Show Notes

Hi all,

Christian Hu from Alibaba’s Qoder joins the podcast again to talk about the fierce domestic workplace agent competition. For context, Anthropic and OpenAI have decided not to allow access to their models and products in Greater China; thus, the competition for models and agents is largely driven by domestic players. For workplace agents — coworker-style products — the most popular ones at the moment are Tencent’s WorkBuddy, Alibaba’s Qoder, and ByteDance’s Trae. The other agents we see coming from the labs are mostly coding agents. With that, I’ll hand over the floor to Christian, who graciously found ~40 minutes while on a business trip to talk to us about the landscape and Qoder’s own strategic shifts.

This episode was recorded on the day Qwen3.8 Max launched.

For more, check out the podcast lineup here and explore the episodes!

Chapters

* 00:00 — Southeast Asia Expansion and Market Dynamics

* 02:43 — The Shift Towards Business Logic in Coding Agents

* 05:29 — Industry-Specific Applications and Vertical Agents

* 11:22 — Global Strategy: Lessons from Market Differences

* 14:00 — Partnerships as a Key Element in Market Strategy

* 16:41 — The Future of Coding Agents and Market Trends

* 19:22 — Pricing Models and Inference Economics

* 22:08 — Open Source Trends in AI and Market Competition

* 27:34 — Building vs. Buying AI Models

* 29:38 — Future Directions for Coding Agents

* 32:14 — Final Thoughts on Market Opportunities

Transcript

(AI-generated, for reference only)

Grace Shao (00:00)

Hi Christian, friend of the pod, you’re back on. Really excited to have you here. Where are you these days?

Christian (00:07)

Yes. I just landed in Singapore last night.

Grace Shao (00:12)

Okay, perfect. Cause we’re gonna talk about your Southeast Asia expansion. But since we last spoke, competition among coding agents has intensified quite a lot in China, particularly — the market has changed a little bit. Where are you seeing the whole market going, and how do you think Qoder fits into all of it?

Christian (00:32)

Yes, it is a war, you know, between every major tech company in China. Because it’s a war, nobody wants to lose. I think the logic behind this coding agent war is that most companies believe that a coding agent shall be the fundamental path to AGI — artificial general intelligence. So nobody wants to be behind in this race.

But the most interesting thing behind the war, or this race, is that something is changing. When we look back to the last twelve months, everyone is talking about the models, everyone talking about what kind of model will be the most competitive advantage for your coding agent. But now, for most leading agents, they are trying to be more focused on the business logic and workflows. Just like what Qoder is doing — we want to be more into the business logic of our customers. And even for some individual users, they are trying to do something for business. They have one-person companies or one-person workflows. So they need the coding agent to do more about the business workflow, not just code generation.

So that’s the shift behind the war. For at least most of the companies, they are trying to raise not just for a coding assistant, but want to be dominant as a desktop assistant for employees or maybe some individual users. And maybe in the future, they want to be the digital employees for the industry. I think that’s maybe the ultimate race for the coding agent.

Grace Shao (02:28)

And you’ve got players like WorkBuddy from Tencent that’s been doing really well, right? Exactly to your point—

Christian (02:33)

Yes.

Grace Shao (02:33)

I think they’ve been plugging in, kind of operating as an assistant on desktop, very intuitive and user-friendly. You’ve got ByteDance with Trae pushing in a similar direction. So how do you feel about that? Where is Qoder’s differentiating point or offering here?

Christian (02:51)

I have a few things to share with you. I think for the past year, Qoder has accomplished very remarkable business performance. In terms of ARR or revenue, we are the leading one — we are number one. And maybe we are more than the combination of number two to number four. That’s a huge achievement for Qoder in business growth.

And as I just said, Qoder from day one is more focused on building an agentic platform for developers and AI builders. It’s not just a coding tool or coding assistant — we want to be a platform for the next generation of agentic power. So the skill marketplace, the plugins, the connectors, the MCP connectors — all of these features are now shining. All of these features are bringing advantage to our business. That’s the truth of what’s happening in China and even in overseas markets.

