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- [00:00]
Grace Shao
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Grace Shao
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Joe Weisenthal
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Joe Weisenthal
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Joe Weisenthal
That's unstoppable energy.
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Tracy Alloway
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- [01:20]
Grace Shao
JPMorgan Chase Co.
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Bloomberg Audio Studios Podcasts Radio News.
- [01:41]
Joe Weisenthal
Hello, and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
- [01:46]
Tracy Alloway
And I'm Tracy Alloway.
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Joe Weisenthal
Tracy, I love being in Hong Kong.
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Tracy Alloway
I love it here so much.
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Joe Weisenthal
I love it here so much. I would, like, come here a few times a year if we could.
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Grace Shao
Could.
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Tracy Alloway
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.
- [02:14]
Joe Weisenthal
Oh, it's so nice. The weather is actually not great.
- [02:18]
Tracy Alloway
It's not great right now.
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Joe Weisenthal
Incidentally, this week we've come during a. I guess it's a monsoon season, right?
- [02:22]
Tracy Alloway
Yeah, it's the rainy season, but oh well, I like thunderstorms, so I'm enjoying it.
- [02:26]
Joe Weisenthal
Yeah, I'm enjoying it, too. Anyway, one thing that has changed since the last time you were in Hong Kong, you left in 2022. Yeah, we weren't doing as many AI episodes in those days.
- [02:37]
Tracy Alloway
No, we definitely weren't. In fact. So I remember one of the big stories when I was here in Hong Kong in 2020 was China's tech crackdown. Right?
- [02:47]
Joe Weisenthal
That's right. That's right.
- [02:48]
Tracy Alloway
Right. And like, there was all this Concern about whether or not the crackdown was going to destroy China's entrepreneurial spirit. I'm doing air quotes on a podcast. I don't know why, but fast forward six years and there's entrepreneurism basically everywhere. And we talk a lot about how China is producing all these new AI models.
- [03:10]
Joe Weisenthal
Okay, can I like say, like, I know very little, I mean, I very. 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's 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 the heads of American AI labs speak in these sort of quasi mystical terms, et cetera. 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. 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 tech breakthroughs.
- [04:21]
Tracy Alloway
Yeah. Overall, the AI scene in China feels much more utilitarian to me. It's more about like the big companies, the Tencents, the Alibaba, sort of using AI for their existing business models rather than this existential thing, which it is in the US where like AI is the business. That's just it, right?
- [04:43]
Joe Weisenthal
Yeah, that's exactly. 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 could, 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, you know, it's a scientific endeavor. And so it's sort of is this blend. And then you have to figure China is so far ahead of the US 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.
- [05:23]
Tracy Alloway
Yeah. I'm also very interested in like the capital stack for Chinese companies because over in the U.S. we all know that people are flinging money at any 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 tech.
- [05:48]
Joe Weisenthal
I think it's safe to say that both of us have a lot of impressions.
- [05:51]
Tracy Alloway
Yes, right. Big impressions.
- [05:54]
Joe Weisenthal
How many times in this intro I was like, I get the impression, but I actually have no idea. So that is a good reason to actually bring in our guest, someone who has more than quote impressions, unquote, about the AI tech scene. We're going to be speaking to someone whose newsletter I'm a big fan of and everyone should read are going to be speaking to the perfect guest, Grace Shao. She's an independent AI researcher and she has a great substack called AI Prom. And she joins us here in our Hong Kong office. So, Grace, thank you so much for coming on Odd lots.
- [06:23]
Grace Shao
Thank you so much for having me. Joe and Tracy, how did we do
- [06:25]
Joe Weisenthal
on our quote impressions, unquote?
- [06:28]
Grace Shao
Those are pretty accurate impressions, I think.
- [06:30]
Joe Weisenthal
Okay, good.
- [06:30]
Tracy Alloway
That was the episode. Like, let's start at a basic level. So the big impression, the one that everyone knows is Chinese models are open source versus the closed frontier models of the us. Why did it develop that way?
- [06:47]
Grace Shao
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. The founder of Deep Sea, Liang Wen Feng, 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 and D sharing has now formed a layer for, for the whole ecosystem where each of the labs kind of integrate each other's kind of breakthroughs. 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 mentioned. They are a lot more conscious with where they want to put their money, where they want to put their time in R and D. And all of that forms the basis of a strong open source ecosystem.
