AI Proem Podcast

In this episode, I speak with Ang Li, co-founder and CEO of Simular AI, about the rise of computer use agents and his vision for computers that can increasingly do work on behalf of humans. We discuss why computer use matters beyond the current wave of API-based agents, particularly for the vast amount of enterprise work still carried out through legacy desktop software that was never designed to be accessed programmatically.
We explore where these agents could have the most immediate impact, from processing invoices and extracting information from unstructured documents to navigating financial, healthcare and other enterprise systems. Ang argues that the goal is not necessarily to remove humans from the workflow, but to shift the balance between execution and judgment, with agents handling repetitive tasks while people remain responsible for decisions and sign-off.
The conversation also gets into the economics and technical challenges of computer use agents. Ang explains why relying on a frontier model for every click can be expensive, slow and difficult to control, and how Simular’s neurosymbolic approach turns repeated workflows into executable playbooks. We discuss the role of smaller and open-weight models, the changing economics of AI agents, and why Ang sees computer use as a way to make automation more accessible beyond the highest-value technical work.
Finally, we look at what this shift could mean for SaaS and the future of work. Ang distinguishes between systems of record and software that primarily serves as a portal or interface, and argues that these categories may face very different levels of disruption from agents. We end with a broader question about human agency: if AI increasingly handles execution, will the more valuable skill become knowing how to identify the right problems to solve in the first place?
The AI Proem Podcast is part of the AI Proem newsletter, which has ~13k followers globally. To learn more about China AI, the business of AI, and how AI is impacting society, please check out the newsletter here and more insightful conversations here.
Chapters
00:00 Introduction to Simular and Its Vision
05:14 The Future of Work and Human-AI Collaboration
10:21 Technological Landscape and Market Demand
15:31 Real-World Applications and Use Cases
20:40 Challenges and Competitive Landscape
29:55 The Dual Nature of Work: Content Creation vs. Execution
33:50 The Competitive Landscape: Frontier Labs vs. Open Weight Models
37:17 The Rise of Computer Use Agents
43:30 Empowering the Average Person: High Agency Through Technology
48:46 Understanding SaaS: Infrastructure vs. Portals
52:32 The Future of AI: High-End Jobs vs. Repetitive Tasks
AI-generated Transcript (for reference only)
Grace Shao (00:00)
Ang, thank you so much for joining us today. Really excited to have you. To start with, could you tell us a bit more about yourself and why you built Simular? What is your long-term vision for the company? And what really brought you here along your academic journey?
Ang Li (00:13)
Yeah, thank you so much, Grace, and thank you for having me here. My name is Ang Li. I’m the co-founder and CEO of Simular. We’ve been working on this company for almost three years.
In short, we call ourselves the autonomous computer company, meaning we are building autonomous computers. The goal of Simular is basically this: everyone has computers right now, but we have to work on them manually by moving the mouse, typing on the keyboard, looking at a screen, and understanding what’s going on ourselves.
We envision a future where computers will do the work on behalf of humans, on their own. That’s really the technology that we’re building towards. Nowadays people also call them computer-use agents. It’s basically a general-purpose agent that can use the computer just like a human.
A bit about my background: I’ve spent almost 20 years researching AI. My personal research direction has basically been trying to figure out what people sometimes call AGI, artificial general intelligence. The goal is to have a general-purpose system that can learn like humans and perform actions just like humans.
I see this autonomous computer problem as the first possible realization of AGI technology that could have a huge impact on society.
Grace Shao (01:29)
It’s quite interesting. You mentioned computer-use agents. It seems like there’s been quite an influx of capital going into this space right now in Silicon Valley. Has there been a genuine technological shift here? Why is everyone suddenly so hyped up about CUAs?
Ang Li (01:44)
Yeah, so that’s the interesting part. We started three years ago, and when I told people we were building agents, people didn’t understand it. I was telling people, “Okay, we’re building agents that use computers.” And people would ask, “Why? Why are you building agents that use computers? Why not just use APIs?”
Agents aren’t actually a new concept. It’s a word that has been around for tens of years in the research community. We already talked about agents within DeepMind when we were doing research towards AGI. It just wasn’t very familiar to the broader public.
Three years ago, we had the first version of ChatGPT, and we knew the scaling laws for foundation models were working. Foundation models were becoming very powerful.
We looked at the trajectory of the technological shift and realized that, for a computer to work like a human, you need APIs. If you want a computer to go into your Gmail, look at an email, and send an email on your behalf to someone else, there’s already a Gmail API. So if you want to do those kinds of tasks, you just let the agent call the Gmail API.
Three years ago, that was basically the case for tool calling: basic APIs.
Then we asked: suppose we have all the APIs readily available to agents, what’s remaining?
The answer became very natural. What’s remaining is all the software that has no APIs.
For example, lots of companies have legacy software on Windows computers, like old ERP systems that nobody is maintaining anymore. That software has been running for many years, and people don’t really want to change because they’re so familiar with it.
The problem is that because the software is outdated, people still have to manually work on it. There are no APIs.
If our goal is to liberate human labor, we don’t want people spending so much time sitting in front of computers doing repetitive, tedious tasks. Nobody likes that. Everyone thinks, “Why can’t I do something more interesting with my life? Why should I spend eight hours every day doing repetitive stuff that doesn’t require much cognitive load, just looking at a spreadsheet and filling out the same form over and over again?”
Those problems cannot be solved by agents that only have APIs.
So then we realized this is actually a harder problem. It requires technology that can look at a screen, decide, “Should I click this button here? Should I type something?”, move the mouse to the coordinates of the button, click on it, and move to the next page.
It feels a bit like a self-driving car in the digital world. A self-driving car looks at the streets and decides, “Should I turn left or right? Should I press the gas?” In this case, we’re looking at a screen and deciding where to click.
When you combine the two, API agents and computer-use agents, you cover the full spectrum of computers. There’s nothing else remaining.
Once you have API agents and computer-use agents and combine the two, computers can become autonomous. That’s the AGI that everyone is striving for.
Grace Shao (05:25)
It’s pretty crazy. Your vision of the future of work is essentially that computers run themselves.
I get that vision. But wouldn’t work itself, by nature, just change? So much of our work right now, like you said, filling out PowerPoints or spreadsheets, you can almost call it performative because it’s ultimately for humans to view.
But if it’s agents or computers viewing the output, do we still need to fill out those PowerPoints and forms?
Ang Li (05:50)
Yeah. I think first we have to look at what the bottleneck of work is right now.
If we still have a lot of people performing manual data entry, that’s the bottleneck right now. If we remove that bottleneck, people’s productivity could be 100x in the future, and they can spend more time on strategic decision-making instead of doing this performative work.
That’s our first goal as a company. Why not just remove that repetitive work from people?
It doesn’t mean that in the future computers do all the work and humans never look at a screen. It’s more like when you hire an intern. You delegate some tasks to the intern and say, “Can you fill out this form?” The intern comes back and says, “I finished it. Do you want to take a look?”
You still take a final look and make sure everything is correct and according to the company’s policies.
Computer agents will do the same. It’s not going to be that agents just finish the work, make some random mistake, and walk away.
In the end, the human will still be the final gatekeeper for everything. Humans just don’t have to be involved throughout the entire process. You still need a human to sign off.
Grace Shao (07:23)
Right. You don’t need to be hitting enter, enter, enter the whole time when Claude keeps prompting you.
Ang Li (07:26)
Yeah, exactly.
Grace Shao (07:30)
But then my question for you is: do you think the future will still look like the desktop we know today?
What would the interface be? Are we still going to use the current desktop applications we use today? Or do you have a different vision for how humans even interact with AI?
Ang Li (07:47)
To answer this question, I think we have to clarify two concepts.
One is what kind of device humans use. The second is what kind of device agents use. Those two devices don’t have to be the same.
First, we have to look at the natural way for humans to receive information. This seems like a relatively simple problem.
Humans invented paper, and society has been using roughly this size of paper to view documents, do approvals, and sign off on things.
For computer screens, we envision the future as something more like an iPad.
You don’t necessarily have to have a keyboard or trackpad. You can just have a big screen.
Grace Shao (08:36)
You don’t even necessarily have to have a screen or interact with it, right? It could just be like a box.
Ang Li (08:42)
Yeah. I mean, you can still have interaction. You can still click on it.
But there’s a fundamental size that humans are comfortable with. It’s roughly the size of a piece of paper.
Some people say the future is the mobile phone. I disagree with that because the size is limited. It’s hard for me to read books or documents on it.
An iPad-sized screen is actually a good size. It’s kind of like current computers, just removing the keyboard and trackpad.
The second question is: what kind of device do agents use?
That device doesn’t necessarily need a screen. It’s just a machine with computational power, and that’s good enough.
Whether the machine is a laptop or a desktop doesn’t really matter. What’s important is the computing power. You could have GPUs in there. You could host models in there. You should think about it more like a piece of metal sitting in a data center. That’s the machine the agents use.
Then there’s another question along that line: what kind of operating system runs on the machine? Is it still desktop, mobile, iPadOS?
Our view is: why not have a common infrastructure for all operating systems?
For now, the most powerful one is the desktop because the majority of the killer applications for computer use are legacy desktop software.
That becomes the primary direction we’re tackling right now.
But it’s possible that in the future, if a lot of apps run on mobile phones, then we need Android machines in the cloud that help you offload that kind of work.
It’s definitely possible. It’s just that right now, we see the biggest bottleneck as Windows desktops.
Grace Shao (10:40)
So in the future, would you guys look at creating the operating system you were just talking about? Or would you even go into the hardware?
Ang Li (10:48)
Yeah. I mean, we are an autonomous computer company, so basically we’re working on computers. It’s definitely possible.
The future trajectory could go beyond software.
But right now, we already have cloud infrastructure where, if you say, “I need a Windows desktop,” we can give you a Windows desktop in the cloud. If you need an Android phone, we can give you an Android phone. If you need a Mac, we can give you a Mac.
We have infrastructure that can allocate any operating system in the cloud for your agents to use.
It’s general-purpose, and the agents can choose how many devices they want.
Some people might want to use 10 Windows desktops for their own purpose. Some companies may need 100 to run massively parallel jobs for certain software.
That’s already possible today.
Grace Shao (11:37)
I want to double-click on something you mentioned earlier.
You sound more optimistic about the idea that, as AI advances, we’ll be freed up to work on more creative work.
I’m going to play devil’s advocate here. Some people would argue not everyone wants to work on strategic work. Not everyone has that creativity, not everyone wants to do that, and frankly, not everyone has the capability to do that.
Some people have been part of the execution chain for the last three decades. That’s how the workforce has trained them.
So it leads me to this broader conversation. There’s a bit of fear-mongering in Silicon Valley saying AI is going to take our jobs. AI will certainly take over many of the administrative and executional tasks you mentioned.
But others are saying jobs don’t equal tasks.
It sounds like what you’re proposing is that CUAs will help with tasks but not necessarily replace the entire job.
Help me understand your more philosophical view on this and how you see the future of work.
Ang Li (12:34)
Yeah. We talk to customers, and people also reach out to us.
I can give you an example. There was a general manager of a car dealership who reached out to us. It’s a small family-style business in the U.S., only three to five people.
They go into QuickBooks and generate hundreds of invoices every day for their customers, and they don’t like it.
Even though people are willing to do this job, they don’t like it. That’s the real problem for society right now.
They earn their wages through this kind of work, but it’s not really a job they like. If those people had the opportunity to do something else, they would do it.
The real problem is not that they’re trained to do this, therefore they want to do it. It’s because this is the way for them to earn wages.
Suppose we had a way to give these people the same amount of salary and the opportunity to do something else they’re interested in. Everyone would do that.
The real question is: do we have enough productivity that allows people to explore their interests?
My view is that this division of labor exists because we are in a constrained economy.
When you only have a certain amount of money and resources to distribute, you have to make trade-offs.
But if society’s productivity becomes 100x higher, meaning we produce 100x more goods and resources with the same population size, then we can allocate more funding and resources to each individual.
In that scenario, people aren’t going to say, “Because I was trained to do repetitive work, that’s what I want to do.”
People will ask, “Can I use Claude Code to create apps?”
A lot of people are already doing that. People in non-technical industries who used to do tedious work are turning to coding agents and asking, “What kind of thing can I create?”
Everyone becomes a creator. You’re creating something new. That’s what people find interesting.
I wouldn’t doubt that.
I feel like the main problem is that our society has a limited economy. That’s waiting for us to amplify it by 100x.
This digital workforce is an opportunity for society to amplify the economy because, in the future, every company could have 100x more digital workers who aren’t human.
Naturally, the speed at which you produce goods or run operational pipelines becomes much faster. Companies run faster and produce more resources for society.
That’s the opportunity I see. This might be a little controversial, but I see agents helping industry become much more productive, and in return giving humans more opportunity to do creative work or whatever work they’re passionate about.
The problem right now is that we are constrained, so people are forced to do a lot of manual work.
Grace Shao (15:56)
No, I actually agree with you on this.
Technology has always disrupted jobs, but that disruption has also led to replacement and redirection of people’s interests.
Even looking at the last generation of workers, think about people who worked in car manufacturing or factory jobs. As automation replaced some of those jobs, people found new work.
What you’re saying is that now our minds are doing these laborious jobs. They’re almost mental labor jobs.
Once our minds, or at least our time, are released from that, potentially we find new ways to use them.
But that may take a decade or two, or even a generation, to figure out what the new way of living is.
I’m in that optimistic camp as well.
It’s just interesting when you talk about 100x supply: will there also be 100x demand? And how might that affect the economy?
But we’re not going into that today. That’s a whole rabbit hole we could go down.
Ang Li (16:54)
Yeah. In short: use the new technology, find new opportunities, and make the pie bigger.
You want to make the pie bigger so everyone can be relieved from some of the stress and burden of labor.
Grace Shao (17:09)
I feel stressed out covering AI right now because it’s moving too fast. There’s too much to follow every day.
But look, what are some real-life use cases you’re seeing?
I liked your dealership example, but what are some more enterprise-facing use cases where people are already using Simular?
I think on another podcast you were talking about healthcare and pharmaceuticals. Are those verticals something you guys are focused on, or have they naturally become high-demand industries for your kind of technology?
Ang Li (17:44)
It’s organic. They naturally become high-demand industries. Financial services as well.
We’re working on this general-purpose horizontal platform that allows people to take our technology, build on top of it, and solve their own problems.
We have this market pull where people come and talk to us.
We’ve found a pattern across almost all the use cases.
The pattern is basically: “I have a form in a certain shape.”
It could be a physical receipt. It could be handwritten or machine-printed. It’s basically unstructured data, often an image.
The agent needs to look at the image, parse the information on that paper, and move the data into another system.
And that system usually doesn’t have APIs.
It’s often a desktop environment. Many of them aren’t browser applications. They’re old desktop applications running on Windows computers.
This kind of manual data entry is one of the biggest bottlenecks in industry today.
And it’s not just healthcare. People from many other industries have exactly the same problem.
Grace Shao (18:55)
So that’s currently the biggest use case.
Help me understand the technicalities of this. Am I giving you access to my PC? How does this work?
Ang Li (19:05)
There are two types of scenarios.
In regulated industries, people worry about giving a third party access to private information.
Suppose you’re a healthcare provider. You already have your own computer with the software installed.
What you want is: “Can you install an agent on my computer so the agent takes the image, fills out the form, clicks through the portal, and does some calculations?”
That’s the option we call BYOD, bring your own device.
Basically, you have your own device. You have everything installed there. I don’t touch any of it. I give you the agent, and you install the agent as software on your computer.
Then there’s another set of users who want to scale.
They say, “Okay, I have one computer. I can make it an autonomous computer. But what if I want to run 100 autonomous computers for this type of task in parallel?”
It’s impossible for them to stack 100 computers in their office and manage all of them.
So they come to us and say, “Can you provision 100 virtual computers in your cloud?”
That becomes the advantage of having cloud infrastructure for all these desktop computers.
In that scenario, you don’t need to provide the computer. You come to us, and there’s already a computer there with an agent installed.
You just prompt the computer and say, “Okay, do this task. Fill out this form and test our computer,” and then you walk away.
You don’t have to manage anything.
Grace Shao (20:40)
I see. So a lot of it is actually outsourced to you guys to manage.
The elephant in the room for all the newer players is obviously Big Tech and the frontier labs: OpenAI, Anthropic, Google and others.
These companies are becoming very good at almost everything. They’re going around eating everyone’s lunch.
What happens when they become very good at computer-use agents?
What is the lasting moat for a company like yours? You’re three years old, you’ve obviously done very well in your vertical and were very early to it, but how do you create a lasting moat?
Ang Li (21:16)
First, I want to thank the frontier labs for working on this problem.
When we started three years ago, I had a really hard time convincing people this technology was useful. People thought it wasn’t useful.
Another group of people would always ask me, “What if OpenAI does the same thing?”
It feels natural because we are working on a very general technology. It feels natural for OpenAI to do the same thing.
And I didn’t really have an answer to that.
I would literally tell them, “Okay, it’s definitely possible.”
I came out of DeepMind and I’m working on this general technology. I believe there are a few sets of people in the world who share the same vision and are pushing the same technology toward the future.
About a year after that, OpenAI started working on this problem. Then other labs started working on it too.
Even though we were one of the only companies focused on it at the time, we released our first open-source computer-use agent, which ranked number one on a public benchmark called OSWorld. That happened in October 2024.
I still remember that one week later, Anthropic released its first computer-use product.
That was the moment where it became real: frontier labs were working on the same problem as us.
The interesting part is that it actually helped us educate the market.
More people became familiar with the technology. Whenever they talked about computer use, they knew there was a company called Simular that had been working on this for years.
They thought, “It seems like promising technology. We want to try it and see how it can help us as a company.”
So that’s the first answer: having other people working on the same thing may not be a bad thing.
I want to say this to founders because everyone worries about a big company taking over.
Actually, it may be the contrary. If a big company starts working on the same problem, it validates the product-market fit for your technology.
Then we have to think about what’s next.
Our philosophy has always been: if there is something other people can work on, why does the world need us to work on it?
In the beginning, we wanted to build the whole computer agent. We knew it was relatively easy for people to build API agents, so why should we spend time on API agents? Why not spend time on the remaining problem?
That’s why we worked on computer-use agents.
Now the big labs are working on computer-use agents as well. So we have to look at what kind of technology they’re focusing on.
The big labs’ business model is based on the idea that AGI is a single model.
Their business model is to sell APIs for that single model.
Computers are a vertical on top of their model business. Computers are not the whole thing for frontier labs.
They’re still trying to train the models. Computer use is an application for them.
So the way they do computer use is to take the model, treat it as an API, and have the agent ask the model, “What do I do next?” Then the model produces the result.
That approach has three problems.
We were actually one of the first open-source agents using that paradigm, where the agent repeatedly asks the model and gets the result. That helped us become number one on the benchmark.
But we realized there are three problems with that framework.
First, it’s expensive.
For every move, every click, you need to take the whole screen and pass all of that screen information to the model.
Sometimes if you go to Wikipedia, it could be 100,000 tokens. Every step, you’re passing all of that information to a frontier model and asking, “What’s next? What should I do?”
It becomes very easy to spend $100 on a certain task.
It’s expensive.
Even though model prices are dropping in some areas, we can still see relatively steady pricing for frontier models because the model intelligence keeps getting better.
The second problem is speed.
For every step and every move, I need to package everything on the screen and pass it to the model. I may have to wait five or ten seconds for each move.
It’s slow.
The third problem is even worse: it’s not stable.
Foundation models are neural networks, so fundamentally they are probabilistic models.
Every time you ask the same question to ChatGPT, it can give you a different result.
Have you ever copied and pasted the same prompt into a large language model ten times? It can give you ten different results.
That reflects the fact that the model is probabilistic.
But in the agent space, we’re talking about an agent asking the model what to do at every single step.
Grace Shao (26:17)
And in your use case, you actually want exactly the same thing every time.
Versus wanting it to feel like a different human talking to me, your use case makes more sense if it gives you the exact same answer every time, right?
Ang Li (26:29)
Exactly. I just want the exact result.
I don’t want creativity in terms of which button to click.
If I deploy this agent on my server, I need to be able to anticipate what’s going to happen.
That isn’t necessarily the case with agents today.
For chatbots, that variability is okay because sometimes that’s what I want.
But these three problems are fundamental problems with using a single model to build agents.
We have a solution for these three problems, and it isn’t a single-model solution.
We have an architecture that we call a neuro-symbolic approach.
Thirty years ago, when nobody was talking about neural nets, everyone in AI was working on symbolic approaches: logic, reasoning, if-else statements, programs.
We try to combine the two.
We take some inspiration from what humans do.
For example, when I learn to ride a bike, the first time is really hard.
But if I ride a bike 100 times, it becomes muscle memory. I don’t even need to think about what I should do. I’m balancing myself without thinking.
Humans have this characteristic called the power law of practice.
As you practice, your efficiency at performing the job becomes extremely high, your consumption of brainpower becomes extremely low, and your reliability becomes extremely high.
Today’s agents powered by frontier models don’t have those characteristics.
Every time I ask them to do the same job, it costs me the same amount of tokens. I may pay the same $30 for one simple task. And every time, it can still give me some surprises and failures.
That’s the problem.
Our solution is basically something like note-taking.
Suppose I’m performing a job for the first time. At the same time, I write a playbook. That playbook is represented by code.
The agent has a notebook that remembers what it did to perform the job.
Next time, it refers to the notebook and tries to reproduce it.
Grace Shao (28:46)
That’s brilliant. Okay, so you save a lot of token usage in this process.
Ang Li (28:50)
Yeah.
One of our early experiments showed that for some tasks, we can save 90% of token usage.
So it’s 10x cheaper than a typical agent.
At the same time, you get higher reliability because most of the actions are code. It’s deterministic.
When I run it, I know the result.
It also helps make the system more transparent.
In an enterprise setting, when I look at the system, I know exactly what this thing is going to do every day.
I’m not worried about the AI suddenly jumping out of the sandbox and attacking other companies.
It’s much more controlled.
Those benefits help people adopt the technology in production environments.
Grace Shao (29:32)
That makes a lot of sense. That’s really interesting.
On the token-spend point, I want to ask you as a founder: how do you balance which models you use?
How do you decide which model to route to, and how do you decide on token spend?
Explain to us the pain point you face right now as a founder, and whether you have any solutions or suggestions for other founders.
Ang Li (29:55)
It really depends on the work you’re performing.
One observation is that there are basically two types of work.
One is content creation. The other is execution, which is what we focus on.
Content creation can be generating images, generating videos, generating code, generating apps.
I view programming and engineering as creation.
That kind of work requires the most intelligent models.
When a significantly better frontier model shows up, a lot of people will immediately move to it because they feel, “Okay, this model is smarter for writing my apps or doing programming work.”
It’s difficult to do the same type of job with the same efficiency using a much lower-tier model.
That’s one category.
The second category is something like, “I want to go on LinkedIn and see what’s going on, see how many people reached out to me, and click around.”
We don’t need an IMO gold-medalist-level model to do that kind of task.
For computer use, it’s highly likely that you only need a good open-source model to accomplish a lot of these jobs when it’s paired with the harness we’ve built, because most of the job gets translated into code.
When you don’t ask the model what to do at every single step, your dependency on the model becomes lower.
That means smaller models have the opportunity to produce equivalent performance for this type of work.
Computer use is a category that shows up a lot in non-technical corporate functions like finance, marketing, and GTM teams.
They go to different websites and extract information.
It’s not a small category. I would say 80% of what we do on a computer is actually looking at a screen and clicking things.
As a founder, you have to balance the tools.
Do you really need your GTM team to use the most expensive frontier model to click around LinkedIn or go to websites and extract information?
You don’t have to. You don’t need a PhD to do that kind of work.
It’s related to how you manage a team: put the right person in the right position, and put the right agent on the right work.
For computer use, we have an opportunity to drop costs dramatically. That’s technology you can already use to maximize productivity in non-technical domains.
For coding agents, I still think it’s important for engineers to use the best model because you’re doing content creation.
You want the agent to be smart in terms of interpreting your intention, getting feedback, and revising the code.
That’s an area where it’s currently very hard to say, “Let’s just use a worse model and the team will still have the best performance.”
My strategy is to make sure the team has all the tools available to them.
That space is moving very fast. Today we have one model; tomorrow we may have a better model.
As a startup, you want to move fast, and one way you move fast is by using the best technology available.
But I think the part most people overlook is that a majority of work doesn’t actually need the best model.
We have a solution for those workloads, and people should consider reducing their costs there.
Grace Shao (33:50)
That’s a very clear way of putting it, and I really appreciate that.
How do you view the way frontier labs are essentially incentivizing this race toward token maxing?
Over the last couple of months, we’ve also seen a lot of open-weight models push token costs down.
To your earlier point, that really changes the competitive landscape for agent companies like yours, which can be big consumers of tokens.
How do you view the incentives of open-weight models versus frontier models right now?
The frontier labs are clearly still chasing token maxing and more expensive tokens, while the open-weight models can continue compressing costs.
How do you see that dynamic playing out?
Ang Li (34:35)
Suppose you are Sam Altman or Dario. What would you do? Would you give your models away for free?
If I were managing a frontier lab, I would feel like I had no choice.
Their valuations are very high, and those valuations need revenue to justify them.
How do they get revenue?
People use their models, and they charge for token usage.
So you have two choices. One is to increase your price. The second is to increase usage.
That’s basically what’s happening.
People have been saying frontier-model prices will drop over time. But over the last few years, for the highest-end models, you still see premium pricing.
The most intelligent tokens can become more expensive.
Grace Shao (35:27)
They justify the premium.
Ang Li (35:29)
Exactly.
Then you have token maxing.
Those dynamics make sense in the current industry because these companies have extremely high valuations, they’re competing with each other, and they have to raise money and generate revenue to make the companies sustainable.
From our perspective, though, we’re not a frontier lab. We’re not a foundation-model company.
What we want is to serve normal people.
We want people to have more affordable and accessible options, so that everyone has the opportunity to automate their work.
We don’t want only rich people to have this opportunity.
We want to bring this kind of luxury to everyone.
Even for very tedious work, they should be able to automate it so people can be freed.
In that scenario, we don’t have to do token maxing.
We’re doing the complete opposite. We try to minimize the number of tokens you use.
And we don’t always have to use a premium model because this type of work doesn’t require a Math Olympiad gold-medalist model.
An open-source model can work.
That’s exactly why I find this category so interesting. It has the opportunity to allow everyone to offload their work.
But if we’re talking about coding, that’s a different story.
Coding agents need the best models. That means you have to pay the premium. They are going to be expensive.
That’s also why the frontier labs are still focused so much on coding agents. It makes sense for them because that’s one of the fastest ways to drive revenue.
I wouldn’t say it’s a simple question. It’s a complex societal problem where you have to consider economics and how startups work.
After all that analysis, their behavior makes sense.
Grace Shao (37:18)
That makes sense.
Touching on coding agents, last year was really interesting. It felt like the year of the coding-agent wars. Everyone was in an arms race, and that’s still ongoing.
Coding agents were arguably the first major breakout use case for agents.
And now we’re seeing more and more companies move into computer-use agents. Manus was one of the earlier examples.
Why do you think the market is mature enough now to actually consider computer-use agents as the next big area of competition and focus?
Ang Li (37:57)
Because there’s nothing else remaining.
I used to tell people: when the frontier labs start competing on computers, that means we’re getting close to AGI.
Grace Shao (38:09)
But how would you define AGI?
AGI feels vague, right? Everyone gives a different version.
If we’re really so close to AGI, it doesn’t feel like the world is suddenly going into doom, all our jobs are lost, we’re irrelevant, machines are taking over.
It doesn’t feel like that, which is the narrative Silicon Valley has been telling.
How do you define AGI?
Ang Li (38:32)
My definition is basically this: for my whole workday, I don’t need to sit in front of the computer. I just talk to my agents through my phone, and they do my job.
That’s it. It’s pretty simple.
I wouldn’t worry too much about humans losing their jobs.
You still need humans to sign off.
You still need someone to be responsible for the outcome. You can’t let the agent itself be responsible.
Grace Shao (38:56)
But then would your value still be as high?
If your job becomes so easy — and your job technically isn’t supposed to be easy as a founder or technical person — but it becomes much easier, are you still worth what you used to be worth?
Does your market value drop?
Ang Li (39:11)
You’ll focus much more on high-level strategy.
Even what kind of prompts you give the agents becomes important.
That’s one interesting thing about coding agents.
People say, “Okay, agents can write code.”
But if you tweak your prompt a little bit, the outcome can be very different.
If you understand the algorithm behind a coding agent, one of the first things it does when you send a prompt is extract keywords and search through your computer.
If you directly give it the right keywords, it becomes massively more efficient for the agent to do the work.
If you don’t give it the right keywords, or you use language that doesn’t appear anywhere in your files, it becomes less efficient.
So there are still subtle differences in what kind of prompts you give agents, what kind of decisions you make, and what kind of problems you choose to solve.
Those things become more important.
It’s kind of like the product manager’s job.
People keep saying product managers won’t be needed anymore.
But actually, that kind of strategy becomes extremely important once you remove all the other work from the process.
The good part is that this kind of work doesn’t require you to sit in an office anymore. It doesn’t require you to give up your physical freedom.
Grace Shao (40:29)
So should companies be training employees on how to prompt, essentially?
Is that going to become part of training — how to make you more efficient at your job?
Ang Li (40:39)
Yeah.
This kind of training is essentially what teachers have always tried to teach students in school: ask the right question.
I did my PhD at Maryland, and my advisor was a very senior person.
The biggest thing I learned from my PhD advisor was to ask the right question. Find the right problem to solve.
I spent five years constantly asking myself, “How do I find the right problem to solve?”
It’s an extremely hard problem.
I still remember him telling me, “If you find the right problem, 50% of the problem is already solved.”
Once you can write a problem statement clearly, 50% of the job is already done.