Grace Shao (04:04)

That’s really interesting. And so when we caught up in Hangzhou recently, you were saying that Qoder appears to be broadening from just coding into industry-specific agents. That was something quite fascinating, because from the start of the conversation, you say coding capability is the fundamental foundation for all of these agents, but people are moving towards these vertical agent use cases. So tell us a bit more about that. What kind of industries are you guys targeting? What is your thinking behind this new strategy?

Christian (04:34)

Okay, I can share an example. We got a very important customer case with Xiaopeng. Xiaopeng is one of the leading electric vehicle producers in China, maybe one of the best. For Xiaopeng, for the company now, Qoder is not just a coding assistant or code generation tool. Qoder has become an agentic driver for the restructuring of their workflows and business logic. They’re trying to educate their developers and their engineers, maybe even the HR department, to use Qoder to renew their business logic and their workflows.

For example, for the legal department, they’re trying to use Qoder to reduce the legal review cycle from six days to one day, or even less than one day. And it’s not just Xiaopeng — I think there are a lot of similar cases. So for Qoder, it’s not a shift to pivoting to vertical applications or specific industries. From day one, Qoder wanted to be a platform. The platform means we want to be a tool — we don’t want to be just a coding assistant competing with other coding tools. We want to be more embeddable and compatible with the customers’ business logic and workflows. So it should be more vertical. But it’s not for us to do the vertical things — it should be let the developers, the customers, and even some individual power users develop domain-specific agents on top of Qoder. That’s the philosophy for Qoder.

Grace Shao (06:51)

Okay. So the thinking is you guys are offering the tool, but really it’s still on the user to build out the tailor-made agent for themselves. Not that you’re specifically pushing—

Christian (07:01)

Yes, that’s the truth. Actually, we got some organic creation of skills by the power users. They are creating some skills, some creating some plugins on our marketplace. We are going to open the marketplace to all the business users so that the users can deploy different kinds of skills or plugins from the marketplace. That will be the market for Qoder. Maybe in the near future, we could be a marketplace for agents, a marketplace for skills.

Grace Shao (07:49)

Interesting. Okay. Well, this leads to something else you talked about. You said that you guys were building an ecosystem around Qoder. So tell us a bit more about what that means, and how should we understand or expect how Qoder might change as a product in the next few months.

Christian (08:05)

Okay. I think we can change the view of Qoder — from a coding assistant to an agentic platform. I can give an example: we are doing something together with Microsoft. That may be an example. We want to collaborate with Microsoft to make Office more usable and more accessible on an agentic system just like Qoder.

You know, in the past, Microsoft Office is Microsoft Office, ChatGPT is ChatGPT — they’re quite different products. For the users, they have quite different experiences on two different kinds of products. And now we want to be one. For Qoder, for the agents, they need the agent to know how to use Office, how to deploy Office, how to use the best features from the Office suite — the toolkit — to help the users do presentations, do documents, process data. So that’s maybe the very interesting thing for the users in the near future.

This kind of collaboration is happening everywhere. Qoder is trying to partner with partners from collaboration software, finance software, even legal software, HR SaaS providers and vendors. We are doing things like that to be more compatible and more useful in the near future.

Grace Shao (09:55)

Very cool. Let’s take a step back. I want to talk about your business expansion, because I think when we talked about a year ago, you guys were really gung-ho about going to the US, going to Japan. I know you’ve been spending a lot of time in Japan yourself. But recently it seems like you are pivoting, or at least putting more priority and focus on Southeast Asia, and now you’re in Singapore yourself. Tell us a bit about the thinking behind your global strategy, and which markets you’re currently focusing on, given that you’re the head of GTM on the international expansion side.

Christian (10:26)

Yeah, you know, I actually got a lot of lessons from the last maybe twelve months — about ten months for my global journey with Qoder. Everything is different in different markets. I just came back from Paris. I think Europe is quite different from Japan. Most people think Japan and Europe share something in common — they are pretty slow in AI adoption, they care more about compliance, they care more about trust. But things are also — you can still find something different between Japan and Europe.