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Joe Weisenthal
Is the culture as pro sharing and pro open source as it was even two years ago now. The deep seat 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?
- [08:26]
Grace Shao
I think the sharing is an unintentional result rather than an intentional effort in the beginning to even start with. They are for sure extremely competitive and we all know the word involution. So China AI is as well. That means there's evolution 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 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, Jipu Z AI is now publicly listed in Hong Kong. Minimax is public listed in Hong Kong. Moonshot is, you know, in preparation to go public next year. They are competing with each other to capture market share, to capture developer mindshare. But, but Deep Sea plays an interesting role. I want to bring it back to deep sea V4. So V4, you know, on the surface, you know, people said okay, it wasn't as maybe impressive on evals and performance. It didn't catch up with the most frontier labs in the U.S. whatnot. Right. But it was a very interesting move because what I heard from researchers on the ground in Beijing was that the lab actually delayed their release for about three to four months because they wanted to re engineer a lot of the inference onto Huawei. So I'm not saying this completely replaces Nvidia or cuda. Not at all. Because if you ask any developers, they still want to use Cuda if they can. However, it was the first effort to really, I don't kind of like did one for the team. Like they kind of like put the
- [10:09]
Tracy Alloway
resources supposed to be like a signal. Basically we're doing this all on like a Chinese stack.
- [10:15]
Grace Shao
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 Stack. 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
- [10:41]
Tracy Alloway
stack aside from deepsea. Can you kind of describe the differences or what China is trying to do on the actual frontier side? Because there are, there are some.
- [10:52]
Grace Shao
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 the, maybe the foremost relevant startup labs, Deepseek, Moonshot, who has Kimi 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 is capital or even frankly, talent, they have decided out of necessity to basically each focus on a different vertical, capturing a different kind of business share. So Zai is very focused on coding capabilities. So if anything, their GLM plan is much more similar to maybe what you think of Claude, Claude Co or Claude Code, et cetera, 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 trying to play catch up and push the Chinese ecosystem as fast as possible.
- [11:55]
Joe Weisenthal
It's really crazy to look at some of the ones that have already gone public here. And just to put in. So Minimax is public and in US dollar terms, it's a $20 billion company. I mean, there are people in the US who have done nothing but publish a paper on arXiv.org who do not even have a product yet, who have probably VC backed implied valuations over $20 billion. How do they make money? You know, again, open source. Okay, like in these four models that you named, do they have different thoughts on how they plan to make money or different business models?
- [12:32]
Grace Shao
Yeah, so China's VC space in general has not been that vibrant, frankly, since Internet crackdown. And a lot of USD funds did exit, you know, three to five years ago with Sequoia being maybe the most like, high profile. Right. Like we all remember that now people Forget Even in 2022, a lot of these labs that we just talked about, they were struggling to even raise, you know, raise capital. And a lot of them Spun out of academic institutions. You know you mentioned they're valued anywhere roughly between 20 to 30 billion right now, but they went public between like 6 to 8 billion. That's like kind of tiny compared to American valuations right now. However, they are actually making money. You know, the publicly disclosed information I think from Minimax and Drupal indicates that they were making just as much like in their last month. They made the same amount of money last month as they did last year essentially. And their end of year AAR projection is anywhere between 1 to 1.2 billion right now. So they are making money and how? Well, just because they're open source doesn't mean they don't make money. I think people forget, you know, we had open source softwares before as well. 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 get you figure out your own computer, you don't have to figure out your own guardrails, your deployment, your security, your monitoring whatnot. Right.
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Joe Weisenthal
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.
- [14:08]
Grace Shao
Yes, exactly.
- [14:09]
Joe Weisenthal
Got it.
- [14:25]
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- [15:55]
Tracy Alloway
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 US 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 I guess optimization.