The remaining 50% is relatively easy because you follow that problem statement and search for results.
That’s happening in the AI space right now.
If humans have the capability to ask the right question, 50% is done. Agents can handle a lot of the remainder.
The reality is most of us aren’t trained this way.
Most education today trains people to solve problems.
Solve math problems. Take exams where somebody gives you a problem and asks, “Can you solve it?”
There’s no exam that asks, “Can you come up with an important problem and then solve it?”
I feel like the whole education system will evolve in that direction, pushing everyone to think, “What’s the right problem?”
I only have, I don’t know, 60 years of my life. What kind of problem is important enough for me to solve during my lifetime so I can make the maximum contribution to society?
Most people don’t have the opportunity to think about that because they are given homework every day at school, and then they’re given homework every day at work even after they become adults.
Now more and more people are starting to think: what should I prompt the agent to do?
Suppose I come up with an interesting prompt and it creates an interesting app. That’s the excitement people are getting from the current technology.
I view this as a positive change for society.
This could potentially take society’s creativity and innovation to the next level with these agent tools helping everyone.
But the important part is: how do we have a solution, an agent, that’s ready for everyone to use?
Not just people already in this camp. Not just people in Silicon Valley.
Can we let people running car dealerships, accountants in family businesses, dentists, solopreneurs, a five-person dental practice — can all these people use the technology and become maximally productive?
Then they can start asking bigger questions about their lives: what kind of thing can I do to make my life more meaningful to society?
Grace Shao (43:30)
That’s a very interesting way of putting it.
It makes me think that you’re essentially creating a tool that enables the average person to become a high-agency person.
Silicon Valley loves talking about high agency, but high agency, like you said, is also kind of a luxury.
Most people have agency. It’s just that you’re so bogged down by day-to-day mundane tasks, duties, and the work you need to do to earn wages that you can’t actually act on that agency.
If you have someone executing a lot of that tedious work for you, you suddenly have the mental capacity to put your energy elsewhere.
I really appreciate that.
I want to ask you another question.
Earlier this year, we saw the “SaaSpocalypse,” and that was quite wild. It was a wild ride for the market.
More recently, people have started saying that reaction wasn’t very sensible.
SaaS companies have existed for decades because there’s industry know-how, workflows, processes, and systems in place. These things can’t necessarily be replaced overnight by something vibe-coded.
However, what you’re saying is that the agents you’re building can actually operate these SaaS products on the desktop.
How do you view SaaS companies going forward?
Over the last six to eight months, we’ve definitely seen a bit of a flip-flop.
Now people are saying, “Actually, ServiceNow cannot be easily disrupted. My God, how could the market have reacted that way?”
What’s your sensible take here?
Ang Li (45:09)
I feel like the industry has a pattern of viewing something as just one thing.
I always say that when you look at something, there are usually two angles.
When we look at SaaS, we also have two categories.
The first category of SaaS companies stores the data. They are the systems of record.
They record things in their own databases and serve that information to users.
Another set of SaaS companies doesn’t really store the core data. They build a portal on top of other systems.
So there are two types of SaaS companies.
My principle is that infrastructure tends to stay.
Over the past 50 years, infrastructure doesn’t just disappear. Humans are really good at building layer upon layer of infrastructure.
The first set of companies, the ones that have the data, are infrastructure.
Data is kind of like electricity in the digital world.
The data is important because it powers further innovation.
For example, Salesforce has the data.
That’s important.
Those companies won’t simply disappear because they form part of the infrastructure.
Agents can sit on top of these systems of record.
Agents can replace a lot of the human-made portals in between because now everyone can create a portal with an agent, in real time.
You don’t necessarily need a company to spend 10 years building a portal and then sell it to customers.
You can have an agent build a portal on top of the system of record in a day.
So that second type of SaaS company may face existential risk because it’s competing with the agent layer.
At least half of them are fine. There’s nothing to worry about.
This also goes back to the question people always ask: will GUIs still be there? Why not rebuild everything around APIs?
We should view it partly as an infrastructure problem.
If software and its GUI have existed for 20 or 40 years, we should probably view that as infrastructure.
Then you can build agents on top of it to modernize that infrastructure.
But if you have a SaaS startup that built a portal used by a small fraction of users, and the portal has only existed for three years, it may not be strong enough to become infrastructure.
If it isn’t strong enough to be part of the infrastructure, then it becomes easier for a company like ServiceNow or Salesforce to say, “Okay, I’ll build an API for that.”
Then the agent can connect directly to the underlying system, and your portal may no longer be useful.
We really have to identify what is truly infrastructure and what isn’t.
Even within GUIs, there are two parts.
Long-lasting legacy software that is difficult to move away from has already become infrastructure.
But modern software produced by many Silicon Valley startups hasn’t necessarily achieved that status yet.
That kind of software can potentially be removed by a system-of-record company producing an API and connecting directly with agents.
Grace Shao (48:46)
I see. That makes sense.
I just want to ask you two questions I always ask everyone.
One is: what do you think people really misunderstand about your industry? In this case, CUAs.
And the other one I’m going to throw to you now so you can think about it: what is the differentiated view you hold?
It could even be something non-AI-related, if you feel really strongly that the Earth is flat or something.
Ang Li (49:08)
I think on the first question, I’m always being misunderstood because people keep telling me the future will be all APIs.
I hope the future is all APIs, but I think it’s actually impossible for society to become entirely API-based because APIs are not transparent.
It’s hard for you to see what’s going on.
GUIs are more transparent.
You can see exactly what the agent is doing.
I actually feel safer when I look at a screen and I can see, “Okay, the agent is moving the mouse, clicking on a button.”
I appreciate having the opportunity to go in and stop it if I see something going wrong.
I also appreciate the fact that it isn’t necessarily too fast.
If the entire digital world becomes 100x faster than human processing speed, I don’t think that’s necessarily a good thing for us.
It’s nice that everything becomes fast, but you don’t want humans to become overwhelmed by intelligent systems.
That’s one of the things frontier labs are dealing with.
Humans have a limitation on processing speed, so we want to maintain some kind of balance.
That’s why I feel like computer use is actually a relatively safe road for society to move down.
It operates around human speed. Sometimes it’s even slower than a human.
Grace Shao (50:35)
That’s very interesting.
Ang Li (50:37)
Yeah.
And we won’t be spamming the whole internet because it’s slower.
Worst case, I’m just performing a task like a human. Why is that necessarily a bad thing?
People worry about spam, fraud, and security because you could have something processing at 100 times the speed of a normal human.
That can overwhelm the entire infrastructure of the digital world.
That’s why I feel like working on computers isn’t just a technology problem.
It’s also about asking: what’s the best way to make sure AI is safe, manageable, and transparent while still solving real problems and freeing people’s time?
These views didn’t all come from day one.
Over the course of developing the technology and talking to customers, we gradually realized that while frontier labs worry about their agents being unsafe, we’re actually pretty confident deploying this into the real world.
We think it can benefit people without causing damage because people can always look at what it’s doing, and they have time to react if something goes wrong.
Grace Shao (51:50)
That’s really interesting.
Your thoughts are probably also evolving as these scenarios play out in real life.
It’s interesting that you’re saying even what looks like a disadvantage — the latency, the lag, the controlled environment — can actually become an advantage in this scenario.
Ang Li (52:08)
An advantage, yes.
The frontier labs are now saying, “Okay, maybe we should pause. We should slow things down.”
For us, we’re basically maintaining this speed already, and it isn’t creating those kinds of problems.
Grace Shao (52:24)
That makes a lot of sense.
All right, last question. What is one differentiated view you hold? Something wild or non-consensus?
Ang Li (52:32)
I’m not sure if this is really a differentiated view.
My view is that in AI development there are two camps.
A lot of people are focused on the first camp. We’re in the second.
Everything is ultimately about how to do human work.
The first camp is trying to do more and more high-end work, gradually moving towards scientists’ jobs.
Initially, everyone thought AI was going to replace all human work, and a lot of blue-collar workers would lose their job security.
But the reality is that some of the easiest jobs to replace may actually be scientific jobs. Even AI researchers’ jobs.
That’s an interesting pattern.
When I talk to my friends — we used to be researchers training models — I ask researchers at big labs, “Do you think going into a portal and clicking around is easier to automate, or replacing your model-training pipeline is easier?”
Most people will say replacing the model-training pipeline is much easier because the whole procedure is so routine.
There’s already a playbook.
Suppose you want to train a model. There’s already a playbook. You follow the playbook and do it every day.
It’s actually a very tedious job.
I used to be on the hiring committee at DeepMind, and we would hire machine-learning engineers into the company.
Everyone was so excited. They thought, “DeepMind is exciting. It’s a frontier AI lab. If I go there, I must be doing something incredibly exciting.”
That was before they joined.
After they joined, a lot of people realized, “Why am I always cleaning dirty data? Why am I always doing tedious engineering work to build around the system, maintain data quality, and feed it into the model? Am I supposed to be training models? Am I supposed to be changing the model architecture?”
But in reality, for many AI researchers, you spend less than 5% of your time actually looking at the model.
The majority of your time is spent on tedious things like data cleaning, data labeling, organizing data, and moving files around.
That’s the interesting part.
When I started working on computer use, initially I saw it as a challenging problem that nobody else was working on.
Over time, I realized it might actually be one of the hardest problems, even compared with these so-called high-end jobs.
If you look at the frontier labs’ strategies, they are moving toward high-end work: having AI write code, having AI train models.
Basically, they’re trying to automate more and more of an AI researcher’s job.
We’re trying to come from the other direction.
We’re looking at all of this repetitive work and asking: if it’s so repetitive, why is it actually harder to automate than an AI researcher’s job?
The reason is that most of the AI researcher’s work is already handled through code.
When you handle something through code, the data is structured.
But when you look at a screen, the screen is extremely unstructured.
It’s similar to understanding video content. You have a real human in the real world, and understanding that kind of environment is a much harder problem than solving a coding problem.
Grace Shao (56:03)
That’s really, really insightful. Thank you so much for your time.
I just want to end on one thing you’re saying, which is basically that nobody’s job is as glamorous as it looks from the outside.
It reminded me of when I still worked in broadcast TV.
People would say, “You must just be talking to four or five Fortune 500 CEOs and looking pretty on TV.”
No.
Most of the time, we were squatting in a corner waiting for the guest to come out, eating takeout — or not getting to eat or go to the bathroom for 10 hours — and just waiting around.
We were waking up at three in the morning, doing research and makeup at the same time.
Nothing is as glamorous as it looks from the outside.
So much of the work is actually preparation.
Ang Li (56:39)
Exactly. Yeah, exactly.
It’s like founders.
Grace Shao (56:43)
Thank you so much for your time today.
Ang Li (56:46)
Thank you so much. Nice chatting with you.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



Get full access to AI Proem at aiproem.substack.com/subscribe

Hello everyone, I've been on the road, so I'll be delaying this week’s written post and next week’s podcast episode. Apologies in advance.
In this episode, I’m joined by Asia Bureau Chief Jing Yang and Senior Reporter Juro Osawa from The Information to unpack DeepSeek’s surprising shift toward external capital, its unique investor structure, and what its rapid revenue growth reveals about the economics of AI. We also explore why China’s AI labs remain fiercely competitive despite a far smaller capital market than the US.
The conversation moves from DeepSeek and Huawei to accusations of distillation, ByteDance, Alibaba, and Tencent, and the growing race to turn AI models into real businesses. We also look at China’s emerging advantage in robotics, from its dense hardware supply chain to the vast amounts of real-world data being generated through deployment.
Finally, we discuss the next frontier: world models. As the race moves beyond language models, can China’s hardware and data advantages translate into an edge in embodied AI? And in a provocative final take, Jing argues that Chinese frontier models may never fully overtake their US counterparts.
The AI Proem Podcast is part of the AI Proem newsletter, which has ~13k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.
Chapters
00:52 The Rise of Chinese AI Models
04:06 DeepSeek’s Unique Position in the Market
12:31 Capital Structure and Investor Dynamics
16:12 The Landscape of Chinese AI Labs
19:17 DeepSeek and Huawei: A Strategic Partnership
25:43 The Competitive Landscape of AI Labs
33:51 Challenges in the Chinese Capital Market
36:44 ByteDance’s Unique Approach to Model Training
41:32 The Future of BAT Companies in AI
48:22 China’s Robotics Landscape and Advantages
53:51 Impact of US Regulations on Robotics
54:43 Unitree’s IPO and Market Dynamics
Grace Shao (00:00)
Hey Jing, hey Juro thank you so much for joining AI Proem today.
Juro (00:03)
Hey
Jing Yang (00:03)
Yes.
Grace Shao (00:04)
to start, you know, let’s start with Ox alpha. That was such a teaser. It turns out to be ZAI again, but I think it was very much expected by people who do watch space closely. It was no surprise in that sense. But I think what’s shocked a lot of people is that, you know, the inference was running on Chinese domestic ships and potentially GLM five point three flash.
The price, it’s priced at about like one fortieth of Opus 4.8. That’s quite significant of a gap, right? Tell us about what you think about that, the implication on these continued Chinese open weight models that are coming out strong but cheap, and how that’s impacting US frontier models.
Juro (00:43)
Yeah, maybe I can start on this one, but I think those flash models are coming out, like Deep Seek had the V4 flash, which is a smaller size one, and that became very popular. And then this one comes out. Alibaba just had a three point eight flash as well. So those are smaller size, you know, lower cost models that can still handle a lot of like AI Asian type of tasks really well. So
know, this is like a sort of sweet spot for demand, right? So a lot of Chinese companies with open source models are coming out. So that’s one, you know, one thing about this, you know, that’s part of that broader trend. But I think as you pointed out, the chip part, you know, it kind of shows how far the Chinese chips have come in terms of inference, right? And
They have become more capable, especially Huawei chips, being used, you know, more and more for inference. And you know, Deep Seek came out with V4 and you know it was also optimized for you know to run on Huawei chips as well. And we’re gonna see more and more of this for sure. But I mean to what to keep in mind that when it comes to training, it’s not the same story yet, not quite yet. So, you know, a lot of Chinese companies are using, still using the most advanced
NVIDIA chips and trying to gain access to that and which we also wrote about recently.
Jing Yang (02:03)
Yeah, I mean the only thing I I’ll add to that is I think the bigger picture is that there is a shortage of inference trips in both the US and China, but in China the shortage is much more runs much deeper and you know
A part of that is because of government policies, right? We still have not seen H two hundred actually officially being allowed to export into China. So the right course for all the major leading Chinese AI labs is they have to adapt the inference to domestic trips and to you know this concept of homogeneous compute, which has been discussed in Silicon Valley, but as well as in China.
And then the hemogeneous part in the Chinese context is very different. It’s about being able to run your inference on a combination of NVIDIA and and and say five different other Chinese GPUs. Right. So we saw that Kimi K3 had to suspend our new customer subscription shortly after it was released, and that was directly a result of not having enough inference chips.
to to to you know to to meet the surging demand.
Grace Shao (03:14)
I think Kimi wasn’t the only one that faced this kind of issue. And Deep Seek and ZAI throughout last year, I think, had to like, you know, manage their demand as well. But I wanna bring the conversation to Deep Seek. You know, you guys broke the story on well, actually Juro just broke the story on Deep Seek’s revenue. And I believe Jing Yu wrote the story about Deep Seek’s fundraising. It’s quite fascinating because DeepSeek, quite secretive, you know, their investor letter or investor conversation was leaked.
Apparently the founder was not happy that it was leaked, But, you know, they are fundraising now. And that’s shocked a lot of people because for the longest time they didn’t want to take any external capital. Jing, do you want to take that first and just talk about, you know, their capital structure, their fundraising, how unique it was, and then maybe we’ll pass it to Juro to talk about, you know, his recent findings about the money they’re making.
Jing Yang (04:03)
Sure. Yeah, so both Juro and I and our colleague Qianer as well, we’ve been reporting Deep Seek very closely since the beginning of last year. So I think one thing I like to sort of take the credit for is that I think most media allies sort of moved on after the initial buzzy period around Deep Seek early last year, but we stayed. I think we immediately recognize that this company is gonna be a very unique
like sort of existence in China AI landscape for the time being. and we reported, I mean I was just as shocked as anyone when we got a tip when we had our very first fundraising story. Because knowing the company the way we knew it for the last eighteen months, we were I don’t know if you were about you, but I was quite shocked. I think I edited it, I wrote that story in like huge disbelief. Like it’s a funny moment for our
journalism because a lot of times you kinda expect things will happen in a certain way or the direction of travel, at least you have a sentence on the post, but this one I think it completely surprised me. And then so in the two months that we’ve been chasing every step of the way of the first ever fundraising, one question that always lingered in my head is what
Prompted this change of this dramatic change of attitude toward external money. So eventually we were able to do a sort of the deeper story in which we revealed that it was Anthropic’s mythos preview that contributed a lot to
CEO Liang Wenfeng thinking, because I think there was a period of time, if you remember, like in the second half of last year, people were in AI research community, people were doubting or casting skepticism on whether the scaling law still exists, if if it’s still going to yield kind of you know progress. and then I think mythos showed that scaling law still exists. And then that’s when Liang realized that okay,
even if we have done innovation when it comes to you know in cr improving model efficiency. To really get to the next level, like Mythos demonstrated, we need to fully embrace the competition and we fully in not the competition, but fully embrace the game of, you know, you know, gathering resources. We need a lot more data and a lot more compute and therefore you need money. And and it’s a
money at a scale at a magnitude that Leon himself cannot fully fund anymore. That’s essentially the reason. And I think the broader takeaway while having covered that story is it is kinda like, you know, how the the peer pressure and the the the competition really makes it very difficult for any any lab
to just you know be stay on the sidelines because you know up until April when DeepSeek D came out with the first funding pitch, they were I think I believe the only holdout right around the world’s major AL labs to not have done any funding, fundraising.
So that’s quite striking and I’d like to keep reminding people that I’m not passing judgment, whether it’s a good thing to join this arms race, but just that, you know, when the last holder had to succumb to the broader pressure, it tells us something.
Grace Shao (07:08)
It’s
just a very, very capital intensive game that they’re playing. but I wanna kinda throw this to Juro then. Tell us about the recent story you just broke.
Juro (07:17)
Yeah, so we wrote about their finances. So their revenue you know for the first seven months of this year was about seventy million dollars. and that’s you know seems very small still, but that’s still you know, like about ten x compared to all of last year. So just considering how this company last year, you know, seemed like you know they had no interest in really making money. And so this year they’re just really getting started.
Right. And they’re starting to have this growth. And looks like a lot of the revenue also came, you know, quite recently as well, in the past few months. And so even though the number is small, also another remarkable thing is that their gross margin is quite solid, so forty four forty four point six percent. And if you look at their API sales for models, that’s like eighty three percent. And so they’re maintaining this like a very high
quite high margin, even though you know what they charge is you know they’re charging very low prices. And so this also shows what they’ve been doing to kind of lower the running cost, running costs of their models, right? And so a lot of those numbers are telling us, you know, kind of where they are. But I think this is important because they’re raising money and even you know talking about an IPO as well. So
No, investors will be looking at those, you know, this progress very, very closely.
Grace Shao (08:43)
I think that’s really interesting.
Jing Yang (08:43)
And and Grace you asked earlier
about the capital structure. I I’ll just
Grace Shao (08:48)
Mm mm.
Jing Yang (08:48)
briefly talk about this because I think it’s also quite important. so
Bear in mind that I think between Juro and I, we have probably 15 to 20 years of recovering venture capital as a journalist. And this is a unique deal for both of us. The way I like to talk about it is that if you categorize the types of investors in deep sea first around, I would categorize as four types. For number one is Leon himself. U number two is the sort of strategic, you know, in you know, the tens and the end of the net, right? And say it’s all. And then third type is the VC firms. And then the third the fourth.
type is the national air fund. so we don’t care, you know, Leon, he’s his money, he is he’s putting, you know, you know, in he’s putting his money in his in his own company. We don’t care what he used he he does with it. But for this following three types of investors, only the national air fund can invest in the SIC corporate entity directly. And then the other two types of investors have to input have to wire their money into a SPV structure.
like a LP structure that’s actually managed by Leon himself. And as part of that arrangement, they also do not have voting shares. They have to essentially make Leon the proxy for their votes. and they also have to agree to lock up their shares for five years. I guess with the exception of you know, events like an IPO. This is all very unusual, right? Because you are putting money into
not into the company or funding. And well, you are putting mu money into the controlling shareholder of the company are funding and at the same time you are giving up on any voting rights. and the reason for that sort of rigorous or strange structure is because as we reported that Leon wants to make sure that he only attracted in you know investors who are there for the long run, not for a quick exit. he is I guess in today’s popular
speech in Silicon Valley, a very AGI appealed. So he wants to keep Deep Seek open source and he does not like commercialization or generating revenue despite as Juro you know mentioned that they generated a pretty quick and revenue growth but that’s not their goal, right? so you can debate whether the end justifies the Minx.
but this is the structure that they came up with. And I know that maybe maybe a lot of us would tend to have an impression that while this is such a hot company and when they finally come to the market with the fundraising obviously investor would like to would just like, you know,
I’m swarming and then regardless of the terms, but actually that’s not the case. I know definitely investors who passed off on the deal exactly because of the structure, they feel like they could not accept those terms. and I think Deep Seek has actually taken that feedback because in the current, you know, the second funding round, they actually are loosening some of those requirements that were in the first round.
Grace Shao (11:39)
That’s
super interesting because I think, okay, just let me process both what both of you said. On one half, what Jing said is that he still wants control of the company and like he doesn’t want to run it like a commercial business, right? Like it’s a very unique setup. And then on the other hand, there’s a clear sentiment change or at least a strategic shift that is being pushed by the fact that he has to get money because everyone else has money. And if he doesn’t have the money, he can’t do the RD that is required, right? And then obviously the IPO that’s imminent, or at least, you know, in the pipeline. Now
I do want to ask one question. You know, he’s openly talked about not wanting to frankly make money for the sake of making money, but he said that I want to make money, but that use that money for continued RD. He’s kind of openly talked about, but even then, the margins, like Juro said, are still extremely high. So what does that say about the other labs?
Jing Yang (12:29)
Maybe I’ll I’ll try to take a stab and then
I just point out as we reported that they have this really high API sales margin, like 80% plus, because there is actually real renovation behind it. The industry term is that they have a very strong infar team. But basically what that means is that they have managed to bring down the inference cost of their models so that for the same number of tasks or to tokens consumed by you know, you you you need it, you need less chips.
fewer chips. and then whether that’s the way to go to get a higher margin. For example, Juro Feel determined by like, you know, we saw Minimax results just recently and then they have a much lower level margin for example, right? So
Juro (13:14)
Yeah, I think definitely Deep Seek, people have written about this too, but some researchers have pointed out that their, you know, innovation is in how they run their AI, you know, with like just chip requirements, are very low. Like memory chips, especially, a lot of companies are, you know, dealing with that cost right now. And so yeah, that’s where Deep Seek seems to be able to make a difference. Yeah.
Jing Yang (13:40)
Yeah, so so I think I’m trying to sort of like try no
channel my Wenfeng here not that I’ve never ever met him or anything but based on enough that I’ve heard about from people who do know him right he believes that AI should benefit all any advancement in technology and AI should benefit all. And then to make it benefit all the first thing you need is to make it cheap, make it affordable. And then and then therefore you need to first make the deployment of AI cheap, cheaper and more efficient. That’s why they have this, you know improvements on infra.
And then as a result, as long as you have something that’s both good and cheap, then obviously you are gonna generate a lot of revenue, right? Right, and profit eventually. I think for people who know him, they would say this is the train of logic. that not that it’s this high margin shows the company is so profit driven, but
When you are pursuing the greater good, money will naturally follow.
Grace Shao (14:40)
No, I think that’s definitely something you’re hearing more and more coming from the AI builders. Like I think it was Neil Mova that recently we just saw Patrick Shaughnessy. The goal is to make AI as cheap as possible. And this ‘cause there’s definitely a sentiment shift from like even a year ago where the narrative is really about like how much money can we make with AI or like, do we pay premium for intelligence, et cetera.
Let me bring the conversation back to the Chinese labs. like you just mentioned Jing, you covered Minimax as well, obviously Moonshot, Zai these are some of the leading labs in China. What’s their current structure? there seems to be a lot of them. Should we be expecting some consolidation? I guess this is two two questions separately, but just kind of give us a high level landscape breakdown.
Jing Yang (15:20)
Yeah, I’m happy to do a micro thing and then I’ll let Juro talk about the individual labs since he knows them better than me. It’s just something that I’ve been actually discussing with my own, you know, contents and investors a lot these days. If you look at in the US, we have it’s been consolidated to like only just two to three major labs left. And in China we still have, depending on who you include or not include the list general, you have about six to seven or seven to eight.
AI model makers that are still firmly fully in the race and any kind of consolidation is not on the cards anytime soon. And this is sort of like a characteristic of the Chinese economy and the Chinese market. If you look at EV, how many years has the EV battle been happening?
And how many companies are still in the race, right? So I think there are several structural reasons. Number one is that you know, the capital markets in China are highly regulated. It’s very difficult to do once you are already a public company. It’s very difficult for a public company to acquire another public company. Or or or in an and I transaction, if any either party is public, the transaction will become
a lot harder. and then we are still sort of we’re just a few years in the aftermath of the antitrust crackdown, right? I think so that makes a lot of the tech incumbents, you know, the Alibaba and the Bidowns of the world also a bit hesitant about that. And then number two is is sort of fundamentally the the the both the private and the public markets in China lack
like the kind of breaths and depths l as in the US. And that made up that created and the lack of, you know, when you do not have enough capital, either in private or pa or public markets going around, that makes potential acquirers very hesitant or reluctant about paying a premium. Right? there were opportunities for there were discussions.
that say for example I can tell you that one of the major, you know, tech companies was seriously considering acquiring one of the smaller labs, that then by the
And then that kind of talks actually happened several times. It was involving different sort of combination of companies, about two to three years ago. And then none of that happened because even back then, the the established tech companies are not willing to pay what they consider a premium, right? that’s number two. and so I think
I think it’s gonna be really quite hard to see any real consolidation and I I think with if Deep Seek does go public and we’re also look if Deep Sea Moonshot and Stepfund, all three of them say go public next year, then it’s just gonna gonna make it even harder for any kind of consolidation to happen. And the world is gonna be in a place where companies continue to compete.
and then the fight to the competition would just have to drag on for years, for a lot more years.
Grace Shao (18:25)
Very
interesting take. Juro, do you have anything to add on that?
Juro (18:28)
Yeah, I mean, I guess it’s also quite difficult to imagine one of those founders working for another one of the founders. Or also, you know, the idea of even Alibaba or ByteDance or Tencent acquiring them because I mean they do also have their own labs and you know they brought in Tencent brought in Yao Xing Yu from OpenAI and you know ByteDance has the biggest AI team in China, so it doesn’t really have the rationale, you know, for those companies to just suddenly
you know, turn around to buy one of those, right? So
Jing Yang (19:01)
Yeah, the the third
reason actually, sorry, I forgot to mention, I gave you two before. The third one kinda touches on what Juro mentioned is talent. There’s just so much more talent in China. So it’s very easy for someone like Jiang Y Ming or or I don’t know, Polima or whoever, where they would think, Okay, if you don’t want to sell to me at a discount, then I’ll do this on my own. I’ll roll up my sleeves and do it on my own. I can’t hire people. So
Grace Shao (19:26)
Feel like, yes, I agree with you.
Jing Yang (19:27)
Yeah.
Grace Shao (19:27)
But then you look at you read the book, the Google Deep Mind story, right? It’s like no one thought Demise would go in and work for a big tech either, but eventually it happened. I mean, now that it’s clearly proven that it didn’t work out, but it was an interesting phase where I wonder if at one point like you just can’t keep on Juan right? Like there’s like you hit a limit of the Juan and then you just like you’re forced also to consolidate for resources for talent and for the like.
For the lack of compute out there. So if you believe in AGI, the some as some claim they do, and your goal is really to have the resources to pursue that scientific research and breakthrough, then isn’t it in some ways going to the company with the most resources the best outcome? I’ve heard people in the industry argue that, right? Like, I don’t know. What are your thoughts on that?
Jing Yang (20:14)
No, I think what you said is a very sensible, rational take. But the problem is for I think the three reasons I mentioned earlier in China, this kind of rational, you know, economy of scale, right? Economics one, right? Just somehow does not apply to China that people would rather trend than
Grace Shao (20:35)
They’re not rational. They’re not making rational decisions, is what you’re saying.
Juro (20:37)
Well I think there’s another
Another factor is that you know Alibaba for example has invested in so many of those AI startups, right? And some of them have gone public as well. And for them, because they have Alibaba Cloud, which has a big business with a lot of those AI labs as well. So, you know, like they would rent chips from Alibaba Cloud and you know use their infrastructure. So yeah, I mean for them to kind of decide to just buy them, I mean
when they can keep them as major customers and, you know, also hold a stake. So I guess that’s another logic for, you big cloud platforms, right?
Grace Shao (21:15)
Yeah, and they have exposure
Jing Yang (21:16)
And and the
Grace Shao (21:17)
anyway. Mm.
Jing Yang (21:18)
yeah, and then the last reason I w I would really I think also important is is the enterprise market is still
very massively underdeveloping China, which which has so far limited the the commercial, the revenue prospect of all these companies. And then when when you are projecting your revenue, that is only like 10X in the next five years instead of 100 X, then then of of course you are gonna be very you’re gonna be stingy when it comes to MA. Right. I think we can see the kind of consolidation that happened in the US. It’s it’s because they these companies actually you know can generate a lot of revenue from it.
enterprise market so they can afford to spend on MA.
