¶ Welcome to Hong Kong and Chinese AI
Bloomberg Audio Studios, Podcasts, radio News. Hello and welcome to another episode of The Odd Lots Podcast.
I'm Joe Wasental and I'm Tracy Alloway.
Tracy, I love being in Hong Kong. I love I hear 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've 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's shade, nugget, it's not great right now.
Like 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 thunderstorms, 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 loved in twenty twenty two, Yeah, we weren't doing as many AI episodes.
¶ China's Open Source AI Landscape
No, we definitely weren't. In fact, So I remember one of the big stories when I was here in Hong Kong in twenty twenty 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, 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 and like, do 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 card 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 JADGPT in late twenty twenty two, 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. Yeah.
Oh, for all that AI scene in China feels much more utilitarian to me. It's more about like the big companies, the ten cents, the Ali Baba is 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, 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 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 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's 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 developments of the AI.
Yeah, I'm also very interested in, like the capital stack for Chinese companies, because over in the US, we all know that people are flinging money at anything with the word AI in it, but in China it's very different. I get the impression that it's like much harder to raise enormous sums of capital, and so I'm very curious how that limited capital actually influences the development of these models and the tech.
I think it's safe to say that both of us have a lot of impressions. Yes, right, impression Manytimes that its intro is 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 Shao. She's an independent, a researcher, and she has a great substack called AI Prome, and she joined us here in our Hong Kong office.
¶ Grace Shao on Open Source Models
So Grace, thank you so much for coming on od Lave.
Thank you so much for having me, Joe and Tracy.
How did we do on our impression? Quote?
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 US. Why did it develop that way?
Yeah, I think people like to think of these myscal reasons, but really it was a very pragmatic business reason to start with. To start with, a lot of the labs have cited that, you know, for Western companies or Western developers to trust them, they needed to open source their models to build that trust and credibility. So in many ways, it's a branding decision. Then on top of that, I
think you can see it as a philosophical drive. You know, the founder of deep Seagalmen Fung, 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 the whole ecosystem where each of the labs kind of integrate each other's kind of breakthroughs. You know, you see them congratulate 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 and D and all of that forms the basis of a strong open source ecosystem.
¶ DeepSeek's Role in Ecosystem
Is the culture as pro sharing and pro open source as it was even two years ago now. The Deep Seek moment was right around Trump's inauguration in early twenty twenty five, so about a year and a half ago. Since then, has the culture stayed the same or has that sort of competition bug, that intense competition bug that we know among American AI labs, has it spread to the Chinese labs at all.
I think the sharing is an unintentional result rather than you know, an intentional effort in the beginning to even start with. They are for sure extremely competitive, and you know, we all know the word involution, so like China Ai is do adding as well, that means like there's evolution in this ecosystem as well. However, I think bringing up deep sea deep sea plays a very interesting role in the whole ecosystem. Like you mentioned, V three, propel the
whole industry forward. Everyone kind of start taking China Ai more seriously. You know, it brought a lot of interests from investors globally back into the internet companies that Tracy mentioned, you know, project that there was a bit of a slump for three to five years. However, you know, Jiarpoolzai is now public listed in Hong Kong, Mini Max is pubably 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 DEEPCV four, so V four, you know, on the surface, you know, people said, Okay, it wasn't as maybe impressive on evails and performance, They didn't catch up with the most front of your labs in the US 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 in video or Kuda, not at all, because if you ask any developers, they still want to use Kuda 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 hunt them.
It's supposed to be like a signal basically, yeah, 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 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.
¶ Frontier Labs and Niche Focus
Stack aside from deep Sea.
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 I.
Think if you really have to have to look at the ecosystem, we can kind of put side the big tech for now, but looking at the maybe the foremost relevant startup labs Deep Seak, Moonshot, who has Kimmi, Zia who has GLM, and then Mini Max. They are still
probably the most committed to frontier research. However, because the constraint we mentioned that they face, whether it's compete when it is capital or even friendly talent, they have decided out of necessity to basically each focus on a different vertical in capturing a different kind of business share. So Zi is very focused on coding capabilities. So if anything, their gl AND plan is much more similar to maybe what you think of Claude cloud co wor cloud 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 deep sea.
Again.
Really is just focused on pushing the frontier and 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 they have already gone public here and just put in So Minimax is public and in US dollar terms, it's a twenty billion dollar company. I mean there are people in the US who have done nothing but publish a paper on archive dot org, who do not even have a product yet, who have probably VC backed applied valuations of our twenty billion dollars. How do they make money?