In Japan, the buying cycle is pretty long. Maybe six months or maybe even twelve months is very common. But in Europe, the cycle maybe is not too long — maybe one month or maybe one week. Because they are trying to catch up with the AI wave in Europe. But there’s still some obstacles in Europe. They care more about the law, the compliance, the GDPR, the AI Act. That’s the truth.

So for every AI marketer, or anyone doing good marketing in Europe, you need to care very much about the legal — the law, the act, the things changing and happening in Europe.

But for the US — I spent almost the first quarter this year in the United States. I met a lot of AI developers, AI startups, AI founders. The story is quite different from China, from Japan, from Europe. In the United States, speed is the most important thing. Speed means you are innovating. Speed means you are upgrading your product. Speed means you are accountable. You’re telling the users that you are accountable because you are innovating, because you are upgrading your product day by day, conversation by conversation. You need to upgrade your product.

Now I come back to Southeast Asia. Singapore is the first stop for Qoder to be a global product. And now I came back to Singapore, I will go to Vietnam and Thailand in the near future. For me, Southeast Asia may be the mixture for my go-to-market strategy, because in this place, in this region, you’ll find you have competition against some American AI vendors or AI producers. You will also find some Chinese competitors. That’s the mixture. This is a very competitive market, but it also has very high potential in this region.

Grace Shao (14:00)

Really fascinating, because you’ve done a world tour and given the high-level vibes of each area. And I can totally see that it’s also quite fascinating you say, like, ending up in Singapore. In some ways, it’s almost the most competitive for a sales role, because you know you have all the options in the world and nothing is actually off limits, and it’s really a price war as well. It’s quite fascinating compared to maybe other areas of the world that will be leaning towards certain companies or certain countries’ technology, given maybe geopolitical concerns or compliance reasons, regulatory reasons, whatnot. And other areas might be purely driven by price sensitivity. So anyway, fascinating. Thank you so much for that. But why then? Why are you guys now doubling down on Southeast Asia after your big global world tour?

Christian (14:51)

Yeah. Okay. I guess I forgot one of the most important things in our last conversation. Partnership is the key element and the key part of our go-to-market strategy. Because we believe that local partners, a local ecosystem, is the best way to get a connection with local community and local industries. Around the world, we have different kinds of partners. For the past twelve months, the most important thing I did was to find partners as many as possible. That’s the strategy for our global markets.

Okay, let’s go to Southeast Asia. Actually, I don’t think Southeast Asia is the most important one. Maybe I think for now it’s too soon to nominate which region will be the most important. But Southeast Asia should be a very important part, because Southeast Asia is a fast-growing market. It’s not too much affected by geopolitical concerns, and it’s fast-growing, so for every major AI company, there are huge opportunities, huge market potentials.

And for Qoder, we are growing very fast. We are evolving every day, every conversation. People in Southeast Asia — the developers really find that it’s very interesting and they can get much from Qoder’s evolution. Because for most users in Southeast Asia, to use closed-source agents from the United States or some other place is maybe too expensive. Maybe it costs too much for most users or most developers in Southeast Asia. But if they want to try Qoder, they maybe have the best position and best time to catch up with the evolution of AI and coding platforms. That’s a good starting point for most developers in Southeast Asia. And Qoder is evolving, so they will be evolving every day. I think that’s a good point for both Qoder and the developers and industry in this region.

Grace Shao (17:30)

Interesting. Let’s take a step back and look at just the overall industry at this point and the trends that are taking off. So the underlying coding models are improving quickly, and we’re seeing that it’s increasingly becoming commoditized, or at least the price is coming down, right? As all of that is happening in the background, how does that actually affect the tools that are built on top of these models, such as your product Qoder?

Christian (17:56)

Yeah. I believe the token, or maybe some of the large language models — I think the cost of token may be zero in the near future. Most of them. Maybe in twelve months.