- [16:28]
Grace Shao
So some of these big tech are actually even buying out the contracts that data centers have with some of these labs and they are taking over the comp, essentially are now optimizing for the highest quality inference demand, if that makes sense. They don't actually have enough even supply, they don't have enough compute power to even meet the demand that's coming through. So that's on how they're servicing clients, how they're changing, I guess, or how they're optimizing their training is that a lot of them are really focused on the post training. And this goes back to. So you know how OpenAI has like three buckets where they chuck money at. There's like the R and D, there's pre training, there's post training R and D. A lot of times a lot of money is spent. But say like 1 out of 10 things stick. But you need a lot of compute and resource and people to be figuring out where to go. For a lot of these labs in China, they frankly don't have that luxury. So they've even given me a metaphor and said it's kind of like knowing what the answer to the homework is and working backwards. So they will wait till the frontier labs to come out with where the right direction is for the next frontier model and, and they will work backwards and actually focus all their resources on post training. So with post training they will optimize a lot of times the data they collect. For example, if a data provider like Mercur provides a very, very niche set of data set for like an OpenAI whatnot, maybe they would charge them 10, 20 million dollars. The Chinese lab will wait out that exclusivity. Contract three to six months time let's say, and then pay a fraction, if not like a tenth of that price the same data set and that kind of plays into that like six to nine month lag that we hear about as well.
- [18:07]
Tracy Alloway
That's really interesting. Let's talk about energy then because the story in the US is that electricity is really the big constraint on AI use. 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. It seems to be in short supply. Is it a similar story in China?
- [18:28]
Grace Shao
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, 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
- [20:15]
Joe Weisenthal
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 OpenAI. So a company Like Merkor will hire a bunch of people to say, build PowerPoints and then they'll collect the data on how they do that, and that is fresh data that they can sell. So those have exclusivity windows after which they can then sell them to anyone.
- [20:44]
Grace Shao
I'm not saying Mercour specifically, but supposedly there are these data providers that do this and they have exclusivity windows. And then the Chinese labs kind of weigh that out so they can pay like maybe a million dollars versus like 10 million for the same data set.
- [20:57]
Joe Weisenthal
So this gets to something generally speaking, which is that people are around the world correctly, like quite impressed by how high quality the Chinese models are, even if they're behind. But then you have things like that. And then you also have accusations from the likes of Anthropic that they're distilling models and that they're finding ways to collect the outputs of American models for training. So then you could say, well yes, sure, it's great, this open source model and it can stay close to the edge. But then the counter is that they can only be so advanced because there is this extremely capital intensive closed source model in the US that's really establishing the frontier and that these Chinese companies wouldn't be anywhere near where they were if they weren't sort of, I guess you would say, drafting off the American labs.
- [21:49]
Grace Shao
Yeah, I think the compute constraint and the capital constraint is real and frankly, like no one's hiding that or pretending that that's not an issue for them right now. Like Deep Sea has openly said they even were struggling, right? Like they needed more compute. I think on the distillation allegations or accusations it is quite interesting. Like recently 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 Shun Yu who is a Google DeepMind researcher. He said there is smart distillation and dumb distillation. Dumb distillation is something I think most of us who are frankly non technical think about. It's like, okay, you take like 1000 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, if 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.
- [23:09]
Tracy Alloway
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?
- [23:32]
Grace Shao
So actually split that into two parts on the data itself. People often think China is so data intensive and you just have mass 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 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 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 can provide, such as Mercourt, like we just mentioned now, on the big tech side, it's a bit 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 a agent that can be plugged into WeChat. So this has been very controversial and it has actually had a lot of pushback Even internally, because WeChat's product manager, Alan Zhang, has been famously or notoriously known to be kind of hard to work with if you want to push something within WeChat. Because he's so protective of that user experience, it's his baby, right? And Tencent, like you said, has WeChat, which is a super app, has more than 1.4 billion MAU globally, like mostly 1.3 billion people in China and the Chinese diaspora globally, or people who work with China, that is immense value. But the risk and compliance risk of potentially an agent going rogue within that chatbot, or that of agent going rogue in executing, whether it's a purchase or whatnot, that risk is very high. So they've been working on that. And on top of that, tencent itself has been lagging behind compared to other big tech in their own proprietary models. And they've really been trying to really play catch up. They actually last year poached someone from OpenAI who is a researcher called Yao Xunyu as well, same name as the other researcher we just mentioned to lead this whole initiative. And their whole goal is to basically build a tencent agent native model. And that is their biggest goal because end of the day, like you said in the very beginning, their goal is to optimize existing businesses already and bring AI to the mass consumer as fast as they can.