¶ Monetization of Open Source AI
You know again and open source? 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 Saquoia being maybe the most like high profile, right, like we all remember that. Now, people forget even in twenty twenty two, 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 their value anywhere roughly between twenty to thirty billion right now, but they went public between like six to eight 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 mini max and DRUP who 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 your AR projection is anywhere between one to one point two 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 softwares before as well. People are paying for managed services, and when you're paying for an API through that AI 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 gown compute. You don't have to figure out your own guardrails or deployment, your security or monitoring whatnot.
Right, So, just to be clear, you can self host all of these models, but for the most part, they do offer the inference part of the stack, and that is a profit center for them.
Yes, exactly, got it all right.
¶ Compute Constraints and Optimization
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 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 compute. So these labs essentially are now optimizing for the highest quality inference amount, 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 optimizing O 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 ai has like three buckets where they check money at there's like the R and D, there's pre training, this post training R and D. A lot of times a lot of money is spent, but say like one out of ten things stick, but you need a lot of compute and resource some 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 homeworks 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 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 Mercore provides a very very niche set of data set for like an open AI whatnot,
Maybe they will charge them ten twenty mille dollars. The Chinese lab will weigh 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 leg that we hear about as well.
¶ China's Energy Advantage for AI
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?
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, China High Engine 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 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 about each state. So when they pushed out the East State to 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 guat Zo Province like even Sinzong, Inner Mongolia Suchwan you know, those were like very easily executed, frankly, and then ninety percent of the population actually sit on the eastern coastal lines, like we think about Beijing, Tianjing, Shanghai,
sh Engine, 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, I want to.
¶ Data Acquisition and Distillation
Get back to something you said. So first of all, just to clarify, you mentioned companies like Mercore that sell proprietary data that they are able to collect and manufacture in various ways. Then they sell it to an open aira So a company like Merkcore will hire a bunch of people to say, build power points, 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.
I'm not saying Marcos specifically, but supposedly there are these data providers that do the cell 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 time and folk same data side.
So This gives 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, yeah, sure, that's like, it's great this open source model and it
can stay close to the edge. But then the encounter is that they can only be so advanced because there's 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.
Yeah, I think the compute constraint on the capital constraint Israel 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 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 y'all showing you who is a Google Deep Mind researcher. He said, there're smart distillation and dumb disolation. 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 claud 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 valuation and even the data labeling itself. So you're using
it as almost a teacher for your own model. It guides it 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 to me, So it's all kind of a bit of a mercury area.
To be honest.
¶ Chinese Data Ecosystem and WeChat
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 ten cent I mean you've got we chat, 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 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 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 mercre like we just mentioned now on the big tech side, it's 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 an
agent that can be plugged into we Chat. This has been very controversial and it has actually had a lot of pushback even internally, because WeChat's product manager, Alan Jung has been famously or notoriously known to be kind of hard to work with if you want to push something with a WeChat because he's so protective of that user experience.
It's his baby, right, And Tencent, like you said, has we Chat, which is a super app, has more than one point four billion MAU globally, like mostly one point three million people in China and the Chinese ask for globally or people who work with China. That is immense value. But the risk and compliance risk of potentially of agent going rogue within that chatbot, or that of agent going rogue in executing you know, whether it's a purchase whatnot, is 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 open Ai who is a researcher called y'all shoam U as well has the same name as the other researcher we just mentioned, to lead this whole initiative, and their whole goal is to basically build a ten cent 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 their existing businesses already and bring AI to the mass consumer as fast as they can.
¶ AI Talent and Personal Choices
You know, you mentioned poaching a research from open Ai, and it's like the way I see it, AR will definitely be built by the Chinese. The question is whether it be built by the Chinese working the American labs or whether it will be built by Chinese working and Chinese labs. Has there been a gathering of steam of researchers from that had been working at American labs going to Chinese labs or are they still sort of one off and somewhat rare.
I think even during the Internet era, we saw a lot of Chinese nationals or Chinese I think 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, yea. I hate to sound so crass about it, but you know, sometimes it's what your package look like and over generalize.
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 you transfer to.
A new market.
They're like, 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 US. And on top of that, I think that something is lost in the nuance is my parents immigrated to North America thirty 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 the city center. You want that fast paced, like techno evy 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 I 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 US? Because we get headlines all the time about you know, so and so is joining whatever company, and people treat that new whose 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 hafty amounts. How much they don't usually disclose, but at least even in the Internet era, like a byte 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 one hundred 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 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.
¶ Pragmatic vs Existential AI Discourse
Tracy, Can I say something that might be sort of a sacrilege for a podcast host to say? Uh, Okay, I'm only speaking for myself here, 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 Dabi Sasabi's 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 us or 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 as on a speaks in like the biggest generalities.
Okay, well, now Sam Altman needs to like invite himself on the show just to test your your commitment to not having AI CEOs.