Grace Shao (18:13)

Wait, how would it become zero though? What’s the thinking behind that?

Christian (18:17)

It should be. It should be. Maybe it’s science — it’s about science and engineering, but it should be zero cost. Looking back to the last twelve months, if you want to buy one million tokens, it may cost you about twenty dollars. But now it’s about half a dollar, just in twelve months. Maybe in the next twelve months, the cost may be quite near zero.

But I think in the future, there’ll still be some very expensive and exclusive models — some frontier models. There may still be expensive and exclusive for some users, for some industries. But most models will be very inexpensive, and even cost zero for most developers. And I think maybe ninety percent of the daily tasks can be accomplished by these kinds of models — the public models or maybe some cost-effective models. I think that’s the future.

Grace Shao (19:22)

Okay.

Christian (19:23)

So on this kind of zero cost, the agent layer — I mean the context layer, the agent layer, and the business layer — will be the most important one for the builders, for the users. So that’s actually what Qoder wants to do in the future.

Grace Shao (19:43)

I see what you mean. But coding agents can be very token-costly, right? So how are you then thinking about the inference economics, usage limits, or even your own pricing models when you are selling your products to users?

Christian (19:57)

Yes. I just said we want to charge our users — we won’t charge by token consumption. We will charge [differently].

Grace Shao (20:03)

Mm.

Christian (20:04)

We won’t charge our users by token consumption. We will charge.

Grace Shao (20:10)

Okay. So it’s not directly token consumption. If token costs come down, your cost of your product also comes down with it. Okay.

Christian (20:17)

Yes, actually, the shift is happening. We want most of the profit of Qoder to come from the agency layer — the workflow layer, the agentic layer, the context layer, the memory layer. That’s close to your daily work and operations, not just token consumption.

Grace Shao (20:42)

How do you view the subscription model? We still see that dominate coding agents today.

Christian (20:47)

I think for today, if you are just a coding assistant, you will be dominated by the large language model. That’s the truth. If you’re just a coding assistant, you should be dominated by the large language model. For now, the best model determines the best coding tool, because you’re too close to the large language model. If a lab has a very powerful logic model, the large language model can easily change to be a coding assistant. You just need an interface — you just need to type your language, you can be a coding assistant for every user. So if you are just a coding assistant or just a coding chatbot, you will be dominated by large language models.

But if you are an agent — an agentic workmate, an agentic layer that empowers users to regenerate their workflows, to connect with their collaboration layers, connect with their ecosystems — you will not be dominated by large language models.

Grace Shao (22:08)

I see. Okay. I want to pivot and kind of look at the model layer right now. I know you don’t work in the model layer, but you’re very familiar with the ecosystem. So help me understand. Chinese labs right now have actually used open source very effectively, right? And I think they’ve built up a global reputation and taken over a lot of the market share. And obviously, there’s a lot of discussion on whether they will continue to open source some of their best models, or maybe become more and more similar to US frontier labs, what we’re seeing with what they’re doing. What do you think of this trend? Will they remain open, or do you think they’ll gradually start closing up some of their best models?

Christian (22:51)

I think I can give you two angles. For the angle of the regulators — for the government — they want the open source strategy. They want the AI wave to be a new engine of growth for the national economy. That’s actually the national strategy for the regulators. So open source should be the trend, should be the future.

And from the angle of the market side — the market players. Actually, for most players in this industry, in the AI industry, open source is maybe the only strategy, or maybe the only path, for Chinese players to surpass their counterparts in the United States. I think maybe the only path.

Grace Shao (23:53)

Why is it the only path?

Christian (23:54)

For now, we can’t see the advantage from chips. We have no advantage about chips. We have no advantage about human resources, even some capital. We have no advantage. So maybe open source is the only path for Chinese companies to surpass United States companies, to be dominant in the industry — in the applications. We have some figures to prove that — Xiaomi’s language model and Zhipu’s language model are the most used around the world, right? They are the most used models, not a Claude, not an Anthropic model, not a Google model, in terms of usage. So actually, open source is maybe the only path, or maybe the only way, to compete against the counterparts in the United States.