- [25:51]
Joe Weisenthal
You mentioned poaching a researcher from OpenAI and it's like the way I see it, AI will definitely be built by the Chinese. The question is whether it'll be built by the Chinese working in the American labs or whether it will be built by Chinese working in Chinese labs. Has there been a gathering of steam of researchers that had been working at American labs going to Chinese labs or are they still sort of one off and somewhat rare?
- [26:18]
Grace Shao
I think even during the Internet era we saw a lot of Chinese nationals or Chinese ethnic people returning to China. Right. I think this, 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? I hate to sound so crass about it, but you know, sometimes it's what your package look 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's 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 lifestyle. 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 met a lot of researchers, actually decided to come back to China or this part of the world simply because they wanted to do it for personal family reasons.
- [27:44]
Tracy Alloway
Are they paid as much as they are in the US 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?
- [28:00]
Grace Shao
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 display, 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 like in China. But look, they made their money with the IPOs. They made their money recently with all that. 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.
- [28:43]
Joe Weisenthal
Tracy, can I say something that might be sort of sacrilege for a podcast host to say? Okay, I'm only speaking for myself here. I'm not necessarily speaking for the team, but it occurred to me, like, I'm not really sure if I'd be that interested in say, getting the CEO of like an American AI Lab on the podcast as a guest. I don't know what I would ask them because, like, like, do I really want to hear like, Sam Altman or demise Hassabis or whatever, like the future of work and all this stuff or like these all the big, you know, I love doing AI episodes. I just feel like at that level I would rather talk to sort of like someone in actually the engine room rather than this sort of big picture person who may have some degree of AI psychosis and just like, as on it speaks in like the biggest generalities.
- [29:36]
Tracy Alloway
Okay, well now Sam Altman needs to like, invite himself on the show just to test your, your commitment to not having AI CEOs.
- [29:44]
Joe Weisenthal
No, I would, I would do it. But I like, let's just agree here that if we ever like get one of the really big like lab CEOs, let's just ask the very like, sort of mundane questions about operations and not like, what are we all going to do? And other, you know, is what is the meaning of life going to be when we don't have jobs? Because I'm so sick of those conversations. They may be important at some point. But Grace, I'm sort of curious from your perception, it does feel like the heads of the American AI labs have some degree of AI psychosis themselves. Either they talk about all white collar employment is going to disappear or that they're going to build a monster that if done wrong is going to be out of control and that they're not. You know, it'll escape, it'll escape the sandbox. Is there the same sort of existential discourse in the Chinese AI community?
- [30:36]
Grace Shao
Yeah, I think to start with, in the AI community themselves, I would say people are a lot more pragmatic. And I think recently I was talking to Nathan Lambert, who was open source researcher who just came to China, visit all the labs. He said, look, I was shocked to see majority of labs are so young. As in like a lot of the researchers are still students, a lot of them are interns, and the core research teams are maybe led by a handful of people. And then these people are academics by training. So maybe they're a bit less commercial, maybe you can say they're less like sophisticated to manipulate the market, whatever you want to call them. So I definitely feel like there's less of that kind of psychosis or high level narrative going around. However, I would say that there is obviously anxiety from the public in some degree. Not as much of a pushback. But recently there was a very interesting court case in Hangzhou, which is home to Alibaba, and a lot of these AI labs. Basically a company tried to lay off a person saying you are being replaced by AI. And the court literally ruled, say that is not allowed and you can, no company can use AI as an excuse to lay off or replace or even cut short their contract time. So that was a really swift reaction from regulators and I think it really did serve as a calming factor for the public, obviously. I also think I want to preface the fact that the knowledge worker economy makes up less of the overall economy in China as well. So that kind of fear maybe doesn't feel as imminent, but that conversation is being had. But I do think in Asia in general, not only in China, you look at South Korea, Singapore, all these countries are approaching AI in a very pragmatic way. And the tiger moms are trying to train up the kids to be AI native, the students are trying to train themselves up to be AI native. People are preparing for the future versus pushing back on the future.
