DeepSeek’s fundraising, unique A-share features, and what’s ahead with The Information

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…

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