How China Caught U.S. AI — With Grace Shao

**Alex Kantrowitz** (0:00)
How does China keep catching up to the US in AI despite having much fewer state-of-the-art resources at its disposal? And what does it mean if US. AI labs permanently lose their lead to China? We'll talk about it with a leading China analyst, Grace Shao, right after this.
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**Alex Kantrowitz** (1:40)
Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. Today, we're going to talk all about China's rise in AI, which is really now a multi-year phenomenon, and whether the United States can maintain any semblance of a lead over China, now that Kimi K3 has effectively equaled maybe not the frontier frontier of USAI models, but enough frontier to make us question whether the lead will be maintainable at all, and then of course what the implications are for the AI race. We're joined of course by Grace Shao. She is the author of AI Pro-M on Substack, and a leading analyst on China's AI efforts, and of course the front of the program. Grace, it's great to see you. Welcome to the show.

**Grace Shao** (2:28)
Alex, so great to be back. Thanks for having me.

**Alex Kantrowitz** (2:31)
So let me sort of set the stage here.
We had this moment last year where DeepSeq was able to produce really great results on reasoning at a cheaper price and the market flipped out. Okay, we know that happened. But even still, you know, despite the fact that people in the US and Europe, and maybe all over the globe, have known that China is very capable in its AI research, the release of Kimi K3 largely had this reaction of like, how did they do that? Right? It's confused a lot of people, even though it shouldn't be a surprise necessarily because China's done it before. You know, it's sort of happened again. And this new model from Moonshot, Kimi K3, we've talked about on the show, it's doing really well. This is from Nathan Lambert's Substack Interconnects. It comes number two on the VALS AI index, number three overall on artificial analysis is intelligence index, number one in the front-end Cone arena, and has many more impressive results. So let's just start there.
Should we be this surprised and how did Moonshot do it?

**Grace Shao** (3:43)
I can't comment exactly how Moonshot did it, but I'll start with a comment actually Nathan even shared with me when we talked about after his China big big China trip when he visited all the labs. I think it's a lot of it's in the talent and people are really shocked by the talent. And this is something we talked about as well a year ago when DeepSeek, you know came out with a whole domestically educated and domestically, you know, trained talent pool. Now, I think that's something really under looked right now.
You know, the talent pool in China right now kind of is because the base is so big. And then there's such a strong STEM education, which feeds into right now the AI, you know, researcher realm. And then we see like the leading researchers really a lot of them, if not, you know, maybe 40, 50 percent of them are of Chinese descent or heritage.
Chinese talent right now is kind of having a moment, I think. And then I think within AI research, you know, what I've heard from a lot of labs are saying, they're like, look, it's not really rocket science, actually.