- [32:33]
Joe Weisenthal
That's interesting. You know, in the US obviously companies announced that they're laying people off and they cite AI even frequently when there's no evidence that AI had anything to do with it. So it's interesting they're not allowed to do it. In defense of American AI labs, most of them lose a lot of money and yet they actually do spend at least the big ones spend quite a decent amount on so called like alignment research, safety research, making sure the AIs don't go rogue, et cetera. How big of a part of the Chinese labs, how much do they spend on? I guess what you would put but the American labs would put into the safety bucket.
- [33:11]
Grace Shao
I do have to say I'm not a policy expert so I don't work with a lot of the safety people as much. But there are organizations in China that are definitely working like the regulators as well as the private sector working together. And for some context in China there are various moving parts in the government, there's MIT, the CAC, etc. These agencies basically some are to propel economic development. So in this case AI diffusion, the whole idea of AI plus AI plus every single sector you can think of, some act more like a guardrail as a protector. So they are working hand in hand. And on top of that every single AI gen application as well as LLM company have to go through the national registry in China so they actually disclose what is being trained, what is, you know, the potential risk. That said, I think right now no one really knows what the real impact of AI will be on economy, but definitely that fear mongering narrative is not mainstream in China.
- [34:31]
Ryan Reynolds
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- [36:33]
Tracy Alloway
describe, I guess the approach of the Chinese government to AI in general? Because it feels like the trade off for maybe not being on the cutting edge with frontier models is, well, you're further along with software and AI and the government maybe has like a better handle on what the labs are actually doing.
- [36:52]
Grace Shao
The Chinese government probably sees this in a much more pragmatic way. You know, just like how there was an Internet plus policy 15 years ago, there's now an AI plus policy. When deep seat moment took off, you know, there was a frenzy of private companies even in home appliances trying to embed Deep Seek integrate deep seek. I'm just like, what is a AI vacuum going to do for you or AI like electric toothbrush going to do for you? It was wild, right? But the government picked that up. And I think what the Chinese government, going back to what we talked about earlier, is that they have the advantage of having the ability to push things down from top down. And at the very high level, they're seeing AI as an economic driver to propel maybe efficiency to address some of the labor shortage that is coming as the population could decode continues to age and decline. It also addresses a lot of issues where a lot of the young people don't want to work in manufacturing roles. They want to be automated actually, and they want to work in urban areas. So they have that now. And then each of the provincial governments will take that as kind of like a KPI. They're like, all right, let's go act like VCs essentially and go find, you know, future deep seats and fund them. However, how much these companies want to take government money is a different discussion. A lot of them will then Even support them by providing infrastructure like buildings, offices, even some of them heard dormitory for these young entrepreneurs and then give them money and capital to try things out. There's also these AI pilot zones being rolled out across the country. I think now about 11 or 12 of them where people can try out new AI products. I met with the largest AI developer community founder in China a couple weeks ago and she was saying there's more than a couple hundred thousand developers in this ecosystem and they are working with regulators and the private sector. So if, say, ByteDance have a new product, they might go to them first and say, can you try this out? And then they will report and debug and see what, what's happening and then tell the whoever local government that's funding them with providing the infrastructure say, hey, this product might come out. Do you want to be part of it? Do you want to give it money? Do you want to provide it with whatever resource you want to? So there is kind of this like cohesive ecosystem where they kind of all dance together how much these companies actually want to take state money. I think that's debatable. However, as AI and robotics become more and more sensitive and being recognized as not only an economic driver, but potentially a military use or geopolitical, I guess, talking point at this point, it is becoming more and more nationalized, not only in China, but globally, in the US and so on.
- [39:35]
Joe Weisenthal
Robotics is obviously an area where China is just straight up ahead of the United States, or at least according to all the videos on my Twitter and Instagram feed of humanoid robots and so forth. How much does you know, when we were all kids, when we thought of AI, I think we thought of robots, right? We thought about the T1000 or some version of it. And now when we think of AI, most people think of chatbots, but that's just one aspect of AI for the advanced labs, whether we're talking about the deep seqs or the minimaxes or GLM and so forth, are they actively working hand in hand with some of the unitrees and advanced robotics companies to figure out how you can actually have that, the true AI robot of the future?