Grace Shao (21:57)
Fair. Well, we’re all speculating. Let’s watch and see and what happens the next few years. Sometimes there’s a wild card thrown at us. I I do wanna ask you guys, give us some color on what happened between Deep Seek and Huawei. I mean, we started a conversation with Juro telling us about, you know, how GLM five point three Flash was tr you know, inference on domestic trips. But I think Deep Seek really took one for the team there and made the first step over. What was I guess an incentive? Was it really just because, you know
They wanted to show the ecosystem that this could be done. was there a commercial reason on the Huawei side? Did they approach Deep Seek? Was there a top-down mandate from Beijing? Like, how did that play out?
Jing Yang (22:35)
Yeah, I’ll take this one. so we and some other outlets reported throughout twenty twenty five that Deep Seek has been adapting to How Richards.
And then we’re trying to and just by the way, in order to make the inference you know adaptable to domestic chips, you have to basically reengineer your training process, right? Once you’ve done the training within video chips, then you have to then recreate that process to domestic chips. to like I’m butchering this, but to to oversimplify. and so I think
Us including many other observers, Chubash, John Warder, Elam, masses, just assumed that this is because Gypsy has been asked by the government that you now are a national champion. You have to take a lead in adapting to domestic semiconductors. and which is why you know when we reported, I think that was a story in June that we did.
That it was actually DeepSeek that took the initiative in adapting to Huawei chips. And Huawei didn’t and it wasn’t until DeepSeek spent some time tinkering with Huawei GPUs that the Huawei engineers got a wind of it. And then they approached DeepSeek and said, how can we support you? So why did DeepSeek do this?
The reason we report it is that as I said earlier, Leon is someone who believes in AGI and believes that AI should be inclusive. And so inclusion in here means you know you need to have a diversity of technologies in both AI and the semiconductor, right? He based on the leaked investor call, we can see that that’s the case, but bear in mind I I’d like to point out that we when we reported this the
the investor call map was not leaked, right? Had it not been leaked. So we reported that, you know, he believes that NVIDIA should not be, there shouldn’t be just one dominant GPU designer. There should not just be one dominant technology. And then and then Deep Seek wants to play its part in increasing that diversity. That’s why they did it. There’s nothing sort of forced or you know political behind it.
Grace Shao (24:35)
Yeah. Juro, so this question’s for you. I think that’s a really good context and understanding why DeepSeek did what DeepSeek did, but help us understand just who plays what role in the ecosystem right now, if you had to generalize in one or two sentences about each.
Juro (24:51)
I mean each AI company in China.
Grace Shao (24:53)
AI lab at this point. We’re talking about I guess just the labs. You can include the big tech if you want, but just like the let’s just a h eight companies that keep on pushing out models.
Juro (25:04)
So yeah, I think for Moonshot, you know, Kimi, we can say that they are trying very hard right now to be the sort of anthropic of China and really focusing on the you know coding, API sales and and especially they are you know really expanding globally, right? And so they try to show that they can, they are the ones that can really compete at the frontier level.
And the frontier coding kind of you know capabilities. So and they know that they can really build a big business like Anthropic did. So that’s the kind of path that they see, I think. And I think Jupu is you know ZAI, what they call it now. so they are trying to also you know focus on that kind of market, but they also have a you know kind of
much stronger domestic background with the Xinhua University, you know, researchers that this you know that studied it. So I mean they have been trying to kind of transition from the previous business focus was you know doing a lot of work for domestic companies including like state owned companies and you know helping them deploy their AI models. And but then they want to also become this more
More globalized, you know, also selling AI models, you know, and also like a little bit like Anthropic, you know. I mean, that’s the path that they a lot of those guys see because that is the path to revenue right now, right? And I think deep seek, as we discussed, you know, stands in a you know a little bit different position, interesting. I mean because they do you know, they do seem to kind of, you know, they do keep their prices very low.
And also not just chasing like Frontier in the same way that Kimi does, but you know, how their models can be also easily deployed, right? And by many, you know, I mean there are many different requirements from you know different kinds of users. And so they are going for, you know, like they may not necessarily be the most powerful, but you know, they are kind of trying to
like you know AI for everybody kind of approach right now as at least with the pricing they have, right? And I think they also do have a lot of, you know, do handle a lot of domestic demand as well. Like so, that’s the national champion aspect. And then the minimax I think they have chased you know both video and LLM. And I think recently they have
They have kind of made a comeback with the latest video model. But I think it’s still quite difficult to see and they do seem a little bit confused to me that in terms of deciding which direction. And because it’s very resource intensive to try to chase both markets because video market you’re competing against ByteDance, which has you know just infinite amount of money and resources. So but
You know, they’re definitely not out of the race. You know, they are, you know, trying to keep up. So yeah, that’s and and the big labs, you know, they try to do everything. And Alibaba, especially with the cloud, you know, they try to, you know, be in every segment of the market. And you can say that about byte dance, but maybe we can talk about byte dance later when we talk about distillation stuff. But yeah. Yeah.
Grace Shao (28:24)
I like how you’ve set
set the tone. We’re gonna be talking about distillation stuff later. All right.
Juro (28:28)
Yeah.
Grace Shao (28:29)
No, no, that’s really good context.
Jing Yang (28:29)
There there is a popular saying now in
The AI space in China is the, I don’t know if you have a smart translation for that into English, but DeepSeek Jansen.
So basically the Deep Seek has set the survival line for everybody else. Either you go for Solta, Solta, Solta, or you go cheap, cheap, cheap. and then but you cannot go cheaper than Deep Seek. So that’s the survival line. And I like to just one more thing, I like to sort of compare, you know, Kimi and Memex a little bit because I’m sure you guys all remember, right? Kimi, I think back in 24,
you know, actually spend quite a bit of an amount of money marketing, advertising for the chatbot, right? And then I think at some point we realized there’s no way that they could compete against Dobao and by Bay Dance. So then they switch to, you know, working on SOTA models. And whereas Minimax continues to be
You know, Juro, actually I wanna ask you, like ‘cause they continue to still be dabbling in everything. Yeah, I think I think yeah.
Juro (29:26)
So actually
On that point I guess Minimax has kind of moved a little bit away from consumer apps, especially something like if you remember talkie, like that was the early sort of hit.
Jing Yang (29:38)
I remember talking.
Juro (29:40)
But I think internally Yeah, yeah. But internally that’s that’s definitely not
Grace Shao (29:40)
Yes, and High Law. Like they had so many products. Sorry, go on.
Jing Yang (29:42)
Yes.
Juro (29:46)
That’s definitely not the priority now. I think over the past year, the priority has been, you know, really the models, right? And models and API. So I think that’s definitely and you know some would argue that Kimi has kind of moved ahead more quickly. and they had big success with K three. But essentially that’s the direction that you know, all these guys want to move in. So I’m I think Minimax is definitely trying to
move in the same direction as well.
Grace Shao (30:15)
So then my question is if every single lab is somewhat doing the same thing, like it kind of goes back to what we were talking about earlier, it’s just gonna be like a price for a never-ending duet, right? Then are we seeing any interesting application innovation right now you think that is being a bit undercovered or underappreciated? Maybe not coming out of these labs.
Jing Yang (30:36)
Juro, do you want to
Juro (30:37)
I think the application side battle for consumer apps is definitely, you know, very fierce. But we just happen to pay more attention, like I guess everybody’s been paying more attention to the SOTA models. But I think Dobao from ByteTance and Qwen and Alibaba’s app, I mean that competition continues, right? And yeah, it’s not like they don’t emphasize that. So yeah.
It’s just that the people I think have been talking more about something like Chimi K three, but that domestic competition for apps is definitely there and it’s gonna just keep going for sure.
Jing Yang (31:12)
I think there’s been you know increasing appreciation for model rappers. I I remember when Manners just became rebel viral in March last year, people were like, this is just another rapper, and there’s no yeah, and then there’s yeah, there’s no modes
Grace Shao (31:24)
It used to be a dirty word. Now people are like harnesses. Like
Jing Yang (31:29)
if you are just a rapper and now yeah, now people call it a harness. but I think that is sort of on the right.
Course, I think something that I would like to write about more that I have not been able to for various reasons is that actually after the Manners Wave and then the open call of friends in China, what we have seen is actually there’s a lot of flying under the radar Chinese startups that actually have built really smart harnesses targeted at what I call small business like small enterprise customers. and then there’s actual mode.
in doing that in offering that business and there’s actual demand and they are offering this to not just a male and Chinese customers, actually a lot of them offering them to like you know overseas customers. and imagine if you are a company that has a loyal employee size of like about 50 to 100 in like a non-tech, non-digital industry, you want to use AI, you don’t know how, you don’t even know where to start. It’s not possible for these kind of companies to go come to go on like I don’t know, open
Router and say, okay, I’m gonna use the one, two, three, four, these models for this different task, right? They don’t have that know-how. and this is why the kind of you know AI, I think they call it AI employees, right? This AI employee business, which essentially harnesses built on top of you know models, it actually are our take our gaining traction actually proven to be quite useful. so
So I think that’s something that gives people, including me, a little bit of hope and the optimism for the enterprise market in China in the future. Yeah.
Grace Shao (32:59)
Yeah,
a lot of very strong vertical products coming out. I wanna direct the attention back to Jing for a few questions on, you know, the capital market. So the secondary market in the US for A Labs obviously been incredibly active. But we’re seeing nothing like that in China. And valuation obviously has it’s like digits away in terms of like the gap. How do you view that? Why is that? Obviously, kinda touched on it or alluded to already, just in general, China’s
You know, Deep Seek and P space is not as vibrant, but is there other reasons behind this phenomenon?
Jing Yang (33:33)
When you say secondary, you mean like the secondary share sale market, not the public market, right? I think the number one reason still is what I mentioned earlier, there’s just not enough money, right? You know, I think the American LPs have, I think a big part, big chunk of the American money has left, right? And then that void still has not been fully replaced and may never be. and that has
Has really suppressed reven you know, funding, therefore valuation. but then if you actually look at let’s say you know I actually wrote a column several months ago back when Drupal, Z.I.A. and Minimas just went public. Even though that was around the time that ZIA was less than 100, I think their highest point it was about $100 billion US, right, in valuation. That was before that. It was lower than that. But still, if you look at the price to
cells multiples, they are a lot, lot higher than the open A and Anthropic. Anthropic Open A in my memory was around thirty X and
mini mass and z.ai are like a hundred or two hundred X. So I think just looking at the pure valuation numbers are kinda like not kinda misleading or not really useful. You need to look at the multiples. And if you look at the multiples actually the Chinese labs, the ones that have been published are a lot more expensive.
so I don’t think at first that, you know, it’s just because it’s the order of magnitude less, right? Fifty billion versus five hundred
Grace Shao (34:56)
That’s interesting.
Jing Yang (34:57)
billion. That does not mean that Chinese labs are cheaper. So we you can easily calculate based on
the numbers reported on Deep Second to get their PS modules. And then you can see it’s also very high, right? So and then you c if you look at the P and S and then it’s very high, not because the P is high, it’s because the S is too small.
Okay, that’s why the modules are a lot higher. the multiples are actually one order of magnitude higher than the US ones. And then that’s because the S is too small and the S is too small again, going back to my earlier point, I don’t want to repeat myself, but it’s because the enterprise market has not been developed. It’s just a lot more limited mm compared to the US.
Grace Shao (35:38)
No, that’s interesting. I think usually we just only read the headlines and just think Chinese models are very undervalued, but you provide a lot of context there. Juro, I I wanna go back to you. You wanna talk about distillation, right? Let’s talk about distillation.
Juro (35:52)
yeah, so
Yeah, one thing that kind of is interesting about Byte Dance is that you know we wrote about how Jiang Yi Min, the founder, you know, said in the internal meeting about how you know ByteDance is not going to rely on this and they haven’t. And so you know, unlike you know many other Chinese labs that you know have really relied on distillation to as a shortcut, right, in their and to improve their models.
And that I think this is one of the reasons is that you know f if they really want to compete at the frontier level and you know, distillation, you know, like putting distillation as you know as your using that as your main strategy is not gonna really get you there. So, you know, you do need to kind of really properly train your model using, you know, your own data and you know, the data that you can actually, you know.
use for you know really advanced training. And that’s really the path to really kind of come up with your own frontier models that really sort of match or even surpass US models. And so that seems to be part of the thinking. Also another reason we heard is that they also are you know aware that you know byte dance is under so much scrutiny already with TikTok in the US and
You know, distillation has become such a sensitive topic between the US and China, right? I mean the US has accused a lot of Chinese labs of distilling American models. So if Python did that, you know, what kind of you know, what kind of criticism they would get in the US, right? And and would TikTok, you know, face any, you know, more challenges because of that or so those concerns also were, you know, we heard
were behind that kind of approach. But yeah, this definitely makes them quite interesting because a lot of Chinese labs do, you know, when we talk to people, they may not say publicly, but they do acknowledge that yes, distillation does help. And so yes, I mean but I mean there are indications that other labs also, I mean, we are starting to see more progress that maybe cannot be explained, you know.
solely based on distillation, right? Like Kimi K3 achieving, you know, really you know, strong performance, right? So but yes, what ByteNAS is doing is quite interesting. And whether they can really get to the sort of frontline and you know really emerge as the you know leader that way is gonna be also very interesting.
Grace Shao (38:26)
Yeah, I think I think you like hit the nail with that. It’s like distillation and genuine innovation are not mutually exclusive. Like you can get the benefits of distillation but still innovate on top of a certain engineering techniques. One thing I also heard was that, you know, with distillation is you inherit the good but also the bad. And apparently Jiang Ming is a bit like sensitive or anal about the fact that potentially he can always still inherit the bad or the
values or whatever of you know a distilled model. I mean it obviously that story went viral domestically as well. I think a lot of people were like, Jiang is coming out and saying we’re like we we we are not gonna go distill. We’re gonna do it differently. However, the flip side of the argument’s like, well buddy, you’re not gonna distill but n your seat is not actually Soto or anywhere near the last few iterations, right? So
How do you view that? Do you think at this point for a company as big as Spite Dance and as as much pressure they’re faced with and as much capex it put into this, is it better to stick to the principles right now and hope or continue to work hard on potentially coming out with something completely original, completely innovative on their own? You know, they want to go for the best of the best in the world, or you know, the other kind of strategy, let’s not name names, but maybe other labs or certain big tech.
I would say we catch up first and at least then it helps with diffusion and then AI becomes a flywheel in our existing business and we can let this money churn and then help put more money into AI and et cetera. How do you view that?
Juro (40:04)
Well, I think it would be hard for them to make that shift now. Having s you know, after Emin said that in the meeting and we wrote about it and if they do make that shift, you know, we or somebody else is gonna probably write about that shift. So then then that would make them look, you know, pretty bad, I think, if they
Jing Yang (40:22)
There there
be
a massive off ramp for that to happen, I think.
Juro (40:26)
Yeah. Right.
Grace Shao (40:27)
It’s not the first day one of the big techs decided to change a strategy.
Juro (40:31)
Mm.
Grace Shao (40:31)
I love how you also refer to him as E Ming Juro. You guys must be best buddies. to my first
Jing Yang (40:35)
Yeah.
Grace Shao (40:36)
name bases. okay, let’s talk let’s talk about the BATs though. Let’s talk about
Where
you see the BATs right now. Juro, you’ve been covering them in terms of their businesses for a long time. You’ve covered the executives. You’ve done like, you know, extensive profiles of these some of these leaders. Where do you see these big tech going? Like what is their end game here? You kind of alluded to Baba, you know, really going in for cloud, but is that enough in the AI air?
you know, is Qwen still a priority? is embedding AI into commerce still very important? How important is really Yao Shen Yi? Is he really the kind of savior to Ten Sen, Huen Yuan, for supposedly coming out very soon? How do you view that? And then of course, seed and seed dance. I mean, everyone knows seed dance is good, everyone knows seed dance has a lot of data, but
They’re very secretive, very hush hush, right? and given that they’re not public, they’ve actually had the luxury to not have disclosed what they’re doing publicly. Can you give us your views on this? This is an open ended question. Just see however you want to take this.
Juro (41:40)
Yeah, so for Alibaba I think Qwen AI being really center and front is that’s pretty obvious. And also how much of their investment is going into it as well. So they have been kind of
spending more kind of ahead of their plan. Like they had a three year plan. And so and their CapEx, you know, was really increasing at a very I think 10 billion dollars for the was it the quarter recent quarter they reported and so yeah it’s it’s growing at a very fast pace and so it it’s very clear that it’s their priority. And obviously if you look at the revenue
still the biggest part does come from e-commerce. But there are very few questions that come up during earnings call about e-commerce anymore, right? So a lot of the questions about the future and also whatever they want to emphasize is really about, you know, Qwen and especially cloud, because that’s how you know the main platform for selling the models. So yeah, they even Alibaba’s trying to be a bit like anthropic, right? In that sense that
You know, we can sell a lot of models. And and plus they have the cloud, so they, you know, they think that we’re gonna sell everything, you know, the infrastructure. And so that has definitely become yeah the priority. And that shift is pretty clear. And e-commerce, I think they are saying that you know they are incorporating AI into e-commerce, and that’s definitely another big theme. But the future, the big part of it.
depends on how successful this you know AI model push is gonna be. Yeah.
And others ten cent. So yes we’ve we’ve written about them and you know Yao Xing Yu’s role and definitely I mean general view is that they their model has improved and I think you know because the perception before he joined was pretty, you know, kind of negative, right? And a lot of there are people who are even saying that, you know, they’re kind of not part of the race anymore, you know, like in terms of you know
the leading models, but I think they have you know come back. And what they do have is the huge you know platform for applications and you know consumers with WeChat. And we have written you know previously about the WeChat agent and that was a scoop earlier. But I think something like that, you know, they could still kind of make a huge impact by
you know, rolling out something because they have the ability to really, you know, engage their users, right? And that’s still very powerful. So we still can’t, I mean and no matter how this model race goes, Tencent will be very important. And and then I guess yeah byte dance of course yeah we talked about it but they
They are still the biggest AI lab in China and I think the amount of effort that goes into it, I mean they are trying to do everything as well. And they also just like Alibaba, they are really going for, you know, their cloud business, you know, selling models. That has become the biggest sort of emphasis for the cloud business. So previously, before this AI boom, people didn’t really talk about byte dance as a cloud player, right?
But now they do because of that. The model business, you know, that’s really elevated the position in that area too. So yeah, I think all three of them, I mean, are definitely very important. And AI is really the center of what they do now.
Grace Shao (45:16)
For sure.
Jing Yang (45:16)
Just have one thing too quickly to add. I think
But I’ve been pretty much looking forward to the release of the Xiao Wei, right? With the Witch Hat or the Wasting Agent. Not everyone
Grace Shao (45:25)
Did you try it?
Jing Yang (45:26)
I’ve s what do you think?
Grace Shao (45:27)
I tried the beta and it’s like okay,
It’s on beta. I tried it honestly, cause this is I think exactly to Juro’s point. Like it’s really convenient, so you can’t see it. Okay, there’s a glare. But basically, it’s really convenient. It’s at the top of your like B chat interface, so there’s a lot of functional adjacency. That sounds like it’s really easy to get there. You don’t have to log out to another app. But like the whole
experience it’s more like what do I need it for, you know, and how good is it? At least for our jobs I would assume, you know, or desktop jobs, AI is seen as a very, very strong research tool at this point, if you’re not doing it like if you’re not running your own agents. But for the day to day SAO it just feels like a supercharged search engine. And I don’t even know if it adds that much value, frankly, if I’m just like, like
I don’t know, when does this shop open? Like that’s when I think a consumer application, you would open up a consumer application. Do you know what I It’s not I’m gonna open up Sellway, be like, tell me about you know tree’s IPO and like what’s your assessment on this? Do you know what I mean? It’s like I feel like it’s not it’s kind of sounds good, but right now they haven’t found a really clear use case. And then obviously on top of that, Selway doesn’t even use Huen Yuan, they use their own model, which is this other wild piece of the poll.
Tencent strategy. Look, guys, it’s almost like an hour in, and I still have a lot of questions for you guys. I’m so sorry. I’m gonna like redirect the conversation. We spent a lot of time on labs. I want to ask you guys about the other side of the hype or the international interest right now, which is all about the world models, the robots, the neol labs in China. Juro, you recently wrote about this topic. I actually just spoke to Many Core CFO for this podcast like two weeks ago.
And it was very interesting to hear about, you know, a spatial, like a 3D data company or 3D intelligence company is now pivoting to spatial intelligence and thus building world models. It just seems like a lot of companies are in this space. It’s very crowded. Can you give us a high level thought an overview of China’s strongest advantage in the space, China’s current landscape, you know, in the hardware space as well as the software space?
Juro (47:30)
Yeah, sure. Yeah. So I think for robotics, China definitely has a very broad, you know, supply chain and covers almost all kinds of components, right? And like motors, sensors, you know, structural components, batteries, camera, you know, everything. And this also partly because there’s a big overlap between you know, supply chains for electric cars or drones or you know, other products, right? And
And then there are also like a huge concentration of those suppliers, like in you know, Shenzhen or you know, Shanghai, you know, Yanzi River Delta kind of areas. They have a lot of different suppliers all in one place. So like when I was talking to a robotics you know founder in Shanghai, the same person Jim had lunch with, I think, today, so he was telling telling me about how his team can just, you know, like take a DD or
you know, even ride a bike to, you know, many of their suppliers. They’re just in the neighborhood, right? So this obviously speeds up everything, right? So I think that’s the major advantage. And then the bottleneck I think a lot of people point out, but it is the software part. And I think a lot of robotics founders also agree that you know securing that kind of talent, I mean because
That same talent can go work somewhere else that pays more. So, you know, in China that talent is still kind of limited for those guys, whereas the hardware talent is so plentiful, right for this area.
Jing Yang (49:05)
but there’s
I want to add about China’s advantage in robotics. I think it’s very easy to just say it’s a supply advantage. I think there’s also actually there’s more to that.
I’m going to talk about data advantage.
We have not seen real AI powered, real intelligent robots or robot robotic models is exactly because the lack of data.
lack of spatial 3D environment manipulation data. Right? And so even though so that in China when you have you know so many pilots happening in the warehouses and the fa on the and on the factory floors, when these robots get deleted and get deployed in these pilots, they are gathering a lot of data that can be then used to improve the model. And this is something that is not happening.
I think with very few exceptions of maybe Tesla and a couple others. But in China, this is happening in every you know, major logistics you know, Korea company, warehouse company, and car EV makers. This is why you see CATL, company like CATO and JD and Metron have invested in so many robotic startups. And this will create this data flywheel.
That will eventually, hopefully, at least the people in the industry believe, right? That makes China maybe ahead in the breakthrough of the brain, the real intelligence of the robots. That’s something I think that’s less appreciated, but I should bring it.
Grace Shao (50:33)
Yeah
no, that’s really interesting. And think just kind of adding to that, what I’ve also been thinking a lot about is just like because like you said, Jing, there’s so many moving parts in this ecosystem and they’re all done in China, whether it’s data collection with the data fine-tuning or actually the understanding of the manufacturing of these robotic parts and all that. There’s also a lot of talent that’s in this space. That again is being underappreciated. I think people don’t realize that the hardware self-integration is actually the bottleneck for a lot of these products, it’s not really just the manufacturing.
Even some say you can manufacture these products like say in Vietnam or wherever. But like you know, I think Patrick McMickey wrote about it in his Apple book. It was a lot of it is just understanding how to hardware or software or like these more like a niche, like the one percent of the top hard blue collar actually is even very, very hard to replace and train up. And that’s been done through decades. So guys, let me ask you a relatively kind of sensitive question.
So we saw that DC’s been talking about banning robotic imports from China, but from all of us, I’m sure we’ve talked to a lot of robot companies in the US, like I would say like easily like 90, 90% of them have 90 to 95% of them have some kind of arm in China, be either supply chain or certain even some parts, right? So like if this ban really comes to force, what first of all, what’s driving that decision? Second of all, how practical can that be and what kind of impact?
Will it have American robots? Because American robots at this point are already priced significantly higher than these Chinese robots.
Juro (52:03)
Yeah, so what they cited when they made that announcement about the ban was the you know national security concerns, right? So the robots can, you know, gather data and for surveillance or you know, whether they could be controlled remotely from China or you know, that was the kind of concerns that were cited behind the decision. And I think the definitely the immediate impact is that a lot of US startups or
university labs by Unitry robots or you know other robots from China because you know they need to use them for software development or research, right? And so this means that they have to find it somewhere else, but it’s really hard to find, you know, anywhere else because most of today’s humanoids that are affordable and also available are really from Chinese companies. And I think that’s the most kind of obvious impact.
Grace Shao (52:58)
Right.
Jing Yang (52:59)
I I the one only thing I’ll add, I think the impact on the US robotics development would be that
the leading companies that I can produce at scale, you know, can assemble, like I should say, because you know, they can produce all the parts, right? That can assemble in the US at scale would be fine. And I think this regulation of this ban will, whether it’s intended or unintendedly, strengthen the leading players’ position. And then those who have not been able to assemble at scale will have an even harder time.
Grace Shao (53:30)
I see. Let’s talk about the Unitree IPO. Unitree at the time of recording was late August. Unitree just went public like a week ago. Jing, what are your thoughts on that? And I think you guys wrote something quite fascinating. To be honest, for people who are familiar with A Share, it was nothing too shocking, but I think you exactly pointed that out. You’re chuckling. Why don’t you tell us about it?
Jing Yang (53:51)
No, I mean, okay, that story was not something that I pitched. I was sort of asked to do a piece that sort of explains why Unitry’s first day like a debut popped so much at 416%. I did not think maybe I’m too close to this because I covered, you know, capital markets before. And so I’m like, okay, this is not
News. This is to me it’s just like another Wednesday. You know, I mean they went public on Wednesday, right? And and then
Grace Shao (54:16)
It’s just how Ashare works. It’s just how Ashare works, yeah.
Jing Yang (54:20)
yeah, and then and then then I had to then I realized okay, okay, actually maybe that’s exactly why I should write the piece. And so then my boss was my editor was initially not convinced. I told him I say, you know, this is because of the John Tin butt, the cap on the stock prices ceiling and floor.
that is only exempted on, you know, the first day or the first couple of days of IPO. And then he did not think that this was relevant. We’re talking about first day pop. I’m saying what are you talking about? Of course this is relevant. It’s because if you don’t a lot of people believe it, if you do not get in on the first day when there’s no cap on the stock movement, then you will not be able to get into the stocks for days in a row because though immediately the RP market opens, they’ll hit the ceiling.
Right? And then say, okay, then do you have data to back it up? I said sure. And then I went to Bookflow Data. And then there were a hundred and nineteen IPOs on the HR market last year. The mean of the first day share price was two hundred twenty-five percent. So let’s just put that in context. And then in the US is about twenty-five percent or so, right? So so this is why I think
for you know, for anyone who’s not familiar with the Asian market, this looks like really striking. But I think for people who are used to this it’s quite normal. And then do you know why people call you you must know this term in Mandarin called that sing, right? And then f like fight for new is what I sort of loosely translated. But why was it called that sing? Because actually in
This is a dialogue in s the you know, like southern part of China, like in Shanghai when China first had a stock market, right? So in the eighties. And the people would be there without digital training, right? People would be lining up on the street outside of the stock exchange, waiting for it to open so they can get in and then and then put place an actual paper, like a hand in their money and get the the the the the the stock order placed. so
So now that whole phenomenon still exists, it’s just not happening in the digital world. But because of that, you know, like sort of fighting tooth and nail, that sort of hardship we are willing to endure to just get into an IPO that people call it DASI. so that’s a big reason for why we see these POPs. The highest first day pop last year in Asia was 1211% and/or 12 times.
Grace Shao (56:45)
Which company
Was this?
Jing Yang (56:46)
that
That was a company called HAR being Big Bird Industrial or in Chinese Da Pong Yi. And it’s a company that does what they call high precision industrial cleaning. So they offer solu cleaning solutions to, you know, like machines that produce EV components. I mean you can debate
Grace Shao (57:03)
So they’re also part of the autonomous
robot kind of wave as well. They’re just not that sexy.
Jing Yang (57:08)
Yeah, but you you can debate
whether this is like a very high tech but but
Grace Shao (57:12)
Mm.
Jing Yang (57:13)
but definitely, you know, if you compare a company like that that had a twelve times first day pop and you and Uniture is like only a four point six, I was like, Okay, now I get it, right? So yeah, that’s my point.
Grace Shao (57:25)
No, that I
I thought that because I saw on your I think it was your WeChat moments. I was like, yes, Ding, this is like quite correct. Like and I think it’s a relevant context to people who are not familiar with Ashare.
Okay, guys, let’s talk about China robotics. I think because like Jing said, we’re so close to it. Sometimes I completely normalize like seeing robots on the streets in China. And like you have like cleaning robots, you have like hotel delivery robots, you have like and these things have been around for like I’d say at least three to five years. So when you look at the big trend right now with the robotics hype, and let’s just take a step back, go look beyond humanoids.
Where do you think the whole industry is going? Like what are some interesting use cases you’re seeing that’s really scaling robots? and that that is maybe being overlooked by the rest of the world.
Juro (58:10)
Yeah, I think humanoids, a lot of the applications they’re talking about, especially like industrial applications or even like a home household, you know, chores kind of thing, there’s still a lot of you know, hurdles, right? And but as you said, there if you are talking about sort of robotics and automation broadly, there are a lot of very impressive, like a very fast growing applications and
Another one I can think of is like as those sort of logistics vans kind of thing and that are you know autonomous and so those are kind of sometimes in the robotics kind of category, but I think those are growing very quickly as well. And then what you mentioned about cleaning robots, yeah. And but I think with humanoids we’ve seen so many impressive sort of you know demos and robot performances.
And those are very impressive in terms of how robots can move so well, like what they call locomotion, like both software and hardware, right? But I think there’s still a lot of problems to be solved for like robots interacting with the environment and interacting with other objects and people, and especially if they involve like you know unpredictable situations or kind of you know, situations that cannot be really controlled.
