Odd Lots - Grace Shao on What the World Should Know About Chinese AI

Episode Date: June 22, 2026

China's AI industry has changed a lot since DeepSeek released its cheap frontier model last year, and briefly sent US tech stocks falling. After being locked out of the most advanced chips, Chinese companies are now allowed to buy some Nvidia H200s. In fact, many of the big Chinese tech companies — like Baidu — are making a push to become full-stack players, with their own chips, models, and cloud infrastructure. Today's guest is Grace Shao, an independent AI researcher and the author of the AI Proem Substack. She's a bit of an insider when it comes to China's AI industry, and when we were in Hong Kong we spoke with her about the latest in open-source models, the competition among Chinese frontier labs, DeepSeek's place in an increasingly crowded Chinese AI market, China's manufacturing edge, where bottlenecks exist right now (spoiler: it isn't data centers), if Chinese grandmas are actually using OpenClaw, and finally, of course, AI psychosis. Read More:China AI Lab’s 170% Stock Surge Cements Winner-Loser Pair Trade China Plans Mechanism to Evaluate AI Impacts on Job Market Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

I'm Francine Lacqua, an award-winning journalist, and I've got a new podcast, Leaders with Francine Laquois from Bloomberg Podcasts. I've interviewed everyone from Heads of State to fashion icons about the news of the moment. But I've always been curious, who are these people as leaders? I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision. Listen to new episodes every other Monday. Follow leaders with Francine Lacois wherever you get your podcasts. Bloomberg Audio Studios.

Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthal. And I'm Tracy Allaway. 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. Incidentally, this week, we've come during a, I guess it's a monsoon season, right? Yeah, it's the rainy season. But oh well, I like thunderstorm, so I'm enjoying it. Yeah, I'm enjoying it too. Anyway, one thing that has changed since the last time you were in Hong Kong, you left in 2020. Yeah. We weren't doing as many AI episodes in those days, to say the least.

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? That's right. That's right. 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. Okay. Can I, like, say, like, I know very little.

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'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 in, like, you know, the heads of American AI labs speak in these, like, 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 focused 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 Chad GPT, 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 10 cents, the Alibaba's sort of used. 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. 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 as 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 U.S., 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 tech. I think it's safe to say that both of us have a lot of impressions. Yes. Right? I feel like how many times

in this intro 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 is 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 Schau, she's an independent AI researcher, and she has a great substact called AI Prom, and she joins us here in our Hong Kong office. So, Grace, thank you so much for coming on Oddlots. Thank you so much for having me, Joe and Tracy.

How did we do on our, quote, impressions, unquote? Those are pretty accurate impressions. I think. Okay, good. 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 U.S. 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 why. 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 know, can see it as a philosophical drive. You know, the founder of Deep Sik Liang 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&D sharing has now formed a layer for the whole ecosystem where each of the labs kind of integrate each other's, like,

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 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-opensource? 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. other 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, like, China, AI is dreading as well. That means, like, there's evolution in this ecosystem as well. However, I think bringing up Deep Seek, Deep Seek 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, ZAI is now publicly listed in Hong Kong. Mini Max is publicly 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 mind share. But Deep Sea plays an interesting role. I want to bring it back to DeepC V4.

So V4, you know, on the surface, you know, people said, okay, it wasn't as maybe impressive on evals and performance. They 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 Kuda, not at all,

because if you ask any developers, they still want to use Kudab if they can. However, it was the first effort to really, I kind of like did one for the team. Like they kind of like put the resources. Was that supposed to be like a signal basically? Yeah. 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 a very pragmatic reason, to start shifting some reliance on 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 time. 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 is 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 Claude, CloudCoer, Claudecode, 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 kind of trying to play catch-up and push the Chinese ecosystem as fast as possible. 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 U.S. dollar terms, it's a $20 billion company. I mean, there are people in the U.S. who have done nothing but publish a paper on archive.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. It's okay. Like in these four models that you name, do they have different thoughts on how they plan to make money or different business models? 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 money, 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 public disclosed

information, I think, from Minimax and Jupu indicates that they were making just as much, like in their last month, they've 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 their open source

doesn't mean they don't make money. I think people forget, you know, we had open-source software 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 compute. you don't have to figure out your own guardrails or 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.