- [40:20]
Grace Shao
Yeah, I think China, having been the manufacturing hub of literally everything under the sun over the last three decades, has definitely a, an advantage have owning all the supply chain, right? And it's like not only just owning the supply chain, but, you know, there are literally regions where that whole supply from raw material to like the end product from OEM is all within like say 50 kilometers of each other. So what we're seeing is a lot of American investors and entrepreneurs coming into China to kind of get a sense of that. And like you mentioned, because we've been so fixed in software, I think China, having a very strong hardware background, is now thinking about how can we actually integrate the software into the hardware, how ready that is to the mass market. I frankly don't think it's really there yet. So recently I just met with some robotic companies. They actually can't just plug in a minimax, you know, that's like, for them, they need to actually get physical data. That is where like, you know, now all the hype is on world models. Physical AI, you know, that is a complete different set of kind of technology essentially where without the 3D data that these models need right now, the bottleneck right now is that, you know, these hardware, these humanoids, quadrupeds, dogs, whatever you want to call them, they cannot be powered by LLMs. That's number one. Number two is, despite that China being very strong on hardware, the bottleneck is actually a lot of times in the integration as well as the battery solutions. You know, you think of China having very strong battery solutions, but most of these gadgets can't last more than like say two hours. And there's no one that's really come up with a better solution so far. What I've seen the most creative thing so far is like, you know those glasses you wear, like the meta glasses, they kind of die within two hours. But China, like Ifly Tech or Rocket, that's kind of a newer player startup, they created these battery capsules where you can just like stick onto your glasses. It's very lightweight, doesn't really affect your user experience, and that's actually able to kind of extend it by a few hours. So to go back to your question, is China trying to do physical AI? Definitely. What is their edge? I think it's still in manufacturing. Is their software good enough? I don't think anyone really has good enough software right now as of now.
- [42:39]
Tracy Alloway
Wait, I'm just going to press you on this. So if we fast forward 10 years, what would you say is most likely to be China's comparative advantage? Is it like the cheap open source, super optimized models? Is it software, AI software that's integrated with industry and existing business, or is it robotics and the sort of hardware side of AI?
- [43:02]
Joe Weisenthal
10 years is a long time.
- [43:03]
Grace Shao
10 years is a long time. I know, sorry.
- [43:06]
Tracy Alloway
We want to challenge you.
- [43:08]
Grace Shao
A lot of these companies didn't exist 10 years ago or not even five years ago.
- [43:11]
Tracy Alloway
That's fair. Okay. I can shorten the timeframe in three years.
- [43:15]
Grace Shao
All right, so don't chase me down if I'm wrong in three years. But I think, you know, there's two parts. One is, I think I agree with you, hardware side. China's definitely going to have, I think, more breakthroughs and have a lot of edge. Not only is the supply chain all domestically there, I think something overlooked by people is the fact that a lot of the know how is also there and that's not easy to transfer overnight. You know Patrick McGee's book recently in his Apple book saying how Apple tried to move this whole supply chain to India. The biggest bottleneck is actually these like highly skilled laborist jobs that actually are so technical that cannot be even trained in one generation. It took decades to really train up the local community labor force whatnot. So that's still there now because of that ecosystem. A lot of these robots, home appliances, whatnot, these tech gadgets are produced at less than 50% of the cost of where you could produce that anywhere else in the world. They are also extremely innovative. I've talked to people at EV companies just for example, to ship out a new model from ideation to production to hitting the floors, that takes maybe less than 15 months.
- [44:27]
Tracy Alloway
Wow.
- [44:27]
Grace Shao
But for a traditional OEM, like wait for at least three to five years. Right. So there's the hardware side. I think another very underappreciated fact on the Chinese open source model is that people don't realize. So a year ago when I spoke to startups in Silicon Valley, they were the most cost conscious, frankly less compliance conscious and gives very little care about geopolitics. They were building on top of quit now. It's actually a lot of AI Native American enterprises building on Chinese open source because we're seeing headlines on the ROI is not like showing it's extremely expensive for these token maxing projects, whatnot. So Harvey Cursor, they've talked about using a hybrid model where they will build majority on GLM or Kimi, but kind of like what we talked about earlier where they use like OPUS to act as a judge or a guidance. So I think that's something where we continue to see and these companies are generating a lot of revenue and going back to the fact that like a lot of them don't even have enough capability to support the demand that's coming through.