So because a lot of real world applications contain those kinds of unpredictable, you know, situations. So when it comes to real, you know, I mean there’s still a lot of hurdles. And I think that’s where there is a little bit of a or maybe not a little bit, but there is a hype you know, as to you know, looks like those robots can do all the work tomorrow, right, in factories, but it it’s not that simple. And
A lot of people do say that the AI brain part of the robot is still where they need so much more, like so many breakthroughs are necessary for a lot of those applications to actually happen. And that’s another reason why, you know, I wrote about world models, but that’s, you know, a big part of the discussion because that’s where they need a lot of breakthroughs.
Grace Shao (1:00:21)
Who are the main players in the world models right now?
Jing Yang (1:00:22)
I think I want to
Sorry, before we talk about that, I just the only one thing I want to say ‘cause I just learned about this. I actually
Something new that I learned. I don’t know if your audience would know, but I want to share. So there’s a big difference between embodied AI and humanoids. And I think somehow these two terms have been used interchangeably. And then the difference is that humanoids are just robots that are shaped like humans. embodied AI actually are robots that have real intelligence. and if you look at the Chinese government’s fifth 15th five-year plan where they supported the sector, they actually
actually
spelt out both say, you know, support the development of embodied AA and humanoids. That level of sophistication from the Chinese policymakers, I I don’t think that’s been widely appreciated, right? I just want to point that out.
Grace Shao (1:01:11)
That’s an interesting take. Look, I wanna ask a question, what do you think is something you still want to share with the audience that, you know, you guys are focusing on in the near future? something that’s exciting you as you cover China Tech, Asia Tech?
Jing Yang (1:01:24)
Sure, do you wanna go first?
Juro (1:01:26)
yes, I think I mean just the speed of you know changes and you know I’ve written about some you know trends like for example earlier this year when open claw you know briefly became a huge you know phenomenon in China, right? And then last year there was like a AI agent manace and all kinds of you know clones of manace, right? And yeah, I think
These kinds of things will keep happening and I always find them after all these years. I’m still fascinated just how quickly they move. And all those founders jump in and you know I mean there’s so much of the domestic competition that’s also you know not fully appreciated, just how intense everything is. And you know, those guys launched something, you know, when OpenClaw came out, everybody, you know, immediately worked on something, you know, product and
because they know that everybody else is gonna do the same and if they don’t do it now, you know, they’re gonna look like they’re falling behind. And so yeah, I feel like sometimes like you know part of my beat is just like you know the new hype beat and but you know where I’m still fascinated by just how intense you know things change and you know like yeah how intense the competition is.
Grace Shao (1:02:44)
Juro, it can be very exhausting, can’t it? I feel like there were like eight models in just the summer. It’s very hard to keep up at times.
Juro (1:02:52)
Yeah. Yeah, I mean we
We were talking about world models and you know Jean was talking to also some investors and how they were talking about, you know, even founders with backgrounds that have little to do with world models also, you know, starting those, you know, launching those startups now. And yeah, just how that happens so quickly, right? And everybody kind of jumps in.
Grace Shao (1:03:17)
It’s kind of the new gold rush.
Jing Yang (1:03:17)
I made this prediction before I made
this prediction before but I’ll make it again. I think we’re gonna assume we’ll see the War of A hundred War models. we saw the War of A hundred L Ms back in twenty ninety three. And I think this year will be the year where we see the beginning of the War of A hundred.
or models and and the scary part of this is that at least LM is built on something that has like a a c a piece of technology or fundamental like architecture that has a consensus riched upon and and word model is nothing close to that. This is sort of the scary part.
Grace Shao (1:03:50)
No, I agree with that. Guys, one last question I ask every single guest that comes on and Jing you’re familiar with this. What is one difference you view you hold or something non consensus?
Jing Yang (1:03:59)
Okay, maybe I’ll say this. I hope it doesn’t get me into trouble, but I don’t think Chinese models are gonna like the frontier Chinese models are gonna really be able to catch up with the US frontier models. Is sort of my maybe differentiated view because
Grace Shao (1:04:14)
Well, if you put that out there, you
I have to elaborate now. Why?
Jing Yang (1:04:17)
If we assume that a lot of the progress or distillation contributed to a lot of the progress we’ve seen recently, then that just means if you continue to distill, then you continue to be playing catch-up. you may reach
you may reach, you know, to be very close to be on par, but you can never overtake. like a student can never be better at than a teacher, right? So I think that’s just simple logic. I mean I’m making this prediction or this sort of differentiated view based on the fact that distillation continues to be rampant and continues to be commonplace, right? Whenever that’s changed, then obviously my view will change too. So that’s just yeah what I think.
Grace Shao (1:04:56)
I think that was one of the arguments people made about why Zhangyiming was so against distillation, because he was under the impression that you can only catch up or distill as good as the model that you’re distilling from, right? Jira, what is yours?
Juro (1:05:08)
Huh. I haven’t really thought about this, but I I guess maybe this is not necessarily non consensus anymore, but a lot of the US China, you know, restrictions on tech and you know with chips or yeah, I mean all kinds of you know, especially US measures for China and yeah w
Every time just what I see is that how there are always you know ways to get around it and and you know people involved or you know Chinese companies kind of even talking about it as if those restrictions didn’t exist or so you know for example like access to Claude or you know or Blackwell chips or so yeah, I mean those would be like I’ve kind of stopped
seeing them as, you know, like when new restrictions come in, the sort of first instinct is how are they gonna get around it this time? Because I’m sure they will, right? So yeah.
Grace Shao (1:06:07)
It’s like a when there’s
a w will there’s a way kind of thing. Yes.
Juro (1:06:10)
There was a wheel there’s a way. So that’s the kind I
I mean, maybe this is well known, I don’t know. But yeah.
Grace Shao (1:06:18)
no, I appreciate both of your time. I really appreciate your insights today. We covered a lot. We’ll love to have you guys back on another time, but thank you so much.
Jing Yang (1:06:26)
Thank you.
Juro (1:06:26)
Thank you.
Grace Shao (1:06:27)
Okay.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



Get full access to AI Proem at aiproem.substack.com/subscribe

In this episode, I’m joined by Robin Zhu, one of the sharpest observers of China’s technology and AI landscape.
We talk about some of the biggest names in Chinese AI, from Z.ai, Moonshot and DeepSeek to Alibaba, Tencent and ByteDance. But rather than just looking at who has the biggest or most talked-about models, we get into what each company is actually good at, how they’re approaching the frontier, and what Robin looks for when trying to separate real progress from the hype.
We also touch on how Chinese AI companies are operating under very different constraints than their US counterparts, yet they’ve continued to make impressive progress through techniques such as model compression and reinforcement learning. This raises a bigger question around where the value in AI ultimately sits: if models become increasingly capable, cheaper and more commoditized, who actually captures the economics?
From there, we get into the business of AI — how open-weight labs can make money, what AI monetization might look like, and whether the biggest opportunities will sit with the models themselves or with the applications, infrastructure and orchestration layers built around them.
Finally, we zoom out to the bigger picture: what China’s progress in AI could mean for geopolitics, model sovereignty and international adoption, and how investors should think about valuing these companies when the technology is moving faster than traditional financial metrics can keep up.
The AI Proem Podcast is under the AI Proem newsletter, which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.
Chapters
00:00 Introduction
01:06 China’s AI Race and the Rise of New AI Labs
06:39 The Compute Bottleneck — And How China Is Closing the Gap
11:37 AI Monetization: Who Captures the Value?
13:06 Why China Has So Many AI Labs — And Who Will Survive
16:10 When Does an AI Model Become “Good Enough”?
19:10 Token Rationalization, AI Harnesses and the Future of Work
25:21 Models vs. Applications: Where Will AI Value Accrue?
30:31 How Open-Weight AI Labs Can Make Money
33:07 Can Chinese AI Capture 30–35% of Global AI Revenue?
45:02 How Should Investors Value AI Companies?
49:56 Robin’s final thoughts on AI’s future in China and globally
Transcript
(AI-generated, for reference only)
Grace Shao (00:01)
Hey Robin, good to have you.
Robin (00:03)
Thanks for having me. Good to be here.
Grace Shao (00:05)
Yeah, yeah. Tell us about your coverage and your recent initiation on Z.ai and MiniMax. I think that was quite exciting. It was a huge report — 50 pages or 80 pages, was it? What made you decide that now was a good time? And what is your main takeaway there?
Robin (00:23)
Sure. Yeah, look, you know, I’ve been covering internet at Bernstein for a long time now. I’ve been covering gaming for the last number of years in Japan. Year to date, I think something like 80% of our research has been about some form of AI or other. I’ve been using Z.ai and MiniMax as the examples to effectively fill my exhibits and illustrate different points. The stocks kinda ran away from me as we were doing that. We initially thought, okay, we were gonna you know, work out what AI does or what these businesses do and then they all went vertical. there came a point in the summer I was just like, All right, you know, the stocks can do whatever they want given such small free floats and we’ll wait a little bit and the lockup expiries were coming up at that point and yeah, we picked a week Shortly after. I was in the US for a month to kinda network and do different things. and I think we got lucky on the timing, to some degree. But yeah, you know, now there’s more price discovery. There’s, you know, it seems to be a new model launching every other week. So yeah, fun times.
Grace Shao (01:34)
Yeah, sorry, we were just talking about how there’s such AI fatigue. Like, there were literally eight models over the summer and there was no summer for any of us covering AI, right?
Robin (01:44)
Are you not excited about Ox Alpha?
Grace Shao (01:47)
Everyone’s excited about Ox Alpha, but we all have different conspiracy theories, right? Well, because like I don’t wanna like you know go into these conspiracy theory holes today. Let’s focus on some of the big pictures. I do want to ask
Robin (01:58)
Okay.
Grace Shao (01:59)
You are one of the rare people who gets access to these labs and their executives. When I last spoke to you, you said you were hanging out in Beijing, meeting with some of the executives at Z.ai, MiniMax and whatnot.
Robin (02:09)
Mm-hmm. Mm-hmm.
Grace Shao (02:12)
Obviously not sharing anything sensitive, but what’s the vibe? What are the cultural differences?
Robin (02:16)
Yeah.
Grace Shao (02:17)
You know, do you think any of their personalities or cultural makeup actually, you know, differentiates them from how they go to market, how they build their products or technology, or maybe even their philosophy on AI?
Robin (02:31)
Yeah, sure. I mean, I think it’s kind of interesting, you know, I deal with investors day to day a lot. you know, the debate there is, are these labs raising prices? Are we gonna get competition? Do models get commoditized and pricing goes to you know, gets hammered and so on. you talk to the guys at these labs and it’s a very kind of singular focus on, you know, everybody thinks they’re changing the world. AGI is very much top of mind for everybody and Iterating the model is much more of a focus, obviously, compared to investors. But the vibe is very much: yeah, just keep going, keep cranking and see where we can go. Culturally, there are some quite big differences. You know, Z.ai came out of Tsinghua University. Dr. Tang is still kind of on both sides of the fence in some ways. You know, somebody else described it as being monastic. I’m not sure I’d go that far, but it is a much more kind of academic and nerdy organization. MiniMax and all the dealings I’ve had with them seem to be more commercial. You know, they’ve had a couple of pivots in terms of what the main focus has been. certainly more international than Z.ai. but yeah, Kimi’s kind of I guess in some ways halfway in between. You know, they are More international than Z.ai but yeah, you know, you’ve got kind of the more si how do I how’d you describe it? More kind of science based aspect of you know what they’re doing. So yeah, you know, these are they show through in how these companies behave, and the results that you’re seeing in terms of model progress. And yeah.
Grace Shao (04:24)
How do you think they’re defining AGI? Is it different from what SF is saying?
Robin (04:31)
What’s SF saying? It seems to be different every few weeks.
Grace Shao (04:34)
Huh.
Robin (04:35)
I don’t know if there is a single kind of monolithic, you know what — like we’re going to do AGI and it’s this thing. you know, I think the common analogy is summoning the machine god, which... But I think it’s a little bit narrower than that. I think it’s, you know, how do we get AI to iterate our models for us? How do we get into kind of, you know, I guess some definition of loose RSI or narrow RSI? I don’t tend to get into discussions about broad RSI with people, you know, where it does actually just become a little bit more religious. But yeah, I think, you know, everyone is just focused on kind of iterating the next generation of models.
Grace Shao (05:17)
And they’re all kind of facing the same issue, right? End of the day it’s compute. But potentially there’s domestic compute becoming more abundant. do you think that’s really gonna change the game here? Or is that even something that’s happening in the near term?
Robin (05:33)
Yeah, I think it is a bottleneck. Everybody is short on compute. Everybody makes comments on, you know, if you compare the FLOPs per engineer here versus in SF, there is a big difference, and it does hold back some of the progress. But despite that, you’ve seen some of the Chinese labs come up with cache-compression tricks, RL tricks to Close the gap, and I think it has been quite remarkable to see where they’ve gotten to on quite limited resources. I mean, Z.ai in particular, getting to what they’ve done with a 750B pre-train has certainly surpassed what I thought was possible without getting to a bigger model.
Grace Shao (06:22)
Okay, well then let’s take some. I wanna double click on that later for sure on what the potential implications of all that progress means later. But first, start big, high level. What’s your sense on each of the labs? Like who’s good at what? What are they each gunning for? What’s a good mental framework for us to when we’re evaluating these different labs? Because the one thing I wanna lead to the next question really is we just have so many Chinese like lab providers, I sorry, model providers right now. Like, There’s Z.ai, MiniMax, DeepSeek, let’s just call them somewhat the first tier labs. Then we have like the BAT here, I mean like ByteDance, Alibaba, Tencent. Then like randomly over the last like three to six months, we get Xiaomi, Meituan, RED, Huawei, all crowding that space. And then you have like StepFun and a few others kind of dabbling in this. Well, not dabbling, but they’re also doing this. Well, maybe considering them second tier. How do we understand this landscape and
Robin (07:18)
Ni
Grace Shao (07:18)
How do we evaluate? These labs.
Robin (07:21)
Yeah, I think the way that we kind of framed it and when we launched coverage was, you know, I think there are three frontier labs if you look at the latest models. The concept of the Pareto frontier is quite important because there isn’t sort of a monolithic kind of first place. You either have to be the smartest model at your price point or vice versa, the cheapest model for a certain level of intelligence, however you define that. So you can have different spots on the frontier. For example, when Kimi K3 came out, it obviously was smarter than the latest GLM, but it was also something like three times as expensive. So I happen to use both in my day-to-day. and they’re not kind of direct substitutes immediately to each other. but let’s say, you know, we said and I stand by this view that there are three frontier labs: Z.ai, Kimi/Moonshot and DeepSeek. I think amongst themselves, the consensus is that Z.ai is good at post-training, Kimi has the biggest pre-training scale and is good at pre-training, and DeepSeek has this crazy infra capability, so they can run super-high tokens per second and so on. So, you know, they are good at different things based on their different backgrounds. The internet companies — look, Alibaba’s probably spent the Most time and effort to try and develop a frontier suite of models. You know, Qwen is frontier-ish. It’s kind of up there. And then Tencent’s come back really into the conversation in the last six months with Hunyuan 3. I think initially people ignored the preview and then more recently it’s become clear that, you know, the machine that builds the machine is now working and they’re iterating towards Hunyuan 4 sometime this year. So you know that’s that.
Grace Shao (09:13)
I think Hunyuan 4 preview is coming out this Friday actually. It is. Somebody just told me. I think that’s public information soon
Robin (09:17)
Is that right? can I quote you on that? Yeah. Cool.
Grace Shao (09:24)
Or now. Anyway, yes, go on.
Robin (09:26)
It is now. Yeah. well, You know, they said it’s coming this year. I kind of assumed it was coming in Q4, but cool. That sounds encouraging. ByteDance is funny, right? Because they kinda raced out into a big lead with Doubao. It was, you know, it was considered to be the — well, it’s, I guess it still is the chatbot app in you know, compared to Qwen or compared to Yuanbao. And then I think they ran into the issue of, well, okay, we’ve grabbed the users that we can grab, and then when they tried to monetize it through subs, the paying ratios were very low. and so now I think there’s been a bit of a pivot towards, do we do enterprise? My understanding is they’re gonna start doing ads within Doubao in the second half of the year. so there’s a couple of ways that they’re going through. But yeah, I would say that Tencent and ByteDance are more focused on applying AI through their ecosystems. Alibaba’s much more focused on
Grace Shao (10:22)
But they want to use their own models, Right? They want to use their own models. So it’s like without really strong models, how do they apply? And this brings me, sorry, I’m just hijacking this whole conversation, but it brings me back
Robin (10:30)
Mm.
Grace Shao (10:31)
To like Ben Thompson’s recent interview with Patrick Shonas. He was really interesting. He just goes on about, you know, end of the day, it’s advertising and consumers will not pay. So it seems like, you know, all these AI application companies will just become like they will just monetize the same way the internet companies monetize. And then it’s
Robin (10:45)
Everybody sells ads in the end, C Yeah, I think that’s I think that’s A plausible yeah, I think that’s a plausible end outcome. where I would differ a little bit is something like a WorkBuddy, where people are just paying for effectively tokens and task completion. but yes, on the kind of base consumer layer then yeah, advertising, monetizing merchants who Effectively pay for access to people doing stuff on WeChat or elsewhere ends up being you know, Douyin started to monetize some of the local service recommendations recently. again through a kind of quasi ads model. So yeah, I think we do move in that direction medium term.
Grace Shao (11:27)
Then what about all the rest like I just talked about? I think like 10 other companies are serving up models these days. How do you make a or actually let me re-reshift the question? How why
Robin (11:34)
I tend to think of those as
Grace Shao (11:40)
Let me reframe the question: why are there so many? Should we be expecting some calls consolidation? Like, because I don’t expect you to comment on every single one of them, how they’re different. But the fact that we just have like 20 different labs
Robin (11:50)
Mm-hmm.
Grace Shao (11:52)
Offering models at this point. It doesn’t seem very economical, but and also aren’t they all just fighting for the same compute, same talent at this point? How do you view all Of that?
Robin (12:00)
Yes, there’s only so many PhDs that you can hire out of these places. Look, I think there will be fewer players at the frontier or frontier-ish. you know, I’ll pick on Meituan since it’s a company I cover. But they came out with LongCat-2.0. I think initially there was some excitement, and then people realized that it’s got the reasoning capabilities of Hunyuan 3, which has got a quarter as many parameters or something. So or less. and I do think that over time, you know, if you think about what’s important here, it’s access to a data pipeline, it’s the ability to turn that into RL environments and then train these models. and I do assume that the you know, the labs and the very biggest internet companies will kind of, you know, be thereabouts. Some of these smaller names that you’ve just mentioned, I think you know, in the Meituan example, like what’s the difference between having your own model versus using DeepSeek or something? Like, just You know, fine-tuning DeepSeek or something. I do think there’s a kind of open debate there. and you know, certainly costing them a decent amount if you look at their accounts. So I do think there will be fewer players at the frontier. You know, I think
Grace Shao (13:14)
So
Robin (13:14)
If you want to have some kind of basic search engine that is AI powered, then so be it. Do you need a complicated long horizon agentic model to underpin every internet platform? No.
Grace Shao (13:27)
What is driving all this kind of effort to even build their own models? Just FOMO.
Robin (13:33)
I think it’s partly, “we want our own thing.”. I think it’s innately because there’s so much competition in the internet space that there is this kind of insecurity around you know, if we don’t have a model then do our peers who do then find a way to get an upper hand somehow. And so we need to at least understand the technology. That bit I get. I just don’t think that translates into long term commitment into, you know, training ever larger models infinitely.
Grace Shao (14:02)
Why don’t we see that kind of phenomenon in the US as much? Like the internet players aren’t just all rolling out their own models.
Robin (14:11)
They’re all too busy serving compute to the two guys at the front. Seems to be what’s happening. Yeah. I think generally
Grace Shao (14:16)
All right, let’s
Robin (14:19)
Generally, there’s a lot more duplication in China than
Grace Shao (14:24)
Mm.
Robin (14:24)
In the US where the you know these players kinda keep to their own lanes a bit more.
Grace Shao (14:29)
Yeah, yeah. Well, on that topic, this is like relevant, but much of a conversation around AIs that these models are being commoditized, but you do have a more nuanced view. reading your recent report, you know, you’re saying
Robin (14:40)
Mm-hmm.
Grace Shao (14:40)
That, you know, frontier models versus good enough models kind of have different value within the long term ecosystem. At what point do you think a model becomes good enough that the user simply stops caring whether another model is theoretically more intelligent?
Robin (14:55)
Yeah.
Grace Shao (14:55)
Do you think it’s use case dependent or you know? Client-, customer-, end-use-dependent or how do we understand that?
Robin (15:04)
Yeah, I think initially, you know, agentic AI kind of exploded in Q1, and then everybody got super excited and you know went both feet in to try and work out what they could do with it, hence the token maxing movement. and then subsequently I think as especially as we started using agentic AI more and more, the framework that I kind of zoomed in on was more around User perception, right? And you can define that however you want in terms of use cases, in terms of you know who the user is. And I guess the point that we tried to make was instead of there being some kind of objective standard by which AI models become good enough, they become you know, AI becomes good enough when you can solve a task that you’re trying to solve. And If you then have more than one AI model or if you have multiple AI models able to solve the same task, then the arbiter of who you give the task to then moves away from reasoning capabilities because at that point, you know, by default, if multiple models can solve the problem, then you move on to cost and availability and you know, in some cases UI/UX and your kind of taste of one model versus the other in some cases. So yeah, I you know, I think If you kind of lead on to that, then we do think in a couple of the companies have talked about this in different ways, is that you will have a frontier where people will pay higher and higher ARPUs for more and more specialized and longer and longer horizon task completion. you know, you go from ordering bubble tea to doing agentic commerce to doing, you know, more serious work stuff to frontier science. With the number of tasks that you solve probably get fewer and fewer and the ARPUs just scale infinitely almost. And meanwhile you’ve got this kind of behind the frontier bit where yeah you can solve a task with a good enough model. you know, we were in discussions with or talking to execs at Tencent who said that on WorkBuddy there’s probably thirty percent you know performance-driven tokens and versus seventy percent what I think was said was value-driven tokens where it’s like, you know, you can solve the task with A cheaper model. the way that Z.ai tries to frame it is, you know, you have thirty percent and then maybe fifty percent, which you can monetize and then the last twenty percent is just given away for free on Doubao. but yeah, you know, you have a split in the market as a result of you know, using models to solve what it can versus solving problems that you know multiple models can do versus the really simple stuff.
Grace Shao (17:50)
And is that the responsibility of the, I guess, the provider of the tokens or the user? As in, should I be routing my own models left, right, center to optimize my cost or what? Or should the platform be
Robin (18:02)
Yeah.
Grace Shao (18:03)
Doing that for me?
Robin (18:04)
I think people ran into that themselves because I mean the irony for me is that Fable being as expensive as it was was probably the catalyst for people to realize, you know, maybe we shouldn’t ask Fable for the weather because it costs you five dollars to do that or something. and and then at the same time, I think in June, July there was this explosion of open-source Chinese models that you know meant that the number of alternatives Then expanded and people started to kind of think about, maybe I should rationalize or, you know, at least match the value of whatever task I’m solving with the cost of the tokens that you know, that I’m spending. In the first half there were these crazy kind of anecdotes like folks working for the internet companies having quotas of upwards of like a thousand dollars a month and, you know, me Saying to them like I know what you do, you make slides for your boss. You don’t need a thousand dollars of tokens a month. and eventually I think the internet companies backed away from that. So yeah, you know I think we’re seeing that kind of rationalization movement happen.
Grace Shao (19:13)
Yeah, yeah, there’s definitely been a cap I’ve heard for especially what they call like people who are just working with documents, definitely definitely don’t need to be token maxing at all. I am an advocate for not using AI for simple things. Like when you’re asking about the weather, maybe you should just turn on your weather app, like honestly, and not like burn compute
Robin (19:29)
Yeah.
Grace Shao (19:30)
On that. Anyway, you use the term token rationalization in your recent report. Tell us about that, because you kind of alluded to it already.
Robin (19:39)
Yeah, yeah, yeah. So I think it’s, you know, again, it’s this idea of matching the value of what you’re doing versus the cost of the tokens that you’re spending. and it kind of ties back to this Pareto frontier where you know, you have different levels of complexity, you have different levels of cost associated with different models based on their scale and other factors. And you know, you try and be smart about You know, how much you spend, I guess. Like one of the kind of ways I’ve settled in is to have worker bots. I run my own Hermes setup with different bots and I have my worker bots that are relatively cheap and do what they do. and then I have a checker of homework at the end that makes sure that nothing stupid happens and they red team each other and so on. So I think, users kind of find their own ways to do that. Maybe You know, outside of the kind of pro users, then there is the role of the harness or of the kind of you know, the WorkBuddy-like app that then decides for you, this goes to this model, that goes to that model. That makes the overall experience more optimized.
Grace Shao (20:49)
Why don’t we just jump it straight into how you use AI for research? Like I wanna hear more about it. Like, how do you use Hermes? How do you run your own models? Like it’s not a Very
Robin (21:00)
Yeah.
Grace Shao (21:00)
Common sell-side analyst approach. I think you’re definitely like AI-pilled to the max out of all the cell
Robin (21:06)
I am
Grace Shao (21:07)
Sell-side analysts I speak to. Why don’t we talk about that first before
Robin (21:12)
Yeah.
Grace Shao (21:12)
I get more into the analysis? Like I’m curious.
Robin (21:15)
God, how long do you have? Look, I started with OpenClaw, round about the same time as when everybody got excited about OpenClaw. And then I very quickly fell out of love with it and then almost by accident happened upon Hermes around the same time. I use it for a number of things now. I use it for kind of work research as a way to gather information. these bots have a way of scraping data from websites. That I can’t manually. there was one instance of a website where you know the website would show you the last 12 months of data and then the bot went inside and after a while said, here’s an API that allows me to download the last 10 years of data, which is great. maybe it’s less great for the guy operating the website. But it there are things like that, or I can now I’ve set up bots to monitor Twitter and Reddit sentiment when a new game comes out, or you know, I’ve built You know, reasoning loops to try to forecast game sales, look at or actually one of the biggest time savers of all has probably been the ability to summarize podcasts. Like in the old days, pre AI somebody sends you a two and a half hour podcast, you’re like, great. Like I’m sure this is super interesting, but I’ve just lost my Saturday morning. Whereas now I can basically have the Hermes bot gonna summarize it, give me key quotes, key insights, timestamps, you know I probably then go back and listen to what was actually said on maybe 10–20% of the two and a half hours. So yeah, it’s been, you know, a good time saver. The other thing is you know, just the ability to knock around ideas on my phone while I’m walking around. Like there was one time when, you know, my wife and I took our daughter to some tourist spot that I’d been A lot of times and they were kind of going around sightseeing. I was behind them having a chat with my Hermes bot and by the end of the four hours of walking around I developed most of a note to write down. So yeah, it’s it’s you know, there’s different ways that I’ve tried to use it. I’m sure we’ll find more over time.
Grace Shao (23:26)
So you just exposed yourself For not actually listening to my AI Proem podcasts when you do say you listen to them. You’re probably just getting Hermes to summarize them for you.
Robin (23:36)
No, I listened to you.
Grace Shao (23:38)
All right. You listen to this episode.
Robin (23:39)
Hahaha.
Grace Shao (23:41)
Okay, let’s get back to the serious stuff. Okay. I think I think that’s really interesting because I think someone who actually uses it, like understands it differently from just pure observer. But do you think then, just going back to the last conversation, but do you think then frontier model providers still capture the most of the economics? Or do you think the value is gonna move to applications, orchestration layers? Because early in the conversation, you know, we were talking about like, you know, people are like more mindful of the costs now. but this is how these frontier labs make money. So doesn’t it then challenge their existing business model?
Robin (24:15)
Yeah, I mean there is this ongoing debate about value capture, you know, between the semis industry where all the stocks have gone vertical this year and then less so in the last couple of months. and then the hyperscaler layer, I mean that you know, you if you look at the US internet companies, spending AI capex is alternately good and bad every other few months, you know, depending on what gets reported. And then the labs themselves, and then, you know, increasingly you’ve had these kind of harness type debates. The one that has struck me as being very interesting lately is that the competition between first and third party harnesses. If you’re an AI lab, then you know the harness is something that basically puts — if you think of the model as being an answering machine or a reasoning machine, the harness actually adds persistent memory, reference files,, you know, tool calls and The ability to do stuff on a kind of recurring and ongoing basis next to the model. And that’s actually what makes it so for example, the Hermes construct is what makes these bots be able to work with you much more like a human worker can. Right. And so there’s been the debate of, well, you know, it’s strategically necessary for every AI lab to have its own harness, whether it’s Claude Code, whether it’s Codex, whether it’s Z Code and Kimi Code and so on. Or do you just end up with kind of WorkBuddy and that acts as an orchestrator across multiple models? or do the first party models then allow other models into their own kind of space? So that’s a debate I think is not going to be solved anytime soon. I think it’s gonna be useful to kind of see how it plays out. but at the end of the day, especially in the Chinese context, I do think that, you know, if the strategic need is for Frontier reasoning capabilities, then on some level these labs have to survive. Right. If you assume that essentially they get zero part of the value, then they will cease to function or they will stop being able to fund themselves and fund the next training run. So I think they will have to retain some of the value in order to keep doing that. and if you think about which layer of the Chi of the tech stack in China that’s most likely to get overbuilt, it’s probably the compute layer. Which also argues in favor of the labs getting, you know, some of the value. So I think it’s yeah, I think it’s an ongoing debate.
Grace Shao (26:41)
Interesting, you just brought up actually like a lot of these labs will have to create their own products to actually still capture some of the value beyond just infrastructure layer, right? But actually, I think Sam Altman was just on a podcast like a few days ago. He was talking about how he’s like actually bringing everything back. they’re cutting products, right? Like no longer doing a bunch of different products. They’re saying that they’re a platform business instead they only want to use ChatGPT’s interface, they’re even like renaming Codex or something. Like, do you think that is the future for like the kimmies and the mini sac max of the world ‘cause then my argument or my challenge against that is then how could they compete with the big tech and channel where they have just like such broad reach, I guess. They’re like with these super apps and everything.