Grace Shao (24:57)

Question on Qwen then, just because you guys just released another very large model — it’s not getting as much attention. I feel like the headlines are being taken all by K3 right now. But Qwen3.8 Max is claiming to be just as good as Claude. What’s your view on that? And it’s available on Qoder right now, right? So how do you view the model?

Christian (25:22)

I’m not a model expert. I can’t give you an expert view about which one is better, which one is not the best. For developers, for users, you can try Qwen3.8 Max — the preview — on Qoder. I think maybe positive. It should be positive. And we believe that, as I just mentioned, the model gap is closing. In the near future, the model gap is closing. So for Qwen3—

Grace Shao (25:53)

Actually, why is that? Why do you think model gaps are closing, closer and closer?

Christian (25:58)

That’s my belief. I’ll give you an analogy about this. I think the best analogy is education. You know, for education — university education — we’ve still got some top universities, right? The Ivy League, the best in the world. And maybe some of the best two or three universities in China, they are at the top. But most universities and most education in university, I think they’re universal. I don’t see too much difference in the education in most universities around the world. People can get educated, people can learn the skills, people can learn the knowledge in most — I mean ninety percent of universities around the world. That’s not the biggest difference.

There should be some frontier models, there should be some ecosystem models in the future. But mostly — more than ninety percent — will be the same. Almost the same.

Grace Shao (27:05)

So you’re saying basically it comes down to talent. Talent is strong. So therefore, more and more talent going into the industry, thus it’s catching up. But why wasn’t it catching up a couple months ago? Why was the gap, say, six to nine months prior to that — it was claiming to be nine to twelve months. All of a sudden now people are saying maybe it’s only one to two months. Where does this number come from? I’m always fascinated by these claims.

Christian (27:28)

I’m not a scientist, I’m not an engineer. I just have the philosophy, I have the belief.

Grace Shao (27:34)

Yeah. Okay. Well, let me ask you another question that’s kind of taking over the broader industry right now. The broader debate right now is about whether enterprises should be fine-tuning privately and deploying their own models, or even — you’re seeing more and more so-called tier two, tier three smaller internet companies in China leading the way in already building up their own models. Obviously, some people are saying they have the edge of having a very strong open source ecosystem in China, so the barrier to entry is easier. Anyway, from your point of view, do you believe this trend where companies are actually going to start building their own models or hosting their own models and fine-tuning on top of it? Does that make sense? Or should companies continue to do what they were doing, which is, you know, paying for the most frontier models from the frontier labs?

Christian (28:20)

Okay. I think for most companies, even some large enterprises, it doesn’t make sense for them to fine-tune their private models. I can’t see the sense, I can’t see the business logic behind this. Just like the analogy of education — I don’t think you need a private school for your own children. I don’t think most people need a private school for your own children, just for your two or three children.

Maybe ten percent of enterprises have very complicated, very sensitive applications, sensitive data. They need to deploy their own private models, fine-tune their own models. But I think most companies don’t need to do that. Instead, I think they should invest more in the context layer, just as I mentioned. They must invest more in the business logic, in the business, in the workflow, in employee management. That’s where they should invest. Because that will benefit their business growth, benefit their management, benefit their governance. I don’t think it’s necessary to invest in fine-tuning their own large language models.

Grace Shao (29:54)

Makes sense. Not every company should be working on the R&D of this, but really should be putting more energy into managing their own data, context, memory, expertise. Okay, well, I have a last question for you, which is, stepping back and looking at the whole industry right now, where do you expect the coding agent market to evolve over the next year? Because you alluded to a little bit throughout our conversation, but where do we see this whole industry going? Are we going to continue to see standard products that are going to be the main driver in coding agents, or like you said, model labs will swallow everyone’s lunch, and then it’s really all just putting up an interface on top of them? Are we going to see more enterprise-grade, specific use cases and systems built? How do you see this direction?