- [45:35]
Joe Weisenthal
That's such a fascinating idea and it makes a lot of sense that at the application layer that probably a lot of different models can go into it. By the way, I was talking to someone at a dinner recently and he said he thinks that AI writing will get a lot better when AI is embedded in humanoid robots, because then we'll have this sort of groundedness in the real world where I have no idea if this is true. But he said the reason, his theory was that the reason why AI writing is still so weird is because. Because it's in this disembodied data centers, and that as soon as they're really in robots, then they'll have a sort of real world groundedness. All right, I have one last question. You know, after OpenClaw came out, I started seeing again my entire Twitter feed and Instagram feed. I have done this to myself, but all I do all day is consume Chinese propaganda. But I started seeing all these videos of like all these grandmothers and stuff, like setting up their claws and stuff like that. And I saw these videos and I said to myself, I just don't believe this. I think this is fake news. I do not actually believe that there's all these 80 year old grandmas or whatever, really excited about setting up their open claw or whatever. Are those real? Like, what's the deal with that?
- [46:45]
Grace Shao
I think that was definitely a bit of a hype.
- [46:47]
Joe Weisenthal
I knew it.
- [46:48]
Grace Shao
No, no. But I will say there were grandmas lining up to get it done. Okay, I will answer this twofold. On the kind of the surface is I think Chinese aunties, uncles, whatnot, they are just much more open to technology because, you know, you go around whether it's by force or by nature. You know, you can't really navigate modern Chinese life without being on Alipay.
- [47:12]
Tracy Alloway
And you really can't. You can't like buy Starbucks in Beijing without like having WePay.
- [47:17]
Grace Shao
After Covid, I went back to, I think Shanghai for the first time. And I was sitting at a restaurant just like waiting for someone to like, help me order food. No one came because they're just like, why are you so, like, why are you a caveman? Don't you know how to like, scan the QR code on your table? Okay, so that tangent aside, I think the overall optimism around technology is very different from the west because in the last 20, 30 years, a lot of rural areas in China literally could not access resources, information, goods, whatever that, you know, like big cities could not until these super apps came about. So a lot of people don't have TVs in their homes and they live in a village and their maybe annual household income is Like a thousand dollars, but they will have a smartphone. On that smartphone will be able to actually enable them to get micro loans, to purchase goods, you know, to help their kids access information online, whatever that is. So technology is very much kind of accepted and respected and actually like, big tech is loved. Like, if you work for one of big tech, you are like a pride of the family. So there's that very cultural aspect of it then is like going back to the super apps. So I think the Open Claw frenzy was interesting because some people say it was the first agent that, you know, Chinese people could get their hands on, like a Western agent that they can get their hands on because, you know, anthropic and OpenAI doesn't actually operate in China. You can't access that. So when Tencent and Alibaba tried to embed Open Claw products into their own, like, series of products or business products, whatever offerings, it got people really excited. And because of these super app models that they have, it was a very natural way for people to access them. There's a functional adjacency to, you know, the search bar and then opening up an openclaw and then trying to, like, run like, you know, manage your mini programs within Tencent, WeChat and then trying to order something. So all of that kind of took off. But that said, actually, the Chinese government, again, regulators acted very swiftly in the beginning. I think local government, like Wuxi, try to even encourage local businesses to embrace open cloth. But the Beijing government immediately said, guys, actually be very, very careful of your data privacy, your security. Banks shouldn't do this, SOEs shouldn't do this. Be mindful of what you're doing with this technology. And then the big tech kind of rolled back a bit of their marketing. And you can see, actually, hilariously, there were advertisements for helping these aunties and uncles how to uninstall these open cloth on their gadgets. So anyway, that's a bit of kind of background on that.
- [49:57]
Joe Weisenthal
All right, great. Shao, thank you so much for coming on. Odd lot. It's great to connect with you here in Hong Kong. And we'll have you back in three years. No, hopefully before then, we'll have you back in at least, least certainly in three years to see how your predictions held up.
- [50:11]
Grace Shao
Thank you so much,
- [50:25]
Joe Weisenthal
Tracy. That was fun. There was a lot of interesting ideas in a fairly short conversation, but one thing specifically and then that sort of stands out to me is thinking about some of these application companies, how much it makes sense for them to sort of, yeah, they'll use like a state of the art American closed model for like some of the work, but then other just, you know, almost as capable models underneath. So you have like a legal AI app, like a Harvey or something. I'm sure some of these open source models work well for some tasks within that context. And how much it makes sense to sort of combine them under one app layer.
