level of business requirements and then autonomously design, implement, test, and deploy a complex feature across multiple systems, that’s still two to three years far away. So what this means for software development is a fundamental shift in how developers spend their time. ⁓

Hang

So right now, developers spend maybe, I think, 30 % of the time on creative problem solving and 30 % of the time on mechanical work, like writing border plates, fixing syntax errors, ⁓ updating documentation, writing tests, et cetera. But as agentic tools mature, that flips. So AI handles the mechanical work. Developers ⁓ can focus on what AI can do yet, like understanding what actually needs to be built.

Hang

making architectural decisions that require domain knowledge and validating that the code actually does what it should be.

Grace Shao

Mm-hmm. So in the end, it’s still like, there are still aspects of humanity that can’t be replaced by machines yet. Let’s get a bit more practical. I want to understand how precisely Qoder can actually turn a prompt or product spec into working code? What types of tasks or repos does it handle best today, right now?

Hang

So Qoder handles best what we call a bounded, well-defined task today. For example, implement authentication for our user login system or add relimits to our API endpoints or generate a dashboard for monitoring system health metrics. So these are tasks where requirements can be clearly specified, the scope is contained, and the success criteria are measurable.

Grace Shao

Mm-hmm.

Hang

But what’s harder today are tasks that require deep domain knowledge or ambiguous requirements like improved user engagement, that’s too vague, or like a required entire authentication system, that’s too large, that’s too large and risky for autonomous execution.

Grace Shao

So it seems like a lot of the tasks it can do is still pretty much something that’s very easily verifiable. It’s a bit more like a black and white kind of answer kind of task, but not so much things with nuance, right? I want to understand better. So, thinking about the developer workflow, and correct me if I’m wrong, there’s the planning stage, the code writing stage, the running tests, the debugging, version control, and deployment, right? Where does Qoder natively sit the strongest, like in the most helpful, and where do you hand off to other tools?

Hang

Great question. Let me break it down. So the first is plan and design stage, So Qoder is strong here through the spec generation. We can help the developer translate business requirements into technical specs. And the second stage is writing code. This is our core strings. The Qoder can write code across multiple files, handle complex logic, and generate boilerplate. And for the run and test stage,

Qoder can generate unit tests, integration tests, and run them either locally or in cloud sandbox. That’s building feature. And for debugging stage, Qoder can diagnose its errors from test results and fix them autonomously. But for production debugging with live user data, you still need the traditional tools. That needs to be taken care of. And as for version control or call out, we integrate with Git, GitHub, and GitLab. So a coder can create branches, commit changes, and create pull requests. But the actual code review and collaboration discussion happens in your existing tools, like GitHub, GitLab, whatever you use. But yeah, in the final stage, the deployment, we hand off here. So deployment involves your CI-CD pipelines, like infrastructure, monitoring system. So Qoder creates the code and the tests, but you own your deployment process.

Grace Shao

I see. I kind of want to take a step back and understand another big kind of hovering question a lot of people have right now, which is if we’re going to have agentic tools really implemented the work process, how do we understand developer productivity for the future? Will developer productivity still be measured the same way that it’s currently being measured?

Hang

Yeah, I think the fundamental shift here is this. So AI changes what developers spend their time on, not just how fast they work. So again, right now, developers spend maybe 30 % of the time on mechanical work, like writing boilerplate, debugging syntax errors, searching documentation, setting up environments. And only 30 % of the time goes to creative high-value stuff, like understanding what needs to be built. Like understanding what needs to be built, making architectural decisions, and validating that solution actually solves the problem. But with AI, agent AI flips that ratio. So AI handles the mechanical work. Developers focus on the part that actually requires human judgment. So when we measure productivity, we are not counting lines of code or tickets closed.

So we are looking at, can developers spend more time on higher value work? Can they ⁓ ship features faster without burning out? So what we are seeing is promising. 99 % of our paid users actively use agent model. Nearly 99 % of our paid users actively use agent mode. So it’s become core to their workflow.

80% renewal rate tells us that people see the real value here. And enterprise reports two to three times improvements in the deployment frequency. So the real metric here is simpler. Developers tell us that they are less frustrated. They are not stuck debugging environments or writing repetitive code. They are solving interesting problems. And that’s the productivity gain that matters.

Grace Shao

I see, I see. That’s really interesting because I think as a writer, when people really initially rejected AI, the idea was also that there was a lot of mistakes, AI slop, hallucination, whatnot. But then what people started realizing is that you can actually use it as a productivity tool. And like to your point, it doesn’t change how you think as a writer building out the framework, using your critical thinking and really still rely on your own creativity. But you know, you what you’re outsourcing is actually just like the execution that was a lot of the grunt work frankly. Right. Interesting, interesting.

Hang

Yeah, yeah, exactly. Yeah, you free up your hands and you, yeah, yeah, you free up your hands and focus on your head.

Grace Shao

Okay, so I wanted to ask another question on productivity and bottlenecks. So what is, I guess, one of the top bottlenecks or what are a few that you see currently in this space for developer right now? And I guess can we actually separate the separately answer this question, the first half being what are professional developers bottlenecks and what are kind of the new age, like casual developers bottlenecks and how are you helping these two differently?

Hang

Good one. Let me hit the main ones. So usually the bottlenecks, like the first big bottleneck is about the environment setup, So this is a huge time thing. The coders, and then the coder can help you set up the environment automatically, whether you are running it locally or in the cloud, pre-configured environments, automatic dependency resolution.

So you don’t have to spend two hours debugging your Python version complex or just trying to build your dev environments. And then for the flaky tests, coder can also detect flaky tests by running them multiple times and identifying inconsistency. So you can also suggest fix based on the failure patterns and the test outcomes. And then for...

For another bottleneck here is the legacy code. This is where the Ripple Wiki comes in. So remember how I said the documentation is always out of date for developers? Ripple Wiki uses... So Ripple Wiki delivers value here. So Ripple Wiki features here can use AI to generate up-to-date documentation from the code itself, plug the Git history. So it’s not just...

Here’s what the function does, documentation. But here’s a business logic and why it was architecture this way and what changed and why documentation. So the documentation itself stays fresh. When code changes, the documentation regenerates automatically. So we have measured about five times speed improvements, so from 60 minutes down to 12 minutes for team documentation.

And the last but not least bottleneck here is the context limit. So this is a technical challenge. Models have tokens limits, So we have our context engineering figures out what’s actually relevant to the task. So we don’t just dump the entire code base, which will crush your token limits. We intelligently select what the task needs and what the AI needs. and we ⁓ gain a better solution from the ⁓ context we select.

Grace Shao

That’s really interesting. So I think I want to talk about the models and orchestration here. So Qoder is a multi-model platform, right? You guys use Qwen, your in-house built models, but you also use other models like you mentioned earlier, use whatever is like kind of state of art model for the right task, right? How do you route between the models, thinking about latency, cost, evaluations, languages? Do you usually like have a preference for Qwen or the third party models when you’re assigning these models to tasks?

Hang

OK, so what we are trying to do here is integrate the globally optimal models. ⁓ So what we are trying to do here is to integrate the globally optimal models and give users the best results. We are not limited to just Alibaba’s model. So if one frontier model is better for a specific task, we use that. So like if GPT Excel somewher, we use GPT. If Qwen is the right fit, we use Qwen. So now, why does this matter? So first, it best has time. I’ll be direct. So Qwen model isn’t the best model in the world for every coding task yet, but it’s improving fast. So by using the best global models today, can serve users well while our own models can catch up. And if Qwen becomes the best model globally in your year, is our goal, then naturally we’ll use it more. And secondly, yes, sir. Yeah.

Grace Shao

But actually, just want to jump in on that. But actually, Curser is even using Qwen isn’t that fascinating that we just found out recently.

Hang

Yeah, so the cursor’s composter is... They didn’t officially admit it, it’s... So the community thinks they’re fine-tuned and post-trained based on Qwen or some Chinese open source model. Yeah. So first is the best time. And second, the cost optimization without compromising quality is our second goal. So not every task needs the most expensive frontier model. So simple completion is a smaller, faster model. But for a complex reasoning, we use the frontier model. And last but not least is about the reliability. So you don’t want your entire product to stop working because one API provider has an outage. Here we saw OpenAI has multi-day outage in 2023. we want to our reliability to our customer. Yeah, that’s why we use multi-modal strategy. Yeah.

Grace Shao

I see, I see. I was actually going to ask you on that. What’s the thinking behind designing the product using a multi-model design versus only proprietary Qwen? I guess you already answered that partially. ⁓ I also wanted to kind of double click on the fact that Alibaba is throwing 53 billion US dollars into AI infrastructure right now. know, isn’t it, is there not some pressure coming from up top to really hone in on Qwen or, know, like, I guess the question’s more about - Is Qoder really developing based on what’s best for the user and use whichever model that’s best for them? Or is it more focused on being part of an ecosystem, another tool out of the Alibaba set of tools that they provide? Does that make sense?

Hang

Yeah, great question. So it comes down to serving users best today while building towards to the future. look, Qwen models costs us like 1 fifth or 1 sixth of what Frontier model API costs. So that’s an 80 % cost reduction, right? That’s a structural advantage. But Qwen isn’t as good as the top Frontier models on context reasoning tasks.

That’s why we use a multi-modal setup at the balance cost, capability, and reliability. But the key in size is that this isn’t a permanent state. Qwen improves, as coding capability, sorry, as Qwen.

So the key inside is that this isn’t a permanent state. As Qwen improves, as its coding capabilities get stronger, the balance will shift. So we are not philosophically opposed to vertical integration. And we are actually pragmatically choosing what serves users best today while building towards our ownership tomorrow. So for enterprise customers in China, components actually require a domestic model anyway. But for international customers, they often trust well-known frontier models or GPT models. So multi-models board let us serve both.

Grace Shao

I think that’s a really interesting business strategy because end of the day, to your point, it’s a very pragmatic approach to actually serve your customers best and to serve your customerbest, you actually get more business. That’s just how it actually the virtuous cycle works, right? Instead of building up these guardrails and the paywalls. I want to kind of pivot to strategy soon, but before we do that, I really want to ask you a few other questions just on more, a more broad general question. One is, you’ve been working in this space for a long time. There’s a lot of hype around Agentic tools, not just a Agentic tools in coding, right? How do you view a Agentic AI in the next 12 to 18 months? And how do you think it will actually affect what even the general mass think of AI use AI?

Hang

I think agents will swallow the whole market. ⁓ People will use more more agents in their daily workflow. So when you’re trying to use a chatbot, you need to copy paste a lot of data, your contacts, your own data, your domain knowledge.

To chatbot and then generates some specific task results. But by using agent, agentic AI, you don’t need to do that. The agentic AI will live in your workflow. It knows and it contains your domain knowledge. And it knows your context by nature. So this means agentic AI knows ⁓ what you’re trying to do, what you did.

What you are trying to do and what you want. That’s actually a big difference between the big difference between the LLM and the agent AI. So I think, yeah, yeah.

Grace Shao

I see. I think that that leads me to the next question, is like, so for teams who are customizing tools or agents, like you just said, they would have domain knowledge. They already know your work processing really well. And how does it actually work? What’s the plug-in API story with Qoder in this sense again?

Hang

Yeah, so this is about the domain knowledge question and the accessibility work. Let me break down how the domain knowledge and the accessibility works. So first, we have MCP supports. We support the modal context protocol, which is becoming the industrial standard for connecting AI agents to external tools and data source. So this means the coder can ⁓

integrated with thousands of tools in the ecosystem, database, API, version control, and project management through a standardized interface. And then we also support subagents and skills. So teams can create their own specialized subagents. Think of this as task-specific AI workers with their own contacts and capabilities. So ⁓ you want a code review subagents turned into a

So you want a code review, sub-agents turn to your team standard. You can define it. You need a security scanning sub-agents for your compilers requirements, build it by your own. So these are the version controlled and shareable across the whole team, and can also run in parallel. And as for the domain knowledge, you can inject your own documentation, design system guidelines, and your coding standards directly into coders context.

And when Qoder generates code, it will follow your rules automatically. So now, ⁓ here’s the key part. How you actually use all this. So you can think of Qoder like a Stripe for payments or Tailor for communications. You just embed it into your existing development workflow. You don’t need to replace your IDE. You integrate Qoder into VS Code, into JetBrains, into Temrano, into your CI-CD pipelines, whatever and wherever you already work. And there’s actually a precedent here. So Alibaba’s Model Studio platform has enabled over 800,000 custom agents across different domains. And we are also trying to bring the same extensibility here to coding workflows.

Grace Shao

That’s really really interesting. Thank you so much for sharing all that and I do apologize if any of the bit that I didn’t like sound that smart because it’s so technical but I really appreciate you breaking it down for me and really explaining it to me in very simple language. I have one last question for you which is something I always ask all my guests. ⁓ What is one differentiated view you hold? And this doesn’t have to be about work. It could be. It could be about your space could be about agenda coding. It could also be about anything else in the world. It could be about your experience in Silicon Valley versus China. Just a differentiated opinion review of something that you think is not that mainstream or it be a bit against consensus.

Hang

So we talk about something related to agentic coding. actually, I don’t think the transformer-based LLMs in the future. Because I actually do agree with the point that LLMs is just a compressor of the tags. They don’t really understand what they are talking about.

But I think as the develops, as the technology goes, one day we will have the truly AGI. But it’s not based on Transformer.

Grace Shao

What does AGI mean then?

Hang

AI means the AI can really understand what it’s talking, what it’s doing, and really have emotions. Yeah.

Grace Shao

Fully human-like, emotionally, intellectually functioning being.

Hang

Yeah, like human are…From this perspective, they have emotions, have logic, they understand the work, the 3D work, and they have logic of, okay, why I’m doing this, and what I’m going to do in the next.

Grace Shao

Is that scary to think? Because in many ways, they would be more powerful than us, right? They can speak every single language on Earth. They will know more knowledge than us as an individual. But can we though? What if, you know, what if there’s charge and they can charge themselves these days, these humanoid robotics?

Hang

we can cut off the power. You mean to take off and charge by themselves?

Grace Shao

Yeah, like, do you ever worry about this AGI achievement going rogue?

Hang

To be honest, I’m both a bit excited about this and a bit scary.

Grace Shao

Yeah, I think especially for the next generations, right? Like what does it mean for them? Yeah. Thank you so much for your time. I really appreciate it.

B. Christian Hu, Head of Global Marketing and Operations at Qoder

Grace Shao

Christian, thank you so much for joining us. I just spoke to your colleague, Yu Hang. he was super helpful in explaining to me the technicalities of Qoder and the design of the product. My interest right now is really shifting towards the strategy and business side of Qoder. I’m really glad that you can join us today. So why don’t we start with the beginning? Why build Qoder inside of Alibaba? What was the thinking behind that? And what unique advantages does that really give you?

Given that you have someone who just threw $53 billion into AI infrastructure that’s backing you, right? In terms of distribution, your infrastructure, your data, your model access, how does that really advantage you in many ways?

Christian

Okay. Thank you. Thank you, Grace. Thanks for having me here. You know, Alibaba has a very big plan and has very big ambition for AI ⁓ and his position in the future in the AI industry. And as you know, Alibaba has a full stack. We call the full step strategy from the cloud to model and to application. So that’s the full stack strategy. And for, ⁓ you know, a home computer, you know, we don’t build Qoder in a vacuum. Yeah, we build Qoder from the real context because we can find, we have got so many, maybe thousands of engineers from inside Alibaba. They are facing very real software problems in real software development. They have many issues in interface and how to fix the issues. But for the existing AI coding tools, they are maybe reactive. cannot...resolve some existing problems, maybe some complex and sophisticated problems in the real software development process. So they are trying to refer to a new product. we build Qoder just from inside. We have so many inside, the mind from the inside engineers. also, as you know, be part of Alibaba give us, mean, have so many advantage because first of all, we own models.

Because you know, you know, we have early access to Qwen and that’s how I’d propose large-scale models We don’t just use Qwen we also reinvent and feed the Qwen with our real data from our AI coding Qoder base. So we just really reinvent the Qwen and we are coordinated with the Qwen team to improve the performance of Qwen models and

Secondly, we own the cloud. I mean, the bottom layer of Alibaba’s AI strategy. that’s the cloud. Every digital tools and maybe even every AI coding tools should be based on large cloud infrastructures, consume very large computing powers. So cloud is also a very important factor in AI coding platforms.

The third, think we, I think the most, last but not least, we also have the advantage of Alibaba has so many enterprise customers and so many business, you know, we got some business lines maybe from the consumer to the from the SMB to large enterprises, from the consumer to industries. So we got so many data across, as real data for us to, how to do involve the, the area AI to, you know, to ⁓ upgrade our coding platforms from the real data. that’s the, so, ⁓ so back to the, the origins of the Qoder So we just not, you know, we’re just not create another AI coding platform just for the tool, which we want to build a Qoder as a new platform for a Agentic platform for the real software. We want to build the Qoder for real software, not just for fun, not just for the fun making, but for the real software.

Grace Shao

Thank you, that’s really helpful for everyone to understand. I think saying we have a model is the most humble thing anyone can refer to Qwen as, because you guys have one of the best leading open source, open weight models in the world right now. Actually, so on that right now, you know, we’ve been hearing a lot of news about people adopting Quinn globally, and it’s really being used not just in China right now. ⁓ Who are the people using Qoder actually?

Are they mostly Chinese developers or are you guys actually expanding globally? That’s, I guess, the first half of that. And the other half is, are these mostly professionals or are they like students or are they enterprises? Like, how do we understand the demographic here?

Christian

Yeah, good question. We are building Qoder for global developers from day one. Yeah, so that’s not just for Chinese developers, but for global ones. And for now, ⁓ actually for now, our users are mainly come from the, we call them individual developers, not just the enterprise users because know we the enterprise editions on it our way we are just we want to start with the individual you just at the first and and the funding you know geographically from China to the overseas market so we want to some difference between you know preference different preference for different you’re just in different regions yeah from in China they would prefer to the more, more integrated coding system existing with the existing customers. It may be fully integrated with existing systems. And in the Western countries, maybe in some in US or maybe some in ⁓ Malaysia, the users will prefer to more flexible workways. So they’d have a different preference for different coding languages. And for now, we are trying to solve the real software development problems. the most of the users come from the professional users. Because they have a real problem to resolve in the real software development process. Because they doing their work. Because they doing their work and they want to increase their productivity levels to solve more problems. So the main users come from the professional users.

But we also find the increasing adoption of the individuals and some new learners, maybe some product designers, maybe some UI, maybe some UX designers, they want to deploy the AI coding tools to to expand some ⁓ new interface or maybe some new mini apps to increase their productivity is, yeah, so that’s the trend. we believe, uh, Qoder is created to solve the real problem real software development problems. But we also find that the, our agentic, uh, models and the questing models can allow, uh, more users, just like the new learners and new indies, new learners to, uh, new individuals to use our software to create something new, something more powerful. Yeah.

Grace Shao

So my understanding is that Qoder itself came out of the desire to help professional developers. But Qoder Quest, that mode you can go into, can actually help people with less technical backgrounds to be able to play around with it and potentially still build their own thing.

Christian

Yes, Quest Mode is maybe the key to unlock more space for web coding. What is web coding? Web coding is for non-professionals to use the Qoder tools to create something new. I think the Quest Mode may be the new way to unlock more web coding. But our Quest Mode is different from the existing agent model of other competitors or maybe other coding platforms because our Quest Mode is always is really is also a Agentic is a Agentic about also ⁓ we called a delegated and you can control the workflow and control the result on the question model. So that’s that’s for real software. Yeah, so the Quest Mode of coding is also is for real software.

Grace Shao

I see. I think I have a question for you. I asked Yu Hang earlier as well. But basically, my question was that, you know, there are a lot of tools out there already, like the co pilots Curser co whisper Warp you know, there’s there’s a whole array of them. How do I understand where Qoder sits so Yu Hang explained it to me in the technical sense of where it sets between co pilot and a agentic. Can you explain to me where Qoder sits in terms of like the business positioning.

Christian

Okay, we have a slogan for a Qoder We Qoder a agentic platform, a agentic coding platform for real software. So we got two keywords, Agentic and the real software. I just explained what is the software. I can explain more about the software because many developers, mean, no matter professional developers or non-professional developers, they found something critical in their developing process because they need to know the existing Qoder base. They need to understand what the existing Qoder means, what the existing documents mean for the codes. So they need to understand so many documents, so many files to understand what the coding process will be like.

So we created a Ripple Wiki and a non-context memory function to understand the codes, understand the documents, understand the behaviors of the developers. So for the new entrants, for the new developers, they know how to get the real software down. So that’s the issue in real software development context.

Yeah, that maybe seems different from what we call the rubber coating. So that’s for real software. And for Agentic, for most coding platforms, we call it your prompt and the Agent, the React. for Qoder we call it your delegate, then Qoder delivers. So then Qoder that deliver the real software and real results and the real apps for your delegations. So that’s the difference from the existing, I mean, maybe some most of the competitors to agentic platforms. So we are just not want to respond because of the delegation that can, you know, to free you from the desk. mean, for agent, for most agents, you also need to speak to the agent.

You need to communicate with the agent while you are sitting alongside the desk. But the dedication can free you. You just don’t need to communicate with the agent. don’t need to interact with it for line by line, word by word. You just need to monitor the per science and maybe some need to check with the workflow so that the Qoder can deliver the result. That’s the difference between way from the other competitors.

Grace Shao

Actually, let’s take a step back. I’ve noticed that when I was doing some research on you guys that, you’re, it’s not just Alibaba that is creating these coding tools. You know, we have even just in China, we have ByteDance creating something similar. Obviously, Microsoft has things like has Copilot. So then we are looking at obviously then the startups and the Frontier Labs all kind of swarming into coding tools. Why is that? What is really the reason for focusing on coding right now as the next kind of use case?

Christian

Yes, actually, coding, as we just talked, coding may be acceptable for every engineer, maybe a non-engineer, maybe you’re just a digital learner, maybe just an analyst for the AI. So coding is the most certain way for token consuming, for the ⁓ infrastructure consuming, the tool to the cloud infrastructure. Maybe the coding is the most important way. And for different players, they are playing different games. For giants, you just mentioned the Bydance, maybe Microsoft, maybe AWS, they have very large cloud infrastructure. They just to integrate the new AI application to the infrastructure. And for SRAPAC, maybe some other front-end labs, are... ⁓

They are trying to find the new path to the developers, maybe some to the application levels. for Cursor, they are just a new service. They are deploying the large-scale models to make it accessible to the new developers. Cursor has...

I think for new startups, have their weakness, maybe they have their cost structure weakness. mean, they are the users of the large-scale algorithm. They can’t dominate the large-scale algorithm model and maybe they will be at risk to disengage with the large-scale algorithm model owners. So that’s the cost structure that is at risk for them.

And for Qoder know, as I just mentioned, the Qoder is born within Alibaba. Alibaba has full stack from ⁓ cloud infrastructure to model to application. So I think that’s a great advantage. As you know, ⁓ as you just mentioned, Qwen is very popular around the world. some, you know, I just kind of found the news that Airbnb, Airbnb,

Grace Shao

Yeah, Brian Chesky

Christian

Yeah, yes, I said adaptedQwen open source model to the real invent their own infrastructure That’s very popular and for it for cursor. Yeah, it’s just anything for a cursor itself They just came up with a new composer composer model because it’s a small small model for coding but We have a speculation that the this small model comes from the Qwen or maybe some cheap. Both of them are from China. So we will find that the full integration and full connection with the model and the client structure will benefit a for Qoder in the future.

Grace Shao

Actually, on the point of how Qoder sits in Alibaba, can we kind of zoom in on that? Can you help us understand the big picture? Where does Qoder sit in the Alibaba Grand AI strategy across the stack?

Christian

Yeah, I think Alibaba AI strategy is a very big picture and also very long roadmap for what we call the super artificial intelligence. We call it super artificial AI. Yes, wait, wait, wait, ASI, that’s the roadmap. so from the map, so when we look at the map, I think it will form the bottom to the top level.

I just mentioned is the cloud infrastructure. Yeah. The middle level, mean, the Qwen, maybe some other large Niagara models in the middle layer. And the Qoder, I think it sits on the application level alongside with some other applications. Maybe, you know, Dink Talk, maybe Quark, maybe some other applications on the application levels. think that’s the full, we call, so we call the full stack strategy for Alibaba in the near future. Yeah.

Grace Shao

I understand you lead Qoder, international operations and marketing, right? And that’s a pretty big title you got here. What’s your GTM focus right now? Where are you kind of focusing on essentially selling your product to? Like which markets, through what channels?

Christian

Yes.

Grace Shao

How is this kind of working out for you?

Christian

Yeah, so I think it’s a tough task. I know because we are new, we’re just a baby. We just launched our products about two months, no longer than three months. We’re just a new one in the market. our ambition is here. We want to ⁓ become the top global coding platform for global users.

