Z.ai/ Zhipu: one of the first major LLM start-ups to go public. Competition with giants and aims for AGI

In this episode, I sit down with Zixuan Li, who leads the chat API and global partnerships at Z.ai, one of China’s leading LLM labs (one of the four tigers) and now one of the first to head toward an IPO.

Z.ai started as THUDM, a Tsinghua data-mining lab best known in open-source circles for GLM and CogVideo, and has since grown into a model-as-a-service platform powering millions of devices and thousands of enterprises in China and beyond.

We talk about what it actually means to be an “independent” lab in a market dominated by platform giants like Alibaba, ByteDance, and Tencent, why Z.ai pivoted from SOE-heavy infrastructure projects to a product-led GLM stack, and how they landed on a different business model, and the creation of the GLM Coding Plan, instead of charging by tokens. Zixuan is very candid about pricing (“If Anthropic charges $200, we charge 200 yuan”), the realities of on-prem-first China vs cloud-first West, and what it’s like to race against Minimax and Moonshot with fewer GPUs and less cash.

We also zoom out and look at China’s AI talent pipeline (and the meme that the AI race is “Chinese in China vs Chinese in the US”), how he thinks about AGI as self-learning agents that live on your phone, why he’s comfortable being a white-label backbone in the Global South, and where he sees China’s AI landscape in the next 6–12 months. If you want a ground-level view of how a Tsinghua spinout is trying to survive, and maybe win, in the LLM wars, this one’s for you.

Newly launched (Dec. 22) GLM 4.7:

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01:20 – From THUDM to Z.ai: rebrand, Tsinghua roots, and model-as-a-service

03:30 – Quiet period & IPO: pride, pressure, and the business challenge of LLMs

06:33 – Pivoting from SOEs: infra projects, agentic models, and why strategy followed capability

07:25 – Competing with Minimax, Moonshot & DeepSeek: focus, compute, and capital constraints

08:34 – Chasing benchmarks vs real-world IQ: math, humanities, and alignment trade-offs

11:05 – On-prem vs cloud: why Chinese SOEs still won’t touch APIs

13:43 – Zero-retention and trust: can China’s culture around data ever shift?

14:07 – Inventing the GLM Coding Plan: subscriptions, stickiness, and “pay by value, not tokens”

16:00 – “If Anthropic charges $200, we charge 200 yuan”: pricing strategy and margins and GLM’s open-source flywheel

19:41 – Who really pays: sticky indie devs, big tech customers, and bargaining power

23:32 – GLM Coding Plan vs Cursor/Qwen/Claude: plans, agents, and avoiding lock-in

25:57 – Z.ai’s AGI ladder: AutoGLM, self-learning, and personalized weights

27:03 – Independent labs vs platforms in China: speed, resources, and “dirty work”

29:34 – Moonshot vs Z.ai: chasing the moon vs being “down to earth”

30:53 – Will China’s LLM market consolidate?: 5–10 players, Doubao, and video-generation winners

31:44 – Doubao phone & Honor partnership: bargaining power with OEMs

34:11 – Beyond China–US: Global South strategy and being a white-label backbone

35:29 – Being comfortable as infrastructure: letting others own the brand

38:05 – Who joins Z.ai and AI talent: thriving with scarce resources

40:07 – Culture, 007 hours, and survival: what it takes to be infrastructure

42:33 – Social welfare, AI safety, and cheap tools in India & Indonesia

44:38 – How China actually talks about AI safety (or doesn’t)

47:29 – Differentiated view: why Zixuan believes you should “enjoy lacking resources”

AI-Generated Transcript

Grace Shao:Hey Zixuan, thank you so much for joining us today. Really excited to have you on. Walk us through your journey and what led you to Z.ai to start off with.

Zixuan Li:Yeah, so currently I’m the head of Zhipu AI’s chat API services and also head of global partnerships. I collaborate with LMSys Chatbot Arena, OpenRouter, Vercel, these large companies, and ship our products through their platforms.

The reason why I joined Zhipu is it’s one of the leading AI labs in China and I can do overseas businesses, because I have a background at MIT’s Schwarzman College of Computing. So that brings my knowledge into real-world practice.

Grace:I see. Was there any incentive for you to move back to China versus stay in the US?

Zixuan:I think it’s more personal, because my wife’s based in China and she’s used to her work, so there’s no way she can move to the US.

Grace:Fair enough.

So let’s talk about the company’s mission and origins, because I think it does seem a bit mysterious, especially to people outside of China. From the outside, people know Zhipu, Z.ai as one of the leading Chinese LLMs. But that doesn’t really capture everything you guys do, right?

In your recent prospectus, you describe yourself as a MaaS — model-as-a-service — company first. So tell us about that.

Zixuan:Okay, so before Zhipu AI, we were called Zhipu or THUDM, because we named ourselves by the AI lab’s name. We originated from Tsinghua University’s data mining group — THUDM. But I think it’s hard to pronounce, and also “Zhipu” is also very hard to pronounce. So this year we bought the Z.ai domain and finally changed our name to Z.ai.

When we were called THUDM, we were very famous inside the open-source community because we had a lot of repos, a lot of models under the THUDM name. And we open-sourced not only text models, also CogVideo, CogView, these models. I think they were sold at that time.

But with the launch of VEO, Hailuo, and also a lot of current top models, we began to be more focused — basically more focused on text models, visual understanding, and so on. So I think that’s the origination of the lab.

But as you said, there’s this terminology called model-as-a-service. From our side, when we compete with large companies like Alibaba and ByteDance, we need to be more focused. They have their inference level, they have their cloud services, but we don’t. So we try to let the model itself provide the service — like the API, or technologies like visual understanding — and try to use the model itself to be the selling point.

Grace:I definitely want to double-click on how you position yourself compared to peers — a few of them you just mentioned, whether it’s Minimax and Moonshot, and then you also mentioned the BATs.

But to start off with, you’re currently in your quiet period as your prospectus just hit the public. And if successful, you will become one of the first major LLM startups globally to be listed on a stock exchange. How does that feel?

Zixuan:I think we are proud of it, but things are very challenging, because it’s really hard to do LLM inference. Both OpenAI and Anthropic have very high revenue, but a lot of loss on their income statements. So we have to figure out how to make money from large language models and also provide cheaper service to the customer.

So I think it’s only a starting point for us.

Grace:Definitely. I think right now only the big tech companies in many ways are essentially seeing ROI, and the model companies and the model labs themselves are really finding it hard to make a profit.

I want to ask you about the branding. You did say you guys changed your company’s name to Z.ai this year, partially because Zhipu is just hard to pronounce. But was that also related to the fact that you guys seem to have made a pivot into really focusing on going global? Z.ai seems to be a lot more non-Chinese-native-speaker friendly, right? So is that the push right now?

Zixuan:I think that played an important role, be…

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