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Chapter 1: What recent changes have occurred in China's AI landscape?
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Bloomberg Audio Studios, podcasts, radio, news. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.
And I'm Tracy Alloway.
Tracy, I love being in Hong Kong. I love it here so much. I love it here so much. I would like come here a few times a year if we could.
I'm sure you would. I lived here for like, I guess, almost four years. So it's kind of weird coming back. But Hong Kong has a lot of pluses, like great food, great weather for most of the year, beaches. I once heard someone describe it as Manhattan meets Maui, which I think is like pretty accurate.
Oh, it's so nice. The weather is actually not great.
It's not great right now.
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Chapter 2: How are Chinese companies adapting to chip export controls?
I mean, I know very little about AI, but I know even less about Chinese AI. But here are some of my general impressions, which is, A, it seems like there are so many open source. Okay, so I know they're largely open source. It seems like every random company you see, like some toothpaste company, and they'll have produced an LLM. So I'm very curious, like, how they're making money on it.
I also get the impression, like, you know, the heads of American AI labs speak in these sort of quasi-mystical terms, etc., It doesn't feel quite the same here where it feels like a bit more of like yet another technology.
But I'm glad you brought up the point about the tech crackdown because at the time, the whole story was like, oh, there needs to be less focus on sort of digital tech and more focus on hard tech. Which has been done extremely ā that's been an extraordinarily successful endeavor.
And then my last impression though is that since the release of ChatGPT in late 2022, that was the moment it's like, no, we really have to also compete on sort of this next era of software and sort of consumer-facing tech breakthroughs.
Yeah. Overall, the AI scene in China feels much more utilitarian to me. It's more about like the big companies, the Tencent, the Alibaba sort of using AI for their existing business models rather than this existential thing, which it is in the U.S. where like AI is the business. That's just it.
Right. Yeah, that's exactly ā AI is sort of weird. Like it sort of sits in the middle of what you would call like software and hard tech because we consume it through the browser, right? Sort of the same way or in many cases through the browser. The same way that we would go to an Amazon or an online gaming or something like that. But it's clearly ā it's a scientific endeavor anyway.
And so it sort of is this blend. And then you have to figure China is so far ahead of the U.S. when it comes to things like robotics and EVs and batteries. And one thing I don't know anything about is the degree to which that melding of hardware capabilities with AI capabilities, how that influences the direction of the development of the AI tech.
Yeah, I'm also very interested in like the capital stack for Chinese companies, because over in the US, we all know that people are flinging money at anything with the word AI in it. But in China, it's very different. I get the impression that it's like much harder to raise enormous sums of capital.
And so I'm very curious how that limited capital actually influences the development of these models and the technology.
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Chapter 3: What role does DeepSeek play in the Chinese AI ecosystem?
Why did it develop that way?
Yeah, I think people like to think of these mystical reasons, but really it was a very pragmatic business reason to start with. To start with, a lot of the labs have cited that, you know, for Western companies or Western developers to trust them, they needed to open source their models to build that trust and credibility. So in many ways, it's a branding decision.
Then on top of that, I think you can see it as a philosophical drive. You know, the founder of Deep Seek, Liang Wenfeng, has openly said he wants open source, his most frontier research to really help propel the whole industry as a whole. And that kind of R&D sharing has now formed a layer for the whole ecosystem where each of the labs kind of integrate each other's kind of breakthroughs.
You know, you see them congratulating each other, even on X when they have new models announced. So you can say it's a bit more collegial. I wouldn't say they're not competing though. However, because of the compute constraint they're faced with, talent constraint, and the capital constraint you even mentioned, they are a lot more conscious with where they want to put their money.
where they want to put their time in R&D. And all of that forms the basis of a strong open source ecosystem.
Is the culture as pro-sharing and pro-open source as it was even two years ago? Now, the deep-seek moment was right around Trump's inauguration in early 2025, so about a year and a half ago. Since then, has the culture stayed the same or has that sort of competition bug, that intense competition bug that we know among American AI labs, has it spread to the Chinese labs at all?
I think the sharing is an unintentional result rather than, you know, an intentional effort in the beginning to even start with. They are for sure extremely competitive. And, you know, we all know the word involution. So like China AI is dreading as well. That means like there's involution in this ecosystem as well.