So for me, think the go-to-market strategy for ⁓ us to go global, think we need more partnerships, we need more integration with the local communities. So for me, that’s why I’ve followed to different regions to meet up with local community developers, maybe the leaders of local communities. We want to talk about it.

What kind of preference for them, what kind of products they want to prefer in their context, in their context, maybe within the enterprise, maybe within the individual developing context. And also we are aiming to the global products, we are not going to separate.

China with the global markets. We want to offer two different products. We just offering one product, one platform, one user interface for all the users around the global. So that’s the challenge. But we believe we can do that because we are trying to interact directly or maybe talk directly with ⁓ the developers from the different corners of the world.

Grace Shao

Yeah, that’s definitely quite different from how Chinese companies used to sell their products abroad, right? Because it used to always be a one app location or one interface, domestic one interface for the globe, for the rest of the world. So actually on that, think my final question for you really is just how are you navigating the current climate? Obviously it’s not.

Probably not easy given just current situation with geopolitics, with the competition, with everything, right? How are you navigating differences between China and the global markets in terms of adoption, compliance, data residency requirements, and even developer culture? Because I know you were just in Singapore, like you mentioned, you’re flying around a lot, you’re meeting people from different parts of the world. How are you making it all work?

Christian

Mm-hmm. Mm-hmm. Yes.

Quite different. I think it’s quite different. I have seen huge difference in the developers in China from the developers in Southeast Asia. Because I just flew to Singapore and met up with some local developers. The difference comes from different angles.

User interface and the language and maybe some other features of the software. So the difference, is huge difference between the different rappers geographically and demographically. Yeah, so all the differences are very, very huge. And we also find some, because we are trying to offer our enterprise edition in the near future. So we also started a very

We have made a study for the enterprise levels in different markets. We find quite different stories because in China, the developers in enterprise, maybe in the small companies, in large small companies, they would prefer the company to purchase the software. They prefer to use the existing software. The truth is using software can be embedded into the existing OA system or existing software ecosystem. So they prefer the existing, they prefer the integrations. But in other countries, maybe in other markets, they prefer more flexible and more athletic applications. They don’t care about the integration, they don’t care about the...

I mean the sophisticated integrals, maybe sophisticated workflow and controlled by the company owners. So that’s the different culture. So you can find it’s very common for individual users to buy our software, buy our products in other countries, mean outside China. Yeah, so that’s the huge, that’s the huge difference.

Grace Shao

That’s really interesting. I would have thought that integration part is something that everyone kind of wanted because it makes your workflow so much more seamless, right? And that was the selling point for all of Microsoft’s tools essentially, right? In the software era. Interesting. Yeah.

Christian

Yes. But it doesn’t mean that we don’t need to make an integration or some ecosystem with the... You just mentioned Microsoft, mean, the OA or maybe some Workday, some other software platforms. We also want to explore more connection with existing software.

Grace Shao

Thank you so much, Christian. I really appreciate your time. Thank you.

Christian

Thank you. Thank you so much.

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“Retail is simple. Retail is just how do you sell something, and make someone’s eye light up. AI or any technology you add to it, is just another way to do that,”

— Sharon Gai, retail tech and AI expert, former Alibaba executive.

Joining me today is Sharon Gai, an expert in AI and innovation, with a focus on retail. She was an executive at Alibaba, where she advised brands and heads of state in crafting their digital strategy with programmatic marketing and AI.

In this conversation, Sharon shares her journey from working at Alibaba to becoming a consultant in AI technology for global companies. She discusses her experiences in e-commerce, particularly the evolution of live streaming and innovative marketing strategies in China. Sharon emphasizes the importance of AI integration in retail operations and the future of shopping with AI avatars. The conversation concludes with insights on simplifying retail to focus on core selling principles.

Sharon was selected as a RETHINK Retail’s Top Retail Expert and a LinkedIn Community Top Voice in 2024. She has two books, E-commerce Reimagined and How to Do More with Less Using AI. For more of her work, go to sharongai.com.

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

02:27 Experiences at Alibaba: The Global Leadership Program

05:11 E-Commerce Evolution: Insights from Tmall and Live Streaming

07:54 Innovative Marketing Strategies in Chinese E-Commerce

10:23 The Rise of Live Streaming in E-Commerce

31:33 The Evolution of AI Avatars in Retail

34:09 The Impact of AI on Shopping Experiences

41:00 Challenges in Retail During the AI Revolution

43:22 Integrating AI into Retail Operations

48:43 The Simplicity of Retail: A Unique Perspective

Transcript [AI generated]

Grace Shao (00:00)

Hey Sharon, thank you so much for joining us today. It’s great to reconnect with you. We met like, I think four or five years ago back in Shanghai and you were still with Alibaba, right? So why don’t you start with telling us about your journey? Like you grew up in Canada, you worked in Hangzhou, Alibaba. I know now you live in New York. How does it like, how did you kind of bring together all those expertise to what you do today, which is help?

Sharon Gai (00:27)

Sure. So when we met, I was working at Alibaba still. For me, as somebody who was born in China and raised in North America, and then I chose specifically to go back to China to work for a bit, the reason why I did what I did is I just knew that going forward 10, 20 years out, the two major superpowers would be the US and China. And I already had a pretty good understanding of things that were going on in North America.

⁓ Going back to where I was born and getting the chance to work there at one of the tech companies there really opened my eyes to how both countries work. I think in the future it’s going to be a ping ponging back and forth of I’m sure different ⁓ globalized companies and projects between the two places.

⁓ And so that had blended pretty well to what I do now, which is a lot of writing and keynote speaking and consulting for global companies that both have a footing in China and the U.S. So it all ties together pretty well now, but definitely as I was going through my through line or life trajectory, it seemed very confusing in the beginning phases.

Grace Shao (01:35)

Yeah, tell us a bit more about your time at Alibaba, because I know you were part of quite a special cohort. was a of a test and trial group of international cadets, per se.

Sharon Gai (01:47)

I’ve never been in cadets before. But I guess with anyone joining Alibaba, it feels like going into entering some, a ⁓ corporate army of sorts. But the program was originally set up by, or it was a brainchild of Jack Ma’s. He had always, I think he had similar thoughts, which was, you know, eventually you’ll hit the ⁓ bottleneck of about a billion or so. ⁓

Sharon Gai (02:09)

internet users in China, where do you then grow the company beyond the billion users? You have to find it outside of China. And so the first place of external search was Southeast Asia and then into the Middle East, Africa, Europe, and then eventually the US. And so his long-term vision was

to recruit people who came from those places, those corners of the earth, to get them to come to China to be in green with Alibaba’s culture, a way of working, and to bring them back out again, and then ping pong back and forth, just as I thought. So I think his vision and my own personal vision aligned pretty well. So that’s what got me to join the program. And yes, it was definitely an experiment. There were many, what I would call seasons of us or cohorts.

every single year there were new people that came in, from different cultures based on the strategy of the company at the time. think in certain years, they really wanted more, a certain language to be spoken. So they, really hired for that specific language. and it, definitely changed at different versions. but the idea was to bring in.

people who are bicultural, multicultural to eventually lead some of the business units that was trying to expand outside of...

Grace Shao (03:22)

And actually on that point, what were you doing at Alibaba? I believe you were involved with Tmall, right? So the international business, flagship business of Alibaba’s e-commerce sides. Could you tell us a bit more about that?

Sharon Gai (03:34)

So the first sort of business unit I was in was called Tmall Global. Our larger BU was called Tmall Import Export. of course, as you would know, Chinese tech companies always like to change names and just change things. Embracing change is one of the values.

So at first it was called Tmall, import and export. And I was first on the import side. And then I went to the export side, which is what we call Tabout Tmall world, where there’s about 50 million Chinese diaspora around the world. And they also will use Tabout as a shopping app. At the time was also the growing footprint of the Shiians and Tmus of the world, where these local Chinese e-commerce apps were trying to leave China.

And so Taobao Tmall World was also part of that exercise. And so those were my main two. the first was, or sorry, one of them was Taobao Tmall Import-Export. And then I moved to Tmall Classic, which is the domestic side of Tmall.

where the brands, most brands were either Western brands selling into China or Chinese brands selling to local Chinese consumers.

Grace Shao (04:47)

So you’ve really got a good look into how the retail e-commerce digital evolution happened in China. You’re super plugged in. And you were there for like six, seven years, right? So when you left, you published a book, you published E-Commerce Reimagined. And I believe at that time it was COVID, pre-COVID, and just everything kind of changed again. And we saw the rise of e-commerce really.

⁓ being part of the daily lives of North American consumers as well. Tell us a bit about like, I guess, first your book and then tell us about what you witnessed over the 10 decades, sorry, about six or seven years while you were Alibaba.

Sharon Gai (05:25)

Yeah, so in 2017 when I joined, was the height of live streaming starting in China. When I joined T-Mall Classic, I was actually one of the first teams to set up a live stream and take it to the US. So funny today, I’m back in, funny today I’m in Kuala Lumpur, Malaysia where I’m joining you for this podcast, where Jackson Wang had a concert here yesterday.

And he was one of the celebrities that we collaborated with first to do one of those live streams. At the time when we were trying out and testing out live stream rooms, we didn’t know about the flow, how to direct things, what questions to ask him, how do you showcase the products in a very natural, organic way. All of those were tests and experiments that we figured out throughout the process.

But during that time, I mean, in a lot of, so I do a lot of keynote speaking today and in a lot of the keynotes that I do, I always start off with comparing just the size of the consumer economy of China, where it is the largest one in the world. It has the most number of internet users. It also just has a very voracious consumption habit. It also has the highest,

internet penetration in the world, the number of mobile users in the world, and people, and out of those users, people who are buying things online. There’s a high amount of trust online because historically from Tmall, from JD, there’s been very, very high standards by merchants. So when merchants enter a marketplace, there’s usually very demanding.

terms for them to host returns, be able to accept returns, to deliver things on time. And that standardization eventually increases trust in the marketplace that even if there is a new seller, new tab out seller that emerges, the consumer will most likely trust them versus if you had that same transaction happen in the US, there’s a lot less trust in the marketplace.

So 2017, 2018, we’re laying out all of these foundations. And I think what I took away is the immense competitiveness of that space. And so out of competition, naturally there’s more innovation because as a merchant, you’re fighting for the same eyeball that your competitor is. So either you’re going to lower your price or

better your brand or better your quality. There’s some sort of lever that you have to tweak to be better than your competitor. I’m sure you’ve heard the term involution. In China, it’s an involutionary environment in schools, in academia, and definitely in business. And so from a brand’s perspective, there’s just a lot of...

⁓ playful and competitive things that they can do and that’s definitely

Grace Shao (08:20)

Can you give us some examples?

Like what kind of playful gimmicks or tricks would people have to use to kind of draw more eyeball over?

Sharon Gai (08:28)

So back then I was part of ⁓ the Tmall food team and I was in category management at the time. And this is a traditional sort of key account structure in all sorts of countries or categories where your top accounts will produce roughly 70 to 80 % of your total GMB.

And then the next tier, mid-tier type of brands will produce about 10%. And then the long tail produce another roughly 10 % or so. And so in...

Grace Shao (09:01)

So is that just in China for Taobao and Alibaba? Is that like a reflection of the whole industry similar kind of to how the marketplace play out in the US?

Sharon Gai (09:10)

It’s similar in the US for sure. If you look at the large FMCG companies, Procter & Gamble, Unilever and Nestle, the large three will take up about 70 to 80 % of the market share. And then depending on the subcategory or the category that they’re in, they will pretty much dominate the market or they’ll dictate a lot of how the market shakes out.

Grace Shao (09:12)

Okay.

Sharon Gai (09:36)

So during that time, or let me preface by saying that’s what you see in a traditional offline heavy space. So in a supermarket, for instance, where things are a lot more traditional. So in 2017, 2018, around that time, it was when online, there was that huge push into online. And in the...

food space, there was a lot of DTC brands that sprung out of that. At the time, China didn’t have that green light, red light policy. So the consumption sector was really robust. lot of consumption startups, FMCG related startups raise a lot of VC dollars and wanted to IPO. So at the time with their VC dollars in their hands, they were able to create all sorts of new brands.

So the Chagees, the Nice Nose, these sort of tea brands and bakery brands that you see often in Southeast Asia or in China, those were all sort of created around that time, 2017, 2018 time. And I remember...

going back to your earlier question, so what sort of creative things did I see? There was this milk brand that experimented with adopting cows. So in China, depending on where your listeners are, the dairy industry is very traditional. And in China, there’s Meng Niu and Yili are sort of the two very, very archaic, hundred-some-year-old companies.

been in China just for so many years and everyone knows them. And around that time, would not really do much in the e-commerce space because they knew they had that foothold in the market. And so out came this new brand called, in Chinese it’s called Zhinyang Yitou Niu, which translated is Adopt a Cow. Funny name, and I think part of their success was also

the cleverness in developing that name. I wrote about this case study in my book too. Their goal or the goal of their founder was traceability. So a couple of years earlier than that, there was this huge milk powder incident where several babies, I think infants died because of this milk powder was poorly made. And so his...

Focus was could we have every family in China quote-unquote own a cow that they can milk from afar? And that when they get their milk delivery, it’s from that cow that they’ve raised or that quote-unquote they adopted So that was his ultimate vision That’s how that’s how the name came about

⁓ and the things that we did was, selling milk cards. we, so, so traditionally on an e-commerce, have skews where you’re buying the actual product. This was the first time where we’re buying, a, almost a gift card, but this card would just be for this one brand and it would be sold at a much steeper discount. so.

That’s why on the consumer side, you would want to buy it. On the merchant side, you would want to sell it. And we would roll this product out to a lot of other categories for a lot of other products where people knew they would be spending that amount of GMV. So anything like toilet paper, rice, flour, milk, health supplements, any sort of category where you knew you were gonna spend that much.

in a year’s time for you yourself and your family because it’s just one of those products that you frequently shop for. And so that was something that eventually the entire platform rolled out to the entire team all rolled out, but it started with that one company. And the reason why that company did that was because there were two very large incumbents.

And the only way that he could compete is if he was innovative enough to think of something else. And throughout my time, I worked very closely with that team to see them from day one where they had two followers on their store to now if you go and look at that brand, it’s the millions of followers they have. think by now they’ve also IPO’d because they were able to IPO before the red light, green light policy.

and they set a new standard because, out from out of that, there was a new type of, DTC, all sort of mindset that came out of T-Mall where, you know, if you were a no-name brand, if nobody knew who you were, as long as you were able to think of attractive enough and innovative enough things to keep attention and to keep your consumers.

to for you to for first of all your customers to discover you and then for them to come back again and again you you were able to Survive even if you were a newcomer and even if there were very large established incumbents in your category and so that was a slice of what I learned and witnessed and there was many many more examples, but

It’s a that one’s a pretty notable and memorable one for me.

Grace Shao (14:47)

That’s like so interesting. have so many questions, but I don’t know if we’re gonna be able to double click on everything. Number one is, do you think it had anything to, this is like, we don’t have to go fully into this, but I do feel like 2017, 2018, like you said, it was a peak of like also private equity in China. And like, did you see consumer brands were just like going nuts? Like whether it’s like makeup, cosmetics, like FMB, definitely like a lot of brands peaked.

Sharon Gai (14:49)

No.

Grace Shao (15:12)

I’m surprised that this one’s still living, which is great, but it’s just like a lot of the makeup brands, like, you know, Perfect Diary also kind of did this, like they kind of just fizzled out, right? There was like these coffee shops all across the country that were coming up that were selling like specialized filtered coffee, whatnot, right? So it’s interesting to see that this company was able to utilize innovation in their marketing and branding, but actually sustain this business.

Number two, comment is how can they actually track which cow? It seems pretty crazy. Like surely they weren’t actually getting the milk from that specific cow, right?

Sharon Gai (15:49)

Which one should I answer? On the note of the cow, that was his vision. No, today, think, well, I also stopped following the brand after I left the company. To my knowledge, it did not go to that extent. However, the extent that it did go to was the founder did do a lot more beyond just the cart.

Grace Shao (15:53)

Yeah, so it’s not actually like played out, right?

Sharon Gai (16:14)

He also wanted to and I’m sure you also probably covered a lot of it 2017-2018 was a big push in agriculture from a Chinese government standpoint. He also wanted to create these he wanted to turn his factory into like a like a like a touristic activity where people could go visit the cat like your cow or you could go and

Grace Shao (16:35)

Okay.

Sharon Gai (16:36)

and experience how milk was made, how it was pasteurized, the entire process. It’s actually a very, very complicated process before it gets, know, we sometimes take for granted like packaged or milk from our fridge is taken and drink it, but it goes through so many steps. he, so within that sort of touristic factor, you could see,

certain cows were penned off and there was a video camera on each of them and each cow was numbered. So you could technically see it, like see your adopted cow. But to date, I don’t think it eventually, or that model stayed, but there were many, experiments that he did. And I’m sure there were...

Grace Shao (17:11)

Okay. Okay.

⁓

Okay, we’re going way off track.

session with these cows. I’m just like bring it back and I’m going to look up these cows afterwards. But it reminds me also this Alibaba thing where people try to gather points and they can own their own trees and they can plant a tree on Alipay. Right? Okay. So anyway, let’s bring it back. Okay. Let’s rewind and go back to retail and AI. and technology and innovation and retail. So live stream, you mentioned it, 2016, 2017.

Sharon Gai (17:37)

Mm-hmm. That’s happening.

me.

Grace Shao (17:51)

You’re one of the first, you know, part of the teams that were kind of, you know, really, I guess, leading the frontier of live streaming technology or even the strategy. It’s still not that mainstream in the West. So to start with, could you tell us what even is lives from e-commerce and how has live stream shopping really change in e-commerce in China? Is that something that you’re seeing? And like, I guess, ⁓

adoption in the US right now with TikTok or anything else.

Sharon Gai (18:22)

Mm-hmm. So live streams, e-commerce is when a seller and you can be a brand or you can literally be a person who owns something that you want to get rid of on the internet. You turn on your camera and you showcase this product live to an audience. And this audience could be some could be your actual followers or just be strangers. And then this sale is made technically the old in the old days on the in the western side.

The first adoption of live stream shopping was on Facebook live where Facebook live used to be a big thing. People would go live all the time. And then you host a room and then you make a bunch of sales and you’re actually recording a lot of the addresses and whoever bought you bought something by hand. It was very, very manual. And then on tab outside, they started with live streaming in app.

And in the US, think, you know, the Walmarts, Amazons tried their shot at live streaming. They invested a lot of dollars in creating these very posh and professional looking streaming rooms, setting up streaming studios. And all of that didn’t really go anywhere. So it had its spurt of interest. I think I think at the end of the day, it’s a it’s

One, it’s a timing thing and two, it’s sort of the way that it’s done. Yeah, and then I think the third, the host has something to do with it. So first timing in China, when I played with it, it was in 2017. That was still newish in China. China didn’t really take off with it. It didn’t really become mainstream until 2019-ish. That’s when we really understood. That’s when every single.

brand, merchant, you that, and if I’m outsourcing my operations to a TP, my TP better have a streaming room in their company. A ⁓ TP is a team all partner. if you don’t want to run your own e-commerce operations, you can outsource this to a company that knows this space very well. And really,

Grace Shao (20:12)

Sorry, TP is like a, a TP is. T-Mall partner, yes. And to give the audience, sorry to interrupt, but to give the audience some context, like how big are we talking about live stream e-commerce? Like give people like the headline numbers, like a leading influencer during Double Eleven or their annual GMV. What are we talking about?

Sharon Gai (20:40)

I think in total, I don’t know the exact numbers, but it’s north of a couple billion per year for sure for some streamers. Which is crazy.

Grace Shao (20:55)

which is crazy. This is like a single salesperson

if you think about it.

Sharon Gai (21:00)

Well, on camera they’re one person, but behind them is hundreds of people. And I’ve been through their product selection process. I’ve worked with them throughout the night. They do not sleep. Their streams start at 8 p.m. That’s not actually when they wake up. A streamer’s day is at 2 p.m.ish in the afternoon. They’ll wake up.

Grace Shao (21:03)

Yes.

Sharon Gai (21:21)

and they will start getting prepared. Their director will usually tell them today, is the, you know, we’re going through the final list. This is the final price that we’re gonna sell all these products at. At 8 p.m., usually 8 p.m., maybe sometimes six, sometimes nine, the show starts. You’re streaming for about four to five hours, so you usually finish at midnight-ish. And then midnight.

Starting midnight to around 5 a.m. You start to review what you just did because you have literally just talked for 45 four to five hours You sold a bunch of product products during double 11 It might be tons of millions of dollars that you’ve just sold per show And then tomorrow it’s gonna start again. So

You’re going to review everything that’s worked, that’s not worked, what you said was right or not right because some of these products repeat again the next day. So they’re always fixing their script and they’re always fixing how to say it better.

Grace Shao (22:15)

they buy the inventory or they take a cut from the brands? How does a business work?

Sharon Gai (22:19)

They don’t buy the

inventory. Streamers will not buy the inventory. The only fee that’s given to them from a per brand basis is a, is a, a, is is a, like a slot fee. So if I’m a brand and you’re the streamer, I’ll pay you $5,000 for you to show my product. But beyond that, you’ll also take a 10 to 15 % commission per product that you’re selling.

Grace Shao (22:48)

So this is very different

from how Instagram influence is kind of where my point is. Like for Instagram influence, it’s like, okay, you place this product here, that’s one off fee versus a live streamer in China is actually making money continuously as long as like people are buying through their link, right? And people sometimes don’t realize that.

Sharon Gai (23:07)

They don’t. Yeah. So this very traditional financial model for streamers in China is not the same in the US. In the US, it depends. Some streamers are just hired to talk for two, three hours at a time, and they’re almost paid on a per hour basis. Some streamers are paid.

They’re paid a set fee for the entire stream. It’s not even commission based. Their whole job is to, if they’re selling clothes, they just put it on, turn in front of the camera, comments will fly in and say, can you try this on another color? They take it off, put it on the other color. And so it’s almost very robotic and you sort of follow what the comments are saying. It’s not commission based. So they think less. They have to think less. They’re a lot less strategic also with placement of products. Which one goes first? Which one goes second?

because that also changes, that also impact GMB. So the two financial models are pretty different. But going back to your question earlier, is this happening in the US? It is absolutely happening in the US. It’s actually, the last numbers I checked was about $60 billion in the US, and that was last year’s number. So it should be a lot more now. The biggest US player is this app called Whatnot. I think that’s the most successful app.

They’re about as there. think they’re a series D startup now. They started with collectibles and I know some of their early collectible sellers. They sell trading cards. That’s the start of live streaming in the US is trading cards. Like there’s a huge fan base for these trade. There’s this Italian brand that makes this specific card and there’s a bunch of also intricacies with how to detect if a card is fake or if a card is real.

⁓ but, that’s the starter product is very niche.

Grace Shao (24:52)

So still quite niche. It’s not mainstream

and prolific in China right now, like how everyone knows what live stream e-commerce is, like any auntie on the street will know about it. So it’s just a very different kind of model. I wanna bring it back and talk about what that means. Does it mean it’s like...

shopping felt more personalized or did it mean like you said it was more rapid? Did it mean more interaction? Like I just want to see does that kind of foreshadow the future of technology and shopping experience?

Sharon Gai (25:23)

in the US? ⁓ I think it does. does. The way that I look at it is at the end of the day, you just want to be more, you want to reduce your barrier between the merchant and the buyer. The reason why live streaming works so well is because it’s live.

Sharon Gai (25:42)

Um, it’s so the, in some of the keynotes that I do, one slide that I have is the evolution of e-commerce. So in the nineties, you Craig list type listings, where it was just a product title and a description. There were not even photos because you didn’t have to put any photos. So some sellers were lazy and they just wrote down that I’m selling this leather jacket for $200. Um, and then Amazon came along and enforced the a plus content or, um, their, their product detail pages have to be.

put together in a certain way. So it became photos and then there were, was mandatory to have product videos. Then it was mandatory to, well, that part wasn’t mandatory, but then e-commerce became different influencers showcasing it. And then TikTok started Instagram, all of that. And so it’s pretty natural that things will just evolutionize. I think this goes back to competition.

which is China is just more competitive. So they jumped into live streaming a lot faster because we noticed that the merchants that live streamed did better than the ones that didn’t. It was as simple as that. And so the ones that didn’t would start doing it and they would have to, they were forced to learn it even if they didn’t really want to because that’s just what worked. And so in the U.S. it’s

it’s worked for these collectibles, toys companies. And I think other brands see that. now have, now what not has become cross category. Now it’s, they started with collectibles, but now a lot of fashion brands have jumped in, food brands have jumped in. Supplements also. And then in the TikTok shop world, it’s pretty, it’s more and more common for D to C brands to start streaming also.

⁓ to outsource their streaming to. Now there’s a new term called TTPs, which are TikTok Shop Partners, to do this for them.