Robin (27:27)
Yeah, so that’s that’s the interesting part where if you’re an AI lab, you know, right now you’re using, you know, some of these third party harnesses as distribution. over time, you know, do you have to retain some parts of the reasoning capabilities like, you know, for example, cyber is one of these niche things that you can do with an AI model. Does that need to go into a you know, power user grade or consumer grade AI harness? Or, you know, there are different Flavors of model that do different things, not you know, maybe you don’t put all of them onto the generic kind of third party harness. but that is something to work out. I mean in Sam’s case, I think there’s a kind of semantic thing here where you can name it what you want. to me the accumulation of personal context behind the model through repeated engagement with it and it kind of learning what you do and You training it to do different things as skills, setting up a network of expert tool calls that you can kind of call on. Like that stuff is actually in my mind, where a lot of the long term value lies, or where the usefulness of the AI kind of goes and resides in the you know, as you use it repeatedly. So yeah, and you know, there are a few ways where I think this can play out. One is potentially, you know, there is obviously the The version of the world where everybody goes on to a third party harness within you know that’s run by one of the big internet companies. The alternative is that, you know, if you are a more pro user, more specialized user, maybe you do need some of the specialized functionality that, you know, the AI labs keep to themselves. and then you have a range of these outcomes. yeah, very complicated question. I don’t know if I have a fully formed answer at this point.
Grace Shao (29:18)
Fair enough. but let’s look at monetization in general for the open-weight labs. You know, obviously I think from the Western perspective, it’s like the biggest question
Robin (29:25)
Mm-hmm.
Grace Shao (29:25)
Is always like how do they make money? How do open-source models make money? How do you see like the current API sales? Is that just a durable revenue pool? or is that not gonna be enough to sustain the capital they need to continue to train?
Robin (29:43)
Yeah, I mean right now, you know, back to your earlier point about compute constraints. you’ve trained these models, there’s been this explosion of interest in them. I think one of the biggest constraints that they have faced is the lack of silicon is constraining their ability to serve up APIs. You know, I feel this pain every morning Asia time where, you know, I ask a Chinese model to do something at nine AM Hong Kong time and it’s rate limited all the time because everybody else is trying to do that at the same time. And as that gets resolved, presumably, you know, that induces some demand and your ARPU goes up. yeah, I and there are probably lumps around new model releases and whatnot that means that you know it’s spiky rather than that being this smooth curve upwards. But yeah, I do assume that expands over time. You know, all of these companies distribute via the internet platforms as well through, you know, ModelScope and workbody and You know, other things. and then I think to me the interesting thing that’s kind of happened recently is this idea that they are now starting to try and charge or take rate from the global inference providers, right? Like Kimi has gone and signed these deals with different inference providers, essentially charging them you know, you can call it a royalty fee or a or it’s some kind of licensing agreement. But essentially altering the license so that if you are Looking at the weights for academic use or whatever, then that’s fine. If you’re using it to actually just host it and make money, then you have to pay them something, which I think makes sense. So that’s a route that they can kind of pursue to try and capture some of the global economics.
Grace Shao (31:26)
Yeah. So going towards like a bit more controlled commercial economics. Okay, you estimated Chinese models to be able to address roughly thirty to thirty-five percent of global AI revenue, despite the current headwinds with geopolitics and whatnot. Walk us through that thinking. Thirty to thirty-five percent is quite a large pie. I think even a year ago when I spoke to some of the labs, they were jokingly saying, even if we get five percent, that’s enough money for our business, you know.
Robin (31:54)
Yeah.
Grace Shao (31:55)
I mean, but that’s one lab. I guess cumulatively it’s it’s it adds up. So tell us about your thinking on that.
Robin (32:01)
Sure. I mean it was more of a top down kind of estimate based on what was Attainable or what was kind of in a you know in the context of geopolitical realities what was realistically kind of accessible to these labs. And you know we had made these TAM estimates by region. I think US was like half of global or you know maybe even a little bit more than that. And then China obviously we assumed was a captive market. We ended up assuming a very, very limited access to the US market because of the geo issues. and you know The thing that there’s been a few things that have happened since, like, you know, potentially well, Ramp, I think at one point said that the like f you know, five percent of their highest engagement AI users were playing around with AI models from the Chinese labs. and then you had Microsoft that was allegedly thinking about using different AI models from the Chinese labs to power copilot. So, you know, have we been Conservative, have we been kind of you know, is it that there truly is no access to the US market? Question mark. But you know, in Europe you know, we’ve assumed some access, you know, not unconstrained access. I guess if you’re Airbus, you’re probably never gonna use a Chinese model for obvious reasons. But then you’ve also had you know Mistral Mistral’s kind of turned itself from being a you know frontier lab to something that now helps Z.ai to distribute GLM. And so, you know, there are these kind of future permutations I think are gonna be interesting. That means that, you know, that Europe is accessible to the Chinese AI labs to an extent. and then in the rest of the world, I mean you’ve got, you Z.ai, for example, Z.ai doing sovereign projects with Malaysia, with the Middle East. that presumably then acts as a bit of a kind of beachhead for them to go and do other stuff within these markets. So Yeah, you know, we assume more access to these other markets where, you know, the geopolitical picture is probably you know, more more kind of open to the Chinese labs. So it’s a top down estimate. You know, that it doesn’t mean we think the Chinese AI labs will have thirty, thirty five percent market share. You’re still kind of contesting these markets with OpenAI, Anthropic and others. but yeah, it was more of a kind of, you know, how much of the TAM is actually open to you?
Grace Shao (34:24)
And let’s just say like geopolitics side, like you mentioned, like obviously government agencies are not going to use Chinese labs, but like a lot of companies might, like, you know, companies with less like strict compliance on this, then what does continued progress with China’s open-source models mean for like what would it mean for these US frontier labs, especially as of now realistically, there’s really two labs left at the kind of frontier really fighting it out. and they’re not you’re not seeing them lowering their Cost or opening up their weights.
Robin (34:53)
You’re gonna get hate mail from Elon Musk after you upload this.
Grace Shao (34:59)
Well, if he watches this, it’ll be great.
Robin (35:03)
No, look, I think you know, I think there’s going to be a a mix of model use in in the market, right? Like isn’t something I think a lot of people have underweighted is what Alex Karp has been saying, where, you know, companies need sovereignty over their own data, you need ownership of what you’re doing. He’s obviously talking about his book, but you know, I do think that there will be a variety of solutions. you see, you know, folks from Databricks and DoorDash posting about testing Chinese models on Twitter and I think it’s interesting that’s, I think that will continue. I think you know these companies will find ways to orchestrate across different models. And the fact that certainly compared to the US labs, these are generally smaller models. if you can get most of the reasoning capability from for much lower token costs, then yeah, I do I do think that you know in a growing section of Use cases that you need, that these will be good enough. Like within the home market, obviously they fight to be frontier. but outside of China in the global market, they are that kind of, you know, eighty percent cheaper for or, you know, much cheaper for most of the capability kind of market positioning.
Grace Shao (36:21)
All right, let’s zoom in on the companies themselves. I want to kind of touch on the labs, especially the two companies you just wrote about in your initiation report, and then we can talk about your long-term coverage of ATs. Just start with what’s your bull case on
Robin (36:33)
Okay.
Grace Shao (36:34)
Z.ai? It’s no secret that you love them. Why? Well, what’s your thinking on that? Like, do you think they would
Robin (36:43)
Yeah.
Grace Shao (36:43)
Just be like the leading research engine research lab in China?
Robin (36:48)
Yeah.
Grace Shao (36:49)
Frankly, not nationalized the same way that DeepSeek is likely more to commercialize. Like, I don’t know, but you just raise your eyebrows. Maybe I’m understanding that incorrectly. Help us understand. What do you think of Z.ai these days?
Robin (37:00)
Yeah, look, I’ll save the DeepSeek comment to the last. But you know, I do like their ability to iterate these models. You know, they came out of Tsinghua University and I think that relationship helps on some level when it comes to expert domain training data. You know, when you talk to them they emphasize that they have this data advantage, which I think you’ve seen through some of the RL progress that they’ve shown. and, you know, the ability to
Grace Shao (37:27)
Sorry, what Is their data advantage? What is their data advantage? They’ve said that
Robin (37:30)
As an
Grace Shao (37:31)
To me too, but I don’t know what that means.
Robin (37:33)
Yeah, I think it’s, you know, if you can buy data and you can, you know, acquire data from experts, you are effectively paying people to write down what they know. but there is also the process of turning that into verifiable you know, like RL environments where you have verifiable kind of end goals or checkpoints that the model needs to hit, or how do you verify correctness or not? And Turn it into something that’s a lot more structured and you can feed it into the RL pipeline to actually train models with it rather than just you know, I sit there and write a hundred page thing on how to do equity research or something. Like I you know, there it has to be there yeah, there has to be kind of reasoning gates and a way to kind of let the model kind of as assimilate that information. so you know, the I think in the GLM-5.3 Release paper is actually really interesting in the sense that they emphasize look RL is all we did and effectively they’ve taken you know data and you know translated that into different RL environments and then they’ve used that to try and iterate the model in different ways. So you know I do think that’s a useful skill to have. The fact that they have a seven fifty B model that’s as good as it is is interesting to me. The fact that, you know I think this is known. I mean that the next big boy model is coming later in the year, you know, call it October, maybe a little bit earlier, maybe a little bit later. But at which point you start having probably the best seven fifty B class model, or certainly it to me it is at the moment. and if you have something that’s competitive in a much bigger model size, then you actually occupy two parts of the of the Pareto Frontier front potentially, which is Interesting strategically. so I but I think most of all it’s just that, you there are these three labs I think are frontier. one of them is a little bit captured in terms of, you know, having to answer to the government to some degree. Kimi I like as well. It’s you know, I think Kimi’s doing some really interesting things on a number of fronts.
Grace Shao (39:50)
Yeah, I was just gonna say actually, like Kimi obviously you don’t cover officially now given that they’re not public yet, but you know, they kind of reset expectations around CI. I think when five point two came out, they felt like the world felt like CI was the leading l lab coming out of China. Kimi K three kind of put themselves on the global stage again. In fact, I think they did a you know, really, really big marketing splash globally, and captured a lot of attention. And then given that they don’t have the kind of CAI ent like Was it entity list complication. They actually
Robin (40:23)
Yeah.
Grace Shao (40:24)
Have it easier with international expansion, but it just kind of looking at, you know, Kimi K3, how do you view Moonshot and theirs their positioning right now?
Robin (40:35)
Yeah, I mean the fact is that they have the biggest Chinese pre-train, right? And the Kimi K3 is a very capable model. it’s significantly more expensive, but at the same time, you know, if you’re among c corporate customers, I think there is the argument to say that you just you know, it’s cheaper than the US labs anyway and you just pay for the best capabilities on some level. But yeah, look, I think, I think they will be up there in the fullness of time. They will hopefully get listed before too long. You know, the last time I asked them the answer was soon. so we’ll see. But yeah, I I think they’re you know, what happened with GLM-5.2 and K three was was kind of interesting because there was a point before the summer where we could have launched coverage on these on these AI labs and almost Around the same time basically GLM-5.2 happened. It was great from the perspective of somebody that used these models, but then I think Z.ai’s share price went up like fifty five percent in the week or that week or something. And it like, All right, maybe we’ll take a break and see how see what see how it goes. And then it got to the point where I think, you know, Z.ai’s share price was pricing in being a winner-take-all type winner, at which point, you know, when Kimi K3 came out then there was an unwind of that expectation. I would argue that there shouldn’t have been that expectation, but you know, go figure. So I think now, you know, I still think that these are the two to watch. DeepSeek obviously will always be up there, but I personally find the Kimi and GLM models way easier to use.
Grace Shao (42:18)
And Why was it that, you know, when Z.ai and MiniMax went public that it felt like MiniMax was more of the market darling, or at least investors in Hong Kong were buzzier around them.
Robin (42:29)
Yeah, I think there were two reasons. I think one was MiniMax was considered to be a lot more international. or it was it was much more international. And then the other thing was given the Entity List listing that Z.ai had had, that it was kind of thought that they would find it more difficult to expand internationally. And the other the other problem that investors had with Z.ai was the on prem Segment, which, you know, was kind of this it reminds people of the bad old days of China Enterprise Software, right? Where, you know, you had these companies kind of toil for years and years without really getting anywhere. and so that was, I think, the initial impressions. we put out something in quite early on, I think after CNY, where you know we took a deep look at these companies. I think I was always of the view that You know, model reasoning capabilities are more important and the state of these companies today versus six months ago versus six months in the future is gonna be so wildly different that it’s yeah, you need to evaluate the machine that makes the machine more than kinda where they are at any given moment, which I think was you know, people were guilty of in January.
Grace Shao (43:41)
And you make the somewhat sacrilegious sell-side argument that traditional financial analysis of these labs can be almost irrelevant essentially. Like are we effectively valuing these companies right now? Like what do you think we should be actually looking at when we are putting valuations on these companies right now?
Robin (43:56)
Yeah, I think the market really struggles to value these companies because, you know, everybody agrees that AI is a big deal and you can have these debates on how many trillion dollars of TAM AIs going to be in the fullness of time. I’ve largely given up having that conversation. It’s just it’s going to be big. and you know, investors do value these stocks on the basis of multi-year ARR trajectories and you know, revenues multiple years out. But yeah, like, you know, these companies are about to report first-half earnings. we’re about to I mean, there are things that you can watch out for, like inference margins and you know, obviously the revenue growth and How AR converts to revenue and so on. But you know, f for example in the case of Z.ai, like you’re you’re basically staring at a bunch of numbers that reflect GLM-5, GLM-5.1, which is ancient history in AI terms. So it’s kind of useful, but not really at the same time. yeah. So to me it’s much more important to just kind of look at the iteration, look at the architecture tricks that they are coming up with and the ability to kind of, you know, scale these new innovations to much bigger models. And you know, if you can do X then you should be able to do Y, and then what does that unlock in terms of capabilities? so yeah, I do think it’s you know, I do think that Stuff is at least as important as kind of scrutinizing the numbers. Even if you know it’s kind of my job to multiply two numbers together at the end of the day.
Grace Shao (45:31)
It’s more important to be looking forward than kind of looking back. but like then I have a question that’s like, you know, StepFun has already
Robin (45:37)
Mm-hmm.
Grace Shao (45:37)
Filed for the IPO. we just said Kimi is likely going to go public in Hong Kong too somewhat, sometime this year, next year, whenever.
Robin (45:44)
So yeah.
Grace Shao (45:46)
We’re looking at like four leading labs already, just the Hong Kong Stock Exchange. Then we have obviously
Robin (45:51)
Mm-hmm.
Grace Shao (45:51)
The BATs, which I want to talk about later as well. Like, how do we understand? Is this not a pretty crowded space? Like there’s a lot of labs going public in China.
Robin (45:59)
Okay.
Grace Shao (45:59)
Does that make sense? How do we understand that? How do we pick the winner? Robin, no one How do you pick the right stock?
Robin (46:07)
How do we push the right? I’ll push back and say that this is the least crowded new-tech cycle in China that we’ve seen so far. you look at you know in the past, you know, EVs and batteries and you know, or what’s going on with humanoid robotics at the moment. you know, the AI labs piece has probably been one of the more concentrated fields that we’ve seen. And within the names that you’ve Mentioned, I do think that there are kind of there’s a clear hierarchy of, you know, which ones are closer to the frontier. I think DeepSeek has decided it wants to embrace the national champion role and potentially list in the A-share market, which is fine. But yeah, I think, you know, I’ve I’ve said for a while I think Z.ai and Kimi are, you know, my picks for the frontier. I think, you know, based on what they’ve done, based on the, you know, I the architectural innovations, based on the adoption of their innovations by other labs is kind of one thing that I watch for as is being quite telling. yeah, I you know, I guess there will be more than just two players in the Hong Kong market. But yeah, I think my view is, you know, like in the US where you’ve seen fewer players at the frontier over time, I think that will show through here as well.
Grace Shao (47:37)
All right, let’s move up the stack. while you’re covering BAT, you’ve been covering them, well, Tencent and Alibaba
Robin (47:42)
Mm.
Grace Shao (47:43)
For quite a while, just given that byte dents is not public. you know, what is your mental model thinking through these big techs in China right now? Clearly they have a bit of FOMO, they don’t wanna be left behind, they don’t want to just be known as their internet as the internet phase. They’re all in IAI. They have the benefits
Robin (47:59)
Mm-hmm.
Grace Shao (47:59)
Of talent, they’re the benefits of money, but somehow the like just like the US, they’re not the ones actually pushing
Robin (48:04)
Yeah.
Grace Shao (48:06)
The frontier. How do you think we should think about that?
Robin (48:10)
Yeah, I mean psychologically, they are quite different businesses. Like, when you talk to Tencent, the focus is a lot more around the application layer and how do you apply AI and agentic functionality within ecosystems like WeChat, within, you know, WorkBuddy is potentially a new platform for them, you know, on the AI front. you’ve got the games and ads businesses, which are I actually think are good platforms on which to apply AI and generate kind of benefits. But you know, it’s much more focused on the application layer. And there was a comment in the latest slides from the Q2 earnings that said, If all else fails, then we’ll just rent out the compute to whoever else. so that’s that’s considered to be
Grace Shao (48:53)
I’m dead. I love how candid they are.
Robin (48:57)
So that’s kind of their psychology around AI. Alibaba obviously has you know you’ve got the e-commerce business, which is kind of stuck in this retail growth environment that’s not really growing. And the cloud business is you know has — well, you know, two years ago it was growing like plus eight, now it’s growing plus fifty, in the upcoming quarter, give or take. and so Yeah, it’s become the new thing, right? Where, you know, the hope is that they have Qwen, the hyperscaler layer, and T-Head, which is one of China’s better ASIC programs, which is worth something. And so, you know, to try and integrate that as a stack. But yeah, you know, if you look at the kind of growth algorithm of the business itself, it’s probably the compute layer that’s driving a lot of it. Yeah, just in terms of renting out capacity. So yeah, these are these are quite different businesses.
Grace Shao (49:56)
Yeah, and But the thing is they’re still going ahead, like you said, there’s Qwen, there’s Hunyuan, there’s Seed. should they still be in this model game, in this in this extremely competitive game, or do you think they should be
Robin (50:07)
It’s
Grace Shao (50:08)
Focusing on, like you said, like just plug in all the other models, focus on growing their existing business, right? Like how do you make up it? How should
Robin (50:18)
Yeah. I mean I
Grace Shao (50:20)
They balance that?
Robin (50:21)
Mean cloud and AIs probably Alibaba’s core business at this point. Like the e-commerce is kind of the cash cow that funds everything. but this is clearly the future and you know they’ve guided explicitly for cloud growth to be more than forty percent for the next bunch of years. so to Alibaba this is the core. in Tencent’s case I think there have been There have been multiple debates, you know, that I’ve had with investors around, you know, do they need a super frontier model? Do they need the best model in the market? Or do they you know can they just be the orchestrator layer? Can they just be WorkBuddy? Can they just, you know, use games or ads as a way to kind of monetize AI? I think right now the approach seems to be let’s do everything and see what sticks. or you know, like hopefully everything sticks, but you know, that’s That’s still the strategy and the you know the I guess one benefit that Tencent has is that they do generate a lot more operating cash flow through the core business that then funds a much bigger bonfire of capex over the next whatever number of years that allows gives them some level of optionality.
Grace Shao (51:30)
So obviously a lot of that money is also going to building out compute right now, right? So do you think China’s compute build out could be a double-edged sword at one point? It might erode some of that scarcity on pricing power and inference. Or, you know, some people are writing about overcapacity on compute. Like, is that a thing?
Robin (51:53)
Yeah. I mean, look in the in the in the infinite long run, and if you just kinda take that Tencent comment of if all else will rent out the compute, like if everybody builds compute with that as the fallback option, then the reasonable terminal outcome is that you get a big overbuild of compute, right? Just logically. but so that is a concern. And in the long run, you know, I guess you can make the argument that every new tech Cycle in China has ended up in some kind of overbuilding in the end. to me that’s most likely probably to happen in the compute layer because the level of specialization and tech and you know differentiation that’s required in semis is reasonably high. In AI labs, if you really wanted to be frontier, then there’s a level of math and science capability that you need, whereas standing up boxes with servers in them feels less Complicated. you and I probably couldn’t do it, but with enough money and help you know it should be doable for large corporations. so yeah, I do worry about that. I mean, like it’ll probably take a long time because you know even growing 100% a year, it’ll take a while for the domestic semis industry to catch up with demand. But you know, for now, I think compute tightness and You know, cost pressure in the supply chain is probably you know, it’s almost a good thing in the sense that it reduces the risk of everything kind of collapsing on itself and pricing you know, price wars and things of that nature happening in AI in China.
Grace Shao (53:34)
But compute abundance will be good for consumers, right? Well, at least for end users. Which is not a bad thing.
Robin (53:39)
Well yeah, so yeah. Well, I think there will be the, I guess obviously initially when there’s more compute, yes, you know, everybody has more capability to serve up more inference demand. And so the industry grows. I think and then obviously, you know, the hope is that there is a balance between demand and supply. In practice that almost never happens. and when you end up with an environment where there’s a you know, there’s a number of good enough models, there’s a lot of compute. Then it’s probably more likely that you know on one hand, you know, the cost of compute then goes down, but yeah, there’s more likely to be price competition in AI inference at that point than today.
Grace Shao (54:19)
And we start another round of juan. There’s never-ending juan so you talked a lot about you know how much the harness around a model can change the actual output. And I think it’s quite topical around the big tech right now. do you think we’re putting too much focus on how smart the base model is? do you think eventually really like the focus should be on memory, tools, routing, verification?
Robin (54:42)
Yes.
Grace Shao (54:44)
And then therefore like these big tech companies actually have A lot more experience in building these products, understanding consumer user behavior,
Robin (54:53)
I think both layers matter. I think it’s you know, if you’re gonna reduce the question to extremes, then if you just have the orchestration layer and you don’t have AI, you know, the reasoning capabilities of the model, then that doesn’t work. And I think the vice versa also doesn’t work in the sense that, you know, then you kinda kneecap the model’s ability to complete tasks and so on. So I think you’ll see the labs and The big internet companies all try and compete on both layers. one you know, back to your earlier question of, you know, why should all of these companies be developing AI models? one of the fears that I always have around these big Companies is that there’s always there’s always going to be big-company bureaucracy in politics and who takes credit for what and you know that sort of thing. And then I was in the when I was in the US over the summer, I had conversations with a couple of people who described working, you know, we were talking about Google and DeepMind at the time because it was during the week when everybody seemed to leave. And one of the ways it was described to me was you know, if you want if you think you’re going to change the world and you want to make AGI happen and so on so forth, then the most convex place where you can go and do that is at the frontier labs. And you know, doing the same thing at a big internet company feels like you’re designing a better toaster. which I mean it’s it’s not I don’t know if it’s a completely fair comparison, but yeah, and financially at the individual level, if you’ve done a super difficult PhD in something, you come out and you want to monetize that, then You know, today the most convex way to monetize that is probably to join Kimi ahead of their IPO, right? So, you know, there are those types of incentives at play as well. So, yeah, you know, so you know I do ultimately think the labs will be up there in terms of their ability to deliver these reasoning capabilities. and then the first and third party harness question ends up being kind of something that iterates in real time.
Grace Shao (56:58)
Right, right. And I think it’s kinda like going back to your earlier comment, like I think it’s what happened with Tencent when DeepSeek came out, they’re like, you know what, our Hunyuan is kind of meh. So maybe we’ll just focus on building products that are like a harness around it, like, but it just didn’t work because their own models weren’t good enough and then you can’t always rely on other people’s models, right? So these big tech are still going ahead with their own models now.
Robin (57:19)
Yeah. Well, they now own twenty percent of DeepSeek. So, you know, I think Tencent has the deep pockets and has the kind of strategic patience to be able to go down multiple routes where it, you know, you are doing your own model, you know, Hunyuan 3 was good and now Hunyuan 4 is coming soon. and meanwhile you’re still serving DeepSeek within your Yuanbao and your
Grace Shao (57:41)
Mm.
Robin (57:42)
WorkBuddy apps and so on, and over time And they use, for example, GLM in their ima app. and so yeah, they’ve always kind of done both. and you know, I guess it is reasonable that WorkBuddy allows them to see the reasoning traces of different models and that then somehow feeds back into their own model development, which is so yeah, I think Tencent’s in a slightly different position than a lot of the other guys in this conversation.
Grace Shao (58:11)
But wouldn’t Alibaba and ByteDance have the same kind of deep pockets?
Robin (58:16)
They would. But do they have the do they have the kind of social infrastructure and
Grace Shao (58:22)
Hm.
Robin (58:23)
The ability to pull everybody into a you know, an open third party harness? Whereas you know, if you look at Qwen Work, if you look at TRAE, these tend to be a lot more first-party-heavy. I don’t know if they will be as open in the fullness of time as Tencent. So yeah, they they y you’ve got these companies. And then, you know, each of these companies will then have their own internal kind of puts and takes in terms of who gets what. And so Yeah. Tencent historically
Grace Shao (58:49)
Yeah. They’re definitely all trying to follow the
Robin (58:51)
Had a bigger had a better track record of being kind of open and being the
Grace Shao (58:56)
Yeah.
Robin (58:57)
Somebody called them the benevolent gatekeeper of China Internet, which is yeah, it’s not a bad way to describe them, I guess.
Grace Shao (59:04)
Yeah, I think the other two are trying to follow the WorkBuddy route as well. They’re They’re all revamping DingTalk and Lark right now. So I think it’s like putting it into Qwen Work or something, and then TRAE—
Robin (59:13)
Yeah yeah yeah.
Grace Shao (59:14)
—TRAE and Coze were put into Doubao, and now it’s called Doubao Work, with Lark kind of grouped into it. Anyway, I wanna ask a question on recursive self-improvement. So I’m quite curious about what you think about the gaps between a lot of the labs. I don’t even want to position China versus the US, but if you have to put it that way, you know. A lot of times people are saying, you know,
Robin (59:35)
Yeah.
Grace Shao (59:36)
The Chinese advantage — sorry, the US advantage right now is that the labs are putting a lot more money into R&D and into figuring out how to go forward, right? And then the Chinese models some some ways are basically following their footprints and figuring doing the
Robin (59:48)
Yeah
Grace Shao (59:51)
Knowing the answer key to the homework. It’s that kind of analogy people are saying. But if there really is RSI, then would that change things? Then would these labs — sorry, the models themselves — just start figuring out How to improve themselves quicker and quicker and that gap would just shrink and compound with time or how do you see that?
Robin (1:00:10)
Yeah, I mean the I guess it depends on how you define RSI, but I guess the way I think about it is that there will be a gradient of different levels of how of you know automation and how to what extent AI can train AI and you know you can have them write kernels or whatever. But you know, going forward, can you ingest data and going back to the RL kind of example that I mentioned earlier, can you have AI write The verifiers and the gates and the success fail kind of conditions and you know have AI set up RL environments rather than somebody with a PhD doing it. so I think that will happen relatively quickly and then you know you start automating that process more and more. But then you still ultimately I think I’m very big on this. Like I still think you have taste and judgment be very important in terms of what kinds of verifiers you get the AI to to develop, and how you know there’s still ways to you know to do it better than the next guy rather than just have AI brute force everything. there will be some elements of that. But yeah I think you know and more and more it’ll be kind of you know the human supervising the AI doing more and more of it and but still kind of leaning on it and offering a bit of a steer in terms of where it where you want it to go and the types of behavior you want it you want to reward and so on. So yeah, I think I think there will be a gradient of how much AI versus human involvement you have in some of the model training. On the compute gap, then yes, if you have if, say, tomorrow OpenAI figures out RSI at a pretty high level and they’re able to iterate quickly, then that probably means that the gap between US and China widens again to some degree. You know, We seem to be in this Quite circular debate about whether it’s three or six or nine months and you know, I think the reality is that there’s it kind of oscillates. The US comes up with something and then China closes the gap again quite quickly. but yeah I think given how quickly information is kind of you know the flow of information between different parts of AI has been so rapid I think it’s, you know, over time the gap then, you know, maybe doesn’t widen infinitely.
Grace Shao (1:02:35)
And do you think this whole three-, six-, nine-month thing, end of the day, when we take a step back, surely it’s just so minor, no? Like how do we understand that?
Robin (1:02:44)
I think it matters to a bunch of people above our pay grade. You know,
Grace Shao (1:02:49)
Ha ha.
Robin (1:02:49)
A lot of it is being kind of hijacked in the media, in kind of geopolitical discussions and, you know, things of that nature. and if you are a frontier researcher, if you’re doing, you know, I think Anthropic has started to talk about medicine or drug discovery as a as one field that they want to be good at and If you’re trying to discover new drugs or trying to cure cancer, you know, which maybe we are now starting to do, then you do want, you know, the super frontier latest three months of model capability because and y you’ve seen enough c you know, like early-stage biotech whether these things either go up or down a hundred percent or w you know, whatever. But Because either a drug works or it doesn’t. And so for those types of use cases, yes, you do want the absolute frontier. If I’m just having a conversation with my Hermes bot, like if my model is three months out of date, does it really matter? I like to think it does. In reality it probably doesn’t.
Grace Shao (1:03:55)
No, it’s true. do you think there’s anything else that we need to talk about today, just for our audience to better understand the China AI landscape right now, where it’s at, or how to evaluate it?