No, I would, I would do it, But I like, let's just agree here that if we ever get one of the really big like lab CEOs, let's just ask the very like sort of mundane questions about operations and now, like what are we all going to do? And 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, 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 color 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, that 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 is open source researcher who just came to China visited all the labs. He said, Look, I was shocked to see majority of labs are so 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 make manimally 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.
¶ Public Anxiety and Regulatory Response
Say that there is obviously anxiety from the public in some degree, not as much of a back pushback. But recently there was a very interesting court case in Hongjo, which is you know, home to Ali Baba and a lot of these al abs. Basically, a company try to like lay off a person saying you are being replaced with 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.
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 whoverall.
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 tire moms are trying to train up the kids to be AI native, the students are trying to train themselves up to be AA native. People are preparing for the future versus pushing back on the future.
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 one spend there 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 did they spend on I guess what they would you would put but the American labors would put into the safety bucket.
¶ Government Approach to AI Development
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, et cetera. 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 jen AI application as well as LM 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.
How would you 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 sovereign AI and the government maybe has like a better handle on what the labs are actually doing.
¶ AI as Economic Driver
The Chinese government probably sees this in a much more pragmatic way. You know, just like how there was an Internet plus policy fifteen years ago, there's now an AI plus policy. When deep seak moment took off, you know, there was a frenzy of private companies even in home appliances, trying to embed deep seat, integrate deep sea. I'm just like, what is a AI vacuum going to do for you? Or AI like electric truth rush 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 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 heard like dormitory for these young entrepreneurs, and then give them money and capital try things out. There's also these AI pilot zones being rolled out across the country. I think now about eleven or twelve 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 of 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 bitdowns 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 low 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 robot has become morem when 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.
¶ Robotics and Hardware Advantage
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 fifteen one thousand 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 labage, whether we're talking about the deep sea so then Mini Max's or GLM and so forth, like, are they actively working hand in hand with some of the unit trees 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 in the sun over the last like three decades, has definitely an advantage of 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 result end product from OEM is all within like say,
fifty 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 Mini Max. You know, that's like for them, they need to actually get physical data. They 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 three D data that these models need right now, the bottomneck right now is that you know, these hardware is these humanoids, quadrupids, dogs, whatever you want to call them, they cannot be powered by lms.
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 metaglasses, 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 on to 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.
¶ China's Future AI Comparative Advantage
Wait, I'm just going to press you on this.
So if we fast forward ten 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.
I think any.
But we want to challenge here.
A lot of these companies that exist ten years ago or not even five years.
That's fair, okay, I can shorten the timeframe 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 neck 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 appliance is whatnot. These tech gadgets are produced at less than fifty percent of the costs 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 fifteen months. Wow, But for traditional OEM like way 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 as semlic Valley, they were the most cost conscious, frankly less compliance conscious and gives very little the care
about geopolitics. They were building on top of QUID. Now it's actually all 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 KIMMI, 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're 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.
¶ AI Groundedness and Open Claw Phenomenon
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.
You know.
By the way, I was talking to someone out 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 the will have this sort of groundedness in the real world where I have no idea this is true, 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 is, as soon as they're really in robots, then they'll have
a sort of real world groundedness. A right, have one last question, you know, after open Claw came out, I started seeing again my entire Twitter feed and answergram feed. I have done this to myself, but all I do all day is consume Chinese propaganada. 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 eighty year old Grammas or whatever really excited about setting up their open claw or whatever. Are those real? Like, what's the deal with that?
Yeah?
I think that was definitely a bit of a hype.
I knew it.
No, No, but I will say there were Grammas lining up to get it done.
Okay, I'll 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.
No, you really can't.
You can't like buy Starbucks in Beijing without like having we paid.
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 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's side, I think the overall optimism around.
Technology is very different from the West because in the last twenty thirty years, a lot of rural eras 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 there may be annual household income is like one thousand dollars, but they will have a smartphone and 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 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 get their hands on because you know, anthropic and open it doesn't actually operated in China. You can't access that. So when Tensan and Ali Baba try to embed open claw products into their own like series of
products or business 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 open claw and then trying to like run like you know, manage your mini programs within ten cent we chat and then try 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 we see 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 didn't do this as so you 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 claw.
On their gadget. So anyway, that's a bit of a kind of background on that.
All right, Graceha, 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 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. 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 a 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 model then we call AI. That really does seem like a potential leg up that you know, just done 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 US, because I had thought the same thing, like, if everyone's talking over we chat, 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 shen Zen, 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 US.
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 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, but yeah, I don't conglomerated is a conglomerated I don't know whether it's conglomerated or agglomerated. I'm just gonna say glomerated. We know that we are certain industries in the US that are quote conglomerated one way or another. But yeah, I did find that to be And man, these like Mini Mac billion dollar 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 gotta 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 stuffing to down 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.
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