Grace Shao (27:32)

I have a question. seems like in some ways that the China side, you said, the influencers, like not influencers, the live streamers themselves actually don’t need to be influencers. In many ways, it’s not that people go to them. It’s not like they build any, they bring any extra credibility. They’re just the avatar or, you know, the mannequin in many cases. Whereas in the U.S. it’s e-commerce is so heavily reliant on like the actual influencers, like, know, which celebrity endorses what.

you know, makes a big deal. Do you think we’ll continue to see that kind of divergent path? Or you think, you know, people will actually care less and less about who the influencer is?

Sharon Gai (28:12)

It’s actually kind of the reverse if we’re just talking about live streaming in that in the US, the people who are streaming are sort of no name people. And then in China, it’s the well known streamers, not the celebrities. I mean, on both sides of celebrities, let’s take out celebrities because they, ⁓ you know.

Sharon Gai (28:35)

They’re their own world. Also, some of them do

it sometimes. Yeah, there’s definitely pro, there’s definitely very well known live streamers like the Austin Lees of the world. They’re now a semi celebrity themselves because they’re so famous now. Like that type of profile is non-existent in the US where they’ve just gotten so good at the streaming side of things where they’ve in the branding side of things.

Grace Shao (28:53)

Mm-hmm.

Sharon Gai (28:59)

So will it diverge? think for the US at least, you’ll have more full-time B2C, more traditional Instagram influencers jump in and try it out. And it’s definitely gonna be a word of mouth type of thing.

⁓ like what I see on tech talk today is, there are certain influencers who, who are traditional influencers and now they’ve gotten in, they’ve jumped into the live streaming side. so I think in the U S it’s still a testing the waters type of thing. as for China, I think the big concern is the AI piece, where now if you go to any branded room,

If it’s an off hour, if it’s an off peak time, it’s some sort of AI avatar that’s streaming. To date, I’m sure you’ve heard of the Loyong Hall live stream room in 4.6.18. I think it was this year where he did the AI live stream and he sold X million of dollars. And it was more than he would do when he streamed himself.

in person as a human live. And that in the media space, there was a big wave of, are we going to be replaced by these AI streamers now? I think that was definitely a big, was definitely more of a PR push. Like it’s very easy to tweak those numbers so that you do make it seem like your AI version can sell a lot more. ⁓

Grace Shao (30:24)

Mm-hmm.

Sharon Gai (30:25)

You can play with pricing, the product that it sells, a lot of things. Yeah. So, but so in China, I think the future is going to be, are those big name streamers going to want to employ an AI version of themselves to conduct live streams? I think that is something that a lot of them are thinking about. Like once we let it happen, are we going, is it, how is that going to change the industry?

Grace Shao (30:50)

Actually, that’s where I really want to kind of take our conversation to next, which is like, we’ve talked about live streaming and that was, that’s kind of like the next technological, I guess, breakthrough in e-commerce for the US given that like, you know, Asia, East Asia is already like really, have, e-commerce already really, really, sorry, I want to say is like this live streaming model is already very prevalent across East Asia. ⁓

And like you said, the next stage of technological breakthrough for e-commerce in China or even including East Asia is really in avatars or AI avatars. And we already kind of saw Alibaba with their kind of like fake avatar, like these like cartoon avatars for a while now. Can you tell us a bit more about that? Like actually give us the context of like

how the AI avatar technology within Alibaba has evolved over the years and what we’re going to potentially see in the future. Because what you just said with the loyal health thing is essentially this frees up his actual free time, his time, and his avatar can just sell for him. Then do we still need him? Or, you know, is he going to become the brain behind it? And what’s the future of AI within this, this whole conversation?

Sharon Gai (31:57)

There’s I think there’s a difference to note between a real person creating an avatar of themselves and Being like the the Wizard of Oz the Oz and like puppeteering their avatar Versus I think what you were talking about is the Aya ease of the world where it’s like very futuristic looking She is a completely fictitious person person

Grace Shao (32:02)

Mm-hmm.

Yes.

Sharon Gai (32:23)

Her face is made up, the things that she wears is all made up. That type of, maybe for a better term, that type of sort of metaverse looking, futuristic looking thing is not so much an avatar. But it does happen. There’s also, Lil Mikaela is similar in Brazil.

Grace Shao (32:40)

Mm-mm.

Sharon Gai (32:47)

She’s also like, yeah, she’s like forever 19 years old. She’s worked with all sorts of major luxury brands. We’ll work with her now. So that model is proliferating around the world. I think that way of selling something or the evidence of that in the market was pretty big in...

the 2020-21 time of the world, it’s definitely died down a bit. To date, I have not seen new versions of Ayayi because technically if Ayayi worked really well, there should have been many, many versions of her. from a brand standpoint, we should also see, like each brand would have sort of their own version of Ayayi. think this brand called Florisys.

Grace Shao (33:22)

Right.

Sharon Gai (33:39)

which is this makeup brand. created one too, but she’s also kind of disappeared. So I think that that... Right. It’s like another look. Yeah. Yeah.

Grace Shao (33:46)

It’s almost like mascots, the mascots, right? He had to see like, right? Like, and they kind of just died and yeah, but people actually still want

a human looking thing, whatever, like a human being to try on the product. So to your point, like the Loi Yonghao example, it’s like people still like the fact that it looked like him, like a human, but it’s not him, right?

Sharon Gai (34:09)

Well, I think the Lui Yonghao specific one was just the novelty of it. yesterday, I was trying to buy something directly in Chachi BT because of their partnership with Etsy. I just wanted to see if it’ll get delivered, when it’ll get delivered versus if I just bought from a traditional dot com website. Like there’s a novel and I just came out of Capgemini.

panel a couple of days ago before that where every single panelist was like, we’re all shopping and try GPT right now. like, how, I don’t know how long this actually gonna last.

Grace Shao (34:42)

How good is

experience good? Because I can’t do it in Hong Kong. Is experience good? you... Is the whole...

Sharon Gai (34:47)

Yeah, you can definitely buy things within the app for sure. Not every single product.

Grace Shao (34:55)

But is it a good experience? But is it a good experience? how does it differ from going to a Alibaba.com versus like a Taobao.com versus like Amazon.com?

Sharon Gai (35:04)

I think it’s very dependent on if you’re very sure of this product because the weakness of ChadGBT and just shopping via LLMs is it’s way worse display. Because usually product photos are very beautiful and they hired models and background sets to picture this product very nicely. But in ChadGBT, a lot of these pictures are compressed.

Sharon Gai (35:29)

If they were originally long, they’re like short now. Like a lot of these photos are, there’s no standardization. And then also it cannot show videos. It can show YouTube videos inside a chain of chat, but there’s no, like if there was a product video, it can’t show the video. So if it’s something that you already know that you already buy all the time, then I think that’s, it’s.

Grace Shao (35:29)

Yeah.

Sharon Gai (35:51)

It’s easier to buy through an LLM, but if it’s something that you kind of want to browse and look at and maybe like flip through a couple of pages of reviews or photos, it’s not the best shopping experience. I would still go back to our traditional dot com website.

Grace Shao (36:04)

So basically like the agent will just go from your intent all the way to purchase all like in one go. You don’t have to click through a bunch of buttons. You just say, buy me this using this credit card go. Kind of thing. Okay. Actually tell us about more about that. Like are you seeing more brands that you’re working with right now adopt like AI within their purchasing process?

Sharon Gai (36:28)

⁓ From the perspective of a brand, I think all brands right now, the big to do for all brands right now is AEO, if you’ve heard of that term. So it’s a differentiation from the old SEO world where if I wanted to buy like a winter pea coat, I put it in Google, a bunch of blue links show up. I click through the Macy’s link, the Bloomingdale’s link, maybe another one.

Ralph Lauren or something. And I look at it and I browse through it, maybe at Dakar, maybe at Leave, I go to another site. That type of experience is being changed because now a lot of people are searching for their products through a ChachiBT, a Claude, a Perplexity. And so now as a brand, how do you not show up as the blue link, but how do you show up in the answer? So if I’m putting in the same query,

So I want to buy a winter p-coat and this is my budget and this is my type of style. I want it for this warmth because New York is usually zero to eight degrees in the winter. What are my choices? And then there will be choices that show up and some brands will show up and some brands won’t. So to get your link or to get that skew to show up, that’s called AEL or stands for answer engine or optimization.

Grace Shao (37:49)

And how do you get on that list? How do you make sure you’re on that list?

Sharon Gai (37:50)

There’s a lot of things you have to do. And it’s also something that all brands and also startups are trying to figure out right now. So it’s like an empty space for startups. in, give it another two quarters, you’ll see a lot of seed stage startups that do exactly this, which is an AO. ⁓

AEO product, like use me and I will make sure you show up in an answer engine. It’s not a sort of, a, or fortunately, it’s not a very straight cut thing. Like if you do X, then you will show up. There’s a lot of things you to do behind the scenes from just reorienting your product detail page to make it more crawlable.

Some websites want to wear off bots. Whenever you see the Cloudflare pop up where it says, check if you are a human. Most websites don’t want bots to crawl all over their site by default. And so you have to remove that. You have to also make sure that your product detail pages, or any web page rather, is very, very intricate. So much so that if anybody searched for a certain ⁓ question,

that whatever your product detail page says will pull up. So for instance, even with the example that I just said, a peacoat in the winter for New York, and it’s perfect for zero to eight degrees. That was part of my question. It’s very specific because the way that we search has just changed. We went from typing a couple of keywords to Google to now, even for yourself maybe when you look into China GPT or when you’re looking for an answer, your prompts are getting longer and longer.

Grace Shao (39:15)

It’s so specific.

Mm-hmm.

Sharon Gai (39:32)

just what you are looking for is more and more, because it’s easier and easier to find information that directly hits what we want to find. So in the zero to eight degree example, if one coat seller had that listed, would surface in the answer engine versus the coat seller that just said, this is a beautiful coat. And like providing no additional

Grace Shao (39:49)

Right.

Sharon Gai (39:57)

⁓ intricate detail for an answer engine to pull off of. But beyond that, there’s so much more. There’s more on reviews, how well your brand is guarded, general sort of reviews, Google reviews of your brand. And then back in the day,

Grace Shao (40:12)

the credibility.

Sharon Gai (40:14)

Back in the day, these LLMs pulled a lot from Reddit. This month or these months, they’re removing that Reddit portion more and more because people say random things on Reddit. Also, Reddit is 50 % bots anyway. So they’re cutting down the amount of information they’re going to pull from Reddit. They’re now going to pull more from very more credible sources. But to get your...

Grace Shao (40:18)

Mm-hmm.

Yeah.

Sharon Gai (40:38)

a coat to show up, a lot of brands used to do Reddit campaigns. So they would hire an agency and create a subreddit for their brand and make sure that these skews from their new collection was very well reviewed. a lot of them were fake. But that at least it was talked about in the Reddit community because Reddit content was very highly regarded by LLMs. And it’s sort of a moving target.

Grace Shao (40:57)

Mm-hmm.

Yeah. Would you say like,

yeah, would you say then like getting on the AEO is probably like these retailers biggest pain point or choke point right now?

Sharon Gai (41:11)

It’s definitely on the forefront for a lot of them, for sure. ⁓

Grace Shao (41:15)

What other issues are they kind

of facing during this like kind of this AI evolution or revolution?

Sharon Gai (41:21)

⁓

In regards to, well, right now for retail, it’s just a very tough quarter. Like this year, we had no growth in retail in the US ⁓ and the Q4, we might go backwards a bit. So we might go negative one percentage point, but that’s mainly caused by just people want to cut back on spending. People are buying more private labels instead of original, like being loyal to brands. They just want something more.

Sharon Gai (41:48)

bang for the buck, more quality over or same quality for same dollars that they’re spending. And then with all of the inflation, all the tariffs, it also makes goods. There’s just a lot of instability in the market. So everyone is trying to hold on and just maintain and survive. There’s also so many retail chains that’s closed and

Grace Shao (41:49)

Mm-hmm. more expensive. Yeah.

Yeah.

Sharon Gai (42:14)

just gone

bankrupt in the past year. So 2025 has been just a major shakeup for retail. I think a lot of brands want to, their CFOs are telling them to AI-ify their teams and to start employing agents instead of more people and increasing headcount in their teams. But I think for a lot of retailers,

Grace Shao (42:35)

And what kind of agents?

What kind of agents are we talking about?

Sharon Gai (42:37)

Agents for all sorts of every segment of the value chain from product creation, the merchandising piece to buying. So how can you AI-ify your procurement team? Maybe not completely remove the person, but at least stop them from increasing the team and to start outsourcing a lot of the work to AI. To the redesign of websites, writing, copy, creating.

product detail pages, creating marketing content, how do you sell, how do you market this product better to your consumers. All of that can definitely be, your productivity can definitely increase from ⁓ using AI and that’s what a lot of brands are thinking about doing and implementing this year and probably the next.

Grace Shao (43:08)

And that’s where a lot of the work focused on that you do. You help them integrate a lot of these AI tools and the AI agents into their work processes.

Sharon Gai (43:34)

Mm-hmm.

I think a big, actually, missing link from the retail side is there’s no one... When people learn about AI, it’s through today, I read this headline. Tomorrow, I read that headline. The next day, I took an executive education course with MIT and I got a certificate. It’s all... It’s very...

It’s very sporadic and it’s very disparate and everyone has different information points. There’s, I think there’s an absence in learning the foundations and the fundamentals and the applicability, the application part for your business. think those three core things, a lot of retailers are not formally doing because from the tech company side, now you have AI skills as...

Grace Shao (44:20)

Mm-hmm.

Sharon Gai (44:25)

mandatory, a mandatory piece of your job. But retail is so retail is a very traditional business, actually. So they’re there, unless you’re a D to C type of company, so your your team is naturally younger, everybody’s in their 20s, they’re already playing with Sora on their phone every day. You won’t really be part of that sphere.

Grace Shao (44:39)

Mm.

Sharon Gai (44:48)

So what’s needed is a level set of just learning about AI. Yeah.

Grace Shao (44:53)

Yeah.

So if you are, I like this question and I always, I thought it was quirky, but if you were, if you were given a hundred million dollars to really help revamp a traditional retailer, like let’s say Macy’s right. Or Bloomingdale’s or whatnot. how would you actually,

use and spend that money in terms of adopting AI and technology? What areas would you actually spend the money on if you want to make your company future proof in the next five to 10 years with this whole AI revolution?

Sharon Gai (45:22)

I would, instead of looking at where to spend money first, I think I would look back on our business metrics and what number do we want to shoot up more or tweak? Do we want to build our awareness? I think for Macy’s probably not because everyone’s already very aware of this retailer. Do we want our revenues, revenue numbers to go higher? Do we want to increase the number of locations?

Do we want to increase profit? I think I would zone in into an actual business metric. And then from there, cut into what is our bottleneck? So what part of the way that our current team is oriented, are they spending the most time on? Per role, what part of your job should be? Per role, if you were to do a task organization exercise where

There are, an e-commerce manager for instance, in your day-to-day, there’s definitely things that you do repeatedly because every time you upload a new SKU or you’re onboarding a new SKU, there’s definitely repeated things there. And there’s also things that are never repetitive where, know, if for instance, your SKU was just magically picked up by...

Grace Shao (46:27)

Mm-hmm.

Sharon Gai (46:37)

a Sabrina carpenter and now it’s, you know, floating all over the internet. That’s like a one of a kind thing that AI is not really good with in handling and capitalizing on. And so if we figure out the bottleneck and the repetitive pieces of your job to then find the tools that will take away that repetition.

Grace Shao (46:57)

Mm-hmm.

Sharon Gai (47:02)

to free you, to give that time back to you, and to free you in whatever else you can do to make the business better. I think that’s where, that’s sort of the stepwise sequence of things that I would do. And I think that in terms of a dollar figure standpoint, honestly, a lot of these AI companies don’t really know how to best price their products.

Sharon Gai (47:24)

The whole industry is just in a huge moment of experimentation right now, where a lot of people don’t know whether to price it by the more traditional SaaS models, or should we do this in a... A lot of companies are going for a consultancy, like a consulting services play, because Palantir popularized that, ⁓ or like a per time, per query.

Sharon Gai (47:51)

way a usage number and I get a lot of emails from partner vendors from their partner teams and every quarter or so they’re like, we we changed numbers again. This from now on, our pricing is gonna be this for this many queries that come in. So a lot of things are in flux.

But I think the important things that shouldn’t be in flux or that would stay is that those foundational three things.

Grace Shao (48:20)

That’s a very actually thoughtful answer. I was just looking for a headline. But no, I think that that’s really, really fair assessment. There’s just such an early stage in the whole industry right now that these tools don’t even know how to price themselves. But eventually they will figure out their own business models. And then I think for the users, whether these retail companies that you advise or not, they will figure out how to price that into their business.

Okay, mindful of time, I do want to ask, what do think is the most overhyped idea in retail tech right now? This could be AI related or not, but just in general, where do you think people are putting a lot of attention in and whether it’s overhyped or not? What do you think is an interesting retail tech that people might not know about? Actually, that’s a better framing of the question.

Sharon Gai (49:02)

something they might not know about is, well, I have mixed feelings about this, but in 2017, when I, before I joined Alibaba, well, it was right around the year. They actually set up this traveling road show called, Gateway. If, if you’ve heard of that, they did it first in Michigan. So I’ve ever re used to really want to bounce.

Grace Shao (49:19)

No, I’ve never heard of it.

Sharon Gai (49:24)

bond themselves with the Republican Party and go to all these Republican states and say China is a huge consumption power. We’re going to import so much meat and a lot of chicken and a lot of soy and grain. And so they had this traveling conference called Gateway and it would go through different states. And I remember there, there were these Alibaba developers that would make everyone

do this demo of putting on a VR headset and looking at Taobao, like three dimensional. So you can browse through Taobao and through the headset. I think that is something that people might not know about. It’s not so much hyped, I guess. It’s definitely something in the future, but it’s definitely not something here in the next three to five years. I think what we have.

Grace Shao (50:13)

You think like wearables, like wearables for shopping.

Sharon Gai (50:16)

Yeah, wearables for shopping or anything where it’s like a digital 3d store. Like all of the things that we talked about in the metaverse, like those are very, very far away from us. Yeah.

Grace Shao (50:31)

Yeah, I feel like those like VR

sets just like didn’t really take off. They had like a year of hype and then the technology wasn’t good enough. But I do see your point. Like even for myself, like if I could just wear something, I’m sound so lazy, but I can try out outfits and then like, you know, click order. That would be really helpful because right now it’s like I shop on Net-A-Porter and it comes to me that I have to like send it back, right? If it doesn’t fit, but that technology would really change how to shop.

Sharon Gai (50:57)

I yeah. Amazon has that, where you can change your outfit. And then you can also.

Grace Shao (51:02)

Mm-hmm, but it’s not feel like

as real yet, right? Like it doesn’t fit my body, my changing body and you know, like just like, it’s just like a fixed avatar, right?

Sharon Gai (51:13)

Yeah, it’s two dimensional, you, but you, it’s hard to do that too with a VR headset.

Grace Shao (51:15)

Mm-hmm.

I see what you Okay.

Sharon Gai (51:20)

Yeah, you would need some sort of mirror to do like that sort of, that was, that was a tech that was big too and then also died, which is like in-store interactive displays where guests implemented it, where you don’t have time to try on all those outfits. So they uploaded every single.

Grace Shao (51:36)

Yeah.

Sharon Gai (51:43)

item into this mirror and you can just plop plop the mirror and you’re standing in front of the mirror so you can see, so you can check out your outfit without actually physically putting it on. ⁓

Grace Shao (51:55)

Yeah, Lululemon had the fitness

ones, right? The fitness mirrors. then they kind of, it was the same technology behind it, but it also kind of died after COVID. Although I think a few Chinese companies are trying to make it happen again. They’re saying it’s AI empowered. But right now it doesn’t feel like much more than a big screen where you get to see reflection kind of just following the structures and instructions and that’s it, right? It’s not huge breakthrough yet. Yeah. I have one last question for you. Cause we were kind of going off on our

Grace Shao (52:22)

tangent again, the two of us, they first started with cows and now it’s like the mirrors. What is one unique belief or differentiated view you may hold about industry that you think is quite different from others in the industry?

Sharon Gai (52:35)

on the note of retail, I think.

Grace Shao (52:37)

on the note of retail and or retail and technology.

Sharon Gai (52:40)

I think retail is pretty simple. I think people complicate it too much with all these bells and whistles. At the end of the day, retail is how do you sell something and make the other person interested? That’s all. How do you take any product that you have, you can put any sort of shell over it, and someone’s eyes light up.

That’s what retail is. I think all the AI that we add, the VR things, the personalization and this or the other, those are all ways to do it. But I think it’ll just change. So I think...

I think not enough retailers go to the heart of what they’re selling or what they’re doing. And they often, it’s also understandable because, know, it’s most like in any Pyramidic industry, you always look at what the top key account or top brand is doing and they’re usually producing the most GNV. So you say, ⁓ they’re selling a lot. I’m going to do what they do.

And then everyone kind of swarms in that direction. And it’s not that, I think that’s for any industry, that’s just how I guess the world works. But I think the right way to approach it isn’t to look at that model. I think it’s more just to answer the question of what are you selling and how are you gonna sell in a way that attracts attention.

Grace Shao (54:06)

I think that’s like pretty applicable to like every industry. Like when you’re saying that, like really I was just thinking about like the business of media content or anything, right? Like fundamentally go back to like what, what is the problem you’re trying to solve? What answer are you trying to provide? What are you solving? Right? Like, and then, you know, go find your core versus all the kind of frivolous things outside of it.

Those are just tools. anyway, really, really appreciate your time. And I actually really love that you just walked us through the evolution of technology and retail, the intersection of technology and retail from, you know, what we know of e-commerce marketplace to live streaming to, you know, AI adoption. And we kind of even touched on our VR.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Joining me today is Chim Lee, Senior Analyst at the Economist Intelligence Unit. He works in EIU’s China and Asia teams, and is based in the company’s Beijing and Hong Kong offices.

He leads EIU’s research on China’s advanced technologies, Climate change, Energy, Semiconductors, and Artificial intelligence, and also covers how China’s industrial policies link up with the broader diplomatic and macroeconomic dynamics.

Our conversation starts with China’s newly announced 15th Five-Year Plan proposal, which reveals the country's next priority and how it may impact its economy, society, and trade relations with the rest of the world.

We then dove into the current involution 内卷 issue, particularly zooming in on the solar and EV sectors. Then we look at the data center build-out driven by the AI boom and how local and regional governments are making sure involution does not hamper this sector.

Finally, Chim reflects on his work and his analysis of China’s economic planning and innovation direction.

--

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.

They delve into the strategic priorities outlined in the plan, including self-reliance in technology, maintaining manufacturing dominance, and the role of private and public sectors in driving economic growth. The conversation also touches on the challenges of overcapacity and the evolving landscape of China’s international cooperation.

--

* China’s Five-Year Plan signals strategic priorities.

* Focus on self-reliance in technology and innovation.

* Maintaining manufacturing dominance is crucial.

* Private sector plays a key role in economic growth.

* Overcapacity remains a challenge in various sectors.

* International cooperation is evolving in China’s strategy.

* AI and new energy are critical emerging industries.

* China’s economic planning involves both public and private sectors.

* The plan addresses geopolitical tensions and trade flows.

* China’s approach to technology is both strategic and adaptive.

In this conversation, I spoke with Tom Nunlist from policy consultancy Trivium, about China’s AI Plus plan and its implications for the economy and society. We discussed the role of digital infrastructure in AI adoption, the transformation of production relations, demographic challenges, and the government’s role in connecting academia and industry.

The conversation also covers the complexities of navigating China’s regulatory landscape, municipal and provincial implementations of AI policies, and the measurement of AI’s economic impact.

Tom shares insights on how MNCs can better align corporate strategies with government objectives during the AI growth era, and talks about the emerging AI pilot zones and how China balances between innovation and regulation.

Tom Nunlist is the Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores the intersection of politics and technology, with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100 clients covers policy analysis, risk assessment, government relations, and communications.

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

04:27 Understanding China’s AI Plus Plan

10:55 Transforming Production and Society with AI

15:54 Government’s Role in AI Development

24:59 Measuring AI’s Economic Impact

27:12 Local Adaptation in Policy Implementation

28:01 Understanding Chinese Policymaking for MNCs

28:59 Aligning Corporate Goals with Government Objectives

31:19 AI Pilot Zones and Innovation Hubs

33:26 Promising Use Cases for AI Adoption

35:56 Balancing Innovation and Regulation in AI

42:52 Shifts in Government Priorities for Technology

45:56 Tracking Real AI Diffusion in the Economy

48:57 The Skills Gap Created by AI

AI Generated Transcript

Grace Shao (00:00)

Joining me today is Tom Nunlist, Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores intersection of politics and technology with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100, clients, covers, policy analysis, risk assessment, government relations, and communications. Tom, it’s so great to have you here and it was lovely meeting you online actually a couple weeks ago at one of the panels we were on together.