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Subscribe to the Masters in Business podcast on Apple, Spotify, or anywhere you listen. All right. So there seemed 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 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 like meet the demand that's coming through. So that's on like how they're servicing clients. How they're changing, I guess, or how they're optimized their training is that a lot of them are really focused on the post training. And this goes back to, so you know how Open AIA has like three buckets where they check money at? There's like the R&D, there's pre-training, there's post-training. R&D, a lot of times a lot of money is spent,

but say like one out a 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 to the frontier labs. to come out with where the right direction is for the next frontier model, 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 Mercor provides a very, very niche set of data set for, like, an open AI or whatnot,

maybe they would charge them $10, $20 million. 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 dataset. And that kind of plays into that, like, six to nine-month leg that we hear about as well. That's really interesting. Let's talk about energy then, because the story in the U.S. is that electricity is really the big constraint on AI use. And, you know, you've 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 Westerns are visiting, like Beijing, Shanghai, Xinjiang 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 so a lot of the energy plants, you know, the solar plants, hydro plants, whatnot, were actually built out in, you know, 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 State of 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 Guayzhou 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, where you 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, like, the demand right now. 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 manufacturers in various ways, then they sell it to an open AI. So a company like Merckor will hire a bunch of people to say build PowerPoints and then they'll collect the data on how they do that, and then that is fresh data that they can sell. So those have exclusivity windows after which they can then sell them to anyone? I'm not saying Merckor specifically, but supposedly there are these data providers that do the sell, 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 dataset. 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, this like 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 close-source model in the U.S. 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. 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 a issue for them right now. Like Deep Seahs openly said they even were struggling, right? Like, they needed more compute. I think on the distillation allegations or accusation, 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 Shui, who is a Google Deep Mind researcher. He said, there's 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're 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-pacing 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 mercury 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 10 cent, 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 mass amount of data to use for AI training. However, actually, people forget, again, China's interoperating. price buildout 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 Mercor, 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. 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 aspera 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, you know, 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 Shui 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 at the day, like you said, in the very beginning, their goal is to optimize their existing businesses already and bring AI to the mass consumer as fast as they can. You know, you mentioned poaching a researcher from Open AI, and it's like, the way I see it, AI will definitely be built by the Chinese. The question is whether it will 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 recent? researchers from that had been working at American labs going to Chinese labs, or are they still

sort of won off and somewhat rare? 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 would pick that over living in the U.S. 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 very, very clean-cut 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've met a lot of research 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 bite-dance 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 the 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. 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, do I really want to hear, like, Sam Altman or DeBis Hasabes 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 that sort of, like, someone in actually the engine room rather than this sort of big picture, a person who may have some degree of AI psychosis and just, like, it speaks in, like, the biggest generalities. Okay, well now Sam Altman needs to, like, invite himself on the show just to test your commitment to not having AI CEOs. No, I would. I would do it. But, 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, you know, 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. Like, 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 the sandbox. Is there the same sort of existential discourse in the Chinese AI community? 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 an open source researcher, who just came to China and visited 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 Hengzhou, 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 rules say that is not allowed, and 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 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 and general, not only in China, you look at South Korea, Singapore, all these countries are approaching AI in a very pragmatic way. And the tire moms are trying to train up the kids to be AI native. the students are trying to train themselves up to be A.A. Native. People are preparing for the future versus pushing back on the future. That's interesting.

You know, in the U.S., 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, etc. How big of a part of the Chinese labs, how much do they spend on, I guess, what they would

put, what the American labs would put into the safety bucket? 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 AI 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, you know, no one really knows what the real impact of AI will be on economy. But, you know, definitely that fear-mongering narrative is not mainstream in China. Pride Months, Toronto. Pride is an opportunity for you to create your own space, to celebrate your existence. IHeart Radio is proud to be an official sponsor of Pride Toronto Festival, and we won't stop. Celebrate Pride.