However, I think bringing up DeepSeek, DeepSeek plays a very interesting role in the whole ecosystem. Like you mentioned, V3 projects. propel the whole industry forward. Everyone kind of start taking China AI more seriously. You know, it brought a lot of interest from investors globally back into the internet companies that Tracy mentioned.
You know, prior to that, there was a bit of a slump for three to five years. However, you know, CAI is now publicly listed in Hong Kong. Minimax is publicly listed in Hong Kong. Moonshot is in preparation to go public next year. They are competing with each other to capture market share, to capture developer mind share. But DeepSea plays an interesting role. I want to bring it back to DeepSea v4.
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Chapter 4: Why is open-source a significant trend in Chinese AI development?
Like we're doing this all on like a Chinese stack.
Yeah. They were like, look, guys, like you can actually do this. And they became a shared foundation layer for China's model ecosystem. So because, again, everything is open source and open weight, other labs were able to study what they did to actually start inferencing on Huawei stock.
And I think that was the first step, whether it's signaling or actually, you know, very pragmatic reason to start shifting some reliance on, you know, the China AI stack.
Aside from DeepSeek, can you kind of describe the differences or what China is trying to do on the actual frontier side? Because there are some.
I think if you really have to look at the ecosystem, we can kind of put aside the big tech for now. But looking at maybe the four most relevant startup labs, DeepSeek, Moonshot, who has Kimmy. Zia, who has GLM, and then Minimax, they are still probably the most committed to frontier research.
However, because the constraint we mentioned that they face, whether it's compute, whether it's capital, or even frankly, talent, they have decided out of necessity to basically each focus on a different vertical in capturing a different kind of business share. So ZEAI is very focused on coding capabilities.
So if anything, their GLM plan is much more similar to maybe what you think of Clod, ClodCore, ClodCode, etc. A codex, that kind of product. And then you look at Minimax, they're really focused on the multimodality capabilities. Moonshot, they're really focused on agents.
And DeepSeek, again, really is just focused on pushing the frontier and kind of trying to play catch up and push the Chinese ecosystem as fast as possible.
Yeah.
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Chapter 5: What challenges do Chinese AI labs face in terms of competition?
People are paying for managed services. And when you're paying for an API through ZAI or Minimax, whatnot... You basically don't have to self-host. You don't have to get your own GPU. You don't have to figure out your own compute. You don't have to figure out your own guardrails, your deployment, your security, your monitoring, whatnot, right?
So just to be clear, you can self-host all of these models, but for the most part, they do offer that inference part of the stack, and that is a profit center for them.
Yes, exactly.
Okay, got it.
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All right. So there seem to be two major constraints on Chinese AI. Maybe energy is a constraint as well. We should talk about that. But there's the capital issue. So not as much capital available or people aren't flinging it at AI companies the way they are in the U.S. And then secondly, there are the export controls on chips. And we talked about that a little bit
But can you describe how those controls are actually, I guess, influencing the development of the models themselves and like, I guess, optimization?
So some of these big tech are actually even buying out the contracts that data centers have with some of these labs. And, you know, they are taking over the compute. So these labs essentially are now optimizing for data. the highest quality inference demand, if that makes sense. They don't actually have enough even supply.
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Chapter 6: How does the Chinese public perceive AI technologies?
And you know, you got to find a data center that has an electricity hookup, and it has to be reliant and all of that. And It seems to be in short supply. Is it a similar story in China?
Honestly, energy is probably not the biggest bottleneck right now in China. And I think people like to say, well, some people like to say, oh, somehow the Chinese government had foresight on the AI boom driving like the energy consumption, but definitely not. I think people forget that China's economic growth over the last three to four decades also meant a rise of urbanization.
And a lot of the cities that, you know, we are visiting these days, like at least Westerners are visiting like Beijing, Shanghai, Shenzhen with all these robots and EVs or whatnot. These were all really urbanized within the last two, three decades. And because of that, the grid is very new.
And because of that, the government already foresaw that there was going to be a increase in energy demand and, you know, So a lot of the energy plants, you know, the solar plants, hydro plants, whatnot, were actually built out in anticipation for that. Now, obviously, this has coincided with now the AI boom and it's really helped out. Beyond that, you know, change.
China has an advantage in the fact that they can actually drive top-down mandates and provincial governments will follow suit. This is something quite unique to China because it's not like decided by each state.
So when they pushed out the East Data West Compute, where it's basically a top-down initiative where they built a ton of renewable energy for cheap in rural mountainous areas in Guizhou province, like even Xinjiang, Inner Mongolia, Sichuan, you know, Those were like very easily executed, frankly. And then 90% of the population actually sit on the eastern coastal lines.