So today, will unpack China’s AI Plus plan, what it means for the real economy and how AI governance is viewed by China, sorry, viewed in China and compare that to what’s happening really in the US. But to start, tell us about Trivium and what’s your own professional journey. How did you kind of end up in Shanghai?

Tom Nunlist (00:45)

Cool, thanks, Grace. That was a nice introduction and likewise, good to meet you and good to be here on your podcast. Definitely flattering. I’ve seen your upcoming guest list and lots of exciting personalities coming up to be on the podcast. So yeah, a little bit about Trivium. We were founded ⁓ in, I think, 2017. So we’ve been around about eight years now. We are a China-focused, or right now China-only policy consultancy.

⁓ And so we really our kind of like value ⁓ is that we really know how the sausage is made here in terms of policy and politics in China and we help our clients mostly multinationals and investor clients understand that. So, you know, for example, a new policy like AI plus, you know, comes out, you know, we can come in very quickly and, you know, help inform our clients, you know, what this is, where it comes from, its overall context.

and then forming scenarios for how it’s gonna play out and kind of what they might wanna do. As you mentioned, I’m on our tech team, but we cover a lot more ⁓ than tech, really kind of the whole nine yards of ⁓ policy making, be it from economics to labor to kind of everything in between.

Tom Nunlist (02:01)

Yeah, as for for myself, I think I have a pretty typical China story, you know, insofar as, you know, long time expats. I came here in 2008, you know, more or less on a lark as a study abroad students, you know, to figure it all out and, know,

then life happened, got interested in it. I moved here permanently in 2013. My undergrad background is in journalism. So I studied here for a bit. Then I worked at a business review magazine and then eventually kind of made my way into the consulting space. Not too much of a very strict career plan, but again, one thing sort of leading to the next and here we are.

Grace Shao (02:43)

Awesome. So I think we’re going to go straight into it. What everyone’s interested right now is in the AI Plus plan. So that rolled out in August this year, believe, late August. It’s quite new. think people are still trying to understand what it really means. So the Chinese State Council published a high level paper that was basically pushing all sectors to really embrace AI.

It’s said to be the most comprehensive blueprint for AI development domestically, and even touches on China’s international ambitions or diplomacy as well. So to start off with, can you just tell us, high level, what is this really about? How do we understand this policy?

Tom Nunlist (03:24)

So this is the second high level AI policy to come out of China. The first was some years ago already back in 2017. That was about, you know, it was about the new generation of AI. AI Plus, the concept has been around for two years already now. It was originally announced at the two sessions. Hopefully our listeners know what that is. an annual meeting like that sets policy. It was announced two years ago, talked about again this year, but you know, not many

details were revealed about it. There was some assumptions which they were correct that it would be a bit like a former policy from 2015 called Internet Plus, was kind of following in that same vein. And just to sort of like set the stage of like what a document like this is, so it’s called a State Council opinion, you know we’re referring to it as a plan, it’s called Plan in the name, but it’s really a sort of like high level

like political document that is setting the priorities for what the nation wants to do, right? Like here’s the direction we’re gonna move in. Here are some like broad, you know, over the horizon kind of KPIs. This is where we all wanna go. ⁓ And then, you know, the plan will cascade kind of down from there. We’ll get more details over time. In terms of the name, AI Plus, that’s AI Plus or added to everything.

So everything in the economy, in all sectors, in society, we wanna see, the government wants to see AI make its way into there to get the most use out of it, to really ultimately transform the way that the economy and society works. It’s big, it’s a big vision is the answer.

Grace Shao (05:04)

think it’s really interesting you mentioned Internet Plus because I remember when that came out. So you said roughly 10 years ago, It was really embraced by every part of the economy and society. So have you seen any attitude changes or shifts or how people view AI Plus just being on the ground in China right now?

Tom Nunlist (05:22)

Well, in terms of views on the ground, like in terms of people talking about it publicly, not a whole bunch. I think what’s really happened here is these both internet plus and AI plus are responding to things that are already happening. So the internet certainly did not arrive in China in 2015. was ⁓ like.

Grace Shao (05:42)

Yeah.

Tom Nunlist (05:43)

very, very much going strong and was already one of the world’s leading digital economies at that time. And so what it was really seeking to do is kind of take the momentum for things that were already happening and push that further. So obviously, in 2015,

you know, the consumer internet, know, Alibaba, Taobao, like those type of, certainly WeChat Pay was introduced in 2013. You know, these were making waves, making big changes in the way that society just kind of basically works. And then Internet Plus was like, yeah, let’s take this momentum and apply it to everything else. Let’s have Internet Plus healthcare. How can we use the Internet there? You how can we use it in government services? And again, AI Plus is sort of doing the same thing. You know, the, you know, this is an introducing the AI wave to China, the AI wave is here, it’s happening, everyone’s using it, everyone’s excited. And so this is getting behind that momentum that is naturally already here and attempt to build a policy framework around it and like, yeah, really, where are we going with all this momentum, right? What are we aiming to achieve?

Grace Shao (06:52)

Yeah, I think actually on that, I am curious, has China’s highly digitalized society and the infrastructure made AI implementation or diffusion any easier in your eyes? Or how has that kind digital infrastructure played a part in just the mass consumer adoption of AI we’re seeing right now in China?

Tom Nunlist (07:05)

Mm-hmm.

Yeah, it’s a huge part. mean, from just the consumer side, China is like the US or Europe, just an extremely connected society. Everyone, even in the most remote places in the country, has their smartphone, has probably even has like 5G.

WeChat is something of a national infrastructure at this point. It’s a messaging app that everyone uses for work and life. It really is absolutely indispensable. And so having that infrastructure already there, or having everybody with a phone in their pocket automatically makes these tools accessible. I think...

any age, any person you might come across, you know, do they have DeepSeek on their phone? Chances are, yes, they probably do. On the back end, which I think is, you know, just as ⁓ important. So the past few years, or as we all know now, right, the...

know, the biggest part of the biggest spend of the AI boom is building out, you know, massive, massive data centers, right? And making that kind of infrastructure work. It’s a huge race right now in the United States. And so there was already a national plan to have ⁓ a nationwide network of data centers, you know, put in place as kind of before this big AI wave.

It’s a bit to do with some broader reasons of internet and energy and having some of this infrastructure in place. ⁓ Actually, in terms of energy, that’s one of the ways that I think a big leg up that China has in terms of the US is the amount of energy infrastructure it has built out compared to other parts of the world. So they’re ready to sort of do this, where I think other places maybe a little bit less so.

Grace Shao (08:57)

Yeah, and I think we can kind of talk a little bit more about the East data, West compute and all these different government initiatives that’s really boosted the data center built out. like, to your point, priority even the AI boom. But actually, let’s take a step back and kind of look at how the policies really affect a society, right? I think in September, one of your colleagues Kendra, her shape for a road, a blog post saying,

The state council’s AI Plus directive is to reshape the paradigm of human production and life. When I read that, was like, what does this mean? It seems kind of crazy. Like, are we going to make AI babies now? What does it mean to promote a revolutionary leap in productivity and profound changes in production relations and accelerate the formation of a new intelligent economy? So how do we kind of like break this down? What does it mean? And, you know, when we were chatting prior to this podcast recording, you said this is a grandiose term.

It’s three-shaping human production, but what does that actually mean? Like, is this quite literally reproduction? Like, how do we understand that?

Tom Nunlist (09:54)

Well, I don’t know, hopefully reproduction will stay traditional. in terms of, know, these types of policies sometimes have these like really big grandiose framing. know, again, back to what I said earlier, the point of this, it’s a political document at the end of the day. It’s establishing a vision, right?

and the promise of AI, which is not... ⁓

news to a Western audience is that it will be transformative of society that will kind of like change sort of how things work. This specific language that they’re using there, like talking about transforming production, you know, that’s a bit in the sort of like communist Marxist language, you know, of China. And then the context that it’s kind of living in now as well, ⁓ there’s this like really big deep sense of urgency in China of like kind of like

the need to move from ⁓ an economic model that is waning, that sort of reliance on labor intensive ⁓ economy and land sales and things like that into a ⁓ new area where they’re get new types of growth, new and better growth, the switch from quantity or quantity.

⁓ to quality. These goals were kind of already there. There’s another, you know, wonky Chinese policy term called new quality factors, new quality production factors, and what are all of these, you know, types of new things, you know, ⁓ AI, self-driving cars, and so on. And...

it wants to leverage these into making new growth opportunities happen, basically.

Grace Shao (11:33)

Yeah, and I think you touched on one thing, which is like, you know, the traditional economy is very like labor heavy and it really relied on just the mass population of the mass workforce, right? But what we’re seeing right now in China, but not only China, a lot of like actually very developed economies across Asia, including Japan and South Korea right now.

is that there are just not enough people. Like the population is declining, people are not willing to have children, right? And kind of given that backdrop of an aging population, a shrinking population, what is kind of, I guess, the goal from the government when we look at the labor force? And how will AI and technology play a role in that? Are we really just going to see like robots implemented or is it more automation? Or how do we understand this? Yeah.

Tom Nunlist (12:22)

think in some cases, we will see robots replacing physical workers. ⁓ But I think that’s the smaller part of the story. The bigger part of the story is this broader question of actually just avoiding the middle income trap. And so in order for China to take care of its aging population, to sort of weather this big demographic shift that is happening now,

no matter what, even if birth rates double or triple next year, it’s gonna happen. And the way to do that, that the government sees is to raise people’s incomes per capita.

and do that very quickly over the next 10 years, More profitable companies have more prosperous people, have a bigger tax base. And so that the country is just able to deal with this challenge as it emerges. Again, it’s inevitable. And so back to this new quality production factors or this transformative effect that AI is gonna have, the transformative effect is that it will be a productivity multiplier, right?

enabled everyone and companies from big to small to be vastly more productive and vastly more valuable and really help China earn enough and become wealthy enough, maybe right before it ages too much. So I think a bit indirect there, but it’s about the whole economic story together.

Grace Shao (13:51)

Yeah, and I think the whole approach to AI development and progress has been extremely pragmatic and economic driven for China, which is a bit different from what I get the sense in DC and even for sure Silicon Valley. Actually, on the topic of what you just mentioned, which is the government’s role in promoting companies and companies’ profitability, I have heard of this thing where the government is playing a role becoming a networker between the academics.

Tom Nunlist (14:00)

Yeah.

Grace Shao (14:19)

and the researchers and the companies. And I think for the audience, a lot of people sitting in West, we know about Alibaba Tense and Huawei, these mega big tech companies having talent schemes, quite similar to how basically there’s campus recruitment for like Meta and Google, whatnot, right? But how is the government now playing a role for SMEs or even smaller companies in terms of how are they connecting talent and...⁓ policy people and kind of the resources in the public space and the private space.

Tom Nunlist (14:51)

This is a great question, think, and a really important thing that’s emerged just over the last couple years. It’s not just AI, it’s sort of like all areas of science, technology, and engineering. But what it seeks to do is to bridge the corporate world and the academic and research world in a better way, right? So you can have like...

⁓ needs and talents and coms flowing both ways. So for example, this might be setting up round tables or some kind of like platform or any kind of mechanism that brings these parties together. So going in one direction from corporates, setting up links with universities so they can go into departments and say, hey, we’re doing biosciences, we’re doing AI, we’re doing you know, some type of metallurgy, you know, these are the types of talents we need. And can you focus on that? Can you help get us, you know, train that talent that we need?

or going the other way, having researchers see what’s going on in the corporate world and having a solution for that, or green fielding their own research, right? They’ve been doing this for a state institution or a university, and now they wanna take it out of there and find the right entrepreneurial partners to do that with.

Right. You know, as you mentioned, know, like large companies have done this sort of thing for for a very long time and have prospered, you know, because of those links. mean, indeed, I mean, a lot of the American tech giants, you know, came. That’s a famous story. It came out of a university or dropped out of a university, you know, and now, you know, maintain those links. It’s same in China, but, know, that’s a lot harder to do if you’re an SME. I mean, everything’s harder to do if you’re an SME because you don’t have the resources. Right. So providing that meeting place,

facilitating that is I think a really important program and one that I’m pretty confident will see solid results in the next couple of years.

Grace Shao (16:49)

So what agency or what government entity is actually helping facilitate that kind of meeting right now? And this question is to lead to the next question, which I’m going to actually ask now, which is, for me, I’m not a policy person. I’m getting confused when I read these papers, right? Because there’s the NDRC, which is in charge of the economic planning.

Then MIIT, which is in charge of the information technology, the ministry, the CAC, which is a cybersecurity regulator, right? Then there’s the MOST, and then there’s a party central science technology commission. There’s just so many of these government agencies that seems to be all involved in pushing the progress of AI technology at this point, as well as being a regulator for safety and policy work, right?

Could you kind of just break that down? Who’s in charge of what?

Tom Nunlist (17:39)

All right, okay, let’s just address that one, because that’s like a pretty big question. So for this, for the AI Plus plan in particular, the main administrative body for this is the NDRC. So for those of you don’t know, that is the macroeconomic planner. They’re in charge of kind of like setting the big direction.

of the ⁓ economy, right? So NDRC is in the coordinating role of this plan, right? So from there, it’ll go to the other ministries of the state council, some of whom you’ve just mentioned, right? So the industrial ministry, that’s MIT, science and technology ministry, most. ⁓

⁓ CAC, the cyberspace administration, all across the board. And those ministries will be in charge of taking the big idea and making it specialized or setting specific goals for their various sectors. And we’ve already seen that happen, actually. just a couple of weeks ago, the National Energy Administration, the NEA, came out with the very first ministerial AI Plus plan, which is AI Plus Energy.

And we’ll skip most of the details there, but suffice it to say, it is gonna use AI to help make the energy transition happen, which is very cool. From there, it will cascade down further into localities. And localities is really, that’s where the rubber meets the road and where all of the action happens. So we’ll see cities, they’re already AI plus plans.

There’s one in Beijing and Suzhou, those are explicit. And then like Shanghai has one basically, although it’s not called AI Plus, but they have one as well. Interestingly enough, those also actually predate the national plan, which is something that kind of happens in China at various points. And so a lot of the like actual like funding decisions and a lot of where the funding comes from will be at the local level.

And then there’ll be like, you know, a national pool of money as well that will like help support those, right? So, you know, it’s a top, you know, people say, China’s a top-down system. That’s of course true. And what I just described is how that top-down system works, right? So from the central planner down to ministry needs, down to local level, which has all of those ministries at the local level and, kind of being funded ⁓ from there. And then of course there’s like special national projects here and there.

Grace Shao (19:59)

So I’m just trying to understand this. In the RSC, the economic planner basically makes a big grand plan and they push out the AI Plus that we’ve been talking about that was pushed out in August. But a lot of the execution that’s done is actually trickled down into localities like the local governments, the provincial governments, city governments, whatnot. And so something like, just taking this as an example, something like the facilitation of maybe a researcher at Tsinghua meeting private company for potential, let’s say commercialization plan, that could be actually led by say the Beijing Education Department or how does that, I just wanna understand how to execute that works. Okay.

Tom Nunlist (20:36)

Yeah, yes, yes, yes.

Those might exist at different levels, I’m not sure. But yeah, the local level would certainly be implementing stuff like that. Or in another more sort of ⁓ direct way, Shanghai has money now where it can like say, companies that are in AI space are eligible for X amount of money.

funding for their first year, right? And like that funding decision, that’s made at the local level at Shanghai.

Grace Shao (21:03)

And that will be decided, I guess, by what the city might mean. So each city, each province, given their strong, they have their own economic factors, right? Like for example, like I think I was researching Harbin, like, you know, people think it’s just like a really cold place for the ice festival, but actually it’s an industrial city with a lot of legacy in robotics, traditional robotics, mechanics, industrial machinery. So their money might be put into developing physical AI.

Tom Nunlist (21:12)

Yes.

Grace Shao (21:29)

like embodied AI, right? And then maybe in Shanghai, we’re thinking about like maybe consumer driven products, right? Like just, just kind of high level thinking, but that that that’s kind of what happens. ⁓ So I want to understand how does the KPI work then, like in terms of like, how do we understand, I guess, how these, because what I’ve heard also is like these cities to cities, compete with each other, they compete for talent, they compete for, like, bringing in different businesses, how does that work? And then in terms of like, how do they actually

Grace Shao (21:59)

a measure, right? Like the technology or AI’s contribution. Because we talk a lot about, like people talk a lot about like how companies are trying to measure AI’s like actually, ⁓ you know, contributions to the company right now, the profitability. How do we actually understand AI’s contributions to the economy? I guess it’s two separate questions, but yeah, help me understand that.

Tom Nunlist (22:19)

Yeah, this is a really interesting question. And I think frankly, it’s one that the government is just trying to figure out itself. For years, of course, it was just GDP. So you win if you bring your area GDP, which is great for encouraging growth until it encourages the wrong kind of growth or encourages the wrong kinds of projects. And so I’m a little bit less familiar off the top of my head, but it’s something my colleagues have looked into. ⁓

as well is how these KPIs might be changing. And again, from this shift from quantity to quality, I think at the end of the day, probably something like GDP is simply the easiest thing to sort of see. But certainly, and that’s like if you’re like a mayor, I guess. But for people that maybe work within different ministries or in like...

specialist areas, whether or not they do a cool project along these lines, whether or not they brought, they fostered the emergence of a new giant in their district. That’ll be looked on favorably. So in terms of who actually sets these KPIs, I think that would actually go down to the personnel department ⁓ and how they interact, how the personnel department decides

things to include on there, some of which will be from NDRC’s AI plan and some of which will be from like totally other different things. I can’t tell you what their score rubric looks like. But again, the message here of going this like broad top-down kind of thing, what officials will be doing is, you

looking at the communication of these targets, right? Looking at the messaging and interpreting them for their district, right? So what do I need to do to make that happen here? And that’s the way forward for my career, right? And also to connect this with what you were just talking about in terms of local specialization, right?

what’s going on in Harbin, the local conditions there are different from in Shanghai or in Hangzhou. And so I think in the ideal way, and the government uses this phrasing a lot, is to have things definitely specially adapted for your local conditions, right? Don’t just do exactly what we’re saying, like make it work for you.

Right. And so in the ideal world, you would have like different things going on everywhere and they would all be complimentary. I think what happens, what tends to happen is that you have duplicative efforts, you know, which of course we see, you know, everyone’s talking about now in the auto industry. my gosh, there’s a hundred auto companies and they’re all, you know, in a giant battle Royale that is destroying value, you know, rather than. Yeah.

Grace Shao (25:06)

Yeah, the price war right now. Yeah.

Actually, how do we understand this? think because for the sake of, know, understanding Chinese policymaking for say, Western investors or Western companies, like say, MNCs operating in China, and in the day is to help them

better their operations, right? So then how do we understand this from that perspective? Say your client’s M &C and they’re saying, seeing, okay, AI plus is being rolled out on a central level. Then they are like, how do they decide? I don’t know where to put their plan, to build out their operations. How do they kind of make that judgment comparing provinces to provinces? And I think to your point, you kind of have...

answer this in the sense of like maybe if you’re industrial machinery you go to Harbin right but if you’re consumer goods you’re Shanghai but are there any other things that companies need to be aware of or investors investing in companies are coming out of these different problems should be aware of?

Tom Nunlist (26:00)

Yeah, great question. So I think probably the first choice, the first thing to look at is just, you know, where are the hubs for what you’re doing, right? If you’re an automotive company and you’re looking to make any of these, well, might go to, you might go to Enhui, right? Hefei, sorry, I forgot it for second. You might go to Hefei because that’s where a lot of the new energy vehicles are.

Right, and then from there, and this I think is a bit more unique ⁓ to China, is if you’re a corporate and you’re trying to be successful here, one of the first things you need to do is align with whatever the government is trying to do. You know, that doesn’t mean do exactly what the government asks you, right? But you know, figure out what officials there want, what their KPIs are, what their existing programs are, and how do you align your corporate goals with that?

⁓ And that’s how you get support. That’s how you get buy-in. That’s how you’re ultimately successful, right? You know, as in sure it’s no secret to anyone, you know, the Chinese government just has a much bigger voice in the direction that the economy is going, right? And the things that are happening in the economy and, you know, companies and investors absolutely, you know, have to listen to what that voice is saying.

I think for investors as well. So where are these companies collected? Where are the big hubs for the industry that we’re investing in? And also, what is the government itself saying that it wants? And which companies do we think can...

Obviously, of course, first deliver on the market promise, like do what they’re saying you’re trying to do, but are there opportunities here? Will they get this kind of support from the government that is a factor that is larger here than it is in other places? Probably maybe any other place.

Grace Shao (27:46)

Yeah, I think it’s also like the point you’re saying, it’s not really like you have to do what the government says, but it’s like you might as well lean into, like, I guess, lean into it, right? Like there are going to be favorable policies for your industry, certain areas, municipal areas, you might as well lean into it to optimize or to like maximize your success rate or your success possibility, right? So on that

Grace Shao (28:10)

point actually, I’ve heard that there are quite a few AI pilot zones. Like, you know, right now, I think for the West, people only know about Shenzhen, Hangzhou being kind of the tech innovation centers, obviously Beijing, Shanghai playing a big role for corporate headquarters and obviously where investors sit, policymakers sit. What are some other major cities that are actually quite relevant to this like AI growth right now or are considered AI pilot zones?

Tom Nunlist (28:35)

think those would honestly be the main ones. know, Shanghai, Beijing, you said, Shenzhen, Guangzhou, Hangzhou, like these are the places where, you know, a lot like the most action is happening, right? Especially in an area where we’re talking about, I mean, it depends on what we mean, right? So like if we’re talking about just raw AI development, making new LLMs and stuff like that, you know, one of the big, you know, stories is that there’s only so much talent out there that can do that.

⁓ and this talent will gravitate towards some center. And there’s only a few of those, only, not everyone can have those people. Not everyone, those people won’t go everywhere.

Right, AI, but back to what AI Plus is about, right? AI Plus, all of these other things, right? And having that in various sectors, I think where other cities will excel or have the opportunity to excel is where those hubs are, right? So if we’re trying to add AI into auto manufacturing, that’s gonna happen in an auto manufacturing hub.

Right. And I think that actually speaks to the important thing that folks need to be looking out for. You know, at this point, know, we’re, you of course, at the high level, you know, we’re talking about sectors. OK, we AI in the research sector or want AI in the health care sector. But I think what’s most important is going to be looking out for not which sectors it revolutionizes, but which specific use cases, right, are going to be.

Grace Shao (29:59)

Mm-mm, I see.

Tom Nunlist (30:06)

most obvious to implement.

Grace Shao (30:08)

And actually on that point, which use cases, let’s put it that way instead of sectors, do you think are kind of showing the most promising mass consumer adoption of AI, gen AI as we know it? So I’m not talking about like the buildup of LLMs and everything. I’m saying, know, when DPC came out, there was a media frenzy of stories about how China’s like home appliances are even adopting AI, EVs are trying to adopt AI, you know.

Tom Nunlist (30:13)

Yeah.

Yeah.

Grace Shao (30:34)

I mean, obviously that kind of hype has gone, like, moved past us, but like, in terms of whether you want to use sectors or use cases, where do you see actually China right now really leading in adoption? And where do you think we’re seeing the trend going towards maybe in the next three to five years?

Tom Nunlist (30:50)

Yeah, think ⁓ it will continue to penetrate more ⁓ on the consumer side, just on of like AI services that are available to everyone. mean, that’s sort of the biggest thing right now. Whether or not we can get consumers to pay in China, I think is a little bit different of a question. But in terms of specific areas, I think it’ll be where we’ve already seen AI ⁓ have quite a bit of traction. So in like logistics and transportation where, you know,

with like self-driving is kind of almost here and you know we have the the nev is this it’s a software-defined vehicle and we’re going to be like a ready integration for ai into the features of the vehicle that’ll definitely be one you know another one thinking about ⁓ that comes to mind is is agriculture which i you know ⁓ i can’t name a specific company or or a project but ⁓

you know, drones are becoming ⁓ large and, you know, helping to manage big farms, like do things like crop spraying, you know, or inspecting or like, also not just in agriculture, in inspecting power lines, drones are not used to do that. It’s actually physically hard to get up there, right? And so there’s AI use cases for that, right? It can go into like visually inspecting, right? Or visually help, you know, irrigate your crops and so on and so forth.

So it’ll be things like that, right? Where we’ve already started to see new things happen, AI being used a bit. And now these new tools and the growing power of these tools will enable it to really actually happen.

Grace Shao (32:28)

Yeah, definitely. think like, when I first saw and tried out a few of the EV cars, this is even like during COVID, this is like three, four years ago, I was shocked by I wouldn’t say they’re like genuine power, but how tech savvy they already are. had voice control, each of them already had a built in robot, you can control your like windows, you control your heat, like the heat of your seats by voice recognition, voice control. And I think like you said,

Tom Nunlist (32:42)

yeah.