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We bring you deeper dives into the stories shaping your world from the evolution of AI to the shifting priorities of global business. Plus, Silicon Valley power players and the latest tech trends. Catch up on the conversations you miss during the day. Subscribe to the Bloomberg Businessweek Daily podcast on Apple, Spotify, or anywhere you listen. How would you describe, I guess, the approach of the Chinese government to AI in general? Because it feels like the tradeoff for maybe not being on the cutting edge with frontier models is, well, you're further along with sovereign AI and the government maybe has like a better handle on what the labs are actually doing. 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 Deepseek moment took off, there was a frenzy of private companies, even in home appliances, trying to embed Deepseek, integrate deepseet. I'm just like, what is an 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 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 ask like VCs essentially and go find, you know, future deep seeks 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, you know, infrastructure like buildings, offices, even like some of them are heard like 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, you know, 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, Biden's 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's happening and then tell the whoever local government that's funding them with providing with 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 robots 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 U.S. and it's so on. 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 like AI, I think we thought of robots, right? We thought about the T-1,000 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 seeks or the minamaxes or GLM and so forth, like, are they actively working hand in hand with some of the unitries and advanced robotics companies to figure out how you can actually have that, the true AI robot of the future? Yeah, I think China, having been the manufacturing hub of like literally everything under the sun over the last like 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, And because we've been so fixing the 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 mini-max, you know?

That's like for them, they need to actually get physical data. This 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 humanoid, 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 out with a better solution so far. What I've seen the most creative things so far is, like, you know those glasses you wear, like the meta glasses?

They kind of die within two hours. But China, like I Fly 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. Wait, I'm just going to press you on this. So if we fast forward 10 years, like 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 like integrated with like industry and existing business? Or is it robotics and the sort of hardware side of AI? Ten years is a long time.

I know. Sorry. Well, we want to challenge you. A lot of these companies didn't exist 10 years ago. We're not even five years ago. Yeah, that's fair. Okay, I can shorten the time frame in three years. 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 the hardware side. China is 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 bottom next 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 at 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, like, floors, that takes maybe less than 15 months.

Wow. 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, an 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. That's such a fascinating idea, and it makes a lot of sense that for at the application layer, that probably a lot of different models can go into it. You know, by the way, I was, 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 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, 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 sitting their open claw or whatever. Are those real? Like, what's the deal with that? I think that was definitely a bit of a hype. I knew it. No, no. But I will say there were grandmas lining up to get it done.

Okay. I'll answer to 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 with a lot. without being on Ali Pay and on eachot. You can't like buy Starbucks in Beijing without like having we pay. 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 inside, 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 $1,000. But they will have a smartphone. On that smartphone will be able to actually enable them to get microloans, 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 culture 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't get their hands on because, you know, anthropic and open aid 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. opening up an open claw 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 Usi try to even encourage local businesses to embrace open claw. But the Beijing government immediately said, guys, actually be very, very careful of your data privacy, your security, banks shouldn't do this. SOE 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. All right, Graysiao. Thank you so much for coming on Odd Lots. 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 at least certainly in three years to see how your predictions held up. Thanks so much. Tracy, that was fun. There was a lot of interesting ideas in a fairly short conversation. But one thing specifically, and then that like 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. Yeah. Well, you don't need the cutting edge model for everything, right? 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. 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 then 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. Yeah, although it was very interesting, Grace was saying that it's not as developed or as structured. as it is in the U.S.

Because I had thought the same thing. Like if everyone's talking over WeChat, if everyone's paying over WePay, 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. I don't think that can be replicated anywhere in the U.S. I mean, 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. go 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 the Robo Vision 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, but yeah, I thought there was. Glamerated is a good word. I don't know whether it's conglomerated or agglomerated. I'm just going to say glomerated. We know that there are certain industries in the U.S. that are, quote, glomerated one way or another. But, yeah, I did find that to be, and man, these, like, mini-max, $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-A-I things to do, so you can't just keep saying we should. do an episode on X or Y, 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. Yeah, the monetization was really interesting to hear. Okay, shall we leave it there for now?

Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Wisenthal. You can follow me at the stalwart. Follow our guest, Grace Schau. She's at Grace MZ Schau and check out her substack AI Prom. Follow our producers, Carmen Rodriguez at Carmen Armand Dashel Bennett at Dashbot, Kale Brooks at Kail Brooks,

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