Robin (1:04:07)
Yeah. Yeah, I’ll talk about something that’s a little bit of a tangent. But you know, one of these hills I’ve decided to die on is gaming. You know, I cover a lot of gaming both in China and Japan. There was this kind of moment where Google came out with Project Genie. I mean this was in this was in earlier on in the year where you know US software seemed to go down 5% a day every day. And Gaming got thrown in with that. And I think since then I think folks have come back a little bit and you know, back to the judgment and taste kind of thing. yeah, I continue to be of the view that gaming is actually remarkably difficult to disrupt and actually probably benefits from AI evolution over time. that’s probably you know and recently, you know, Google actually Was showing off one of my companies, Capcom, to demonstrate, look at how Capcom’s using AI and therefore, you know, AIs useful in the real world. And so yeah, I think I think the logic has kind of been turned on its head a little bit, but that’s that yeah, that continues to be the hill that I’ll die on when it comes to AI.
Grace Shao (1:05:21)
You think like gaming, filmmaking, these are all spaces where production cost is a lot lower, but you know, consumption will still be there. Is that kind of the thinking behind that?
Robin (1:05:32)
I think yeah, I mean production costs should come down as you automate more and more of these things. you know, you are starting to see AI video start to take over, you know depending on how complicated like you’re you’re not gonna have Nolan-level movies with generative AI anytime soon, but you are seeing short form video platforms essentially become AI-centric. so yeah, I think I think, you know, That will continue to grow, albeit the issue I’ve always had with multimodal models or video models is that, you know, the ceiling for commoditization, or the ceiling for at least you know, me not being able to tell the difference between one and the other is quite low. And so, you know, how do you differentiate video models from each other when that happens is kind of the thing that I’ve not been able to fully resolve. But yeah, if you’re just a maker of visual content, then this is great for you, right? You’re able to kind of Make stuff much more quickly. gaming in my mind is a lot more complicated because it’s not just about, you know, rendering of 3D environments or rendering of characters or whatnot. You have to have a storyline, you have to have, you know, action, music, fe you know, combat and so on and so forth that makes it more difficult. But yeah, or you know AI solves for production, you get a lot more output. I think the question in media and games and movies is like do people then care? Right? but then we’ve also just seen Niu Lai go viral. So maybe, you know, you need a more nuanced version of what people care about.
Grace Shao (1:07:10)
I like how we’re ending this conversation full circle. We started the conversation with having you look at the Ox Alpha thing and then the meme going around is the Niu Lai picture.
Robin (1:07:19)
Yeah.
Grace Shao (1:07:20)
So let’s see where that goes. I was like, is this a representation of niuma (牛马)? ‘Cause the end of the day we’re just all like worker bees and in Chinese we’re all horses and cows. But he said probably not. That’s not where the meme comes from.
Robin (1:07:35)
I’m gonna leave that alone. I’m gonna leave that alone.
Grace Shao (1:07:43)
How do you use AI in your own research, but I want to ask you like there’s just a lot of AI tracking tools out there. There’s a lot of scattered data. AI itself is obviously very broad to even kind of you know, it’s just to say what are you tracking? So actually the question for you is like, what are you using for your own evaluations or what do you track? Like, are you looking at OpenRouter, ModelScope, GitHub? Like what, what do you use to understand? Where demand is going, how compute is used, which models are good, etcetera.
Robin (1:08:15)
Yeah, I mean we track all the obvious things. I mean I’m not sure that’s differentiable necessarily, but you know, OpenRouter, Hugging Face, GitHub, ModelScope. We’ve set up kind of bot routines to try and scrape these things every so often. And, you know, we have these gigantic Excel files of, you know, daily data, weekly data. And then that at a high level paints a picture of you know who’s actually engaging with some of these things, who’s actually, you know creating repos, who’s then downloading the models, who’s doing this, that, and the other. one of my pet peeves with OpenRouter is that, you know, it’s a really, really small chunk of the market. a lot of people, especially on in the investment world, are kind of overindex on it as a as an indicator of and you see the media do it and say things like, You know, Chinese models are now two-thirds of consumption or something. It’s not, it’s two-thirds of consumption on a relatively small part of the market. but fine. You know, we do track it and we do look at kind of, you know, which apps are being used, be it kind of Z Code or some of the other stuff. So we do all that. I guess one thing that we do, which I don’t know if anyone or many other people do, is we do run our own evals. So we When a new model comes out, one thing that we do is we run it through or my bot runs it through these benchmarking tests and you make them solve tasks and it gives me I mean there’s a there’s a variety of reasons to do this, but effectively gives you a more first-hand view on whether you think something is good and then I tend to try and use them day to day because that gives you then potentially a more varied kind of read on Model feel and usefulness beyond just making it crack hardcore software engineering tasks. which, you know
Grace Shao (1:10:13)
It’s not for everyone.
Robin (1:10:14)
Well, that, but also it’s you know, these labs presumably then train on something like Terminal-Bench or SWE-bench. No one’s gonna train on my own day to day nonsense. So yeah.
Grace Shao (1:10:26)
Do you have any other tips for how us like research-driven people or our jobs are just here typing away on our computer how we can better use AI in our research Process?
Robin (1:10:40)
I will say it’s an iteration process. Like I think even you know, you have to kind of use it and be hands on, figure out, you know, what is useful for you the most and and you know find ways to kind of make it multiplicative, right? And just not just kind of have it summarize the news, but also, you know, we’ve built different Constructs on top of my data extraction funnel to try and you know have it actually analyze what’s going on, give it, you know, send me kind of reads on different things that I care about at any given point. And then I use it to iterate some of the stuff that I do day to day.
Grace Shao (1:11:24)
It can be as smart as how you make it to like how smart your inputs are, really. Yeah.
Robin (1:11:29)
I think so. And you know, what I found is you go around in circles, like you give it some stuff, you kinda work out whether it actually can do that or not. And sometimes you know, the answer’s not always yes. and yeah, and you know, try and kind of extend or you know, or try and give it you know more and more things to do until you so you know, just organically there will be use cases that kinda pop out from every so often based on my experience.
Grace Shao (1:11:59)
I don’t condone the fact that you weren’t spending time with your daughter full on one on one and actually on your voice AI tool. But my husband’s gotten to a point where he’s wearing his Apple Vision Pro and he has like seven of his agents in a line and he’s just sitting there like controlling them. I’m just like it’s gone too far. Like I think if there’s gonna be like a pushback by all the kids and wives at this point, not to overgeneralize. Now, look, what has really changed for you or your view on over the last six months? you know, has something fundamentally shifted in your research or something your understanding of AI and the technology.
Robin (1:12:39)
My understanding of AI seems to change every two weeks. So, you know, that the whole process of being in January, staring at these two IPOs, and then you know, fully going down the rabbit hole of trying to understand them and keeping you know, trying to keep track of everything, build a network around, yeah, I mean it’s it’s been it’s been wild, how much everything has changed. I’d love to be able to distill it down to one thing, but I think the reality is just, you know, everything has changed and the thing that I’ve discovered actually that’s been interesting is you know, I run a small team and there are people who normally work for me and in the old world I used to kind of staff them on a small handful of things a day. And then they will go away and do it and they come back and I would have to make, I don’t know, three or five consequential decisions a day or, you know Have kind of deep, deep conversations with myself about how to do something. Now, increasingly, with AI — I mean they’re still doing that, but at the same time I’m knocking around ideas in my head and I’m sometimes using AI bots to help me kind of process what I think. and they come back so quickly that I find myself having to make five consequential actions an hour. And then at the end of the day I’m like, you know, my workload has increased as a result, which is I don’t know if that was the desired outcome, talking about AI making people’s lives better. But there you go.
Grace Shao (1:14:15)
I think it’s just because you’re too much of a doer and too competitive. I think for people who are high-agency people, they are doing more and they’re experiencing AI fatigue. For people who wanna just clock in, clock out and just do the three things they were told to do, their lives are made easier. You know, it’s like you know, writing three emails used to take maybe like a day, now it takes twenty minutes or less.
Robin (1:14:35)
Then I wouldn’t be on this pod, so there are upsides.
Grace Shao (1:14:39)
Look, one last question for you. I asked everyone, what is one differentiated view you hold or you know, something a bit non-consensus you think?
Robin (1:14:49)
Yeah, I think the gaming thing is probably the most controversial. you know, or the maybe the broader idea of I think if you want I think it’s it’s the whole idea of of judgment and taste where, you know, there’s still this belief and it’s almost a religious belief at this point, which is because I don’t know how to disprove it either. which is I think on some level it’s you know That will always be valuable, the idea that I’m still of the view that it’s quite difficult to get AI models to spit out something that’s outside of its own distribution and training data. And so, you know, on some level you still have human input being the arbiter of something that’s ninety percent of the way there and something that’s truly kind of differentiated. So yeah, I vote at the end for humankind.
Grace Shao (1:15:47)
But then I have a follow-up question on that. It’s just like I think it’s easy for people who’ve built up taste or, you know, like yourself, you’ve been in the industry for long enough to build up your own taste, your own judgment. How do people without that kind of experience build up human taste still when they’re joining the workforce or they’re growing up like our children are growing up at an age where, you know, AI is natively embedded in everything they do? So, how do you still differentiate that taste? Because You know, now like we’re seeing these like parallel structures of sentences everywhere you go. It’s driving me crazy. Even Instagram ads are like, you know, they have like these very obvious parallel structures and it’s like driving me crazy. I’m like, dude, like this marketing associate did not write this, but I think what you’re
Robin (1:16:29)
Okay.
Grace Shao (1:16:29)
Seeing is people without that taste judgment and now just copy and pasting whatever AI spits out.
Robin (1:16:36)
Yeah. I mean we’re gonna end this conversation like every good Asian parent and talking about parenting at the end of it. no, look, I mean it is a conversation I have with myself. Like how do you educate somebody or how you know, whether it’s your own kid or whether it’s somebody that you work with or whatever, then, you know, how do you develop taste from first principles and Yeah, I don’t know. I don’t have a great answer, but I do think exposing yourself to original content and doing things the hard way, at least in the beginning, is still important.
Grace Shao (1:17:16)
Well, thank you so much for your time. Very generous with your time today, Robin. I really appreciated your insights and everything.
Robin (1:17:21)
Appreciate our conversation.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



Get full access to AI Proem at aiproem.substack.com/subscribe

In this episode, I speak with Bei Shen, CFO of Manycore Tech, the Hangzhou-based company behind Kujiale in China and Coohom overseas. Manycore started as a cloud-based 3D design software company serving designers, furniture brands, retailers and property developers. Today, the company is expanding into spatial intelligence, using its 3D data, simulation capabilities and software to explore applications beyond design.
We talk about Manycore’s evolution from startup to public company, including its IPO earlier this year and how the company’s story has changed since going public. While its core SaaS business still accounts for the majority of revenue, Manycore is increasingly positioning its proprietary 3D data and technology as a foundation for spatial intelligence and new applications.
We also dive into SpatialVerse, AholoWorld and Manycore’s work in robotics and embodied AI. Bei explains how the company thinks about spatial intelligence—not simply as a data business, but in terms of the systems and simulation environments needed to help AI understand physical space. We discuss potential applications in robotics, game design and filmmaking, as well as the question of how much intelligence different types of robots actually need.
Finally, we discuss Manycore’s global expansion, partnerships and long-term strategy. We explore whether spatial intelligence will become a market dominated by a few global platforms or remain fragmented across industries and geographies, and what Manycore sees as its role as more robotics companies begin building their own physical AI systems.
The AI Proem Podcast is under the AI Proem newsletter, which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.
Chapters
01:18 Leaving Investment Banking for a Startup in Hangzhou
05:29 From Silicon Valley to Going Public in Hong Kong
06:19 First of the Six Tigers to IPO
07:28 3D Shift Toward Spatial Intelligence
14:20 Data, Simulation or System: What Is the Product?
18:14 Learning More of SpatialVerse and AholoWorld
31:59 The Role of Spatial Intelligence in Robotics
34:04 How Manycore Fits Into the Robotics Stack
38:11 Global Expansion and Strategic Partnerships
42:31 Will Spatial Intelligence Consolidate or Fragment?
46:20 Manycore’s Focus and Strategic Pillars
AI-generated transcript (for reference only)
Grace Shao: Hi, Bei. Thank you so much for joining us today.
Bei: Hi, Grace. Good to be here.
Grace Shao: Yeah. So, you guys are one of the hottest AI companies that listed in Hong Kong this year, and a lot of people have a lot of questions. But to start with, tell us about yourself. When I was learning about your background, I thought it was quite fascinating. You’re almost like a Joe Tsai story. You had a very successful finance background and a successful career in Hong Kong, and then you decided to jump over to Hangzhou to join what was still a relatively unknown startup. Tell us what made you want to make that jump and leave your cushy banking role. What was the spark about this company for you? And tell us a little bit about where the company is right now.
Bei: Sure. My name is Bei Shen. I’m CFO of ManyCore. I joined the company in 2019. Before that, I was an investment banker for 14 years. I worked for Citigroup in New York, then moved to JPMorgan in Hong Kong. The last nine years of my banking career were at Goldman Sachs.
Around 2018 or 2019, I started thinking about what I wanted to do with the rest of my career. Traditionally, I focused a lot on clients in more traditional industries. I covered companies in the power, mining and energy spaces. It’s an interesting job, but the sectors are relatively traditional.
So I started asking myself how I could get more exposure to technology and internet companies. For me, it was very difficult to switch industries within the bank, so I started looking around for opportunities. Luckily, ManyCore was looking for a CFO. After talking with the founders and the team, I found the company very exciting, so I joined in 2019. I can’t believe it, but it’s been almost seven years now.
Grace Shao: Yeah. And I know you recently took the company public, but before we get into all that, tell us about your three co-founders, because they have quite interesting backgrounds. They’re quite young. They came back from Silicon Valley, bright-eyed and wanting to start something in China. Tell us about the vision they had in the early days and where it has led you now, roughly 15 years later.
Bei: Yes. The company was founded around 2012. The three founders were classmates at UIUC, which has a very strong computer science program in the U.S. Our chairman, Victor, and our CEO, Chen, actually went to the same undergraduate university, Zhejiang University, which is also where our company is based. We still recruit a lot of people from Zhejiang University, which is a great school. And our CTO went to Tsinghua.
All three of them studied at UIUC in fields related to computer vision and high-performance parallel computing. After graduation, they all went off to cut their teeth in Silicon Valley. Victor worked for NVIDIA for a couple of years, Chen worked for Microsoft, and our CTO worked for Amazon.
They were all working in Silicon Valley, but they wanted to come back to China and participate in this exciting market. Back then, Victor had this idea of putting GPUs on the cloud to serve more clients. He was working at NVIDIA on the CUDA team, so he was involved in the early days of figuring out how to put compute on the cloud and serve more customers. Obviously, this was before AI became what it is today.
They built a demo and came back to China. Luckily, the Hangzhou government was welcoming overseas graduates and helped them start the company.
The original idea was very simple: they wanted to put GPUs and compute in the cloud and make that compute available to more people. In the beginning, it was very difficult because AI hadn’t taken off yet, autonomous driving wasn’t in full swing, and crypto wasn’t either.
Luckily, they found a very interesting application in interior decoration. In the old days, if you used on-premise software, it could take a very long time to render a photorealistic picture. With their technology, you could put that computation on the cloud and use multiple GPUs to accelerate the rendering process. That enabled users to create photorealistic renderings in minutes. Now it’s seconds.
That really changed the industry in a big way. So that’s how they started. It was fundamentally a technology company trying to find applications for its technology.
Grace Shao: How would you describe the company today? How would you position ManyCore in two or three sentences? Clearly, it’s no longer just about Kujiale and 3D interior design.
Bei: Obviously. The company has had 14 or 15 years of history. Before 2023, we were basically the largest 3D design software provider for interior design. But since 2023, the company has increasingly focused on spatial intelligence.
To put it very simply, we’re trying to help AI perceive, create and eventually act in a three-dimensional world, so that AI can eventually move from the digital world into the physical world. That’s where the company is focusing right now.
Grace Shao: Perfect. I think you were definitely one of the hot IPOs earlier this year. You went public in April and were one of the first of the Hangzhou “Six Little Dragons” to list. It felt like a point of pride for Hangzhou and for this new wave of Chinese AI companies. What did going public mean for you and for the company?
Bei: Obviously, it’s a big milestone for the company. We raised fresh capital to fund our future growth, especially in spatial intelligence. We need more compute and we need to hire more talent.
But from a business perspective, it also put us on the international radar. We already have many international clients, but it can still be difficult for a Chinese technology company to sell products to overseas customers. Being a public company, with your company story and financials becoming more transparent, definitely helps a great deal in promoting ourselves and selling our products in markets outside China.
Grace Shao: I want to double-click on something you said earlier about how the company evolved. When you filed the prospectus, I went through it, and it was still mostly focused on your 3D interior design technology. Now you’re clearly pushing a new narrative around spatial intelligence, which frankly wasn’t emphasized nearly as much even a year ago when you filed the prospectus. Things are moving so fast.
Tell us about how that shifted and why you had this moment of pivot. Was there an epiphany during the process of going public, or after you went public? Did something hit you where you realized there was this gold mine you were sitting on? Tell us the story behind that.
Bei: Sure. That’s an interesting question. Just to go back a little bit in terms of our IPO history, we really started preparing for a Hong Kong IPO in the third quarter of 2024. Then we filed our prospectus on February 14, 2025.
The IPO process is relatively lengthy for Chinese companies because every company going public needs approval from Chinese regulators. For us, it took a bit longer because of our structure. We finished the IPO in April this year. So looking back, the process took almost a year and a half.
Obviously, both the company and the industry changed enormously between the day we started the IPO process and the day we actually listed.
Our thinking was that it would be unreasonable, or even impractical, to keep updating the prospectus every time the company changed because this industry moves so quickly. So we made a decision to keep the discussion of the new business and products relatively minimal.
That’s why, when you read our prospectus, you see a lot of disclosure about our older, existing business, which is obviously still important. But the new businesses were changing so much that we didn’t go into as much detail.
After the IPO, we started talking to more analysts and investors and trying to give them a more updated picture of where we stand in spatial intelligence.
Grace Shao: For sure. So what are the top-of-mind questions or areas of interest you’re getting from investors right now about the business?
Bei: This is a very frontier area. Large language models have obviously received a lot of attention over the last couple of years since ChatGPT came into existence. There have been many advances in model capabilities, coding, image generation and video generation.
But we’re focusing on a relatively different type of AI. We sometimes call it physical AI. As I said, we’re trying to help AI understand the 3D world, which is very different from reading text and giving you an answer, or generating a picture or a video.
One of the challenges in our space is that we don’t have nearly as much data as large language models do. They can access internet text and enormous amounts of video. In our space, the amount of data is several orders of magnitude lower and much less dense compared with text, pictures or video.
That’s why it’s difficult. It’s very hard. People also need to spend some time understanding what we’re doing because I believe we’re working at the frontier of what could be the next wave of breakthroughs in AI.
Grace Shao: My understanding, according to your public filings, is that roughly 90% of your revenue is still coming from the traditional business, particularly Kujiale, and that’s really funding the new initiatives.
As you move into spatial intelligence and physical AI, you touched on data as the bottleneck. But data is also part of your moat, right? You’ve had more than a decade of experience working with 3D data. Tell us more about the connection between your traditional business and this new business.
Bei: Sure. I wouldn’t say data is the only reason we chose spatial intelligence, although it’s obviously a very important aspect of the business.
There are many connections between our traditional Kujiale business, or Coohom internationally, and what we’re focusing on today.
Even 10 or 12 years ago, we adopted a very integrated technology architecture. We bought our own GPUs and did rendering using our own GPUs in order to provide the service to customers worldwide.
That’s actually quite similar to what large language models or 3D models are doing today. You’re utilizing compute to provide products and services to people around the world over the internet.
So that’s one connection in terms of the technology lineage. We did a lot of hardware-software optimization to make sure rendering could be provided at the lowest possible cost. Similarly, if you want to do inference today, even if you have a very good model, you still need to keep inference costs low in order to remain competitive. That’s something we’re obviously very good at.
Data is also very important. We’ve accumulated a large amount of data. In hindsight, it’s fortunate that, compared with the on-premise software that came before us, we had all the data on our cloud platform. That wasn’t necessarily by design at the beginning.
But in today’s world, 3D data is extremely valuable and very difficult to obtain. Because of our cloud-based architecture, we’ve been able to accumulate a large amount of data, especially structured 3D data, which is critical for training 3D understanding and 3D models.
So between our technology lineage and our data library, I think we’re in a very unique position to explore spatial intelligence.
Grace Shao: So you have a lot of 3D data, but this is spatial data. It’s not necessarily the movement or motion data people talk about needing for robotics training.
But when I spoke to your team while visiting Hangzhou, it sounded like a lot of your clients may actually be robotics companies. What are you providing to them today? Is it a 3D intelligence system? Is it data that helps robots operate better in physical space? Or is it a bit of both?
Bei: This has an interesting history. I think it was back in 2022 or 2023, during the pandemic, when nobody could really go anywhere. We were all stuck in offices or at home and couldn’t travel abroad.
We received an email from Silicon Valley from one of the large technology companies. They came knocking on the door and said, “I heard you guys have some interior-setting data.”
We said yes.
They said, “We’d like to buy some.”
At the beginning, we thought it was spam or some kind of trick. But it turned out to be a real client doing research.
We didn’t think too much about it. We struck a deal and helped put together some synthetic data they required. We made some money, not much.
Then the next year, another technology company came and asked for something similar. This time, we took notice. We thought, okay, there must be something valuable in our data.
So we started asking these U.S. clients, “What are you actually doing with our data?” We’d had it for over a decade and hadn’t really thought too much about it. Luckily, we hadn’t deleted it just to save storage costs.
Only then did we find out that these were large technology companies in the U.S. training robots. They needed synthetic settings in which they could test and train their robotics policies.
That’s when we realized we were sitting on something interesting and valuable. We started thinking about how we could better commercialize the data we had.
Obviously, we’re not satisfied with simply providing raw synthetic data. Right now, we’re speaking with customers both in China and the U.S. and trying to help them train and evaluate their policies more effectively.
Eventually, we also hope to train our own model.
We believe that if you want a world in which robots, or physical agents more broadly, can become fully autonomous, they need their own brain. The ability to perceive and understand physical surroundings, reason about them and act within them may require spatial intelligence. That’s something we’ve also started working on ourselves.
So right now, we’re still providing synthetic data to customers. We’re also trying to train our own model, which we eventually hope can be put into intelligent robots or embodiments of different forms so that they can really act in the physical world.
Grace Shao: That’s really interesting. There’s a bit of serendipity there. Things just happened and you were there at the right time with the right data.
I was reading through your materials. There’s something called SpatialVerse, and you’re also releasing HoloWorld. What are these things for? Tell us more about these products.
Bei: Sure. These names keep popping up. Sometimes I get confused as well because things change so fast.
As I said, in 2022 or 2023, we started selling synthetic data solutions to robotics companies. Later, AR and VR companies also came to us asking for something similar because they need data to train goggles or glasses.
We put this type of business together under the name SpatialVerse. That’s one line of activity and business we’re developing.
As I said, eventually we’d like to train our own models and put them into robots.
Another branch of research we’re working on is helping agents create synthetic worlds. This is somewhat similar to what Dr. Fei-Fei Li’s company, World Labs, is doing.
Basically, using a simple prompt, text or pictures, you can quickly create a virtual space where you have geometric information as well as information about the objects within that 3D space.
People talk about “world models” a lot these days, and sometimes the term is misused or misinterpreted. But the way we understand it, in order to have this capability, you really need a model that can generate a 3D world.
It’s not just a continuation of pictures. There are very good models today that can give you 20 or 30 seconds of short video. But what we’re after is the ability to use computing technology to generate a 3D world where the objects within that world have certain physical properties, and where you also understand the geometric relationships between those objects.
That’s critical for robots eventually being trained inside that world.
Grace Shao: So rather than a video-generation model, which is where a lot of multimodality efforts are going right now, you’re really trying to create 3D spaces. It almost feels like creating a little Sims world.
What are the use cases then? Off the top of my head, there might be game design, filmmaking and, of course, robotics training. What do you think people are missing when they think about what this tool or product could eventually be used for?
Bei: That’s a great question. Again, this is relatively new.
Historically, 3D design has been a relatively niche market. Before us, you had all these on-premise 3D design software products, but they’re difficult to master and use.
Compared with picture editing, 3D design has historically been quite niche. It was mostly used in architecture, industrial product design and VFX.
What we did was make the 3D world easier for ordinary people to generate.
One area where we’re already helping customers is media. In China, one-minute and two-minute micro-dramas have become very popular. You see many of these productions on Douyin, and a lot of the content is already being generated by AI.
We’re helping some of these creators produce spatially consistent 3D worlds. If you rely purely on video generation today, you can get hallucinations after a certain amount of time. Objects start floating around. You leave a room, come back, and suddenly the objects are missing.
Using our product, these producers or content creators can maintain a spatially consistent 3D world and produce something higher quality. You don’t have the same spatial hallucinations you get from video models.
The other application is robotics training and evaluation, which we’ve already discussed.
It’s inconceivable that you can train robots in every possible physical environment. Thinking through all the corner cases would be too expensive and too time-consuming.
So in order to train and evaluate these policies, I believe it’s critical to have virtual worlds where you can put robots through testing virtually.
Those are two areas where we’re already putting our technology to use. Eventually, I think there will be many more applications.
Grace Shao: That’s really interesting. I want to double-click on the micro-drama example.
To help me understand, are people using your technology in parallel with something more traditional like Seedance? One is more for aesthetics and one is for spatial control? Is it layered?
Or could your technology eventually compete with and replace a more general-purpose video model like Seedance?
Bei: That’s a great question. Right now, the way we serve customers is really a layered approach.
We already have a product out that you can try called LuxReal. We rolled it out about a month or two ago.
Basically, you can upload a script, and it’s an agent that helps you produce a 30-second, one-minute or two-minute micro-drama based on that script.
First, you use our product to create the 3D world. For example, if you want to shoot a micro-drama set inside an ancient Chinese palace, after reading your script, we’ll generate that palace for you. Let’s say you want two rooms inside Beijing’s Forbidden City. We can create those rooms, and then you can move your camera around within that space to shoot the scenes.
Our customers don’t only use our modeling capability. They also use Seedance because we don’t actually do the video-generation part right now.
We help you create the spatially consistent 3D setting, and then you put Seedance on top of that. You can very quickly produce a one- or two-minute micro-drama.
If you only use Seedance, you may have to do a lot of editing afterwards because certain things don’t look right. You have to spend manpower editing out hallucinations.
With our product, the room is always there and the table is always there. The table isn’t going to change when your camera changes.
So it’s basically a very efficient tool for these micro-drama producers.
Grace Shao: That’s very interesting. So essentially, you’re producing one asset layer in the broader workflow for these creators.
It’s funny because when I was talking to people at Kling and Kuaishou, they said people don’t necessarily care about inconsistencies yet. Sometimes the cat turns out white, sometimes the cat turns out black. There’s definitely an understanding that AI content, as of now, isn’t that sophisticated.
I want to shift the conversation to models and robots.
I’m going to put you on the spot here. You mentioned Dr. Fei-Fei Li. Yann LeCun and Fei-Fei Li are both world-renowned scientists working on something around this realm. They’re both trying to push forward ideas around world models. Obviously, this is still in a very nascent stage.
How do you see the field? And frankly, how do you see your company’s position among all these global competitors, which have a lot of technological influence and obviously a lot of capital behind them as well?
Bei: Great question. I wouldn’t say we’re directly competing yet. As I said, this is a fast-evolving and changing industry, and everyone is still doing a lot of exploratory work.
Dr. LeCun’s approach, to be honest, I don’t understand in great technical detail because I’m not computer-science trained. I’ve read about it, and one apparent benefit of his approach is that you may not need as much compute to come up with these highly realistic representations.
We haven’t paid too much attention to his work yet, but obviously we’ll be very interested to see what he develops.
Dr. Fei-Fei Li’s approach is more similar to ours. Both companies are trying to figure out an efficient model for creating a 3D world where you not only see the world as we see it, with the correct textures, sizing, depth and perception, which is the rendering side and something we’re very good at, but where you also embed more information.
We’re trying to add physical properties such as friction coefficients and how wind behaves. As customer requirements evolve, we’ll try to put more and more information into that model.
Eventually, it will be interesting to see what applications emerge outside media and robotics training when people have these kinds of worlds available.
Grace Shao: I listened to one of your CEO’s interviews, and he said that what you’re trying to do is create spatial intelligence that can help translate physical space so LLMs can better understand it.
He also discussed how world models shouldn’t necessarily be produced by every individual robotics company, or at least shouldn’t be viewed as interchangeable, because each use case can be so different.
So I guess my question is: how should we think about a robot being used to remove blood clots, where the work is incredibly meticulous, versus a robot designed to lift heavy objects? What kind of 3D data and 3D model does each need?
It seems like “world intelligence” or “world models” is too broad a category to serve every single demand in the space right now.
Bei: I’m not 100% sure which episode or interview you’re referring to, but I think what he was probably talking about is the robotics industry today.
Obviously, the level of optimism varies depending on who you talk to. But based on our conversations with the industry, a general-purpose humanoid robot is still pretty far away.
I wouldn’t say it’s next year. Maybe it’s five years, maybe it’s 10. But just imagine a humanoid being able to do 100 chores in your household. I think that’s still quite a few years away.
There are so many challenges. One of them is exactly what we’re working on: can you teach a robot to understand different settings?
The minute it walks into a room, can it immediately understand, “Okay, this is a table, this is a desk, and these are the relationships between the objects”?
We’re still working on that.
Building a general-purpose, fully autonomous humanoid robot is hard.
But if you narrow the problem down to more vertical or specific-purpose robots, I think that’s more doable. The level of complexity and intelligence required is much easier to achieve at this stage.
I think the industry is more likely to evolve through more and more specific-purpose robots. One robot might pick up boxes. Another might help with laundry. I’m just giving examples.