Grace Shao (32:52)

implementing GEN.AI into it just means that it can actually embolden it more, right? Do more things or right. So that’s really interesting. I think I want to double click on one question that a lot of people are kind of debating. know, China’s approach innovation often is said to be, you know, innovate and then regulation comes later. Europe obviously takes another extreme case of like hyper or not hyper, but like a lot more.

Tom Nunlist (32:57)

Yes.

Grace Shao (33:16)

cautious and safety, you know, safety cautious and like, you know, regulation comes first. And some people are complaining about how it’s hindering innovation or innovation going into production. Right. So I guess my question right now is you’ve been in the AI safety and policy space for a long time now in China. Do you think that actually you must give up safety for innovation or are there other ways that you’re seeing people actually being able to have safety and

innovation co-exist and co-develop and maybe taking data privacy as an example or how did Deepsea come through if there was so or let’s just talk about that space.

Tom Nunlist (33:50)

you

Yeah, this is an excellent question. And frankly, I think one of the most underappreciated or even like misunderstood aspects of the AI story as it stands right now in China. I mean, there was a point not too long ago before the EU AI Act, which you mentioned where, you know, China had, you know, the strictest AI regulations on the books in the world. And yet, you know, DeepSeq was still clearly able to emerge here and,

you know, become what it is, right? And I think the story here is that, you know, China is, I think, as most people will understand, a very security conscious, you know, country, but it is also highly flexible, right? And the interesting thing, the sort of interesting story, like when ChatTPT first came out, there was this mad scramble.

among regulators to get a handle on it, right? Because it was gonna flood the internet, you know, with these tools and man, what are the impacts gonna be, you know, like just a real sense of urgency to try to like write something immediately. And so there was a period of about a year and a half where you had regulation after regulation and, you know, they...

you know, if you looked at them in line, you’d think they were different, but they were actually kind of rewriting one another, and it was like all very ⁓ messy and a very confusing space. But then, you know, China was able to kind of like find what its bottom line is.

and then be flexible and adapt from there, right? So it was, you know, hurry up, let’s do something. Let’s kind of see what’s gonna work, where it might be too far, and then dynamically kind of like dial back.

So one of the interesting, I think probably the most interesting single event of this story was there was a registration system that was created where if you want to publicly release an AI tool like a chat TPT, it has to be registered with the state and blah, blah. And then some requirements started to be built on top of that. And there was a draft that said at one point,

all of your data that you need to train your LLM with needs to be verified as true. And the AI research community came back and said that this is impossible. Like if this is implemented, will, know, progress will grind to a halt. There is no way we can do this, right? There was no official response to that, but the final version of the rule did not contain that.

Right, was that was walked back. was an idea that was tried out, that was an explored, you know, and eventually, you know, was abandoned because it didn’t work. And so I think, you know, one of the sort of like, again, underappreciated or even unknown strengths of China’s regulatory system is that it can be flexible in that way.

Grace Shao (36:27)

Right.

Tom Nunlist (36:46)

in an ungenerous interpretation of this, which you hear from a lot of foreign companies and rightly so because there are drawbacks of this, is that regulation can kind of seem all over the place and arbitrary and you never know what things are gonna change next.

And certainly in emerging areas, that is true and very challenging, you know, if you’re in a corporate compliance type situation. But the plus of that is it can be, you know, quite flexible and adapt to, you know, what the perceptions of the needs are kind of as they’re coming up, which in, you know, an environment as fast developing as this one, where again, new problems might emerge tomorrow. I think that’s a really important strength or really useful strength to have.

Grace Shao (37:29)

We could be quite reactive in the sense that they would actually react to what the industry and the actual practitioners at the leading frontier, technology development, want or need, right? To really help and regulate the technology, yet also not hinder any progress. I think that’s really interesting and it’s a very fresh take on it. I haven’t really heard that before, but I think it’s

it makes sense. And it also kind of explains what you said about some people’s kind of complained or misunderstanding of this whole like murkiness. so you said that the AI Plus initiative really it’s been around, like not been around, but like the AI policy or the plan has been around or the idea has been around. And then there was a 2017 National General AI Plan as well,

There’s also the made in China 2025 plan, all these big grandiose plans that have been really pushing forward AI or robotics and just technology development in general. as you said, policymakers and regulators can actually be quite reactive. So over the last, I guess, 10 years as these three mega plans been rolled out.

How have you seen these things change or how has the policy makers really a change in terms of their sentiment or the attitude towards this technology?

Tom Nunlist (38:40)

Let’s say the biggest thing, so taking a bit of a longer view, so science, tech, and manufacturing development has been a priority of the governments for a very long time, since the late 90s. It’s been kind of on this top priority list. And so one shift I’ve seen in the past few years is side tech development moving from one of the list of important things into the top thing.

like the most important thing. It’s like that is ⁓ kind of an organizational principle, right? Or like a driving organizer of the whole party, right? And again, that’s because of the perception of what the state’s needs are at this point. In the past, in the sort of like last formulation, right? Of like what the country needed, right? It just needed growth.

It’s like, it’s the late 70s, we’re into the 90s and 2000s. We need to just grow. We need more people and jobs, we need production. That’s what we need. Now that’s not what they need. We need quality growth, we need to move up the value chain, we need to avoid the middle income trap if we can, expand people’s incomes, become a more efficient and a more technologically driven society. And so the sort of prioritization,

and some of the character of these plans have changed sort of in line with that. Some other things I think have stayed the same or strengthened rather, right? So with Made in China 2025, which this not really talked about explicitly anymore because of the political sensitivities it creates in the US, right? But the sort of view, right, was that China

you know, doesn’t want to be vulnerable, basically to, you know, always reliant on outside technology and wants, you know, these things for its own, right? Wants them to be secure and controllable. It wants to have, have its own thing, right? That of course, I think, you know,

in light of the subsequent ⁓ US effort to strangle the semiconductor sector in China is even more of a priority. So it’s not just move the value chain and get incomes up, it’s also create these fundamental technologies which we absolutely cannot have as a vulnerability.

Grace Shao (41:03)

Essentially kind of push more honed in on the self-reliant focus than they previously didn’t really have to, right? It was also kind of a reaction as well. Okay, I think I want to go in some quick questions. ⁓ You did answer a bit of it, but one overhyped and one underhyped province or city that you think people are not noticing enough outside of China.

Tom Nunlist (41:28)

Yeah, again, would say,

Yeah, they’re not really as specific over under Hype City that I can think of. But yeah, I would say go back, double down on the point of like, you know, look at where different specific hubs are, right? So right now, you know, especially the US talking about AI development sort of in general, right? Like the rush to AGI, you know, so on and so forth, right? The AI plus plan is about doing things in the real world, right? So I think where a lot of like really fascinating stuff is going to happen is where those real world

things are in China, right? So like where we have many filtering use cases actually emerge and that’s going to be sort of all over the country.

Grace Shao (42:02)

Right.

Right, like CN maybe for renewables, but like Hefei for you say auto, and then like even like Baoding and Hebei for like auto. You get at least like second, third year cities that are just like actually relevant, but only if you’re in the sector, you would know, right? And that’s a really interesting take. So what is one metric that a policy analyst like yourself should be really tracking or focusing more on?

instead of just, you know, maybe what we’re seeing on the headline is like, you know, this crazy chase for like benchmark frontier technology, frontier of LLM benchmarking. How do we actually track or judge real AI diffusion in the economy?

Tom Nunlist (42:48)

would say it’s probably more along the lines of traditional measures, So penetration, productivity, profitability, wage and efficiency growth. Again, the emergence of those scenarios, Are people actually out there using it in the real world? So I think it’s look for those traditional.

tangible things, right? Again, I mentioned that Chinese consumers tend to not want to pay for consumer-grade AI tools. If that’s something that changes, right? If they’re good enough where people are willing to pay for it, wow, maybe that would be an enormous indicator.

Grace Shao (43:16)

Yeah.

I don’t think anyone’s gonna want to pay for like, you know, consumer app. The culture, right? Like no one wants to pay. I don’t know, I switched my brain on and off when I use like Western apps versus Chinese apps. And when I’m on a Chinese app, they pop up, they’re like, pay for premium. I don’t want that filter anymore. I don’t need this sticker anymore. I’m like, I’m not paying. You just have a different mentality, right? Because you do get too many goodies for free already. It’s very...

Tom Nunlist (43:27)

I think he wants to go to pay for a I know.

Yeah.

Yeah.

Grace Shao (43:51)

It’s very hard, think. The barrier is very high. The threshold. I have one last question for you. And it’s a question I ask everyone that comes on to differentiate understanding, which is what is an unconventional view you hold? And this could be about work or something in life, you know? But what is something that you think about and you’re like, oh, maybe I don’t say this out loud, or maybe this is quite different from what my peers think?

Tom Nunlist (44:13)

What was an unconventional view I hold?

I’ll go one with topic specific here. That’s because it’s come up recently in ⁓ fights I get into, Twitter fights I get into with people. There is this interesting and I think not totally off the mark concern with AI that it’s gonna basically make us all dumber. Students are gonna outsource learning to AI. There was a case study that did the rounds about doctors using AI tools to help them spot

certain types of cancer got worse at it, know, like after relying on the tool. And that’s a real concern. I think it’s something that, you know, there’s some red flags that seem to say that that might actually be happening. But I think the real problem might be a bit more nuanced than that. think it might, my hypothesis is that it will create something like a ⁓ skills or performance gap.

between different parts of the population and exaggerate it. So, whereas some groups of people might become reliant on it and become de-skilled in their job, definitely. And then in some cases, that might be what we wanna happen. I we don’t want everybody, I mean, that’s sort of the promise, not have to do certain boring things. But I think for a smaller portion of the population, it is gonna be a massive learning and development.

accelerator, right, to really help you to get good and improve. And so, you know, I mean, beyond, you know, whether or not I’m right, I don’t know I’m right, it’s just a bit of a guess. You know, I’m wondering where that gap kind of might be and how large it’ll be, right? So is it going to be 90 % of people get dumber and 10 % of people become super learners? You know, or is it, you know, somewhere in between?

That’s my unconventional view. It’s gonna create a skills disparity.

Grace Shao (46:04)

Yeah, I actually kind of agree with I think it would make people who are relying on it for skill set like vocational skill almost like just the art of, know, not art, but the skill or ability to write a press release or draft a basic news piece or you know, build a DCF model or you know, do some quick basic research that might become dumber in the sense that you don’t know how to

do it in a traditional way. But I think the arguments also like say 34 years ago, people are like, you have the internet now. You don’t even know how to use a library anymore, which I think our generation honestly, I don’t really know how to use the library very well. Like I go on my loss. I don’t know how, you know, how to find books essentially from alphabetical order and, you know, like finding the topics, but we do learn how to find more information in some sense, right?

But I think to your point of like, ⁓ it will help people learn a lot faster, but it will require a new kind of skillset, is like, you can access all this information, can you decipher it? Can you dissect it? Can you actually pick out what is correct? What is actually relevant? Because there’s so much noise and clutter, which is kind of similar again to our generation where we had to use the internet to find information, Versus like our parents generation had to like walk into the library and just like.

Grace Shao (47:19)

go through like 10 books, right? ⁓ But that’s super interesting. Thank you, Tom. Really, really appreciate your time. This was super insightful. It was really helpful for me to even learn about how to understand how policy was made in China, how it might affect businesses and investors. And yeah, this was just super insightful and a lovely conversation.

Tom Nunlist (47:21)

Yeah. Yeah.

Yeah, thank you, really. It was really lovely to be on the pod.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Joining me today is Natalia Cote-Munoz, a policy strategist, writer, and International Strategy Forum (ISF) Fellow— a program by Schmidt Futures that supports rising leaders at the intersection of geopolitics, technology, and public service. Natalia has served in the U.S. State Department, leading foreign policy think tanks and crisis diplomacy roles. She is a graduate of the Harvard Kennedy School and speaks English, Spanish, and Mandarin, among other languages.

In this conversation, we discuss Natalia’s unique upbringing as a third-culture kid, her experiences in tech diplomacy, and the evolution of US-China relations in the tech sector. Natalia reflects on her recent return to Beijing after a decade, sharing insights on the rapid technological advancements in China, particularly in AI and digital payments.

We also discuss her observations of how diplomats are trained as an international relations teacher at China’ Foreign Affairs University, how AI cannot be replacing humans in diplomacy, her embrace of AI in productivity and creativity work while she was experiencing a concussion, and lastly her unconventional belief about the societal views on Labubus, highlighting the cultural differences in perceptions of childishness and professionalism.

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

01:30 A Third Culture Kid’s Journey

04:21 Evolution of US Tech Diplomacy

11:32 Reflections on Beijing After a Decade

17:54 Exploring the Red App and AI Conversations - Doomism vs. Optimism

28:35 Education and Talent Development in AI

38:20 Exploring Student Aspirations in International Affairs

40:33 The Role of International Faculty in Education

41:56 STEM vs. Liberal Arts: Educational Mindsets

47:41 AI as a Productivity Partner: A Personal Journey

56:24 AI in Diplomacy: The Human Element

01:01:26 Legitimacy in AI: Who Builds It Matters?

01:02:00 Cultural Perspectives on Professionalism

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Kevin is an investor at a family office, where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. In his spare time, Kevin writes East Wind, which is focused on technology investing.

In this conversation, Kevin Zhang shares his insights on the evolving landscape of AI investments, the implications of hyper-scaler capital expenditures, and the future of AI model training. He discusses the cultural differences between investment ecosystems in the US and China, the valuation of private market companies, and the role of neoclouds in the AI sector. Kevin emphasizes the importance of capital and distribution in determining the success of AI companies and reflects on the future of work in the context of AI adoption.

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

00:00 Kevin Zhang's Journey From Software Engineering to VC to Equity Investment

02:04 The Hyper-Scaler Capex Debate

04:31 The Capital-Intensive Nature of AI Models

07:49 Future of AI Capex and Market Dynamics

11:43 Understanding Private Market Valuations

14:49 Consensus Capital and Investment Strategies

17:10 Cultural Differences in Investment Ecosystems

21:50 The Future of Chinese AI Companies

23:50 Capital and Distribution in AI

27:47 Open Source vs. Closed Source Models

32:22 The Role of Neoclouds in AI

40:40 Investment Opportunities in AI and Beyond

Transcript generated by AI

Grace Shao (00:01)

Hi everyone, this is Grace Shao. Joining me today is Kevin Zhang. Kevin is an investor at a family office where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. So he’s now based in Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. And in his spare time, Kevin writes a blog on Substack called East Wind. Go check it out.

It’s focused on technology investing. So Kevin, thanks so much for joining us today.

Kevin Zhang (00:33)

Hi, great pleasure to be here.

Grace Shao (00:36)

Yeah, tell us about yourself. You’ve had quite a journey, you know, from Silicon Valley and now into your base in greater China. You’ve worked as an engineer and now an investor. You know, that’s quite unique. Tell us about your professional journey.

Kevin Zhang (00:50)

Great. So I guess going all the way back to my college days, ⁓ studied computer engineering, both in Canada and then states for grad school. And then spent most of my career in the States ⁓ in Silicon Valley and New York. So started as an engineer at a company called Salesforce. So they make CRM software before ⁓ transitioning to a couple of venture funds, ⁓ one in the Bay Area, one in New York. So primarily the focus has been on

early stage software, AI investing. And in these days, I look primarily at public equities, specifically focusing on the companies that have exposure to AI or ⁓ businesses who ⁓ will see a re-acceleration of growth because of AI.

Grace Shao (01:39)

Awesome, so we’re gonna go straight into this. I think we have a lot to cover. ⁓ First, let’s get into the hyper scalar capex kind of story. We’ve been seeing jaw dropping capex commitments, alphabet raising, AI capex too, 85 billion, meta committing hundreds of billions, ba-ba 10 cent I think over the next couple years, also committing like 300 billion upwards. It’s just these crazy numbers.

Do you see this as a necessary investment or do you think they’re absolutely overspending right now and they’re creating a bubble?

Kevin Zhang (02:11)

I think if we look at past ⁓ technology cycles, there will always be, or there has always been ⁓ some overspend across the industry. ⁓ So I think ⁓ bring it down to on a per company level, I think things get more nuanced, right? So looking at, for example, alphabet, right? How much of that is internal workloads for search, right? For their Gemini answers, ⁓ looking at open AI where how, ⁓

whatever their, can we maybe start this again? Let’s do a rewind. Okay, great. ⁓ So I think across the industry, if we look at past ⁓ technology waves, ⁓ most famously in the ⁓ initial tech kind of a dot-com bubble, the industry has overspent, right? But as we zoom out, ⁓

Grace Shao (02:49)

Just go ahead, just restart your answer.

Kevin Zhang (03:11)

the spend becomes more normalized and then the demand ⁓ ends up catching up. And so I think the real question is on a per company basis, right? Whether it’s Alphabet, Meta, Azure, with Microsoft, AWS, how much of that is overspending? How much demand can they generate, right? With their ⁓ whale customers? And then if they end up overspending, how many of these players can survive, right? So for some of the smaller players,

who are comparatively more leveraged, who don’t have the cash flows to support some of the CapEx, right? They’re maybe in a little bit more of a dangerous territory than one of the Meg-7, where they’re generating a significant cash flow to ⁓ fund their operations.

Grace Shao (04:01)

And I think this kind of goes into also one of your writings recently. You were saying that, like, look, it’s all these big tech that are able to afford ⁓ spending on, like, LLM training and inference, as well as whatever infrastructure that’s needed to really build out, like, sophisticated LLMs. And you said it’s basically foundation models are a rich company’s game. Why is it the model layer is so capital intensive? And do you think that means we’re going to see the startups just kind of

in this field kind of just die out one by one or acquired or what’s trajectory going forward?

Kevin Zhang (04:38)

I think there are two paths that the industry can take. So the default path is if we look at ⁓ the progression of ⁓ costs for model training, whether it’s pre-training or post-training, ⁓ each generation has been significantly more expensive. So ⁓ many years ago, it might have been ⁓ several hundred thousand dollars. Then it went to the millions, tens of millions, hundreds of millions to train a model.

So assuming that trend holds, ⁓ we’ll see kind of billion dollar training runs, right? So taking a billion dollars to train, let’s say GPT-6 or GPT-7. And so if that is kind of where the world goes, then these companies will need to raise more and more capital to fund their training. They will raise more and more capital to fund their inference, right? So once you train a model, how do you serve it to...

⁓ and users, right? That’s also very, very expensive. ⁓ However, if you’re of the view that there will be, or transformers, which is kind of the models that are used in ⁓ LLM or ⁓ used in things like ChatGPT these days, ⁓ if you believe that there will be other model architecture paradigms ⁓ that are going to be significantly cheaper, then maybe there will be another kind of startup that comes and disrupts.

the entire business model of an OpenAI or Anthropic or any of these labs.

Grace Shao (06:07)

and was DeepSeat one of those that kind of disrupted the whole model.

Kevin Zhang (06:11)

⁓ I think not necessarily. think ⁓ going a little bit into the weeds, the, I think, five, six million dollar ⁓ final training run touted in their paper, ⁓ that was only for the final training room, which is not inclusive of the GPUs that they’ve acquired, ⁓ their human capital.

all the prior training runs and experiments that they’ve run. And then also within kind of AI training, if you basically train last year’s model today, right, it’s significantly cheaper than if you want to train a frontier model. ⁓ And I think Anthropic had a paper where, or had a blog post where basically ⁓ for if they want to train a similarly similar model to the DeepSeq R1 a year ago, it would have been 10x more.

Right, so essentially the costs track. And so it’s less so disruptive ⁓ than I think some of the folks in media might have thought. And certainly they have ⁓ made certain architectural improvements as well as inference improvements at DeepSeek.

Grace Shao (07:32)

So like if that’s the thinking, like looking ahead next three to five years or even a longer run, is this like the hyperscaler arms, I guess, if you put it that way, is it gonna just keep on climbing up that capex or will it eventually plateau or is this question we will never know.

Kevin Zhang (07:49)

⁓ it depends on like, like how long the labs, right? The, the open AI anthropics, even Google can sustain, ⁓ this pace of model improvements, right? So we we’ve seen a little bit of plateauing in, in, in the past year or so, ⁓ as, as we’ve kind of reached the limits of pre-training, right? So, ⁓ right now, a lot of the emphasis is on like these like thinking models, right? So, ⁓ when you actually type a prompt,

into ChatGP, you know, think for a while and then that tends to ⁓ generate better answers for you. ⁓ And so it feels like the like assuming compute requirements continue to wait, what can we start the question again? What was the specific question? Like what is the arm? ⁓ Okay.

Grace Shao (08:43)

Yeah, I’ll just redo it.

So if we use this logic, right? Like, does that mean that in the next three to five years, we will in the longer run that the capex numbers will just continue to climb? Or eventually we will see this hyperscaler arms race kind of plateau out a little bit because I don’t know the day, like these are already like crazy numbers, right? Like we’re looking at a couple hundred billion dollars put into training in the next three to five years. That’s the plan. But how does one keep up with this kind of money?

Kevin Zhang (09:13)

Okay, so there’s two things, right? There’s training and inference. ⁓ And so on the training set of things, the assumption is ⁓ labs will continue to require more and more compute to train more and more expensive models, right? So let’s say the next model takes 10 billion to train, right? And the model after that takes 50 billion to train. Then theoretically on the training side, that tracks. And then I think where a lot of this capex is going, especially if you look at

⁓ the Meg 7 where they’re putting 70 to $100 billion ⁓ per year per company. I think a lot of that is the expectation of inference demand. So as you put these models to production, whether it’s large language models, whether it’s recommender systems, image models, video models, that demand will catch up. So as it stands, there’s a mismatch between ⁓ the capital outlay

into these data centers versus the revenues that Gen.ai companies are generating. So if we look at OpenAI, Anthropic, they are the primary beneficiaries in terms of how fast their revenues have grown and the absolute scale or the relative scale of their revenues relative to even companies like Cursor, who grew very, very quickly to 500 million in ARR.

And so ⁓ in terms of like software revenues, we’re kind of in the tens of billions range, whereas ⁓ for ⁓ hardware CapEx or data center CapEx, we’re in the hundreds of billions, right? So assuming that ⁓ software revenues, two to three X year over year, then eventually it will catch up to CapEx if end users.

⁓ enterprise customers find that they’re not getting ROI from these Gen.ai ⁓ apps, then I think that’s where the house of cards ⁓ collapses.

Grace Shao (11:14)

Yeah, actually, let’s just like talk about the private market valuation quickly. Like right now, OpenAI is valued at like over 500 billion, something like 180 billion, right? Like startups like Hercer, Lovable, Chasing Billions, or what they’re calling trillion dollar ambitions right now. ⁓ I think the Lovable CEO said they want to become the first trillion dollar business in Europe, right? How should we make sense of these numbers? Like, I’m not a quant person. They just sound like humongous numbers.

Can you explain this to us, like how to make sense of this? these are just, ⁓ does it make sense for these companies to be valued at this high in the private market right now?

Kevin Zhang (11:52)

Yeah, so I think that’s a really good question. ⁓ So the ambition for a frontier lab like OpenAI Anthropic is to be one of the big boys at some point in the future. And so taking ⁓ OpenAI as an example, right? So if an investor is of the belief that they will eventually build their own cloud, they will get into robotics, ⁓ their ⁓ core lines of business, right? Chatchi PD as well as their APIs become

really large businesses, right? So let’s say TriGPT is embedded in various enterprise ⁓ kind of customers. And if they’re actually able to charge, let’s say two, 300 bucks a month, right? As ⁓ more and more white collar workers are reliant on open-air technology, that tracks to a market, even in the enterprise side of things, that’s several hundred billion dollars, right? And then if you’re also of the belief that

Google search will be disrupted and Gemini somehow fails to catch up to OpenAI, then they could also run ads on the consumer side of things. So once you add all these kinds of lines of businesses together, an ⁓ optimistic person might see kind of a line of sight for OpenAI to be this like three to five trillion dollar company that some of the Megs have been at already.

are an investor at the $500 billion ⁓ mark, then I think that’s the return profile that you’re looking at ⁓ before kind of taking into account all the dilution from subsequent funding rounds, stock options, et cetera. And then moving down to the application layer, I think these companies are making is if we’re able to replace broad swaths of labor,

⁓ and you are able to command pricing that’s at some proportion of the ROI that you deliver relative to just replacing like a human headcount, then the exit values for these become enormous, right? So then cognition at 10 billion might sound really reasonable. I think as the issue right now is the exits will be very spiky, meaning we’ll see a lot of zeros.

⁓ and you’re going to see a lot of companies really become those $10,000,000,000 companies. And then for a VC fund, you have limited shots on goal. And so as the entry valuations ⁓ increase, you have less shots on goal. And so on a per investment basis, ⁓ your risk ⁓ increases quite a bit.

Grace Shao (14:40)

So in that sense, you don’t think we’re nearing the ceiling of model layer evaluations or anything. We haven’t hit the peak of the bubble or anything yet.