Christian (30:48)

Yes. I don’t think there will be too many coding agents in the near future. I think some of the coding agents, even some existing coding agents, will be swallowed by large language models. I believe that. Because language models can easily build a coding agent. So there should be some — maybe two, maybe three, or some number of coding agents still in the market. But most of them will be swallowed by large language models. And there should be some new agents — business agents, workflow agents, or some domain-specific agents in the future. We’ll have more domain-specific agents in the near future. Legal agents, human resource agents, even some agents for you — just like podcasters or bloggers, we’ll have some new agents for you.

Actually, agents will be just like digital employees. That’s the future. You will have more digital employees. You can ask your digital employees, just like you can ask your teammates, you can ask your friends — but it’s digital friends — to help you do anything you want them to do. That’s not just a coding agent, that’s your working agent.

Grace Shao (32:14)

Very interesting. All right, Christian, I have one last question actually for you, which is a question I ask everyone that comes on the podcast. You’re not unfamiliar with this. What is one non-consensus view you hold now? Or has anything changed since we last spoke?

Christian (32:28)

You mean from last time? What’s changed?

Grace Shao (32:30)

No, just any non-consensus view. Even what you said earlier was against what a lot of the industry is saying already, but do you have any view you think is very non-consensus or against consensus right now?

Christian (32:48)

Okay. I have something, maybe something shocking for many AI builders. I don’t see too much potential in the United States market. I mean for Chinese AI—

Grace Shao (33:06)

You mean for Chinese companies going global, whose first stop is often the US? Okay, very interesting. Go on.

Christian (33:09)

Yes. Actually, I want them to quit. You can invest in the US market, but I don’t think they can benefit, or get real margin and profit from the US market. But it’s a different story between consumer AI and enterprise AI. For consumer AI, there may be some possibilities for Chinese companies, because we still have some advantage — we have cheaper manufacturing, cheaper human resources. That’s the advantage for consumer AI companies to do business in the United States. But for enterprise AI, I think it’s quite difficult. Quite tricky and quite — it’s just not a friendly strategy to do business in the United States.

Grace Shao (34:11)

Where should they be going? Southeast Asia right now, Singapore, Japan, like you guys?

Christian (34:16)

Southeast Asia is much easier for them to do business, compared to the United States.

Grace Shao (34:24)

But the thinking from a lot of these founders is often that Southeast Asia, frankly, doesn’t have as much purchasing power either, or willingness to pay, especially on software.

Christian (34:32)

But they are growing. They are growing. We must invest in the future. And one thing I want to note — there’s much more potential in Europe.

Grace Shao (34:41)

Interesting. Okay. I think Europe has been a bit overlooked. People have kind of — some people have written it off a little bit, frankly, just given the recent years of slower innovation. How do you view the European market?

Christian (34:54)

I think Europeans are catching up. They are awake, they are working to catch up.

Grace Shao (34:59)

Mindfully.

Christian (35:01)

Yes, they’re working to catch up. That’s what I got from the Paris Summit, very intensively. And I believe — I think from the economy side, you must look at the market from the economy side, I mean the macro side. The economy in Europe — they are struggling. That’s my personal opinion. The economy in Europe, for many countries in Europe, they are struggling. So they need a new power, they need a new engine to restart the economy. I think AI should be the best engine for new growth in Europe.

Grace Shao (35:40)

Very interesting.

Christian (35:41)

So what kind of technology? Whose technology do they want? I don’t think they have the time to catch up just building their own AI labs, their own AI infrastructure.

Grace Shao (35:54)

Their whole stack, yeah.

Christian (35:55)

Yeah. They need help from China, even some other places. So I think that’s a huge potential for Chinese companies to do business in Europe.

Grace Shao (36:05)

Interesting. Okay. Very differentiated. Very cool. Okay. Thank you, Christian. Thank you for your time.

Christian (36:11)

Thank you.

Grace Shao (36:12)

Enjoy your trip and travels.

Christian (36:15)

Thank you so much. I enjoy it. Bye bye.

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

AI Proem Podcast

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