That seems like a more likely roadmap than trying to immediately build one general-purpose robot with omnipresent capabilities.
As you build more and more of these scenario-specific robots, maybe eventually you arrive at a stage where a more general-purpose robot becomes possible.
That’s how we see the world and how we’re tailoring our R&D efforts. We’re not trying to go after one extremely general spatial-intelligence model right now. We’re trying to crack these silos one by one.
Grace Shao: So which verticals are you focused on today, out of all the different kinds of robots you’re serving?
Bei: To give you a few examples, we think household robots are probably difficult, at least in China, because Chinese households tend to live in relatively small spaces. The margin for error is extremely small.
So right now, we’re focusing on helping robots in more industrial settings.
For example, in warehouses, we can teach robots to quickly understand the warehouse because the level of complexity is relatively lower than in a family setting.
We’re also helping some robotic dogs patrol power stations. They need to walk around, identify anomalies, record them and report them.
We believe these are some of the low-hanging-fruit applications today, where we can teach robots or robotic dogs to perceive and understand the 3D world.
Grace Shao: Do the economics make sense right now? Frankly, if you’re trying to replace relatively simple tasks or labor, especially in China or elsewhere in Asia where labor costs are relatively low, does it make economic sense?
Bei: That’s a great question. The economic equation is definitely important as we put more effort into this.
Some of the key areas we’re trying to explore are places where it’s dangerous or costly for human beings to operate.
For example, around high-voltage power stations or transmission lines, it’s definitely safer to have robots patrol instead of human beings.
Or in remote areas and underground mines, if you have water leakage or some geological situation, it’s safer to send robots and perhaps drones to inspect first rather than sending a human rescue team directly.
Grace Shao: That makes sense.
But my understanding is that you’re purely on the software side right now. Does it make sense for these humanoid robotics companies to pay for your service and technology, or does it make more sense for them to train and build their own models internally?
How do you view that? There are obviously different camps. Some people say OEMs can build different types of hardware while companies like yours provide the intelligence underneath. How do you see that trend?
Bei: Right now, we’re only focusing on software, as you correctly pointed out.
We’re trying to be model-agnostic, and we’re also trying to be embodiment-agnostic.
Basically, we’re trying to develop technology that different robotics companies can use to train and evaluate their policies.
Obviously, we’re not there yet, but that’s our goal.
We’re not trying to build robots or robotic dogs ourselves. We’re trying to help these companies find a very cost-effective way to evaluate their policies. That’s our approach right now.
Grace Shao: What do you think people are getting wrong or misunderstanding about the industry today?
In spatial intelligence and robotics, there’s obviously a lot of buzz and a lot of hype. Unitree has been getting a lot of attention as well.
Do you think there’s too much hype right now and that we should be more cautious because progress is still going to be slower than the public expects?
Or do you think the misunderstanding goes the other way, and people are underestimating how quickly this technology could proliferate in niche use cases and eventually reach consumers?
It’s a big, open-ended question.
Bei: Sure. From my perspective, I obviously believe this technology has very broad applications in the future. But the capabilities have to get there first, and the cost has to be low enough for the technology to proliferate.
I believe spatial intelligence is a critical part of human intelligence. It’s almost innate to us. Dr. Fei-Fei Li has made a very good argument around that.
After thousands of years of evolution, human beings can see things and quickly understand their geometric and 3D relationships.
That’s something large language models don’t really have today, but it’s critical if AI is going to operate in a physical context.
Right now, it’s great that AI can solve math problems or write poems. But can you actually ask a robot to do your laundry? Can you trust it to do all these tasks?
Right now, we’re not there yet. But I believe we’re on the way.
I wouldn’t necessarily call it a misunderstanding. I think the difference in opinion is really about how long it’s going to take.
As I said, one of the biggest challenges facing our industry is data. We need to find smart and cost-efficient ways to obtain more data because models are a product of that data.
Large language models are really the product of compute multiplied by data. Our industry is no different.
That’s why we’re thinking about different ways of capturing more 3D data. We’re working with different hardware companies. Robotics companies are one category. Scanning companies are another.
We hope to have more hardware companies work with us so that more users can use our technology to capture or generate 3D data.
In the long run, we need a flywheel where applications, data and models all improve in tandem, level by level.
But right now, the flywheel isn’t flying yet. We’re working very hard to push it forward.
Grace Shao: That makes a lot of sense. More users mean more use cases and more scenarios, which give you better data. Better data improves the models, which then lets you serve clients better.
On partners and clients, you mentioned earlier that you already have quite a global footprint. I think that’s fairly unique among Chinese companies that are trying to go global today.
When you think about international expansion and distribution, what’s most important? What kinds of partnerships are you looking for?
You mentioned that some large Silicon Valley technology companies are already clients. How should we understand those relationships?
And more importantly, do you face localization as a bottleneck, or is that less of an issue in your particular sector?
Bei: Great question.
Putting it in the context of Kujiale or Coohom, localization is actually very important.
For example, if you want to sell an interior-design product in the U.S., the industry is very different. China and the U.S. both consume furniture and decoration services, but the industry relationships and dynamics are very different.
Localization is therefore critical.
Just to give you a very simple example, even the measurement systems are different. China uses meters, while the U.S. uses feet and inches.
That’s a tiny example, but there are many localization changes you need to make in order to fully satisfy the local market.
What’s interesting is that this has changed quite a bit in the AI context.
ChatGPT basically became global overnight. I think one reason is that the model itself became so powerful.
You can almost think of the model itself as the product. You don’t necessarily need to build many layers of user interface on top of it.
We’re beginning to see that in our space as well.
If you have a very powerful world model, for example, you can generate 3D objects or scenes relatively easily. There may still be differences in language, but in terms of usability and application, it becomes much easier to promote globally.
That’s a big opportunity for us.
Eventually, we hope to offer a product that has a global appeal similar to ChatGPT, where you have users all around the world.
Obviously, that’s not easy. First of all, you need to come up with an extremely strong model that can actually serve clients and users worldwide.
Grace Shao: So what I’m hearing is that on the consumer-facing side, something like Kujiale obviously requires more localization.
But if you increasingly position yourself as a B2B support technology or an infrastructure layer underneath consumer-facing products, you may need less localization. Is that a fair understanding?
Bei: That’s a fair summary.
Coohom, by the way, is the international version of Kujiale. It’s not just a language translation. We’ve adapted Coohom depending on which market we’re entering, so we made a lot of localization changes.
But for a new product like LuxReal, the micro-drama product, we didn’t have to do much localization besides language.
That gives you an idea of how, in this era, products can become international much more easily because the underlying layer becomes extremely powerful and important, while the application layer on top can be relatively simple.
Some clients can even develop their own applications based on our technology.
That’s how we envisage the future.
We’d like to develop very powerful models and offer them through APIs or SDKs. People can then do their own development and build secondary or tertiary applications on top of the model.
That’s a change in paradigm compared with the past. As a software provider, you used to have to build many of these applications yourself.
Now users and customers can use things like vibe coding to build many applications themselves. We don’t necessarily have to do all of that anymore.
Grace Shao: That makes a lot of sense.
So just one last question on this part: should we understand the future of spatial intelligence as being more fragmented and vertical by sector and use case, rather than by geography, compared with how software evolved during the internet era?
Bei: I would tend to agree with that assessment.
As I said, because the data is so difficult to obtain, I think the industries where we can establish these small data flywheels will develop more quickly than others.
So I believe it’s going to be a more fragmented landscape compared with large language models, where eventually you may have fewer than half a dozen truly global companies. There are obviously more today, but I think LLMs will ultimately become quite concentrated.
In large language models, the data is relatively open to everyone because everyone has access to the internet.
A lot of the competitive landscape is therefore determined by who has more compute or who has the best talent and algorithms. Large companies have a huge advantage in that environment.
Our space is different.
There isn’t a universal 3D data library where everyone can simply start working on the same dataset.
First of all, obtaining the data itself is a challenge.
We obviously have an advantage because of the work we’ve accumulated over the years, but eventually we still need to find more and more methods of getting additional data.
So I think the game is somewhat different from large language models.
Grace Shao: I want to take a step back.
You guys are based in Hangzhou. Like you mentioned earlier, there was an effort in Hangzhou to attract people to come back because of how strong the ecosystem is.
Obviously Alibaba is there, Ant is there, there are a lot of e-commerce players, and many of the startups that came out of Hangzhou over the last decade have somehow been related to e-commerce.
It’s interesting that you didn’t get sucked into that orbit.
When I was reading about your story and looking at the earlier days, I thought it was funny because you could very naturally have gone into e-commerce staging and 3D content creation. That could have been a logical path for serving domestic clients, especially given that you were based in Hangzhou.
What was the thinking behind not going into that vertical?
Bei: We’re no exception. We tried e-commerce. It didn’t work out.
Grace Shao: I love the candidness.
Bei: We’re no exception.
Going back to Kujiale’s early days, we came up with this interesting software for designers. The natural next step was: can we sell furniture?
We tried. It didn’t work out.
I think that’s probably largely due to the genetics of the founders. They weren’t from that industry. They’re not e-commerce experts.
It’s also simply the nature of furniture and home decoration. It’s very difficult to commoditize. It requires a lot of service. It’s not like selling a book or laptop through e-commerce.
Even Alibaba tried, and I don’t think the results were very satisfactory.
So we dabbled in it. We burned some investors’ money, but not too much.
Then we realized, okay, it’s not for us.
We came back and said, we’re going to focus on software.
And lo and behold, we found this opportunity in spatial intelligence.
Grace Shao: Definitely. That makes a lot of sense. Furniture isn’t an easy thing to sell. It’s tailor-made, personal, huge and bulky, and logistics aren’t easy. I can imagine it’s not an easy business.
Looking forward, as CFO, I’m sure you’re thinking about how to invest the newly raised money and looking at the next three- to five-year horizon.
Where should we be looking? What is the company’s focus? What are the strategic pillars for you?
Bei: Great question. We think about this every day.
Going back to the basic AI paradigm, it’s always formed around compute, algorithms and data.
We’re really going to focus on those three things as we try to push the company to the next level.
Data is probably the hardest part because it’s not just about money. You need to think about smart ways of obtaining that data.
Talent retention and talent recruitment are also obviously the number-one priority for management.
You definitely know how expensive data scientists and algorithm scientists have become these days.
Grace Shao: How much are they making these days in China? Give us a range.
Bei: Not as much as their U.S. counterparts, I think. But even for college graduates fresh out of school, if you have the right experience and pedigree, you can make at least five to 10 times what a traditional software engineer might make.
It’s a very highly sought-after pool of talent.
Grace Shao: Okay, so are we talking about RMB 2 million to RMB 3 million? I’m trying to force you to give us a range. Five to 10 times is a big figure.
Bei: No, no. It’s a big figure, but software engineers don’t make as much as they used to anymore.
Grace Shao: The irony in all of this.
Bei: Exactly.
So talent is obviously a huge priority for us.
We’re also looking at interesting opportunities because we don’t know where the next technology is going to come from.
These days, acquisitions are really about people and talent.
If we see interesting algorithms or ideas coming out of labs, we’ll consider making our own moves.
As you know, we work very closely with Zhejiang University. We have a postdoctoral lab with Zhejiang University where we put a lot of effort into computer-vision research together.
Hopefully, we’ll identify talent and interesting early-stage products along the way.
Grace Shao: Would you go into hardware? Would you build your own robots?
Bei: Not right now. It’s already a pretty busy space.
But we’re definitely looking at hardware, probably not robots directly.
As I said, we’re already working with hardware companies to collect data.
We work with some scanner companies and LiDAR companies in China to collect 3D data.
To collect 3D data, you don’t only need cameras. You also need LiDAR, which gives you geometric information.
For example, we’re working with Hesai, which is a very good LiDAR company, to come up with solutions.
So we’re starting to dabble in hardware. We’re not purely a software company anymore.
Going forward, I think the two are going to be coupled together.
Especially in our world, if you want to have state-of-the-art 3D models, you will definitely need help from hardware companies, and we’ll probably do some of it ourselves.
Grace Shao: That makes a lot of sense.
You guys are well positioned given the amount of interest in you right now, and given that you’re in Zhejiang and close to Zhejiang University, where there’s a lot of talent coming out.
I think over the last year, the West has really opened its eyes to Zhejiang University, but in China everyone already knows it’s an absolute top-tier school.
You have people like Liang Wenfeng, and there’s just a lot of talent coming from that region.
Anyway, I really appreciate your time.
I want to ask you one last question, which is something I ask everyone who comes on the show: what is one differentiated view you hold? Something you think is non-consensus?
Bei: I think the TAM, the market for spatial intelligence, is actually going to be bigger than the market for large language models.
It’s still a little early, but if you look at human intelligence, language is only one part of our intelligence. Spatial understanding is also critical from an evolutionary perspective.
Eventually, if we can help AI crack that capability, the applications will be extremely broad.
I think there will be many things that robots or agents can eventually do that people probably haven’t even thought about yet.
It’s still early. It obviously requires a lot of exploration and effort, and there will be many pitfalls along the way.
But we believe this is a very, very large opportunity, and we’re fully committed to it.
That’s one view I think may still be a little bit off-consensus today.
Grace Shao: Thank you so much. I think we still have a long journey ahead.
Bei: Thank you, Grace.
Grace Shao: Thank you for your time, and congratulations again on the IPO.
Bei: Thank you very much, Grace. Nice talking to you.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



Get full access to AI Proem at aiproem.substack.com/subscribe

In this episode, I speak with Robert Wu, the founder and CEO of Baiguan . Our conversation focuses on two questions that increasingly overlap: how AI is reshaping the business of information, and how China’s distinctive mix of pragmatism, markets and state involvement shapes the way new technologies get adopted and financed.
We start with the professional data industry. As AI agents become a new orchestration layer above terminals, APIs, and research products, Robert argues that the biggest disruption may come not to the production of proprietary data itself, but to its distribution. For niche data providers like BigOne Lab, the opportunity is to make differentiated real-time data accessible at inference time. The unresolved problem is economics: licensing, access control and who ultimately captures the value when an AI agent becomes the interface.
From there, we widen the conversation to culture and political economy. Robert explains why debates about AI in China tend to focus less on existential or metaphysical questions and more on what the technology can actually do. We discuss whether that pragmatism comes from China’s history of technological catch-up, whether similar attitudes extend across East Asia, and the potential trade-off between being exceptionally good at applying technology and creating the conditions for more fundamental scientific discovery.
We then turn to the role of the state. Robert rejects the simple idea that China’s technology industries are created through top-down planning. Instead, he describes a hybrid system in which entrepreneurs often discover the opportunity first, while the state later supplies policy support, capital and the resources needed to scale. We use EVs and DeepSeek to explore that model, before moving into state subsidies, local-government incentives, private capital, Beijing’s evolving approach to public markets and why so many young AI and technology companies are choosing Hong Kong for their IPOs.
We close with two of Robert’s more differentiated views: that China could be entering a multi-decade equity bull market, and that outsiders often misunderstand China by assuming it has the same impulse to export its own political or cultural model. And for a slightly lighter ending, Robert explains one of the Chinese stock market’s most vivid metaphors: why generations of retail investors are compared with chives that get cut, grow back, and get cut again.
btw sorry for the weird glitch in the video around 12-13 min of the recording.
The AI Proem Podcast is under the AI Proem newsletter which has over 12k followers globally. To learn more about China AI, the business of AI, and how AI is impacting businesses, please check out the newsletter here and more insightful conversations here.
Chapters
00:00 Robert Wu, BigOne Lab and Baiguan03:09 From alternative data to the AI era06:02 Why AI disrupts data distribution12:11 Inference-time data, licensing and economics15:17 Why China feels more pragmatic about AI21:47 East Asia, belief systems and scientific discovery30:32 China’s hybrid model of state and private innovation45:24 Funding AI: state capital, private capital and IPOs50:30 Beijing’s market-stabilization playbook and policy risk01:07:29 Robert’s non-consensus views and the meaning of “cutting chives”
Transcript (AI-generated, for reference only)
Grace Shao (00:00)
Hey Robert. Good morning. So good to have you join us today.
Robert (00:05)
Good morning, Grace. Hello, everyone.
Grace Shao (00:08)
Robert doesn’t need much of an introduction. If you spend as much time in the Substack world as I do, you’ll know he’s a prolific writer covering everything from capital markets and property to technology and culture. My favorite niche is when he calls out Noah Smith’s articles for being wrong. Those are pure entertainment for me.For today, though, Robert is a student of history, politics and business, and I think it will be interesting to have him walk us through some of the bigger questions people have about China. I’m also curious, because he runs a data company, about how he sees the future of data providers as AI changes that relationship.So I’m handing the mic over to you, Robert. Tell us about yourself, your journey with BigOne Lab and Baiguan, and how you’re seeing the business evolve.
Robert (01:12)
Yeah, hi. So this is Robert. As Grace mentioned, we run a newsletter. But that newsletter is really our kind of side business. The actual BigOne Lab is a team of over forty people, which exclusively most of us work on data products and research products for institutional investors and corporates. Both in China and outside of China. But we have we’ve been very China focused. All of our data and research are about China, Chinese companies, Chinese industries, businesses. The Baiguan to me was partly accidental, but partly also kind of fateful. Actually in the very beginning during college I actually wanted to be a journalist. But I didn’t find a way. So I kind of dabbled in capital markets in investing, corporate finance for a few years. But eventually it kind of hit me that, there’in this new world there’actually other ways to do journalism. Data tracking, data analysis is actually could be a new form of that. And even with data you can do more powerful storytelling and that was the genesis of our newsletters as well. Right. So here we are. We are backed by S&P Global as well, which is I would say one of the most ris backed respectable, respected companies in our industry. And yeah, so it’a brief intro about ourselves.
Grace Shao (03:09)
Yeah, so tell us like what is unique about your data then in that sense.
Robert (03:14)
Right. So we started as a so-called alternative data company. Alternative is alternative to the traditional financial data, macro data, market trading data. It’no longer alternative now. All alternative data is mainstream data now. But it was first happening, it was because the explosion of data and information in the internet and especially the mobile internet age. There are just so many data being tracked. There’payment data, there’online com commerce and social media data, so vast number of data and multiplying exponentially every year. And some investment firms they realize that by harnessing all these data and aggregate them together and put them in the right context, you could actually generate a lot of alpha that is previously not available. Right? So that’how we started the business. It was a very investment firm hedge fund driven business. So that you know kick us to look at a lot of the industry verticals, a lot of the different kind of industries where there’data and we try to find the most granular, the most high frequency data we can find. Perhaps the you know we can have massive amount of data about mobile transactions in China, for example, every day, even every minute, all the transactions that we can have access to and analyze on. So that’different from many of these you know mainstream data providers, which we try to be very granular. We try to be very frequent. Yes.
Grace Shao (05:18)
Yeah, so that’really interesting. I think p one thing that really stood out to me and relating it back to AI is that when we were having our catch-up conversation, we were saying, okay, data plays obviously a huge role in AI. But what you distinctly said, there is the people that are involved in the pre-training data bit, there’like the Mercores of the world. There is the people who are more pivoting towards kind of the post-training data provider, which is what you guys are doing. Just tell us about that relationship and how you think the whole data vendor ecosystem is adopting to AI and or evolving with AI, especially and how like AI is now affecting, say, Bloomberg, Factiva, those mega data platforms that we traditionally know of.
Robert (06:02)
Yeah. So the term data company is really problematic for us. It’really a kind of a name that covers very different kind of businesses serving different needs, entirely different kind of businesses. So you mentioned that there are data companies that are serving the large language model training right now, the Mercor, the Surge AI. So they are they are they are good at massively labeling data, connecting the you know different type of data and help the help build up the data sets that are used for the training. Well for us, we are more on the on the on the more on the real time data end. And it’so for the industry that we operate in, we have Also, we have not a consensus on the name for our industry, to be honest. I call it professional data industry. Some people call it market data, some people call it market intelligence data. But at the end it’it’about tracking and understanding of the real world on a real time basis, if we have to define it. So it’much more about what is happening rather than the logical connections between different pieces of information, which I think is what the pre-training data i is mostly about. And so in our industry, AI is placing is playing a huge kind of disruptive role for our industry. So in the professional market data industry, there are main several main stages, maybe three. There is the production the original production of the data, there’a distribution, and there is the what we call activation. I won’t maybe go to details of each one of them, but if you understand production, production is really where the data is originated, right? For example, if you are Nasdaq, all the trading data on your Nasdaq platform is originated at Nasdaq. That’called production. The second is distribution. Is how you combine all this data into products, right? Companies like SP’marketing intelligence, like Bloomberg, like you know FacSet are in the distribution part. They don’t generate data on their own or mostly don’t not on their own, but they provide the interface for users to interact. Right. And activation is really how data is used. I won’t go to detail for that part. But right now the one the stage that is facing the biggest disruption is not the production side, right? You still need to generate data. You still have to have some kind of source of data. AI won’t help that. But on the distribution side, there’a there’a huge, I would say, change that is undergoing. Imagine if you are an analyst twenty years ago. It’required for you to have either a Bloomberg terminal or you know a FactSet terminal, or if you’trying to a wind terminal, right? It’it’a terminal kind of portal driven business. A go-to portal or source for information. AI is fundamentally going to change that by adding a new what we call orchestration layer above all the data types. You’not going to go to any terminal in the traditional software sense, but you’going to have this advisor to you that is going to massively, quickly, rapidly going through all the data, find the data you need, give you the conclusions, do the comparisons of and all that. Right. So there’a big tension right now between this trend of increasingly more people is rel relying on their AI agents to do research and the existing incumbents. Of the of the market which and then if you look at the incumbents the different companies are adapting differently so you have companies like SP and Faxet they are embracing AI and they signed big contracts with large language models they allow you know clause users or open AI users to access their data through these their AI products and they are they are embracing it. But then you also have company like Bloomberg, which is really at the core, at the at the at the apex of traditional financial and market data industry. I think they’still trying to figure out what to do with this. And I think their natural tendency is to build their you know in-house AI. They still want people to, go into their universe. And to make to like people still go to their universe to check the data. Right. So there is some debates right now and it’going to be interesting, who is going to prosper, who is going to stay. Yeah.
Grace Shao (11:39)
Yeah, Bloomberg definitely still wants you within their terminal. Everything is within their terminal. And once you exit terminal, that’majority of their revenue. They don’t want you to jeopardize that. But for someone like you, it’quite interesting. I said something I made a mistake earlier. What you told me was that you guys are a data provider on the inference end now. How do we understand that? And how do we understand how you are going to work with whether it’the model companies directly? Or how you will provide your institutional clients your data differently.
Robert (12:11)
Right. So at this moment we are still observing. We are actually niche provider of some really high value data, but not needed by most other people. So we’not like the mainstream data sets, but we are observing and we believe that in the end we’ll have no choice but to kind of open us up to the large language models. To us, this will be a new form of access to use our data. Apart from, right now we provide our data to our clients through API, through Excel spreadsheets, through even research reports, this is our current method. But in the end, I think as more and more clients rely on AI to pull data, we will we will we will kind of open us up. And that’the That’in the inference part. That’when people actually are using data to do analysis, to do research. And so we are firmly in that part. And I think it’just inevitable that we will be connected to these AI at some point. It’just the problem right now is about the economics. How do the economics work? How do we kind of get us exposed to it, but also make sure that there’strong enough licensing and you know strong enough gate that we can put on our more exclusive, more differentiated data sets. That’a question that there also hasn’t been a consensus yet in the industry. Yeah. So that’why we are taking this kind of stacking back and observing kind of view of it.
Grace Shao (13:55)
I see, very interesting. Okay. So enough about the dry stuff. The most interesting stuff I read from you are actually your takes, because I think your takes are very nuanced. They’you’a deep thinker. You bring together cultural sentiment, history, political reality, and then the business together. To start with, I think one of the questions I get the most from people right now is just that this general attitude around why Chinese people feel more normal about AI. I wouldn’t even use optimistic. I mean as a as a society as a whole, it does feel more optimistic. But it just seems like whether it’how the government and the regulators are looking at how to regulate this new technology or how people are adopting it, like a trial error kind of feel, there’less of a philosophical push up a pushback towards AI, but more maybe, obvious concerns of what disruption or change might may mean for job displacement, whatnot. But in general, quite optimistic, quite normal. How do you view this right now? If I just kind of bringing together all the different aspects and it because I can’t, I don’t believe people just saying, just because people are more pragmatic. That’that I mean, I made that argument slightly, but I even think it should be deeper, more nuanced than that.
Robert (15:17)
Right. Yes, I mean this is a good question. Without if you don’t ask me that, I wouldn’t even realise it’a question. Because sitting in China, it’true that it is well not you know most people don’t talk about that. Some people do, but definitely not a mainstream discussion on the kind of existential kind of risk of crisis that AI is posing to the humanity. That type of question is not that asked is not you know asked that often in China. For the good and bad, right? I personally I don’t know w which part which approach is better, the more pragmatic one or the more philosophical one. But that’the phenomenon. It’true. Most people don’t think i in that way. The exactly why, you know Probably I would if you are looking for a more subtle, more nuanced answer, probably you won’t be able to find here. Because I was I was also thinking that it’it’really because of the pragmatic and down to earth nature of most things in China. People tend to ask more, what can this be used for? Other than why we have to do this or w what’the bigger contact what’the bigger picture? People tend to focus on the productivity side of things. I mean that’just prevalent in all industries or new industries. And especially China attached a premium to new industries. I think that’the kind of cultural reflex of the last few hundred years, after China kind of fell behind the West. In terms of technology and suffered all the consequences from that. So there was now a kind of reflex to emulate the world, to catch up to the world in all kinds of new technologies out there. Every time Silicon Valley coined some new term, some new idea, there would be some at least some kind of reflection and discussion about that. If you remember a few years ago there was this concept called metaverse. Right. Now nobody talk about that anymore. But back then it was also a very hot topic in China because you know people might think this is maybe the future because the Silicon Valley, the US chose that, and maybe we should think about whether that’the future. When crypto came out first, China was at the very beginning also embracing it, right? My very first Bitcoin was bought in China with RMB, while when there was like many RMB exchanges there. Well, then it hit some problems, it got banned and all that, but that’what happened later. But China ha always had this at this contemporary China had the tendency to learn the new things and to try to, to grow their our own knowledge and strength along these new verticals. So that’the I would say the big context, the big framework that people use, kind of equipped themselves with when they look at these new things. And less so about the philosoph philosophical and maybe not a philosophical but metaphysical, right? The ones that are hard to prove or disprove at this point and just kind of descend into discussion about contact concepts, on the abstract side. That kind of discussion, that kind of discourses really doesn’t have a big market in China. Small circles, yes, but most people Just don’t like to engage in that kind of discussions. Platonic, Aristotle level discussions. Yeah.
Grace Shao (19:23)
Is it just because it’kinda I don’t know, it’just like what’there to gain from that for the average Lao Bai Xing the average Joe? When they think about it, it
Grace Shao (19:32)
Seems like it’kinda Okay, if I embrace it, I win. I don’t embrace it, I lose. It’a bit of FOMO. Especially for the next generation, when I talk to parents, there’less of a concern about what this technology might mean in terms of safety for the kids, but there’more about how do I embrace this technology and teach my kids so my t kids can go basically go ahead of everyone else and come on top? I don’t know. Is that kind of what
Robert (20:01)
Yes and I think big part of that the these better philosophos philosophical questions don’t tend to produce results. Right? It’not a question that w if we debate and discuss we’ll have some kind of agreement. They just tend to stay philosophical. But most people I would say that most people I know here don’t tend to keep going. Keep debating on this type of questions. And maybe that’right, maybe that’wrong. I don’t know. But that’just the phenomenon that we are seeing here. Yeah.
Grace Shao (20:39)
Just how it is. Less of a chatter class, if you must put it, or at least less prominent in.
Robert (20:44)
Yeah, good way to put it. Yeah. Yeah.
Grace Shao (20:49)
I think another thing we kind of touched on briefly when we were catching up for this recording was that we said, look, this Chinese pragmatic approach to technology and like how to even day to day life is not really just limited to China, right? Like it feels like it’a phenomenon across maybe even East Asia. Other markets like South Korea, Singapore, a lot of, studies, whether it’by Stanford or by local communities, have shown that people are also embracing, Singapore’own ministers are coming out talking about how they’claud coding or vibe coding out there. Why do you think I know that you write sometimes with a bit of a this versus that or like a bit of a historical and a eth ethnic and history kind of tied to your analyses? Why do you think that maybe East Asia feels more pragmatic towards AI or contemporary East Asia, like you put it just now?
Robert (21:47)
It’a it’a very deep question. So I think if I have to attribute, and I’m just throwing out ideas right now, religion is definitely huge part. The whole tradition, the tr of the history of thoughts, the history of religion, the history of philosophical discourses, has to play plays a huge role. So we don’t have a tradition of seeing something abstract, either as a natural law or a god in this part of the world, historically, right? So people the w the reason why the people are more pragmatic about things is just like all the things that happen in your life are pragmatic. There’a there’a flood and then we have to fix it. We have to you know do some work on around that to fix it. All the laws, I mean all the folklores, all the all the lessons of history surround about how to deal with these you know disasters, wars in human life with a human way. Right? So it’it’more there’a concrete problem, there’concrete solution. And very seldom you you see like people in China turns to God, for example. For a solution. Maybe
Grace Shao (23:19)
That’really interesting, but I will push back. They are like the Buddhas and the temples that still exist where people like pray for money, which is hilarious. Again, it’a very pramatic result. Or you like you pray for a child. You literally, you’re
Robert (23:31)
Exactly. Right.