Kevin Zhang (14:49)

⁓ The markets are definitely frothy, but the winner will be much bigger than we, ⁓ I think, originally estimated. And so if you are one of those investors that are in these assets, I think you’re going to be fine. If you’re not, then I think, which is probably the majority of these funds, ⁓ I think they’re going to be hurt.

Grace Shao (15:10)

So in your writing on Substack, you’ve argued that, you know, consensus capital is crowding into foundation models and fra robotics, I think. But, you know, are there areas that you think are under invested and still in the private market? Like, where do you see, like, overlooked opportunities right now?

Kevin Zhang (15:26)

I think it’s less so ⁓ maybe overlooked opportunities in AI, right? So like a generalist VC is able to allocate capital across different things, right? So that could be psychedelics, that could be robotics, that could be biotech. so, or consumer as we’ve talked about before. ⁓ so figuring out kind of what the market dynamics are for those industries where

you’re just non consensus enough to be that first check in, but you’re consensus enough that at the next round, ⁓ whatever you’ve invested becomes consensus. And this was like the subject of some Twitter debate ⁓ with Martin at Andreessen where ⁓ he was arguing it’s not bad to invest in consensus deals because like in the end, like some of these deals ⁓

end up generating huge returns. And we know that even with an AI, like if you’re in a consensus bet that ⁓ pans out, assuming OpenAI is that company, then you’re still seeing like a 10x gross return, right? Assuming one of these companies becomes like five trillion.

Grace Shao (16:43)

Yeah, because consensus, guess, it’s for a reason, right? I was speaking to a few VC investors in the Bay Area a couple of weeks ago, and they were saying, like, some of them are kind of complaining that their bosses are just chasing logos rather than the differentiated bets. But I think in some ways, like you mentioned, if it’s an open AI and it’s still going to be the market leader, market winner, you’re still going to come out on top, I guess. ⁓ Yeah. I want to hear, OK, taking a step back from...

Kevin Zhang (17:05)

Yep.

Grace Shao (17:10)

these questions, think from a cultural perspective, you you worked in Silicon Valley, New York, and now like, you you moved around in greater Asia, greater China. ⁓ What do you think, like differentiates the two ecosystems the most in terms of like the investment space and then maybe even just like some kind of high level work, cultural tech tech space observations?

Kevin Zhang (17:33)

Yeah, I mean, there’s a couple of things, right? So one is the abundance versus the scarcity of capital. ⁓ And so ⁓ in the US, there’s still a relative abundance of capital where ⁓ as an entrepreneur, it’s comparatively easy, easier to be funded versus ⁓ a similar entrepreneur in Europe or Asia. And so and the other thing is,

you have capital at every stage, right, from seed through growth. ⁓ And so the market as a whole has more shots on goal, more opportunity to experiment versus China, right, where ⁓ there is a comparatively ⁓ or significantly less capital, especially US dollar funds in the past couple of years. And so on the investor side of things, ⁓ they are

also more risk off, right? Because for some of these ⁓ Chinese VCs, they might not be able to raise another fund, right? So each shot on goal ⁓ is a very heavy bet, right? Versus entries, and if you deploy like a fund very quickly, could probably just raise another one very quickly as well. ⁓ And so ⁓ if, you know, entrepreneurs can be more risk on in the US, investors can be more risk on than

one of these bets will pan out and then that becomes the next big company versus in China where you could raise less capital at lower valuations, less capital at growth. ⁓ And I think where the domestic VCs might be ⁓ extremely careful and maybe not having the same kind of venture parallel mindset. I think that’s...

that reflects on the products that you can build and the scope of ambition for entrepreneurs. And I think broadly this is why more more companies are trying to do the true high model, right? Where they might raise their first round of funding ⁓ in China, but then quickly pivot to a Singapore or Canada, the United States, right? And then raising capital in the US.

Grace Shao (19:54)

Do think it’s like really affected the dynamics between investors and founders as well? Or do you think that relationship actually is still quite similar?

Kevin Zhang (20:02)

⁓ For the US, I think it’s still an entrepreneur’s market for the best entrepreneurs. ⁓ Like given the abundance of capital, that has not really translated to kind of a linear increase in top founders. think capital is still chasing founders every year or

taking a step back, there’s a limited number of great founders per year that can build these generational companies. And so when AI investing becomes consensus and these founders ⁓ tend to be in the US, then you have quite a bit of capital chasing these select founders. ⁓ And then within China, like there’s probably higher pricing ⁓ or

there’s more power on the buy side, right? Where if you’re one of the 10, 20 funds that still have dry powder ⁓ at the early stages or one of the five to 10 funds at the growth stages, then comparatively it’s less competitive ⁓ for the investor versus the US.

Grace Shao (21:18)

How do you think that’s affected, I guess, this generation of AI entrepreneurs coming out of China? you know, like actually, if you look at the six dragons or four tigers, whatever you want to call them these days, ⁓ you know, like they’re definitely nowhere as like valued as high as the American counterparts. ⁓ There are a lot of them actually finding funding from the BATs instead of, you VCs. So like has this affected their

future trajectory or the entrepreneur’s mindset or their business model.

Kevin Zhang (21:50)

Yeah, I mean, I think it’s ⁓ all TBD based on or TBD, like given the pace of innovation, right? So to our earlier conversation, like can one of these labs build ⁓ something that’s post transformer, right? Or ⁓ can one of these win in other modalities, right? Whether it’s video or image or something else. ⁓ But from a capital perspective,

it’s really hard to raise like another 2-3 billion for any of these companies. And so, like my hypothesis is ⁓ some of these companies going public is a way for them to get another turn of the card, right? So it’s like, hey, if we’re able to raise 500 million a billion ⁓ on the kind of Hong Kong stock exchange, that gives us another two to three years, right, to figure things out versus OpenEI or Anthropic where every round is

heavily oversubscribed and they’re able to ⁓ basically chase everything, right? Chase capex, chase kind of their core products, ⁓ as well as some of the moon shots, With opening at going into robotics, ⁓ going into ⁓ video image, et cetera, et cetera. ⁓ Obviously, maybe not to the same level of success as some of the other kind of image players or video players.

Grace Shao (23:12)

When we last spoke, you kind of made this like big statement thinking that eventually these Chinese ⁓ companies will potentially be irrelevant or they’ll stay within their ecosystem, right? It’s a big statement. But I was kind of curious if you could like elaborate on that a bit more. Like, I know you’re very bullish on the American leaders right now, market leaders right now, but why can’t a deep seek or a moonshot, you know, maybe

do well globally as well, or would Alibaba’s open source eventually kind of take on a leadership role?

Kevin Zhang (23:50)

Yeah, so it’s two things, right? I think it’s capital and it’s distribution. On the capital side, like we’ve talked about, that ⁓ model training inference follow the current trajectory, then it’s a capital game. And so the companies that are able to generate the most revenues the fastest and is able to also raise the most capital will win, right? Under this kind of paradigm. And then secondly,

Even for companies like OpenAI, think they realize that there’s a limit to, for example, how much you could charge for APIs. And so they’re definitely moving into applications, whether it’s ChatGP or something else. And that’s where you ⁓ can grab higher kind of margins than purely API revenues. And then as you go into workflows, as you go into the replacement of human labor, as you...

Take advantage of your initial tech advantage to embed yourself into kind of the fortune 500 clients They’re not gonna kind of rip you out versus and then swap swap potential your Chinese competitor in right so I think the speed of distribution and how fast you’re able to embed yourself into ⁓ Customer workflows where those customers have a very high likelihood to pay or a high willingness to pay I think that that’s kind of the name of the game

So capital and then distribution.

Grace Shao (25:18)

Because you’re basically saying monetization still has to be on the enterprise end. What about China’s AI application on the consumer end? Do you see that being one of their advantages or something that they’ve done really well in terms of adoption rate? I deep sea hit like, not deep sea, dobao has hit more than 450 million in the MAU. Deep sea even higher than that. It’s kind of crazy in terms of scale. think about just like compared, it’s almost comparable to the leading American.

⁓ applications right now.

Kevin Zhang (25:49)

Yeah, I think from a usage perspective, for sure. ⁓ The more interesting thing is how do these companies and these products monetize? If we look at Alibaba, Tencent, guess like Baidu, ByteDance, and then even like some of the newer players like Xiaohongshu, how do they monetize? It’s through ads, it’s through e-commerce. And so I think for these,

new Chinese companies, it’s figuring out how to monetize this chat interface ⁓ catering towards a Chinese audience. And I think the company that’s able to figure it out, and it could be one of the giants, existing giants, they’re going to capture that slice of revenue. But as far as the willingness to pay for a Chinese consumer, I think that’s significantly less than ⁓

an equivalent US consumer where the number of people in China who are willing to pay 20 bucks a month or 200 bucks a month for kind of the higher tier of opening, that’s gonna be limited. So you have to win on ⁓ scale, right, scale of users and you have to win on these other potentially less obvious sources of monetization.

My guess is it’s gonna be advertising, it’s gonna be commerce.

Grace Shao (27:17)

Yeah, it’ll be like value added services or like invisible ways of making money. It’s not going to be like a subscription model or anything. I agree with that. Yeah. Let’s just actually double click on China. ⁓ know, Bill Gurley, I think has been one of the more I would say pro China, but more outspoken investors in Silicon Valley. That’s not anti China, at least. I think he recently just talked about going to China as well, doing a big trip with his daughter. ⁓ You wrote about Bill Gurley being wrong.

Kevin Zhang (27:20)

Yeah.

Mm-hmm.

Grace Shao (27:47)

about China’s open source models. Can you explain to the audience what was your whole thesis and why do you think he’s not analyzing the space correctly?

Kevin Zhang (27:58)

Yeah, I first off, like I think where he is right are a couple points, right? So the entrepreneurs, investors, ⁓ folks in technology and finance definitely pay way more attention to the US ecosystem and learn from the US ecosystem way more than vice versa, right? And so that creates a huge blind spot for US entrepreneurs and investors. And then secondly, I think there’s

Quite a vibrant and talent dense kind of ecosystem ⁓ in China here as well ⁓ I think where he is maybe not ⁓ Where he might have like missed the mark one is this kind of like grasses greener on the other side syndrome, right where a US investor might feel like like China’s like the land of opportunity because of potentially lower valuations because of

⁓ very competitive entrepreneurs, ⁓ etc. Whereas, and they might have this feeling that ⁓ China is like kind of the buyer’s market, right, or the investor’s market. ⁓

Grace Shao (29:12)

But wait, that was

what happened with Internet error, right? So it’s not like he has no basis with this mentality or thinking.

Kevin Zhang (29:20)

I think that’s partially correct. I guess the question is how does an American VC ⁓ monetize ⁓ this ⁓ insight if they are potentially unable to invest in these ⁓ underpriced but potentially competitive assets given some of the limitations for American investors? And then secondly,

⁓ I think the open source closed source debate is more interesting. So I think ⁓ Bill Gurley is a proponent of open source. And so the framework is like, if you are a front to your lab and you are training models that cost ⁓ tens or hundreds of millions of dollars per training run, you have to monetize it in some way.

And so if you are a lab that gives it away for free, that truly limits your ability to monetize. ⁓ And that is like very different from Facebook open sourcing their models because they have a very, different business model than some of the labs here in China. So it goes back to even opening in Anthropic where initially they have the tech edge, they have the best models.

eventually folks are going to catch up. So they’re really in this race to maintain being one of the top players, right, top two to three players, and then ⁓ winning distribution, right? So if you are at this Google scale as an opening eye where you have multiple software assets that people are using daily and your model is, let’s say, top one or two or three, that becomes very, very hard to displace compared to a lab that only has a model but no product.

And so I think that confluence of how do you build product on top of your model layer while you have the lead is really the name of the game versus, hey, I’m going to train a pretty good model and then I’m going to monetize via API and then just give it away for everyone else. I think that’s probably not the winning game in this era.

Grace Shao (31:38)

Then what’s the game that Deep Seeker moonshot or any of these, ⁓ the kit moonshot that’s behind Kimmy, what, what, what should be the game for them, I guess, for them to be able to monetize eventually.

Kevin Zhang (31:51)

That’s a great question. I don’t know.

Grace Shao (31:53)

Yeah, I think that’s why like for Baba, it made sense, right? It’s kind of like the meta llama business model because end of the day, they own distribution and they own the infrastructure. but but like it’s interesting to see because even the deep seat narrative like, he’s a billionaire, he funds himself. But then that’s actually not that much money to fund. like we just talked about capex is like this is hundreds of billions. This guy’s got a billion. Like, there’s still a pretty big gap in between that he’s not going to bankrupt himself to fund this. Right. So

Kevin Zhang (31:54)

Yeah.

Yeah.

Grace Shao (32:22)

It’ll be interesting to see how he monetizes or make this into something bigger.

Kevin Zhang (32:22)

Yeah.

Yeah,

I mean, the other thing is like the market is very dynamic and for every one of these labs, you’re a one hit product away from monetizing, right? And I think it’s never a wise thing to count a player out, especially if like a founder is very good and very thoughtful. ⁓ And so if some of these like companies

end up going public and raising, let’s say, their few hundred million or like a billion, they’ll have another two, two, three years to figure out the monetization piece, right? While they try to catch up in some model modality, right? So they might, they might figure out, or mini-macs might figure out, our video models are really good and truly world-class, and we’re going to build a bunch of these workflows and we’re going to be adopted by the world, right? That could be kind of interesting as well.

Grace Shao (33:22)

It could be a business model that we haven’t even seen before. could be something completely innovative, right? Something completely new.

Kevin Zhang (33:28)

Yeah, potentially.

Grace Shao (33:30)

Yeah. Okay, I think I want to zoom out a little bit. When we talked, you said you are looking at Neo clouds, and this is a space where I frankly know very little about. So I want to hear from you and please do explain things in layman terms and dumb it down for me. But alongside the hyperscalers, we’re seeing Neo cloud players like Coreweave, Nebius emerge, right? Like they’re making headlines. ⁓

How does the dynamic really work between the Neo clouds and our old traditional cloud players?

Kevin Zhang (34:03)

Yeah, mean, for some of these, so defining Neo clouds like the way I think about it is it’s GPU as a service, right? So whereas a traditional cloud provider like AWS or Azure or GCP, they might provide a bunch of services, right? So they might offer databases, they might offer compute, they might offer a bunch of these other pieces of software storage. ⁓ Neo clouds kind of simplify what they offer and

The core of what they do is they help companies with training models, doing inference on the models that they train. And so the dynamics so far has a couple of dimensions. So if we look at ⁓ the upstream, NVIDIA, so they basically supply GPUs to both the hyperscalers and the Neo clouds. Their incentive is to have or to not have the hyperscalers be that big because they know that

hyperskillers like Google, like ⁓ AWS, ⁓ or Azure, they’re building their own hardware. so, NVIDIA is very incentivized to build credible competitors to hyperskillers where they reduce their customer concentration. And then for the Neo Clouds themselves, like a few of them were kind of in the crypto mining space before pivoting to kind of this like AI workflow, like AI compute.

And I think the game they’re trying to win is, we have this wedge where Nvidia will give us some allocation of their ⁓ latest GPUs because of those dynamics that we talked about a couple minutes ago. And we’re going to use this as a wedge to eventually become a really big player ⁓ in the AI space and maybe ⁓ for the players with even grander ambitions.

to become kind of the next hyperscaler. And so you could look at like Oracle a few years ago where their cloud business was a very, very distant forth, right? In the U.S. But because of these recent contracts ⁓ with ⁓ these larger customers like OpenAI, that they’re able to ⁓ see significant appreciation in their ⁓ market cap ⁓ and kind of this like...

like exponential increase in their RPR remaining performance obligations because open eyes saying, hey, we’re, we’re, we’re willing to spend a couple hundred billion dollars on, on compute with you, right. Versus, ⁓ within Azure. So, so I think the dynamic is hyperscalers, ⁓ trying to maintain their, their lead, ⁓ while trying to build more, more and more of their hard, hardware in house. And then on the Neo cloud side, it’s, Hey, we have a wedge right with.

⁓ a ⁓ demand for AI, both on the training and inference side. And we’re going to use that to grow our revenues very, very quickly, potentially take on a lot of debt, right? And then become kind of one of these large dominant players tomorrow.

Grace Shao (37:12)

So you said that there are essentially service providers. Could you actually elaborate a bit more on that in the sense that, ⁓ again, this is really new to me, this whole sector, but people have said, NeoClouds are a real estate business. Others saying it’s a software layer value ad service. How do we actually see this? Can you explain that to me?

Kevin Zhang (37:35)

Yeah, so at its core, like, Neo Clouds are just clouds with like GPUs, right? And so how do they monetize? And so there are basically three ways to monetize that we’ve been kind of diving a little bit deep into, right? So one is, hey, I am an OPENAI and I’m just gonna rent your GPUs, right? I’m gonna rent your infrastructure and I’m gonna do everything ⁓ myself, right? And so for the Neo Clouds,

That’s kind of the lowest margin type business because you’re basically taking some profit ⁓ or taking some margin on top of your cost ⁓ to provide that compute, right? So part of that is your initial cost to acquire that hardware. Part of it is your kind of ⁓ ongoing electricity costs or costs to run the data center. And so that’s kind of bucket number one of

monetization for Neo clouds and then bucket number two is kind of the managed services, right? So if you want to do, if you want to provide software for training, if you want to provide software for experiment tracking, for AB testing, things of that nature. So that gets you much closer to software margins, right? Traditional SaaS margins. And so I see more and more Neo clouds going there, right? And that BS included, uh, uh,

CoreWeave as well with their acquisition of weights and biases. ⁓ And then the third part is what if we provide, excuse me, APIs as a service, right? What if we give you inference, we’ll host the models ourselves and then you just pay based on the tokens generated. And so that’s like kind of the third category. And so ⁓ how this space ends up playing out is what’s the purpose

portion of revenues that these Neo clouds will be able to generate from each bucket of services and products, where if they’re able to generate more and more revenues from kind of the bucket two and three, that makes Neo clouds a much higher margin business than comparatively under differentiated, you know,

GPs as a service infrastructure, although running these data centers isn’t that easy. And there’s a lot of nuance between ⁓ even some of these more leading edge cloud players.

Grace Shao (40:09)

I see. ⁓ I think I’m going to actually ⁓ ask you one last question on investment, is I think energy is something that people have been talking about being affected by AI, obviously. And then like you said, software businesses, you know, we’re obviously already spoke about LLMs, cetera. So what do you think is a sector that is going to be affected by AI and you’re looking at it as an investor? ⁓

but are not as obvious to the public or not as obvious right now.

Kevin Zhang (40:40)

Yeah, I think that’s hard. ⁓ So just given ⁓ most of our efforts are on the ⁓ kind of public side of things, we are kind of looking at every layer of the sack, right? So from ⁓ semiconductors, kind of ⁓ the companies that build the underlying kind of infrastructure, right? So your transformer companies, your... ⁓

power companies ⁓ to your application companies. And I think it’s more ⁓ what’s undervalued relative to kind of market consensus and, ⁓ you know, rewinding the clock back one or two months, Nebius was one of those players, right? And Oracle was one of these players. So it’s more which player within which kind of layer of the sandwich is undervalued and then how do we think about

our risk adjusted returns, which is a little bit of a cop out answer. But our objective is to build a basket of these securities across the stack where we believe that these businesses will offer outsize returns. then going maybe a little bit deeper is for a lot of these businesses, they don’t have kind of a pure exposure to AI. Meaning even if you invest in a company like

a Microsoft, right, or an Amazon. They have ⁓ their traditional minds of businesses that you have to price. And then those tend to have, especially if they’re more mature, those tend to have a slower growth rates, right? So even if your AI revenues are exploding, they might be dragged down by some of these at scale, ⁓ mature ⁓ business units ⁓ or products.

And so how do we think about the blended returns for, let’s say, a ⁓ much, much more or much kind of like traditional player in the kind of transformer space?

Grace Shao (42:47)

All right, Kevin, ⁓ anything else you think you want to share with the audience? ⁓ Mindful time or wrapping up our episode? Is there anything you want to talk about that I did not touch on?

Kevin Zhang (42:59)

Sure. I think a very interesting thought experiment is how fast AI will displace work. So I think my perspective is going to be much slower than folks might think in Silicon Valley and much faster than the rest of the world thinks. Meaning next year, 90 % of the code is not going to be written by

or at least like code and production is not going to be written by AI, right? Or I don’t see like broad swaths of ⁓ workers gets automated. ⁓ But I think is AI adoption going to be a 20 year kind of journey? think, no. I think ⁓ for a lot of these professions, it’s going to be this like five to 10 year disruption.

And then we’re already seeing some of this ⁓ for new grad hires, right? Where a combination of AI giving more experienced workers a higher leverage, ⁓ as well as some of the broader macro headwinds ⁓ in the US ⁓ affecting kind of new grad job placements. And so I think my intuition is it’s gonna spill over to kind of the mid-level folks.

as well, right, comparatively soon.

Grace Shao (44:26)

Yeah, I think actually I have an episode coming out literally today, which is with ⁓ Diana David. She’s the director of features at ServiceNow. And she was saying kind of like, instead of thinking about how it’s going to displace jobs, it’s going to change where the workforce will go towards. It’s just that we need that time to kind of figure out where it’s going. But like you said, right now, unfortunately, it’s hitting the young junior staff the most because their work is usually, you know, more

you know, the hands-on kind of like the grunt work that is really easily done right now by AI. Anyway, thank you so much for your time today. I have one last question for you, which is a question I ask everyone. What is a view you hold that is unconventional or you think it’s against consensus? It could be something related to investing or anything, you know, in life.

Kevin Zhang (45:49)

Let me try to think something that’s unconventional.

Okay, ⁓ so I think something that’s unconventional, especially folks ⁓ within finance, think folks in finance tend to be very sensitive to kind of market fluctuations as well some of the macro headwinds or tailwinds. And so, especially right now, where I think we’re in potentially a more challenging part of the cycle.

with employment, with kind of the degradation of ⁓ kind of global connection. think in the long run, ⁓ collaboration will probably win out, at least that’s my hope. ⁓ And that bodes well for entrepreneurs, right, who want to play in multiple ecosystems. That bodes well for investors who want to play at ⁓

these multiple ecosystems right across across Asia Europe and in North America and I think ⁓ the game is Right now is especially for for some of these venture firms in China is like who can survive right because I think those that can survive and Those who can continue to raise they’re gonna be fine and they’re gonna they’re gonna see some very very good vintages ⁓ and I think the same goes for ⁓

when the US also sees their down cycle, the folks that have a lot of dry powder who are able to deploy through those tougher periods, think that’s where you’ll see good ventures as well. so the question of like, given some of these macro headwinds, is that the death of the buy side in Asia, I think that’s probably a little bit overblown.

And then like I five years from now, 10 years from now, like the ecosystem will be mature and healthy.

Grace Shao (47:53)

You think that USD denominated funds will see a revitalization or do you think that we’ll just see a complete different kind of ecosystem from five, 10 years ago?

Kevin Zhang (47:59)

Maybe?

I think it either might be a completely different ecosystem or ⁓ there’s other global capital that’s interested in the broader Asia ecosystem. And I think they’ll come. It may not be at the same scale as ⁓ the era of like Hulianwang, right? But like as long as the ecosystem continues to be vibrant, as long as ⁓ entrepreneurs are able to build

good products and generate or build large businesses, then there will be some capital somewhere in the world that’s willing to back these entrepreneurs.

Grace Shao (48:46)

There’s a lot of Middle Eastern money coming into China, actually, and I think Southeast Asia, family office money, and even, I think, European money. But it’s definitely not the same scale as what we saw during the Indian era with institutional US investors, right? But that’s encouraging. That’s positive note to end on.

Kevin Zhang (48:52)

Thank you.

Yeah.

Yeah, yeah. I think like for some of these LPs, right, there’s definitely a learning curve, right, in terms of LP sophistication and being very long-term oriented. Whereas ⁓ some of these ⁓ OG US LPs, they’ve had decades of experience investing in the VC asset class, right? And so when that learning curve catches up, then... ⁓

At least on the LP to GP side, ⁓ we’ll see some revival.

Grace Shao (49:39)

Great, thank you so much, Kevin. Kevin Jang, thank you.

Kevin Zhang (49:42)

Great, thank you.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

In this conversation, Jing Yang, Asia Bureau Chief of The Information, a former WSJ reporter, discusses the evolution of China's tech landscape over the past decade.

We explore the corporate strategy and positioning differences between established tech giants like Baidu, Alibaba, and Tencent and newer entrants like Pinduoduo and Shein. Jing also talks about her reporting on Shein and Temu and their attempts to be publicly listed in the West.

The conversation delves into the regulatory challenges faced by these companies both domestically and internationally, and how that has led to a belief shared amongst Western investors that “China is uninvestible.”