- [51:05]
Tracy Alloway
Yeah, well you don't need the cutting edge model for everything.
- [51:08]
Grace Shao
Right.
- [51:09]
Tracy Alloway
But like if you can get some of the cutting edge model combined with like the cheapness of the open source thing, like that seems like a pretty good deal for a lot of companies.
- [51:20]
Joe Weisenthal
Yeah, definitely. I also think like how is the massive manufacturing edge that China has not going to just keep compounding itself? I mean this is like the multi trillion dollar question of the entire world. But it really does, I mean when you think about, okay there's this sort of presumably natural synergy, there's all this real world physical data that Chinese manufacturing companies can theoretically get from their various robotic vacuum cleaners and so forth and then feed those into certain models that than we call AI. That really does seem like a potential leg up that, you know, just on the data collection alone from all those physical things. Like a huge potential edge over the next several years.
- [52:06]
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- [52:06]
Tracy Alloway
Although it was very interesting, Grace was saying that it's not as developed or as structured as it is in the US because I had thought the same thing, like if everyone's talking over WeChat, if everyone's paying over, we pay. You must have oodles and oodles of data. But yeah, that was interesting. The ecosystem thing I think is really important and it just seems like really hard to get an ecosystem kind of going from scratch. And I remember maybe it was with Dan Wong, but someone describing how like if you go to Shenzhen, you can basically start like an entire company manufacturing a physical thing because every single supplier is there. You just go from like one storefront to another storefront to another storefront. And I don't think that can be replicated anywhere in the US I mean
- [52:55]
Joe Weisenthal
this is a bit of a tangent but you know, this is. Economists talk about quote agglomeration all the time. The advantage is exactly that of having everything there and then you build these deep networks. One thing I find to be a little strange, and again this is a tangent, is how San Francisco concentrated AI is. Even though at the American level it is sort of pure like desk work. Right. Like it's not the sort of we need the bolts manufacturer, we need the servos manufacturer, we need robovision manufacturer and yet it's still so agglomerated. Or the fact that finance is so agglomerated in New York City, I find that. Or conglomerated. I find that to be a little odd. So.
- [53:37]
Tracy Alloway
But yeah, I thought that conglomerated is a good word.
- [53:39]
Joe Weisenthal
Conglomerated. I don't know whether it's conglomerated or agglomerated. I'm just going to say glomerate. We know that there are certain industries in the US that are quite conglomerated one way or another. But yeah, I did find that to be. And man, these like Minimax $20 billion company, that's like nothing compared to the valuations that we see with American companies. Pretty wild stuff. Also the point we should do more. I mean there's a million we got to do. We have and there are plenty of non AI things to do. So you can't just keep saying we should do an episode on X or yeah, but data markets and like the idea of like, okay, these Chinese companies can buy very pricey proprietary data after some exclusivity window. There's some interesting stuff to be done there.
- [54:24]
Tracy Alloway
Yeah, the monetization was really interesting to hear. Okay, shall we leave it there for now?
- [54:28]
Joe Weisenthal
Let's leave it there.
- [54:29]
Tracy Alloway
This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at traceyallaway.
- [54:35]
Joe Weisenthal
And I'm Joe Weisenthal. You can follow me at thestalard. Follow our guest Grace Schao. She's gracemzschau. And check out her substack AI Prom. Follow our producers, Carmen Rodriguez, Armenarmon Dashiell Bennett at dashbotcailebrooks Alebrooks and Kevin Lozano. Kevin Lloyd Lozano. And for more Oddlaws content, go to bloomberg.comoddlaws for the daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord Ggodlog lot.
- [55:06]
Tracy Alloway
And if you like odd lots. If you want us to do an episode on data markets, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening Sam.
- [55:58]
Ryan Reynolds
Ryan Reynolds here from Mint Mobile with a message for everyone paying Big Wireless way too much. Please, for the love of everything good in this world, stop with Mint. You can get premium wireless for just $15 a month. Of course, if you enjoy overpaying no judgments. But that's weird. Okay, one judgment anyway, give it a try@mintmobile.com Switch upfront payment of $45 for
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