Grace Shao (23:33)
Not you’just give me a child. Like you pray for fertility, give me money. You pray for money. But there’praying in that, but it’not omnipresent. It’like each god has a or like each Buddha has a very clear ask and reward almost. I don’t know, like
Robert (23:51)
Yeah, exactly, exactly. So I mean there are like symbols of belief or faith or whatever, but exactly as you said, people use these very pragmatically, practically. They’the you know when Buddha said that you know w we w you know Buddha doesn’t want it the original Buddha doesn’t want him to be worshipped as a god. Right. It’really a teaching about how to position how to think of oneself and how to position yourself to the universe, to the world. Right. That’the whole teach but then it lost that favor in China. It became this, inf fused with all these other very down to earth beliefs and to become this, now you assign this Buddha. To ask for kids, that Buddha to ask for money. It’definitely not what Buddha originally taught. So it’just it’just it has been like that for not just decades, centuries, even millennia. So I mean that’also that’a very big part about China, which I’m yet to write about, but I kind of touch on it at several points. Is that you know a big part of China is China really I mean Chinese people and maybe East Asian in general, because we don’t have such a strong kind of belief in some abstract things, we also tend not to want to convert other people into our kind of belief, right? So we tend to focus on the practical. That’why we have a lot of business people, for example, that are focused on making deals, right? Trading, benefit you, benefit me, and so it’all very down to earth, but very practical things. And that does definitely have limitations. I would argue that in terms of fundamental pure science discoveries, that kind of mindset creates a disadvantage. You really have to, when you do groundbreaking scientific discoveries, you really have to forget about all these worldly stuff. You really have to forget about things well what’this mathematical formula have to do with my life? You have to forget about that. You have to just focus on this the purity of sciences, of mathematics to have to have some great discovery. Then that’why you know like the people were debating recently you had these metal this mathematicalist, Chinese ethnic ones, but getting their awards not in China, not while they are in China, but because they have further studies in the West. Right now in China there’a big debate about you know whether Chinese college graduates can do you know achieve that kind of level of achievement in sciences if they stay in China. I think right now I’m not that optimistic because overall people are still very focused on the use cases, the pragmatic use cases. But most of the time when some big scientific truth is discovered, they don’t have a direct use cases. And that’definitely not how they start a discovery exploration. Right. So that’well, the thing is, the reason I want to sometimes compare the China and the West is not I want to say which one is better. I actually my main point is we are many societies can be different. But in our world, different societies, different economies, different kind of people can play different roles. You so you need thinkers, you need the people who think about the abstract, but then you also need the people who actually can put things into use and create productivity and make people’lives better. And it’it’it’great that you know you have different kind of people serving their different kind of purposes. And I and I think I love I actually think that you know China being different and the West being different in their own ways, a net benefit for the whole world. And that’you know my actual overarching key point in writing about all of these.
Grace Shao (28:39)
That’really interesting. I think, like offline I wanna dig more into the religious aspect. It was just really interesting. I never thought about it that way. And we might get some heat and pushback on this because obviously South Korea nowadays is like a very Christian country and you know it kinda goes against what you earlier said. But I do think what you meant by East Asian worshipping in general is not so much omnipresent, but it’I don’t want to say it’opportunities, but people often go to these Like Buddhas when they need something, when that thing happens, or when something bad happens. But it’not like you’not taught to be thinking about it day in, day out. So that godlike attitude is very, very different. And I think it does translate to how people are perceiving AI these days, because in the West, right now, AI is seen as like a kind of or a lot of cult like figures are coming forward. And, a positioning AI like a new magic or something that will fundamentally change society as we know it. Anyway, so we can talk more about that maybe offline, but I want to bring it back to then things that you write about a lot, which is how should we understand then China’unique state planning and how it drives economy? Because I think it all relates to what you just said. China itself, in a way, you can say a lot of people are taught to be very, very strong execution and execution people doers, but they
Robert (30:01)
Mm-hmm.
Grace Shao (30:01)
Are maybe less of these creative, wild thinkers. Obviously that’not. All true, but in a general sense, yes. So then when it comes to then how the state interacts with the private sector in terms of innovation and state planning, we see that again, there’a top-down vision or priority. And then companies or sectors as a whole will start executing and create abundance. How does that all work and how do you view that kind of relationship?
Robert (30:32)
Yeah, I think, when we talk about relationship between state and business and innovation, again, people frequently fall into the traps of big concepts, right? People the classic question is China socialist or is China capitalist? And these are also tend to be kind of the Western preference in terms of discussing things. While again in China, People tend to be not so focused on the on these conceptuals, on these, black or white. So when Deng Xiaoping said, black w black cat, white cat, whoever catches the mice is a good cat, it’not just his opinion. He’only a manifestation of the average most of the people in China. Whatever works, whatever can solve the problem of the day. We will use them. Right. So that’the bigger contact context here. And when we look at specifically industrial policy, innovation and all that, I think people, both the government and business people, tend also adopt this view. Whatever works. So if we look at the EVs, for example. When EV became a thing in China, it’a confluence of forces. It’not just like the state said, we want to develop an EV industry, and then it happens. If you look at say BYD, the Wang Chuanfu, when he started to have the idea that we should start an EV business, it was actually earlier than Tesla. And Wang Cheng Fu when he did that. It’not because he think that the state should do it or the state tells him to do it, right? He did it on his own. He has his own vision, his own dream. And it’just how so happens that the priorities, the goals of these business people and the state converged. And really for say something as massive, as important as the EV industry to happen, you have to have all these factors line up. You have to have entrepreneurs who are really willing to take the risk. BYD at the time took enormous amount of risks. But then you also have to have government that have the policies that are friendly to EVs. You know the consumer rebase for EVs, for the infrastructure build out and all that. All these forces are important. Fast forward to today, AI, for example, DeepSeek is a very great example. Of this dynamic. When Liang Wen Feng started the DeepSeek venture, he never thought about Beijing. I mean, Beijing even didn’t realize that a quant fund could you know incubate such a you know important AI company. You know back in 2023, 2024, there was even a crackdown on quant funds, causing some kind of market crash. Back then. We call it the quant crash. That was only two years ago. You know
Grace Shao (33:56)
Why were they being cracked out? Why were they being kind of scrutinized?
Robert (34:02)
So two years ago there was this moment where the market was like sliding down and the quant was like kind of magnifying that sliding down. And the reflexes of regulator was really to kind of hold it, hold them back. There was one episode where the regulator kind of stopped a quant fund to from trading, basically plucked out the cables. So they were, because the mechanics the mechanism of quant trading is usually to kind of magnifying could help the market trend to get even you know more pronounced than it is so there’always some kind of controversy about our industry. So it’hard to imagine that Beijing actually found a quant fund and say, we are going to place a huge amount of money or huge amount of resources and ping our hope on you. Right? It just didn’t happen like that. Now had no state backing. He had his own dream for AI and he had money, he doesn’t have to rely on anyone else. And but after he became successful, after DeepSeek became a an international sensation, then you know a few ye a few days after last year, DeepSeek’moment, he was received by Premier Li Qiang. And then you know they become kind of a national priority. And in the this year’fundraise. There’also very top level state fund from Beijing that invested in DeepSeek alongside with Tencent and all these other companies. Right. So I think that’dynamic is interesting. It’at the same time there is a strong hand from Beijing, but also there’at the same time a huge tolerance for the natural growth of you know companies, industries, people on their own. And Beijing is less a planner, but more a kind of a picking picker of the winner. Right? So they set the long term goal. They say that we want to develop new productive, I mean new quality productive forces, but they never define specifically what are they. They kind of leave that open for the for the markets to explore. To for our own talents to explore. And once there is some clear winner, they come in and back them up with the more resources and help them scale. So that’I would say that’a hybrid. That’really a hybrid model. No single side of it can define this whole model. And this hybrid nature rests on the fact that people again we are flexible We don’t we don’t stick to any single type of ideology or ways of doing things. Whatever it works, right? Some industry needs creativity, then it cannot be top down. It has to rely on these spontaneous ventures and people. But also some industry if they want to scale, they need to have massive allocation of capital to them. And in China, if you want to really get massive amount of capital, you have to have the backing from the state. And that’how it happens. And so I think it’just natural. And it’also it’it’a hybrid model that is proving to be working and maybe for the new other industries it will also prove to be working as well. Yeah.
Grace Shao (37:48)
It’really interesting the way you put it. It’almost like they’a parent. So you get enabled and you get resources when they like something that you’doing, but you get beaten down
Robert (37:54)
Yeah. Yeah.
Grace Shao (37:57)
Or you get scolded and grounded if you’doing something they don’t like you’doing. And that brings me to the next point, which I want to ask you about. And you kinda alluded to this already, you touched on it. It’like the relationship between the state and the prime, it’something I think a lot of people find hard to understand. State as SOEs, state owned enterprises. State subsidies into industries, and then like obviously favorable policy making. It’very interesting because from your point of view, you’saying this is natural. Like you said, it is what it is. You need that kind of parental help or you need that parental guardrail, whatever, or safekeeping in one hand. On the other hand, from obviously a very American perspective or a Western perspective, is why are you involved? Is there for state subsidy than unfair, which I find kind of interesting of an argument. But there’obviously accusations from the West saying these Chinese AI companies are state subsidized, therefore they’not really competitive. I’m but they’still competitive from an innovative perspective. But anyway, and then you obviously have a lot of these AI companies now worried about taking state capital because if they want to go global or even go l like go get listed publicly somewhere non-mainland China. Then there’also concerns about shareholder setup if there is like clear state backing. Anyway, this is a again a bit of a big open question, broad commentary, but I’m gonna throw it back at you. How do you view all these different agents or different stakeholders and their relationship? And how do you view whether it is fair for certain companies to get state subsidy or not? And how to view their then independent competition and innovation.
Robert (39:47)
Right. So this whole kind of debates or controversy about subsidies in China, there’just so many I mean so many ways that I don’t I don’t feel okay with. I mean, like for example the in the West, it’not as if the Western government don’t have subsidies and don’t even have huge subsidies, right? I mean in EU many industries are being subsidized. In the US, if you look at say Tesla in the early days, I mean SpaceX even, all these companies rely a lot on policy support. So I mean maybe the difference between the US and China or EU and China is the I would say the role of the local governments. There is a huge tendency for local governments to go out of their way to support new businesses, which is really part of their own incentive arrangement. It actually helps them to grow the local GDP and help them promote it. So and it also creates some kind of over competition between the local governments. But it’not by design almost. It’just naturally happen that all these government sector support they just come in and out of their own interest They support these businesses. However, I would always argue that all these controversy or debates about subsidy tend to make people believe that it’because of the subsidies that Chinese companies become competitive. I think any basic student of economics would understand this cannot be true. I mean no businesses can be subsidized to be competitive. It’just It doesn’t work like that. Not in China, not in the US, not in EU, in not in Latin America, not in any history, in any human history. No competitive businesses become competitive because they have state subsidies. And usually it’the opposite. Subsidies only create uncompetitive businesses. Because whatever you do, if you are profitable or not profitable, you still have the state backing and which will make you artificially profitable. Who will do that? Who will be competitive? It just doesn’t make sense. And the reason that subsidies or state support or whatever support policy work in China is because every actor in this industry are working towards the same goal. Businesses, owners, the state, central government, local governments, all other stakeholders. It’really about everyone pushing, everyone going, and all the talents engineers in these companies. Everyone agree on something and push for walk forward to it. So it’definitely not just the subsidies. It’it’a whole spectrum of this converted uniform action of every party that make Chinese businesses competitive. And if the West just comes in and says, it’a subsidy that’responsible for that, i it’just not a very effective criticism. I mean and then reflex will be the West will have more subsidies to support their businesses, which, in fact, the wrong kind of prognosis will lead to a wrong prescription, which will be interesting as well. Yeah, I mean I’m pretty kind of I would say it’it’kind of kind of emotionally bit charged topic for me, but I really want
Grace Shao (43:37)
You’passionate about this topic.
Robert (43:38)
Yeah. So I really want to speak it out about this, yeah.
Grace Shao (43:44)
Yeah, so it’interesting then, how do you view this generation of AI companies and kind of the I guess how they’overlapping these space? Because like you said, and we know here at AI Prome where a lot of these labs actually even struggled to get capital in the beginning, before the GPT moment, like your point, Silicon Valley can set the tone. Once ChatGPT took off, Chinese labs. Were able to kind of rally up and garner attention and interest domestically. They got their first kind of pot of gold, set the labs up a bit further, more like bit more sophisticated ways. Clearly they’still struggling to, or not struggling, I would say they still need a capital. So then two of them rushed to go public. Now more thinking about that. All of this indicates, first of all, obviously training models is extremely expensive. But they’still not really getting the funding they need. And some of them are choosing to not get the state backing or state kind of related capital they, that’out there. I guess this question is a bit long windy, but I guess just how do you see the relationship of the AI companies right now with all the different stakeholders and capital players in China? Because the state has the money, some of them don’t want take it. The state clearly is have favoring AI right now and rolling out a lot of strong policies and helping them with compute and energy and whatnot. How are they interacting with SOEs? In fact, how are they interacting with the big tech? How are these different stakeholders now I guess involved with each other?
Robert (45:24)
I think a key variable that was not on the table a few years ago was the role of the capital market. So we have we cannot leave that out when we talk about funding for these new companies. So I think the Beijing is very proactively pushing and helping many of these AI or even right now robotics companies to go list it. Either to Hong Kong or prefer preferably even in domestic A share market. The speed of making these companies public even just a few years after they were founded, it was actually unprecedented by Chinese standard. The y the capital market used to be closed to most of the new economy companies. So that’why when Alibaba went listed they the default was go to Nasdaq. Right. So that default was no longer applicable. No company by default want to go to the US for listing. While at the same time, China Chinese regulators did make it easier for companies to go listed in at least greater China, right? Hong Kong and Shanghai, Shenzhen. And I think that’a yeah.
Grace Shao (46:46)
Jump in really quickly. I think people also don’t understand sometimes and miss the point on a lot of these new economy companies from China are not going to go list in the US is not actually like actually help us explain. Is it a China’regulation reason or is a US regulatory reason?
Robert (47:06)
So it’actually a combination, but I would say the most of the issue is on the US side. Maybe sixty percent US responsible, forty percent China responsible. But anyway, there’a pull and push that make companies think about. So at the same time it’get just getting harder to get listed in the US. There’always a risk to be delisted, for example. And while to apply to US listing now you have to go to Chinese regulator as well, which there was no such approval process before, right? So it’hard. But then at the same time, it’getting easier to list in A share and also in H-share. And also liquidity in Hong Kong is way better than before. So there’both push and the pull. There are still some companies get listed in the US, very few. Recently this year there’this company called Taso Chuo that was just got listed in US. I think they have their own reasons for that. But most companies would prefer to just stay put in this part of the world. Right. So that’a key variable. And I think that’the key leverage that Beijing is using to help these companies raise funding. Like to be honest, I think Beijing is very I would say sometimes like a very strict you mentioned parent, right? Beijing is a very stingy parrot. Actually Beijing doesn’t want to spend too much money on, all the projects. But they are ambassadors at leveraging other people’money to achieve their own goal. Right? So like if you look at deep seeks fundraise, Beijing invested only a small part of that. Most of the money is contributed by you know Tencent or other private investors. For them, it already achieves a goal. It helps the company that Beijing wants to grow raise funds while at the minimum amount of money that Beijing can actually need to chip in. That’pretty smart, you know. It’it’not like it’not like i it is smart to keep resources at your hands and try to leverage other resources other people’resources to support your goal. And capital market is exactly like that. It’not just capital from big companies and big funds, but a capital from all over the market. Everyone, every even retail investor, get to participate. The that only that way you can ensure a everlasting strong stream of support in the in the future. So that’I think a very different that’actually very different, say compared with a few years ago, where you don’t have such a as strong a capital market as we have now. And now Beijing also have a vested interest in support the market. And they have also developed their own techniques and their own muscle memories in supporting the market, which is what we don’t have even five years ago. Right.
Grace Shao (50:22)
Right. But some still argue that the Chinese government could support the stock market more. I don’t know. That’just things I hear. Well, how do you view that?
Robert (50:30)
Yeah. Actually they are now sophisticated enough to understand that you need to be balanced. So what I mean is there are actually two episodes that could remain as lessons for them. One is the twenty fifteen, twenty sixteen market crash. Second is the recent market crash in South Korea. In both episodes, there was a bull market, even a crazy bull market. And in both episodes, the governments initially played a very strong role to boost the market. Back then, in 2020 I mean 20 fif fifteen, there was a People’Daily article saying directly that the market should go above, I forgot it’five thousand or or four thousand points. Which was cited as a kind of a rally call for many people to go into the market because the Beijing says we should, buy, buy, buy. So Beijing actively kind of contributes to the building up of a big, big bubble. And then after Beijing felt it was too crazy, it cracks down on leverage. And a lot crackdown on leverage burst the bubble and it has become a really bad market for the next two years. Same thing as South Korea, right? Like they prime min president of South Korea said, I’m also buying the stocks. Every policy going to support the market. But then the government was too concerned about a leverage. So crackdown on leverage. And then boom, the market dropped. And so I think you know Beijing of today is Pretty sophisticated with that. They want to have a bull market for sure, but they also don’t want it to, turn into a crazy boo. And exactly how they do that, because this is some not something that you can say, I want this, I that so I can achieve that, right? Because it’a market. There’a lot of players. When the sentiment builds up, even Beijing cannot stop people from buying or selling. So exactly how, interestingly, they all have also developed. Dev develop their own technique, which is this so-called stabilization mechanism. So for the first time in history, in the last two years, Beijing was actively employing and deploying capital to act as a stabilization factor for the Chinese capital market. By stabilization I do not mean just a buying mechanism. It’a stabilization mechanism. Which means when the valuation was really depressed and Beijing wants it to go up, they actually now come into the market with real cash to boost the market to help reset the valuation. This is different from before. In the past, I think there’never been an episode where Beijing used real cash to support the market. There was messaging, there was this policy, that policy, this tax policy, that tax policy. But never before was Beijing deploying so much capital directly into the market. But then after the market become more hot, or hotter than what they want, they actually sold what they have. Right. So it’stabilization. It’almost like also recently in the oil market, the moment that Hormuz was closed, Beijing stopped buying oil, waiting out the episodes. Which was a contributing factor, decide a determining factor for right now the oil prices didn’t went through the roof. And same thing was you know the same thing was when in the ancient China. There was a big role of government was to be a stabilization factor in the grains. Right. So when there is a lack of there’more grains than there’needed and the prices are low, the government actually comes out and purchases the grains and store in the storage. And when there is a famine, it’government’role is to release these grains, selling them at maybe a higher price, but eventually serving a social purpose. This is just it’just Chinese regulator is now using his ancient technology to apply it to modern statecraft. And it’working. It’working. Last year the market was just about to be crazy. Last December, last November. And soon Beijing started to sell off their holdings in the ETFs. Which tempered the sentiment, right? Beijing is very smart. They actually made huge profits about after this buying and selling in their own game. And now they have more cash than before and so if the market goes down from some level they are ready to come in again. So this is actually very nuanced and I think I think it’it’it’great that there is not only a desire for market to go up, but also a desire to for the market to grow up in within a safe zone. A zone that’that’that’will not be crazy, that will not cause a lot of sentiment crash, especially for the all of the retail investors. Right. So yeah.
Grace Shao (56:20)
Yeah, I think that’really interesting to hear. I’ve obviously not heard of that like in detail. But then, the question I get a lot is then how do you view the flip side of the government had in the market? Obviously, we’ve seen, kind of internet crackdown, education, property, whatnot. Like you can name a few industries in the last few years, it’been hit pretty hard in valuation can get wiped out overnight. So, How do we view that kind of government hand in the public market? And then I do want to tie it back to then how do we then find confidence in investing in AI and a lot of these publicly listed companies right now coming out of China, like these AI wave companies beyond the model companies that we talked about? Like you mentioned, there are the robot ones, there’infra layer ones, there’even now spatial intelligence companies getting listed. But yeah, just tie it all together.
Robert (57:17)
Hm. Yeah. So my mental model, my personal mental model to understand policy risk in China, is that I think Beijing, the regulators there, are learning. They actually didn’t have as much experience about capital market say even five years ago. So you mentioned the education industry. That was a very important episode in policy making, in expectation management, a very important lesson for Beijing regulators. So when Beijing cracked down on that education industry, actually I don’t think they have realized what kind of you know problems that would cause for the wider you know sectors, especially capital markets. They are narrowly focused on the industry itself. But they actually learn from that. They actually learn that you have to think about all these other factors because all these things are interconnected. There are signs of that, there are evidence of that. Maybe I wouldn’t have time to go into detail, but maybe can go check my newsletter about my years of observations of Beijing’scale. At expectation management and also at thinking this as part of a bigger whole, not just like single policy. Right. So that’my key mental model, which is to treat it as an evolution, to treat all these necessary lessons as part of a bigger learning curve. So here in 2026, I would say today’Beijing. Has way more lessons and way more skills and way more sophisticated than Beijing five years ago. And it’it keenly understand the importance of capital market and also in understand the importance of expectation in the capital market. So they are now very they were they are they are they are way more holistic than before. And I would not think that the double reduction education episode in twenty one would repeat because they have learned. It’a lesson for them. Right. So in that in that policy risk, actually it weakened the risk weakened, lessened considerably than before. And well in terms of investments though, if you just look at these AI and robotic company as you know a pure investment from the pure investment angle. I’d say that it’really not for everyone. The valuation judging by traditional standards is really, really high. But then if you’a believer in AI, displacing ten to twenty percent of global GDP, then all this valuation doesn’t seem high at all, right? So it’really up to the taste and the style of different investors and the risk appetites. In general, I would think the Chinese market will be more and more mature, the capital market will be more and more mature. And the stronger state’hand compared with say the Western market is also I would say understandable given that China’market, especially A-share market, is a is a highly retail driven market. Seventy percent, eighty percent of the money is retail. And retail tend to fall into the traps of herding. Which means like everyone going to one direction. So someone has to come out and be the shepherd. So it’a it’a it’a shepherd to herd model that is different from the West, where people most of the market participants are more mature and more sophisticated, analyzing, researching, which is different from China. So it’just natural for Beijing to play a role, to play a balanced role. Not a like a not a like a very strong role, but a silent, invisible role. Give you one example. So this whole stabilization mechanism I mentioned, actually it’only my name for it. There the Beijing doesn’t even have a name for it. Beijing doesn’t even disclose what exactly are the mechanisms. When they purchase stocks, is they don’t purchase directly. They purchased a list of ETFs and those ETFs purchase the stocks. So they are also very Conscious of their presence and they want to lessen their co their presence. They want to be the kind of the secret shadowy force that is making it making the market stable. But they don’t want to say, we want it stable and this is our message, this is our view. It’not crude, it’actually very nuanced. Yeah. So they are learning. They are really learning really fast.
Grace Shao (1:02:35)
That’very interesting. It’like it just makes me think of like high school teenager parenting again when you influence them, but you don’t directly tell them what to do. You have to influence them in like
Robert (1:02:43)
Exactly. Yeah. Yeah.
Grace Shao (1:02:46)
But one yeah, just like I guess I want to wrap up soon, even though I feel like I can keep on asking you questions. I have another hour of questions for you, but for the sake of today,
Robert (1:02:56)
Thank you. Yeah.
Grace Shao (1:02:57)
How do we understand then, we are seeing a Crazy wave of IPOs right now in Hong Kong. Like we said, a lot of them are directly AI labs, obviously. The others are AI adjacent, or some are pegging to AI. So
Robert (1:03:15)
Mm-hmm.
Grace Shao (1:03:17)
How do we understand these companies? Like or how or why do they want to go to Hong Kong first and not maybe A Shares first?
Robert (1:03:26)
Yeah. It’definitely easier to go it relatively easier to go to Hong Kong for listing rather than A-share. A-share is stricter and mostly because A-share in A-share there are a lot of retail you know mom and pop’investors in the A share. And as you as you frequently alluded to and I agree with is that Chinese political system or regulators, I don’t think the authoritarian is a is a good word, but I do think paternalistic is a good word. They do see themselves as parents. And people do see themselves them as parents, right? So as parents, they tend to be kind of over caring for their kids, which are the people and r retail investors. So the threshold, the bar for listed in A-share is actually very high. And even if you get listed, the pricing that you can place on yourself is also I would say much lower than it should. Like if you look at CXMT, for example, when it first got listed, the IPO price was about one fifth of what it was, t ended up trading at on the first day of trading, right? So why is that? Because they artificially kind of compressed evaluation to make sure that every mom and pop who joined the IPO earn money, make profits, had a good experience. So there’a very clear kind of kind of emphasis on retail investor protection in A-share. Well in the A-share though, not many mainland retail investors can trade in Hong Kong. Some can, but most of the retail investors are not qualified to trade in Hong Kong. Right. So it’very institutionalized. So it’really so for Beijing it’really like a pressure valve for the IPOs. So they actually encourage you to go to Hong Kong. And the Hong Kong exchange, stock exchange, they also encourage you to go listed there. So there’a confluence of interest there. And also at the same time, if you go listed in Hong Kong, you raise US dollars, and which are as which are great for, China based company because there’still capital control in China. Right. So there’a there’def defin just a confluence of interest of all stakeholders to now go to Hong Kong to list first. Unless you are CXMT,
Grace Shao (1:06:07)
Makes sense.
Robert (1:06:09)
They are really good and you qualify for A share. But then you also suffer a bit because of the valuation for the for the kind of money that you are you can raise, but you cannot. Yeah. So
Grace Shao (1:06:23)
It’it’interesting. It’like a balance between over caring and overbearing, it seems like. And yeah.
Robert (1:06:28)
Yes. Yes.
Grace Shao (1:06:30)
All right. Well, look, I wanna ask you one question that I ask every single guest, which is what is one differentiative view you hold or something you think is non consensus? It could be about anything. It could be about China, it could be about the stock market. And I know we touched about touched on quite a few different topics today. I always appreciate again your nuanced view on a lot of these things. I don’t frankly agree with everything you do say, but I do think, what I appreciate is at least you try to really string together different parts of how the world works instead of just over-generalizing China as this one unit. And I think sometimes China observers unfortunately just over-generalize China or oversimplify China. Anyway, I wanna throw this question to you. What is one different
Robert (1:07:20)
Okay.
Grace Shao (1:07:20)
Of you hold? Or maybe you think something that the world still misunderstands about this part of the world, especially when it relates to technology and capital market and everything.
Robert (1:07:29)
Right. So they’actually a lot. I’m just trying to pick through my mind which one is relevant for today’discussion, and maybe this one. I think Chi
Grace Shao (1:07:38)
Give us two then. Give us two.
Robert (1:07:41)
Yeah. Okay. So there’a the there’a small one and a big one, right? The small one is about the capital market. I think China is entering a multi decade bull market. The U A share. There is just so much kind of tailwinds that are supporting it. I’ve already mentioned some of them, like a very sophisticated Beijing. But also RB is trending up. I mean, there it’been the joke of the day that despite the tenfold, twentyfold of growth of Chinese GDP, Chinese stock market is going nowhere. I don’t think it’going to be true in for the next at least one or two decades. It’a new paradigm. So that’you know definitely a big part that all the investors should pay attention to. And I don’t think that’appreciated enough. And a bigger question that I always love to share about China, like if you ask some American or some you know Westerner what’the single most important thing. If just one thing you have to remember about China, nothing else, just one thing. I will always say that Chinese people or China are not interested in changing other people. We are not in this preaching or you know proselytizing mindset. We mind our own businesses. We don’t want to change other people’lives. So much as some Westerners will want to change other people’lives. We don’t. And the reason I want to emphasize this point is that I realize that when you have both sides who want to change the other party, that’a recipe for conflicts and wars. But if you realize that actually one big party of that is not interested in the other party, then I mean in changing other parties. Right. Then you realise maybe there’a chance for peace and prosperity. So I want to I cannot stress this point strongly enough, but I want to maybe use your platform to voice that again. Thank you.
Grace Shao (1:10:09)
No, I really appreciate ending on such a positive and somber note on that. And then I think another thing I wanna ask, which is a bit for fun, is can you explain to us what is good jot hai? Why do people go around talking about like cutting Chinese
Robert (1:10:26)
Yeah.
Grace Shao (1:10:27)
Chives? What does that mean in the capital market space?
Robert (1:10:31)
Yeah. Chives a very interesting vegetable. It tastes a little bit strange, definitely not for everyone. But the key things about chives if you are in the farming business is that chives grows really fast. So when you cut, one chive, a few days or a few weeks later it grows up again. And you cut them down and they grow up again. This is what retail investors are, right? They get cut down all the time, but they grow back all the time. This current generation of chives when they were cut down, they will leave the market. But then you also have a new generation of investors who have no experience, no knowledge of that and they want to try out themselves and they got cut down again. So again and again and again. So that’why they become a term. Each generation have their own kind of symbol for that. Like the last generation, for example, for many of them, they got cut down on Xiaomi, for example. They got a huge IPO, but then it kind of crashed for a few years. Maybe that next this generation for this generation is all these AI names. I don’t know. But every generation, I mean it’a generational thing and it’kind of it is built into the system Right? Because you will have new people coming in. And new people, by definition, don’t have knowledge of the old. So they just kept it’very I would say very figurative, very apt kind of explanation of the mechanism, yeah.
Grace Shao (1:12:14)
I love how technically you got into like people know agriculture and how farmers no, I just thought
Grace Shao (1:12:19)
It was like it’one of those Chinese internet slangs again that are just so hilariously random if you don’t understand the context. But like you said, if you actually understand the thinking behind it, it makes a lot of sense. And so it’actually a very popular internet slang people use to describe retail investors that get kind of hurt and then the joke is institutional investors will just wait for the chives to get cut.
Robert (1:12:42)
Yeah. Yeah.
Grace Shao (1:12:42)
Or chives get cut one around and after another. Anyway, thank you again, Robert, for your time. Really, really appreciate it. I also appreciate that you let me kind of take you in all kinds of directions with this conversation. Please come back again.
Robert (1:12:57)
Thank you for all the tough questions. Okay, yeah, see ya.
Grace Shao (1:12:59)
Yeah, thank you.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



Get full access to AI Proem at aiproem.substack.com/subscribe