She dives into the implications of the AI arms race between China and the US, and the shifting dynamics in the venture capital landscape in China. She explains the differences between RMB-denominated funds and US-dollar-denominated funds, as well as how the VC ecosystem has evolved over the last few years.

—

Jing Yang is the Asia Bureau Chief at The Information. Based in Hong Kong, Jing leads a team of reporters covering the region's vibrant tech and venture capital scene and has written and overseen agenda-setting stories on topics ranging from AI to semiconductors to marquee companies like Nvidia and ByteDance.

Prior to joining The Information, Jing was a Senior Correspondent at The Wall Street Journal where she covered a broad range of topics, including Wall Street’ foray into China, Beijing’s crackdown on internet platforms, and the 2022 Beijing Winter Olympics. Jing also has reporting stints at Bloomberg News and the South China Morning Post.

She has won three Society of Publishers in Asia Awards and three Best in Business Awards at the Society for Advancing Business Editing and Writing in the US. She is an honorary lecturer at her alma mater, the University of Hong Kong’s Journalism and Media Studies Centre, and a board member of the Foreign Correspondents’ Club in Hong Kong.

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

00:00 Introduction to Jing Yang and Her Journey

02:35 The Evolution of Chinese Tech Mindset

08:08 Comparing Old and New Chinese Tech Giants

11:16 The Unique Business Models of Pinduoduo and Shein

17:41 Regulatory Challenges for Chinese Tech Companies

22:04 The Impact of AI on Chinese Tech Giants

32:30 US-China AI Arms Race: Context and Implications

34:55 Bridging the Gap: US and China Perspectives

39:10 China's AI Strategy: Open Source vs Closed Source

44:32 Emerging Players in China's AI Landscape

51:41 The Impact of Decoupling on VC Landscape: Sequioa to HongShan

01:05:44 Future Trends in China's AI Tech Space

Auto generated transcript for reference, typos might occur

Grace Shao (00:00)

Hi, this is your host Grace Shao and joining me today is Jing Yang, Asia Bureau Chief of Information. Previously, she was a senior correspondent at the Wall Street Journal in Hong Kong where she covered a range of topics including Wall Street’s foray into China, the globalization of China’s most prominent tech companies, and the country’s domestic tech regulatory crackdown. Jing, it’s so great to have you on and ⁓ it was lovely running into you actually recently at Karen’s book launch in Hong Kong. We’ve known each other for a few years now, you know, over like our paths crossing.

whether in journalism or when I was working in Alibaba, you’ve covered Chinese business and tech for over a decade across from SCMP, Bloomberg, Wall Street Journal, and now obviously leading the information out here in Asia. So looking back right now, like over the last decade, what’s really changed the most in terms of talent, capital, and company building China, and just your own kind of observations of the whole industry?

Jing Yang (00:52)

Sure. Thank you for having me. Yeah, it was indeed good to run into you at Karen (Hao)’s book talk. So I think the one thing that sort of really struck me is that when I started covering the, started ⁓ working as a reporter, 10 plus years ago, at that time, if you recall, then China was rapidly still integrating into sort of the West led global order.

A lot of the Chinese companies and executives and entrepreneurs, think their mindset was still like there’s so much we can learn from the West, from the US, from Europe, from what companies have achieved there. And then now that has changed for various reasons we can get into later into sort of, know, this is the way actually, you know, we do things in China and it has in so many cases proved to be actually working better or

or there’s more this awareness of things are different in China, whether it’s cultural or economical or social. And I think then you also have seen sort of equally some examples from Silicon Valley or elsewhere sort of looking eastward and looking at what are the things that China or Chinese companies have done that we may actually

there are some lessons or experiences we can draw from. I think that is a very interesting sort of shift. It’s not just one way anymore, it’s more two-way.

Grace Shao (02:17)

For sure. But I think in that sense still, you know, we have people like Bill Gurley going on his podcast saying like Chinese entrepreneurs or business leaders are just so much more attuned to what’s happening in the U.S. compared to vice versa. But there’s definitely more more interest coming from the West to China. Right. I think on that, like one interesting, you know, Chinese tech culture that’s really being adopted and, you know, seeing it being embraced by Silicon Valley right now is the 996. You know, like

That’s an interesting phenomenon. What do you think of that? Do think people are realizing that you got a 996 to really push ahead?

Jing Yang (02:50)

Yeah, that is a very good question. And I think I’d like to actually unpack what we are talking about when we’re talking about 996. When it first started in China, and by the way, that wasn’t just something adopted by tech companies, but also in general, a lot of big companies in the private sector in China were doing that. And I think what in that sense, from the mid to late 2010s,

When companies were adopting 996, it was mostly codified. Remember, that was a pre-COVID world. You are expected, when you’re working, you’re expected to show up in the office, at your booth or whatever. So that means you have to clock in at 9 a.m., clock out at 9 p.m., and then for six days a week. But why you actually do in those 12 hours and six days a week is another question.

And I think over time, some companies realize there’s actually some kind of waste and inefficiency that this codified system has created. For example, a lot of companies, ⁓ employers, in order to entice employees to work longer hours, they provide dinner and then they provide, say, you can expense your cabaret home if you work past 9 p.m., that kind of thing. And over time, then, I think that actually did lead to some sort of inefficiency in the sense that

Maybe some people just want to stay back. If I’m going to go home and have a take on myself, then why don’t I do it at the office when it’s paid for? I’m not saying everybody does that, right? But certainly there are people who did that. And I think while I’m not discounting at all, Codefine 996 has led to burnout and even much worse things. But on the flip side.

is that when you codify something like that, that is the problem. So then if we look at in the US, for example, if you are a Wall Street banker or lawyer or a management consultant, I think you are expected to work long hours when you are servicing a client who has urgent needs or you are just on some really tight project deadline or say if there is a market meltdown.

And then nobody would say anything about, that is 996 or not. And then so I think it’s easy to throw the labels around and then not to actually look at what is happening. It’s not like in the US or anywhere in this world that people just don’t work extra hours. That’s certainly not true. And in terms of, I read the reports. I think there was a wide article that wrote in depth about this.

But the one thing that I think is quite different is that we haven’t seen this being adopted as a policy across-broad, especially by big companies in the US or anywhere else. And in China, when they loosened the 996, what was loosened was just like you are not expected to clock in and out at 9 a.m. and 9 p.m. But you do still expect to work when needed.

And then, and I think in a way, the post COVID world where most workplaces is set up to have employees work, you know, virtually from anywhere has actually made it worse for people everywhere, right? You know, as long as you still have your laptop or even your phone with you, you’re expected to say if your boss messages you at, you know, 10 PM with some questions.

I think it’ll be unthinkable that I just ignore that, right? And so I think that is actually the real issue than when we say, you know, 9-8-6.

Grace Shao (06:26)

Yeah, think it like I totally agree with you in many ways that like this whole working from home thing has actually made everyone feel like they’re more like glued to their devices and more clocked in. But I think it also just showcases that this whole adoption of so-called 996 in the US right now is really driven by, you know, just the excitement and I guess competition now we’re seeing in AI because, you know, for big tech for many years, we’re used to this kind of story of like, you know, ⁓

not a very like, it’s a very cushy job, not that competitive internally once you get in, you get all the perks of like the gyms and the free food and everything and people can just kind of go for walks in the middle of afternoon, right? and that culture’s completely changed. And I do want to really double click on that on the China US AI arms race per se later on. But before we get into that, I really want to kind of look backwards first, try to start of your career, right?

You’ve been covering China’s tech and business space for more than 10 years, essentially. Comparing the earlier BATs, whether it’s the Baidu, Baidans, Alibaba, Tencent ones, to the new companies like the Pinduoduo, Shianti, Moos, what are the kind of differences in their corporate structures or management styles or even their so-called Chu Hai strategy, like their going global strategy? What are some observations on that front?

Jing Yang (07:44)

Yeah, I think overall, I would say, you know, since you mentioned BAT, I think what the B-stands for Baidu are Bytedance, right? But if you look at just the A&T, you know, the Jack Ma and the Pony Ma, I think they represent sort of the last generation of Chinese entrepreneurship. And then moving on to like Jiang Yimin, who’s younger, and then obviously, Kuanlin Huang as well. And then to like the...

the founders of the hottest startups nowadays in China. think one big shift is that the entrepreneurs, the builders who grew up, who were born post 1985, post 1990, they grew up in an era when China was rapidly integrating with the world.

And they likely grew up watching a lot of American TV, really engrossing a lot of American culture. then that sort of... And it was also during that time when China was really just thinking like, we are the students learning from the superpowers from the West, that kind of era, right? So they sort of grew up to have a more global view.

of the last generation of founders and entrepreneurs. then so now this is why the companies that they are trying to build right from Bydowns, which is already a giant today, to the smaller startups that we’re seeing, they want to compete at a global level from day one. Whereas in the case of Tencent, Alibaba, or Baidu, what they did was we

took something that has worked on, for example, Amazon or Paypal in the US, and then we replicate that model, adapting it to the Chinese soil. And then we make it work first in China. Then if that has worked, then let’s see if we can export that success somewhere else. I think that that’s probably the biggest difference.

Grace Shao (09:41)

And I know that beyond the BAT, you know, covered actually, Temu and SHEIN quite extensively. I feel like these two companies are a bit more mystical to the world because, know, they feel like in some ways they were the first companies that really succeeded at not being seen as Chinese, even when they went global. Yet they were caught in the geopolitical rows when the first round of trade wars happened. And, you know, now they’re not really making the front of the headlines anymore. So what do you really make of these two companies?

I guess their positioning, how they’re doing now and their journey to potentially trying to get listed in the West and just frankly failing at it at this point or not being able to.

Jing Yang (10:21)

Yeah, I mean, maybe we’ll just separate PDD slash Temu and SHEIN a little bit because as you know, Temu is a subsidiary of PDD, right? So I think Xin is a very interesting company in the sense that it’s kind of, I mean, of course, you know, people see them as an e-commerce company. But however, if you look back at the roots of the company and the background of their founders, it’s not really a tech company in the sense that, you know, they are

very good at doing international trade, foreign trade in China. And then e-commerce is just internet, it’s just a way through which they made their, it was like a marketing and a sales channel for them. But the company didn’t start as like say with tech or internet in its genes. It was...

For example, in the early years of SHEIN, they really basically went to like all these markets in Guangzhou or somewhere else and then see what we can sell to overseas markets. And then ⁓ they’ve sold a bunch of random things from teapots to wedding gowns and then, know, basically whatever.

sold, they will sell it. ⁓

Grace Shao (11:33)

It was like a glorified

drop shipping business essentially, right? Like a bigger scale drop shipping business essentially in the beginning.

Jing Yang (11:39)

Yeah, exactly.

So that’s how the company got started. But obviously, they then fine-tuned their business model over the years and then did make some true innovations in terms of how to adopt technology and making fast fashion more, not only more efficient, but also more trendy and also through the process, it really drove down the cost.

But all of that was happening against the backdrop of China really has achieved that kind of manufacturing base that enabled this kind of opportunity to arise. And I think that is the part of the Shenzhen’s business model that was most misunderstood and why there was all these allegations of slavery for slavery. Because it’s just the...

the cost efficiency was just mind blowing to a lot of people. And then obviously what Temu has done is to expand, know, Shin’s business model, replicate that from not just apparel, but to many other sectors. But essentially these two companies and or as e-commerce platforms per se, they’re actually quite

you know, different in a sense, because what SHEIN has been selling, apparel, is like a pretty non-standardized kind of merchandise, right? Every piece of clothing is different. And then they change so rapidly in terms of what is the market trend. Whereas when you are selling, say, toys or home appliances, these are standard goods that don’t really change that much over time. And also,

⁓ the marketing and the shipping and everything is also a lot, is quite different. But in terms of like sort of how they were misunderstood, I think the rise of SHEIN team was really to a large extent misunderstood in the West because what I said earlier about, know, because those kind of business models was enabled because of the decades of, you know, China being the worst factory really

accumulated the manufacturing capabilities. when I say manufacturing capabilities, doesn’t just mean factory, the efficiency of the factory floors, but also everything that goes behind it, you know, the infrastructure, the logistics, etc. And then in the process of that rise, I think in the typical Chinese mindset is like, I don’t want to talk about my success or how great I am. I just want to

You know, this very, maybe very pure mindset. I just want to make money. I just want to build a successful business. Right. I only need to satisfy my customers. Like my customers are the only stakeholder I need to think about. Right. They don’t think about, there is also, you know, investors or potential investors, regulators, politicians.

I used to joke back when Shane started to face all this backlash in the US. I used to joke that the people who came out against criticizing Shane in the US are like the pirates of their customers. That shows you sort of where the mismatch is.

Grace Shao (14:52)

Yeah, I think it’s so interesting that you brought that stakeholder engagement part up and it’s really funny, I think. I remember SHEIN when they were like, they made it to the front page of Bloomberg saying, like you said, being accused for labor malpractice or whatnot. They were just trying to hire so like crazy, like frantically hire people who can manage their PR. But it was kind of one of those things where frankly it was a bit too late. Like they really didn’t have the sense to.

actually put out their messaging and put out that what like explain what they’re doing beforehand. But I actually do want to kind of bring it back to the regulatory side of things. So basically, where are we at right now with the two companies? ⁓ Like, well, I guess, and more on the team side, as far as our she inside, are they still trying to pursue a IPO in the West? Or, you know, what where we are right now? And what’s really the hurdle? Is it like an international?

kind of a geopolitical hurdle, or do you think it’s actually a domestic regulatory?

Jing Yang (15:50)

Right. I think it’s sort of a both. I mean, I’m just like repeating what has been reported out there that SHEIN now as a confidentially submitted application to listen in Hong Kong. By the way, that is sort of an exception made by the Hong Kong Stock Exchange because usually, like typically the only times that the Hong Kong listing regulatory regime does not usually allow confidential filing unlike in the US and in the times that they would

usually ground that exemption is for companies that already listed elsewhere. You remember all that homecoming listing wave that happened a few years ago, Alibaba and Baidu, cetera, that when they were applying to list in Hong Kong, they were all exempted to file confidentially. And the Hsing, in that sense, being a company that is not listed elsewhere, that is a of a waiver that the Hong Kong Stock Exchange gave them. And then in terms of hurdles, mean,

All of this happened after the Didi debacle, right? And after which CSRC tightened the regulatory framework for companies, for Chinese companies taking the list overseas. And then SHEIN sort of was caught in, know, SHEIN and many other companies IPO have suffered significant delays because of that.

And in SHEIN’s case, because the company is really quite big in terms of the size and also all the attention it has attracted. But if you look at from, like, say, a pure domestic Chinese angle, what is what SHEIN is as a company? It really, as I said earlier, in the China domestic angle, SHEIN is an employer. SHEIN is a company that that is a big customer to a lot of factories in China.

That’s essentially what it is, because it doesn’t sell in China. So then in that sense, SHEIN is a very big contributor in terms of the whole economy that it has given rise to, as well as the tax dollar, the number of employment that either directly and indirectly has contributed. And then that sort of makes it like a case.

Grace Shao (17:34)

Exactly.

Jing Yang (17:57)

in the regulatory context, that case that cannot be, that has to be really, say, deliberated on. But then does China want a company like this to live, in the US or in London? I think that’s why there is the interplay between the domestic consideration and the geopolitical tensions and also the backlash to that.

that came with it.

Grace Shao (18:20)

Yeah, I think that explains it really well. think for a lot of people outside of China, they don’t realize SHEIN actually is not like a household name at all. The people, the consumers actually are, it’s not a consumer facing company in that sense in China at all. It’s actually to be business per se in China, right? I think it’s perfect. Exactly. I think it’s great you brought up the Didi (Chuxing) situation and I think that’s where I want to kind of bring it back to last question on the big tech space in China, which is like, you know,

Jing Yang (18:32)

Exactly.

is like a B2B wholesale company, essentially.

Grace Shao (18:47)

We all know there was a domestic, domestic regulatory kind of tightening over, you know, between 2020, 2023 per se. know, Didi being kind of at the very top of the epitome of what was happening. And then Baba, Tencent being faced with the, situation as well, right? Like the two choose one between the two kind of camp situation, the regulatory problems. So, but after that.

Basically international investors pulled out of China. People were kind of scared of the China regulatory crackdown. think people outside of China don’t really fully understand why the domestic regulators were cracking down on these companies. Could you give some context on that? And then I think what I really want to also ask you, the second part of the question is, are we seeing a revival of these companies now with

AI being supercharged into their strategies? know, recent BABA and tens of earnings have done really well, all driven by AI, right? Their new AI strategy. Or are they kind of just, you know, the last generation staying there back in 2023, they just kind of stalled and stopped there? Are they becoming relevant again? So I think it’s two parts of the question.

Jing Yang (19:52)

Yeah, so yeah, let me tackle the first part. I try my best even back when I started, you know, this whole thing happened when I was still at the Wall Street Journal. And back then, I think in the beginning, it’s been called the China tech crackdown a lot. And I actually made a point when I was writing about it at the journal, after a while, it has become very clear it’s not a tech crackdown. It’s not a

crackdown on tech companies. Because if you look at sort of the hard tech companies, either it’s a Huawei or any other that’s in that space, they were all fine, right? Essentially, the crackdown was targeted at internet platforms, right? Which sort of is equivalent of big tech in Silicon Valley or in the US when we talk about it. But in China, there is actually a differentiation between

Internet platforms and tech companies. So that’s the first thing. And secondly, I think in terms of that regulatory assault, there are, you know, there are, you know, legitimate sort of economical and regulatory reasons to do so. Right. As you mentioned, the Arsheng, the truth one from two, that was that was indeed a violation of China’s antitrust regulation where

e-commerce merchants were forced to only choose to sell their wares between Alibaba and JD, for example. when this whole thing happened, was indeed sort of the regulatory incentive to do that. was indeed raining years of flouting.

anti-trust regulation and other types of regulation. But then obviously, the problem that came with it is that the way that the Chinese regulatory framework and the Chinese government in general works is that it doesn’t really care about doing a very good job at telegraphing its regulatory intention.

That’s not just on the tech space, like overall, So that’s why if you remember the online tutoring crackdown, Just essentially what happened was just, that was very scarring, by the way, for a lot of investors, right? Because they targeted a sector where the biggest companies are listed in the US.

So then when you can’t just do this, issuing a piece of document that just evaporated the entire sector, that decimated the entire sector overnight. That was very scary. ⁓

Grace Shao (22:31)

Yeah, it was so sudden. That’s what it was. There was no hints or clues. I feel like with the anti-monopoly, was actually, you know, there was like momentum building up to it.

Jing Yang (22:41)

Yeah, so that really, I think in my recollection, that really crystallized the saying that China is uninvestable. When people say China is uninvestable, they mean Chinese stocks are uninvestable because that really crystallized how precarious, so to speak, that Chinese policymaking can be. And then so...

Similar thing with the Didi debacle. think the biggest problem with that is, one can argue on Didi’s behalf that the company didn’t do anything wrong because what happened at the time was there really was just no regulation governing Chinese companies listing overseas. Things were just not formulated. And obviously, Chinese regulators realized that was the problem. that’s why they...

came up with this new framework that has been in effect since early 2023. However, the absence of a formulated framework did not stop them from punishing companies in the first place for flouting rules that are not explicit. Then that is another piece that shows, OK, so this is unpredictable.

this whole virtual environment.

Grace Shao (23:58)

And the second part of the question is, you think we’re seeing a comeback? Because essentially, now you can’t really get into China’s private AI sector, right? If you’re a foreign investor and the way they can get some kind of exposure, I guess, is through the US listed companies, tech companies. In this case, it would be the Alibaba’s of the world, right? So are we seeing kind of a shift in interest again, or a revival of these tech giants?

Jing Yang (24:23)

Yeah, I like to answer that question by going a little bit further back first. So you remember the Shanghai Stock Exchange when they first came out with the NASDAQ-style starboard, right? There was a lot of discussions on what are qualified as innovation so that a company can qualify as being listed on starboard.

Grace Shao (24:36)

Mm-mm.

Jing Yang (24:47)

And you remember Xiaomi actually was going to become the first company and then it didn’t happen. And I remember having conversations because I was covering IPO and capital markets of Wall Street Journal at the time. I remember having conversations with some lawyers who consulted with who advised the Shanghai Stock Exchange on designing the rules for Starboard. And there were still a lot of undecided issues such as, OK, for example, did the other time remain unlisted?

Then the conversation was like, Didi as a company, even though it wasn’t an innovation in terms of the technology of like, ride-hailing, for example, but it was innovation in the business model. Then does innovation in business model qualify as true innovation, therefore qualified being listed on Starboard? There was all these questions at the time. And obviously, we now know the answer with what happened with Didi later on.

And I’m bringing this up because shortly after the crackdown was on the Internet platforms, followed ⁓ the zero COVID and the whole Chinese economy and many other things related to that just were in a really depressing, know, slipped into a really depressing state. Then

Around that time, toward the end of zero COVID, had the arrival of Chagabitie that sort of shocked the core of a lot of tech companies and researchers and investors in China, which we can get into later. But I think what the crackdown on internet platforms made people believe that actually China’s paramount leader, Xi Jinping, is probably not a fan of internet platforms.

He probably does not think the type of innovation in business model that I just talked about qualifies as true innovation. Instead, the things that achieve their companies at Huawei are true innovation, are the real technological advancement that can help bring China forward. And so I think that coupled with the arrival of ChatGPT,

sort of, you know, really served as a wake up call, I think, to both the tech incumbents and the startups in China that we really need to, you know, we are actually behind. We have been, you know, because you remember like in the mid 2010s, you know, China had this four AI dragons in the computer vision age. And then you have a lot of people proclaiming that China is ahead of the US now in terms of AI, right? And obviously, that was all.

That sort of dream or awareness was just shattered. All things came together between late 2022 and early 2023. And then that’s where we are now. So it’s hard to tell whether when we see the BATs nowadays...

really doing some of them doing really good work and innovation in AI is driven by the regulatory reason or else. But I would say just the way that things have played out seem to point out to the direction that this really is the era that they have to go through.

Grace Shao (28:01)

Yeah, like to your point, I think there was like an awakening where the focus or the wanted focus was on the so-called hard hard power competition, right? And like you said, that the kind of slippery slope downward trajectory for the industry was really not just caused by one thing, but it was just the macro situation, the regulatory situation, the companies also not innovating in some people’s eyes, were not doing tech for good for the community somewhat.

You remember the common prosperity rolled down, right? All these things are kind of just adding up together. And then there was the whole COVID zero thing that just really made everything even worse. think that that definitely fed into the fear for international investors and international, I guess, China watchers per se, if you have to put it that way. But I think I want to really now go into the next section of our conversation, which is what you’ve kind of touched on already, which is China and AI, right? And China.

Jing Yang (28:31)

yeah.

Grace Shao (28:57)

China’s AI space and how China is positioning itself right now. I think from the West, especially Western media, it’s very, very, very much focused on this idea of arms race between China and the US. You know, there’s a lot of ⁓ comments about how deep the deep sea moment woke Silicon Valley up, made people realize that China was catching up, you know, there’s some fear mongering, frankly, I think, but there’s some

I think some charged by actual fear or confusion or even surprise. How do you kind of make up that? Like, I guess just a high level context first before we go into details.

Jing Yang (29:34)

Yeah, mean, truly, when we talk about US-China AI race, we cannot talk about without talking about US-China competition, whether it’s, I think people now generally call them this strategic rivalry. That is all happening against this broad backdrop. And then the one thing I would sort of

⁓ note though is that, you know, it’s not like there’s a lot of companies or builders or funders in China from day one thinking about, I want to like outcompete ChatGPT or, you know, Anthropic with what I’m doing right now. I don’t think that’s... Yeah.

Grace Shao (30:13)

Yeah, that’s the point. Yeah, like, like, there’s not that strong narrative in China domestically, right? And I think

the information you guys because you guys write for such a frankly, sophisticated audience and people who are kind of more focused on really the business. I feel like your coverage is not as geopolitically charged. It’s actually just talking about what the innovation is, how the capital market is moving. So I can think from your perspective, like, what how do we make a business? this all noise? Is it just like

because of American domestic political reasons that there’s this kind of geopolitical narrative? Or do you think there’s some sense that in the tech space in the US, they genuinely see China as a rivalry that they have to hold down versus like we said, like the China tech space actually just, they don’t talk about that as much. If anything, I think the China tech space actually admires American tech space quite a lot, like as in they really worship a lot of business leaders. They study their business models, you know, like there’s less of a rivalry sense, right?