Like we think about Beijing, Tianjin, Shanghai, Shenzhen. That's all on east. So that's where the data comes from. So that kind of optimization has also really helped them, you know, with the low that is the demand right now.
Hmm.
I want to get back to something you said. So first of all, just to clarify, you mentioned companies like Mercor that sell proprietary data that they are able to collect and manufacture in various ways, then they sell it to an open AI.
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Chapter 7: What is the impact of government policies on AI development in China?
I've been thinking about this a lot and thinking about what it means for distillation and what it means for the models to catch up, right? So there was this one quote from Yao Shunyu, who is a Google DeepMind researcher. He said, there is smart distillation and dumb distillation. Dumb distillation is something I think most of us were frankly non-technical think about.
It's like, okay, you take like a thousand queries, you take the answers of whatever Claude gives you, right? And then you kind of force copy that into your said model. And then you forcefully make them basically like get the exact same answer.
Smart distillation is like you using the frontier model almost as a partner to help you with the judgment for the evaluation and even the data labeling itself. So you're using it as almost a teacher for your own model. It guides it a little bit versus really copy pasting the answer for that makes sense.
And that part of it is frankly not that unethical or like, you know, that frown upon right now, because that is what enterprises do when they're fine tuning. So It's all kind of a bit of a murky area, to be honest.
Okay. So you mentioned data just then. Talk to us about what the Chinese data set actually looks like. Because I imagine if you're a Tencent, I mean, you've got WeChat, right? That must be a whole load of data on which to actually like build your AI. But on the other hand, I imagine like there are some restrictions around the internet, obviously. What does it actually look like here?
So I actually split that into two parts. On the data itself, people often think China is so data intensive and you just have a vast amount of data to use for AI training.
However, actually people forget, again, China's enterprise build out or, you know, whatever, the knowledge work economy is very new and not as sophisticated, frankly, as the American ecosystem or the Western ecosystem, if you have to put it that way. So data is often unstructured and data thus
a lot of the specific needs for, you know, the kind of training we're seeing today is not as vibrant or the data ecosystem is not as sophisticated as what American data providers kind of can provide, such as Mercore, like we just mentioned. Now on the Big tech side, it's been interesting.
So I'm glad you brought up Tencent because Tencent actually just announced last week that they are working in the works of creating an agent that can be plugged into WeChat.
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Chapter 8: How is China leveraging its manufacturing capabilities for AI advancements?
I think it's easy to blanket statement as geopolitical headwinds, people are scared. But realistically, I think most people are just trying to take care of their families and live a good life, right? Right. I hate to sound so crass about it, but sometimes it's what your package looks like.
And to overgeneralize, I've heard from many researchers say, look, if my wife is a lawyer in China, my wife is a nurse in China, my wife is a teacher in China, that kind of employment opportunity is very, very hard to actually transfer to a new market. If I can get a similar package and a growth opportunity in one of the big labs in China, I will pick that over living in the US.
And on top of that, I think that something is lost in the nuance is, My parents immigrated to North America 30-plus years ago. It was a very clean-cut, like, quality of life. It's just, like, objectively better in any city in North America compared to any city in China. Now that's kind of a personal debate, right? Because it depends on what you really value.
If you want to be close to city center, you want that fast-paced, like, techno, EV, futuristic lifestyleā China actually gives that to you. And then on top of that, if you want to be close to your family, it's a very personal reason.
So I've met a lot of researchers who actually decided to come back to China or this part of the world simply because they wanted to do it for personal family reasons.
Are they paid as much as they are in the U.S.? Because we get headlines all the time about, you know, so-and-so is joining whatever company and people treat that news like sports stars, right? Like teams trading their best players. Is it a similar thing here?
I think you definitely get less of that sports star vibe or mentality here. They're still getting paid like hefty amounts. How much? They don't usually disclose, but at least even in the internet era, like a ByteDance product manager can make just as much as a Meta product manager. Similarly, If you're like an average AI researcher, you're probably making a similar amount.
Although the star, star players, like the ones that are signing 100 million bonuses, I don't know if we had anything like that big in China. But look, they made their money with the IPOs. They made their money recently with all this AI boom. It's just on a maybe slightly smaller scale. Doesn't mean that they're not making much more than the frankly average person.
Yeah.
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