Jing Yang (31:08)

Yeah, so I think in the US there is a bit of a boast of what you just talked about. I’m not an expert on America, but what I have observed, at least the China relevant people that I talked to from Silicon Valley to Washington DC, I do sort of see like increasing bridging. I think in the past, you know,

Silicon Valley, obviously, we know is more left leaning and then, know, this is different. then with, you know, Trump’s presidency, you see all of that, you know, that that gap is bridging. And that actually ended. And when I say the gap bridging, the gap is also bridging when it comes to the China discourse. So on one hand, there is definitely fear mongering. And that fear mongering, think previously probably mostly concentrated in the D.C. and now is like spreading to Silicon Valley. That’s what I.

observed, right? And also, when I was talking about in China, how people were shattered with the release of ChatGPT in late 2022. Equally, think in the US, what do we see about 12 months later is that with the advent of a DeepSeek R1, a lot of people in the US are like, know, America is far ahead, is the indisputable global leader in AI.

from the China-GDP moment to then the deep sea moment is that China is faster catching up or in some cases they even say China is already ahead. And also that in particular is very true, the fear mongering in the semiconductor space as well, because we can’t talk about it without talking about semi. So I’ll just be very quickly talking about here.

from Jensen Huang to like many other people. I think the last year or so they have been talking up of Huawei and other homegrown semiconductor companies in China and the capabilities of their chips. But the reality, I’m not saying Huawei is not progressing, but the reality as our reporting has shown.

the information. The reality is that actually the BATs themselves actually do not want to adopt a Huawei’s ascent chips for various reasons. First is just the tech is just still not there. Nvidia is still the gold standard. And the second is also because all these companies compete with Huawei in the cloud computing business as well. Like why do you want to enable a big competitor? But you don’t see this being talked about when in the US.

From Silicon Valley to DC, when people talk about how Huawei is threatening Nvidia, how US semi control policy has enabled, has forced China to compete and innovate faster. So I hope that makes sense, by the way.

Grace Shao (33:52)

Yeah, yeah, definitely. think that’s something I wrote about as well. just like, it’s actually people are not realizing from a very selfish business perspective. At the of the day, these companies are for profit. they’re public listed companies. Their goal is to make money. They’re not like, you know, following government orders to like, know, create something on a national level for the sake of that. So like, to your point, like they don’t want to give money and give business to Huawei because essentially one of biggest competitors, right?

Jing Yang (34:15)

Yeah.

Grace Shao (34:20)

because like Baba and Tencent, they all have their own cloud business. I think I want to double click on one thing. said, know, there was, when Deepseek came out, R1 came out, well, V3 and then R1 back to back, know, Silicon Valley said, maybe China is ahead in some ways, right? At that point, it was talking about the engineering and efficiency, You know, from your reporting, where is China actually ahead?

behind or really differentiated in terms of their whole AI strategy? And this is a broad question. So it could be about the companies particularly, or do you think the whole ecosystem is operating a different way? How do you see that?

Jing Yang (34:55)

I think obviously DeepSeek sort of made it cool, made it the open source and open weight school, sort of school of thinking. Cool, right? I think if you remember before DeepSeek became phenomenal, from Baidu to Alibaba to Bydance, everybody was just doing their own closed-source models.

If you do open source, only release smaller or more inferior models. You only open source those. And then now that what happened with DeepSeek, just really made in the LLM space, made people realize actually China can be ahead. China can have a real edge if it pushes ahead with open source. And then

And then that and also when you open source your model models, it also just naturally encourages like a broader and wider adoption of your models. It sort of can travel beyond the national borders on its own. Right. And if China were to let’s say if, you know, by the way, as we established, right, it’s not like the Chinese companies that are building large language models are thinking about what the policy.

or what the government officials are telling us. They really truly are just thinking about, like, I want to be better, right? How do I get... If you build a model, obviously you want your model to be used. You want developers to build applications on top of your models, right? You want the cloud providers to include your models as part of their offering. So I think that’s what’s driven that. And I think the open source thing really sort of shown that, you DeepSeek made it...

a lot people realize that a lot of people in China realize actually open source is the way for China to pull ahead. And that has happened obviously with Alibaba’s Qwen and also I think most recently, Zhipu as well as Moonshot came up with their own latest models that also have impressed a lot of AI watchers in the US as well. So I think that’s where,

That is where China is different. And I just want to add one more point as well. In the US, there’s a lot of money to be made in providing enterprise software solutions. then naturally, that’s when AI applications are being built. You want to build for survival. You want to build to be able to become profitable.

naturally the enterprise software solutions, right? You build a 2B business. But in China, it’s completely the opposite. Chinese companies in general just are very stingy in terms of paying for software, right? And then so that sort of is the way the ecosystem works. And it has always been like that, right?

enterprise customers just naturally go to the next cheaper solution. then, you know, entrepreneurs and also venture capitalists that see who see these companies know that this is the environment, then then they also know that, you know, if you go down that route, you’re just basically, you know, waiting to to go out of business. And then so that’s also what makes the open source led, you know,

an open source led business model, you know, like have a better chances of working in China. Right.

Grace Shao (38:04)

Yeah, definitely. I think the open source versus closed source discussion has been even, you know, rooted out in the software era. like, to your point in China, it’s really like a market share like business. you it’s like you commoditize open source models, you try to capture all the market share, right? Like it’s it’s very consumer facing right now. A lot of the LLM they’re all rolling on consumer applications versus the US to your point. You know, it’s it’s you actually make you try to make higher margins on a lot of these enterprise products, but you just can’t in China.

like the willingness to pay is still so low and not just trying to buy things across Asia in general, like the willingness to pay or I don’t know if it’s a cultural thing or it’s just like people just don’t want to pay. you know, I was even shocked I think when I was working for one of the big companies and like a Chinese company for a while where you would even have like pirated software in their company computers. like, ⁓ you definitely don’t need to save from that money. But it’s just a mentality and then there’s a lack of like.

I must pay for privacy, whatever mentality around it. So culturally, that definitely has shaped the markets quite differently. So I think we touched on quite a few of the startups. So like you mentioned, was Drupal, Bytron, Moonshot, Minimax. Do you think there are any companies that are kind of going unnoticed still in the West? Like we named the four that are still called, there’s still a...

call it what was the four dragons at one point? Wait, four tigers. I get the mixed up with just tigers, dragons.

Jing Yang (39:30)

Six little dragons. Yeah. Yeah.

Grace Shao (39:33)

Yeah, they keep on updating them. It’s

like new versions of Dragons and Tigers. are there any online companies right now that you think are going kind of under a notice? Because DeepSeek in many ways actually was not being, they don’t have strong PR. Liang Wenfeng clearly is a very low profile guy. And I don’t think people really knew about him outside of China and China’s AI ecosystem until V3 came out. So like, are there any kind of...

Jing Yang (39:38)

Yay.

Grace Shao (39:59)

companies that you’re eyeing or covering that you think could be the next one, or you think the LLM space is already pretty saturated and incumbents are going to kind of stay as leaders at this point, or we might use the consolidation.

Jing Yang (40:10)

Yeah, so back when we talking about the six little dragons or tigers or whatever, DeepSeek was part of it, of the six, but it was probably the least talked about because it’s just very different from all the other five, right? All the other five had half venture capital funding, have outside investors. DeepSeek remains fully funded by High Flyer Capital Management till this day. But if we’re limiting the...

the scope to just the LLM developers and I would say that I don’t see the possibility of having anyone that we haven’t seen out there. As a matter of fact, I’m actually surprised that the consolidation hasn’t happened at a bigger scale with the exception of of Alibaba essentially acquiring 0.1.AI. We haven’t simply seen other

consolidation happened yet. And I’m actually surprised. If you ask.

Grace Shao (41:05)

For context,

sorry, readers, sorry, listeners, that one’s the one that Lee Kaifu founded. outside of Tsinghua campus, yes.

Jing Yang (41:09)

Yes.

Yes. And then now we see that Jhipu has filed to or is sort of looking at to go IPO in both, you know, Chinese, Asia market and Hong Kong. You know, I think whenever they release their prospectus, people will be reading it with a lot of interest. Other than that, you know, I’m just surprised that the so-called dachang, the tech incumbents in China have not consolidated.

I think there was a time in my, you know, I recall in my reporting, like say in my conversations with sources about 12 months ago, there was a time when some tech companies were seriously thinking of acquiring some of the companies which is mentioned. And for one reason or the other that didn’t happen. And all the tech companies in China actually decided to build their own as well, right? So this is quite different from Silicon Valley if you look at it, right?

we’ve seen from Google to the other companies, are crying pretty sizable startups, or not really are crying, are quite higher. So I do think that the Chinese LLM space doesn’t need some consolidation. It’s just not sustainable. need the level of compute that is needed and coupled with the chip

shortage China finds itself in. It’s just not sustainable.

Grace Shao (42:32)

It’s such a high capex game here, so how do these startups continue to fund themselves? I want to go into the VC space later, but mean, end of the day, it’s only the big check comes to have the money to keep on even chucking into that machine.

Jing Yang (42:39)

Yes.

Yeah,

that’s exactly what I was trying to get to, just not sustainable to have, say, 10, 12 LLM developers.

Grace Shao (42:55)

Exactly. I think it’s

interesting, a lot of them are financially backed by them, but not at a very big scale at this point. It’s like, you know, like a hundred million dollars here and there, but nothing bigger than that rate.

Jing Yang (43:08)

Yeah, exactly. if I were to, I Alibaba has probably backed it the most, right? And obviously, it did it also for the sake of promoting, know, helping the cloud business, you know, expand its market share as well. But if I were to take a guess, I would say that some of these companies will just have to really run out of money.

And then to the point that existing investors are willing to sell, say, don’t know, five cents on the dollar or whatever. So that it becomes financially attractive for any of the tech companies to actually acquire them. Otherwise, you know, it’s just not going to, you know, I don’t know the tech companies. They’re all very, you know, the people sitting on top of this company are all very like sophisticated. They’re not just going to be, you know, spending like, I don’t know.

20 billion dollars or even not 20 billion, like 10 billion dollars or five billion dollars by a technology that they think maybe they already have on their own, or they can do better on their own. So the only way that it can work is that, you know, things have to just drag along a little bit longer for the pricing and expectation to match. Right now, I think there’s a pretty big gap. And also, lot of the entrepreneurs of the

Grace Shao (44:21)

Yeah.

Jing Yang (44:25)

among the six little dragon camp. But they also don’t want to call it quits yet, I think.

Grace Shao (44:30)

Yeah, I think that people are holding on to their dream. And I think I like people ask me about what I think of the AI startup founders of this generation as well. Like to your point earlier, they kind of grew, they grew up differently from the last generation entrepreneurs. And there’s less of a, want to make a quick buck kind of mentality. They are really much more mission driven when you, you know, like hear, hear about them or, know, their media appearances or what they say out loud, you know, like where you’ve been the moonshot guy, Yang Zhilin really, really just talking about how much of a

know, philosophical pursuit it is for him to chase AGI. Actually, you know, I want to kind of pivot into the VC space. We touched on the fact that China’s VC space, frankly, is not as vibrant right now. You know, obviously the majority of the money right now, even to back these startups are from the VATs or the situation like any of the bigger internet companies from the last generation. So how has the regulatory reset

since 2021 really reshaped the local domestic VC landscape because we saw that Sequoia was probably one of first that started the decoupling effort, splitting out their Chinese business called Hongshan, which is like a literal translation to Sequoia, the tree. Are we continuing to see this play out or do you think we’ve kind of finished it, wrap that up already? It’s been a couple of years and the VC space is getting a bit of an energy back or...

recapitalization.

Jing Yang (45:53)

Yeah, I’ll talk about decoupling first. I I think the US China venture capital decoupling is already over, right? Like it’s already decoupled. And it’s decoupled mostly for driven by factors out of the US, out of American politics, right? When the Treasury Department in Biden’s final month, in the Biden administration’s final month,

came up with this long expected rule that essentially restricted any American investors, be it institutional individual to invest in tech and AI related sector in the US. That basically just shows this is over. That piece of regulation is not retroactive. So the money that American LPs have committed to Chinese GPs still remains like, okay, to invest.

But other than that, once the dry powder runs out, the dry powder that’s raised before earlier this year, when the regulation came into effect, then it’s pretty much over. And a lot of American LPs realize that. And then that’s what makes it difficult for Chinese GPs, for Chinese venture capital firms, because

If you are the CIO of a Endowment Fund, you know that, even though you remain committed to about the opportunities in China, but to be honest, DeepSeek only proved that the most potentially lucrative opportunities coming out of China is in the AI and the AI land, which is the various sector that you cannot have any exposure, then what do you do?

So then because of this conundrum, that’s what makes it so difficult for Chinese venture capitalists, the GPs, to raise funds from American LPs. just so you know that American LPs traditionally, think before the decoupling that started from around 2021, American LPs actually made up for about half. I mean, there’s no like sort of

consensus, statistics on this. I’m giving you a number that was given to me by a lot of people in the industry. Before that, before the deep coupling, American LPs make up for like about at least a half in terms of capital raised by Chinese VC firms. And then so that is a big chunk of money that is sort of people know is going to be gone.

is gradually dying. So then, you know, to make up for that, you know, we’ve seen Chinese VC firms going to other parts of Asia, Europe, Middle East, to try to, you know, fundraise. And that really hasn’t gone that well for various reasons, right? It takes time to build relationships, to understand the culture.

and the thinking behind all these ⁓ LPs and all these big institutions that you are trying to basically ask money for. And then that is sort of on the fundraising part. And on the exit part, for Chinese VCs, the biggest issue right now, the biggest bottleneck, the single, if you ask any Chinese venture capitalists, what is the biggest problem, the difficulties that you are facing?

tell you it is because it’s a difficulty to exit. We talked a little bit about the new framework guiding overseas IPOs by Chinese companies. And that has created a significant bottleneck on VC exits. And then so when you are managing a fund, if you can exit and then return capital to investors and then show it in your DPI,

then obviously it’s very hard for you to show that this is what I’ve done so that you can raise a new fund. And on top of that, we still have like Ant. Let’s not forget about Ant. I was recently in a conversation with an LP investor in the US who started investing in the China VC space from

like the 2000s, so pretty early on, right? He was telling me that, you know, almost every big American, you know, endowment or pension fund is still remains like locked in Ant. And without that being unlocked, without some kind of exit in Ant, it also really affects the appetite for this, for them to continue to invest.

in the China VC space. I think we just need to see, I know that in terms of the IPO pipeline that we’ve seen, for example, last year, there was a trail of autonomous driving companies being approved to list in the US or Hong Kong, but that’s just not enough, right? We still haven’t seen like say a billion dollar sized IPO.

from the tech space. We just haven’t seen that yet. And I think with the absence of clear sign of the IPO pipeline being cleared up without clear sign of a sizable exit, a sizable IPO, sizable meaning at least a billion dollar, it’s just going to be very difficult for Chinese GPs.

Grace Shao (51:09)

Yeah, I know you actually cover quite extensively when you’re still with the journal. It’s probably like your main story for the last year where you had the journal read before you moved over to information. What are some potential scenarios actually on that point? Like, you know, like you said, this year in Hong Kong, it’s supposedly Hong Kong Exchange has hit like the most filings since like pre-COVID, right? But majority are relatively small.

AI or tech companies, they’re not making international headlines. The last one that was really like a major international like candy, eye candy was really Ant and where are we kind of at with that? I mean, this is a bit off topic, but I just find it so fascinating they brought it up.

Jing Yang (51:49)

Yeah, I mean, just a quick thought on the record amount of filings we see in Hong Kong. It’s basically a lot of smaller companies as well as a lot of companies are already listing in a share market and doing a share listing. With the H-share listings of say ATL and SF, it’s just something that for the moment that Chinese regulators are more amenable to.

But in terms of your end question, I think what I have come to believe, and I’m willing to be proven wrong because I’m really only just a journalist, but I think if the NIP were to come back one day, will most unlikely come back in its original form, if that makes sense.

It was supposed to be the world’s largest IPO ever. They were on the cusp of raising $35 billion. I just don’t think when Ant finally was going to IPO again, it will come back in that exact shape or form.

Grace Shao (52:46)

Fair enough. think the company has restructured quite a bit as well, right? Since then. So I wanted to kind of get a sense. I’m not familiar with the space, but are R &B denominated VC funds versus what we were just talking about predominantly being like USC denominated VC funds in China? Are the incentives different for their funds? And do they kind of invest in different kinds of companies? Or are they actually fighting for the same deals?

Jing Yang (53:12)

Yeah, so first, the incentives are quite different. It’s really like two quite different worlds, right? Different worlds. So in the RMB world, one of the biggest pockets of money comes from government guidance funds and SOE-related funds. And then these types of LP, they are driven by local GDP growth targets kind of incentives. And then so then

invariably when they write a check to you, they would require you to bring back some of the investment back to their local city or province. Say I write you a 10 million yuan check, then that means that usually there’s a very detailed percentage requirement written saying that you need to bring back, say for example, 1 % of your portfolio investments or you need to invest this much in our province or city.

And then that is obviously some can argue not very market driven. That is the reality. And I will probably also just play a bit of a devil’s advocate if you are asking for money from a local government related fund, then obviously that is sort of the bottom line. That is the profitability, so to speak, that they are thinking about.

Other than that part of the difference, the other difference is that, it’s funny though, like say, know, for a while you don’t see the R &B world and the UST world sort of overlap. For quite a long time, it would just like coexist and then they know that they go into, they all have their own strengths and then they do quite different types of deals or do different types of...

going to different types of sectors. However, since this USD funding drought that we have just talked about, what do you see is that some of the Chinese VC firms that were only raising in USD also have started exploring raising our yuan denominated funds. And then there are mostly two reasons behind that phenomenon. First is obviously driven by

you know, the USD funding. Second reason though is also because of the geopolitics made it as we talked about impossible for a lot of American dollar investor to invest in social sectors. So that some of the venture capitalists in China realize there are sectors, for example, semiconductor, right? That is a sector that has just become like not possible.

⁓ for ⁓ USD funding to get any exposure in, then if they want to get exposure, if they want to invest in that sector, the only way to do that is through raising our UN-denominated fund and invest. So that’s why you see the two walls that previously just co-exist in parallel now sort of start to overlap.

Grace Shao (55:57)

Thanks, Zach, lay out.

Jing Yang (56:06)

love it.

Grace Shao (56:07)

Thank you for that explanation. Actually, that’s really helpful for even me to understand. think I was also noticing this trend. I was like, why are all these former USD denominated funds, like primarily now actually raising RMB funds, but it’s up to your point to make sense. You know, the Manus AI agent company was probably the most high profile Chinese AI company that received USD VC money. If anything, the only one, right?

this past year, what do make of that? And you know, that they moved their operations from Shanghai to Singapore, you know, where do you see this? Is this like the start of a new opening or like a new way for Chinese companies to go find US investment? Or do you think this is like a one-off thing because they did actually receive scrutiny from the US, not so much actually attention from the Chinese that it doesn’t seem like, at least not publicly. So what do you make of that?

Jing Yang (56:56)

Well, ⁓ Manus is just the latest example, right? But if we look at, if you remember a company, Heijin, that also gained some traction about two years ago, think what we’ve seen that what all these companies, all these startups represent is this new generation of Chinese ⁓ founders and builders, right? They were born and raised in China, some of them educated in China, some of them a bit of China and elsewhere.

But they all grew up with that global vision, global view that they want to compete at a global level. However, it’s just so unfortunate that it’s not possible anymore for a company to even try to succeed in both China and global ex-China if you are building a consumer-facing product. That’s just the reality these days. So then realizing that, I think a lot of them now just, you know, if I have to choose China or global ex-China, then...

Some choose the former and some choose the latter. And then for those that have chosen the latter, we’ve seen example exemplified by metas, by hygiene and many others.

Grace Shao (57:59)

I think just one last question, which is if you look ahead three to five years, we can’t predict a future, but what do you think will be the next big story in the China AI tech space?

Jing Yang (58:07)

Yeah, that is a very difficult question. if I were to try to answer that, would say just follow. I was in the conversation with someone else the other day, know, like, what is a Chinese startup these days? Like, how do you define that? The definition has to change and it has already started to change, right? A company like Manus, right? A company like HeyGen, a company like, you know, GenSpark.

Genspar is slightly different than Manus. They actually incorporated as a company in California, but they are funded by two Chinese entrepreneurs who used to work in China. So I think what is really worth watching is these builders and these entrepreneurs. Chinese entrepreneurs, they are definitely going to make waves in the

⁓ AI and tech space globally, not just in China, right?

Grace Shao (59:02)

Yeah, I think we’ve even seen that shift. Remember when Zoom went IPO and everyone was freaking out? Oh my god, it’s a Chinese company, but he’s like an American Chinese. Well, I think he goes naturalized. And just that kind of mentality completely shifted. now, especially in the AI era, more than 50 % of AI researchers are essentially of Chinese descent. So how do you then define it, right? That’s a really interesting take. I really, really appreciate your time. I know you said you had a hard cut off today.

Thank you so much, Jing.

Jing Yang (59:30)

Yeah, thank you so much for having me.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

In this episode, Wency delves into the details of her journey, from working at a leading Chinese-language tech publication to joining an international VC firm, and now writing about China’s AI and tech ecosystem for a global audience. Wency provides a holistic view of the different players from the LLM startups to the leaders in consumer applications and to the rising domestic Nvidia challengers. She is also someone who’s really on the ground and plugged in with the startup community in China, so she sheds light on the real hustle culture and dichotomy of “lying flat” vs. “involution”.

Highlighting the differences between tech events in China and the US. She dissects the discourse in China’s youth today, where two seemingly contrasting sentiments coexist. China’s generational gap feels like it happens every five years because of the vast difference of the speed of technology development. Comparing the differences between tech events in China and the US, the invisible hand of the local governments encourages innovation and commerce.

She then also addresses misconceptions about Chinese startups and emphasizes the importance of understanding the unique dynamics of the Chinese tech landscape and how Chinese founders still see the US as the benchmark. And ending the conversation with a personal reflection of how her father had never dreamed of the economic growth and personal gains he would experience in a lifetime, a look into the average Chinese man’s sentiment towards the country’s technological development, and showcasing her collection of Labubus.

Spotify link here.

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

00:00 Wency’s Journey from VR to China Journalism to VC and to International Journalism

06:48 The Evolution of China Tech from 2020-2025: Trends and Change

11:51 Deep Dive into AI: Observations and Major Players

23:19 China’s Youth: “Lying Flat,” Involution and the Hustle Culture

30:15 Generational Perspectives on Success

32:14 The Mission-Driven Entrepreneurial Spirit

34:58 Contrasting Global Tech Events

38:12 Misconceptions About Chinese Startups

41:13 Trade Policies Affecting The Average Business and Average Person

43:51 Perceptions of Tech Competition with the West

47:58 The Future of Technology and Society

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

For long-term China tech investors and journalists, she needs no introduction. Rui Ma is a leading voice in the space, a trusted China internet analyst. She is the founder of TechBuzz China. She began her career in traditional finance and then early-stage investing. In recent years, she has advised AI companies and established herself as the founder of an AI school to prepare the next generation for the AI era.

In this conversation, we discuss the evolution of the Chinese tech ecosystem and the current trends in AI investment - her bullish view on EVs and robotics. Rui shares insights on the major players in the industry and common misconceptions about Chinese tech.

Based in the Bay Area, Rui travels to China regularly, bringing a nuanced understanding of the two worlds and bridging that information gap. The conversation also touches on cultural attitudes towards technology and the future outlook for AI in China. Finally, she discusses her unconventional approach to introducing screens, technology, and now AI to her children.

Spotify link here.

00:00 Journey from Finance to Tech and AI

02:24 Evolution of the Chinese Podcasting Landscape - going on Acquired

05:43 Investment Trends in EVs, AI and Robotics

08:10 Results > Subsidies

10:50 AI bubble?

13:34 WAIC: Willingness to pay for LLMs isn't there

27:48 Robotics at the front and center

31:35 Energy Innovations and Geopolitical Implications

35:07 Global Interest in Chinese Technology - Esp. from India

40:01 Understanding Cost and Value in Technology Adoption

44:42 Unconventional attitude towards technology and education

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.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Joining me today is Diana Wu David, Director of Futures at ServiceNow. Diana is ranked number #2 futurist in the world by Global Gurus. She ran her own business for nearly a decade called Future Proof Lab where she worked with C-suite executives and boards to help them create future-focused, resilient organizations. She is an expert in guiding professionals and leaders in this ever-evolving business landscape, leveraging strategic foresight to turn uncertainty into a competitive advantage.

In this conversation, Grace and Diana explore the transformative impact of AI on the future of work, discussing how organizations can adapt to this change. They delve into the challenges and opportunities presented by AI, the importance of measuring its effectiveness, and the cultural differences in technology adoption between the US and Asia. Diana emphasizes the need for a shift in organizational structures and the importance of preparing future generations for a rapidly evolving workforce. The discussion also touches on the necessity of fostering critical thinking and creativity in education to equip individuals for the future.

Spotify link here.

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

00:00 Navigating the Information Overload

04:04 The Future of Work and AI Integration

08:54 AI's Impact on Productivity and Organizational Structure

14:48 Measuring AI Effectiveness in Enterprises

19:41 The Evolution of Work and Education

29:40 Cultural Perspectives on Technology Adoption

39:24 Unconventional Views on the Future of Work