Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. Hello and welcome to
Speaker 1: another episode of The Odd Lots Podcast.
Speaker 2: I'm Joe Wasental and I'm Tracy Alloway.
Speaker 1: Tracy, I love being in Hong Kong. I love I
Speaker 1: hear so much. I love it here so much. I
Speaker 1: would like come here a few times a year if
Speaker 1: we could.
Speaker 2: I'm sure you would.
Speaker 3: I've lived here for like, I guess, almost four years,
Speaker 3: so it's kind of weird coming back. But Hong Kong
Speaker 3: has a lot of pluses, like great food, great weather
Speaker 3: for most of the year, beaches. I once heard someone
Speaker 3: describe it as Manhattan meets Maui, which I think is
Speaker 3: like pretty accurate.
Speaker 1: Oh it's so nice.
Speaker 3: The weather's shade, nugget, it's not great right now.
Speaker 1: Like this week, we've come during a I guess it's
Speaker 1: a monsoon season, right.
Speaker 3: Yeah, it's the rainy season. But oh well, I like thunderstorms,
Speaker 3: so I'm enjoying it.
Speaker 1: Yeah, I'm enjoying it too. Anyway. One thing that has
Speaker 1: changed since the last time you were in Hong Kong
Speaker 1: you loved in twenty twenty two, Yeah, we weren't doing
Speaker 1: as many AI episodes.
Speaker 3: No, we definitely weren't. In fact, So I remember one
Speaker 3: of the big stories when I was here in Hong
Speaker 3: Kong in twenty twenty was China's tech crackdown, right.
Speaker 1: That's right, that's right, right, And like there.
Speaker 3: Was all this concern about whether or not the crackdown
Speaker 3: was going to destroy China's entrepreneurial spirit. I'm doing air
Speaker 3: quotes on a podcast. I don't know why, but fast
Speaker 3: forward six years and there's entrepreneurism basically everywhere, and we
Speaker 3: talk a lot about how China is producing all these
Speaker 3: new AI models.
Speaker 1: Okay, can I like say, like, I know very little.
Speaker 1: I mean, I know very little about AI, but I
Speaker 1: know even less about Chinese AI. But here are some
Speaker 1: of my general impressions, which is, it seems like there's
Speaker 1: so many open source Okay, so I know they're largely
Speaker 1: open source. It seems like every random company you see,
Speaker 1: like some toothpaste company, and they'll have produced an LLM.
Speaker 1: So I'm very curious, like how they're making money on it.
Speaker 1: I also get the impression and like, do you know
Speaker 1: the heads of American AI labs speak in these like
Speaker 1: sort of quasi mystical terms, et cetera. It doesn't feel
Speaker 1: quite the same here where it feels like a bit
Speaker 1: more of like yet another technology. But I'm glad you
Speaker 1: brought up the point about the tech crackdown because at
Speaker 1: the time, the whole story was like, oh, there needs
Speaker 1: to be less focus on sort of digital tech and
Speaker 1: more focused on card tech, which has been done extremely
Speaker 1: that's been an extraordinarily successful endeavor. And then my last
Speaker 1: impression though, is that since the release of JADGPT in
Speaker 1: late twenty twenty two, that was the moment it's like, no,
Speaker 1: we really have to also compete on sort of this
Speaker 1: next era of software and sort of consumer facing tech
Speaker 1: tech breakthroughs. Yeah.
Speaker 3: Oh, for all that AI scene in China feels much
Speaker 3: more utilitarian to me. It's more about like the big companies,
Speaker 3: the ten cents, the Ali Baba is sort of using
Speaker 3: AI for their existing business models rather than this existential thing,
Speaker 3: which it is in the US, where like AI is
Speaker 3: the business that's just.
Speaker 1: It, right, Yeah, that's exactly. AI is sort of weird,
Speaker 1: like it sort of sits in the middle of what
Speaker 1: you would call like software and hard tech because we
Speaker 1: consume it through the browser, right, sort of the same way,
Speaker 1: or in many cases through the browser the same way
Speaker 1: that we would go to an Amazon or online gaming
Speaker 1: or something like that. But it's clearly, you know, it's
Speaker 1: a scientific endeavor, and so it's sort of is this
Speaker 1: blend And then you have to figure China's so far
Speaker 1: ahead of the US when it comes to things like
Speaker 1: robotics and evs and batteries, and one thing I don't
Speaker 1: know anything about is the degree to which that melding
Speaker 1: of hardware capabilities with AI capabilities, how that influences the
Speaker 1: direction of the developments of the AI.
Speaker 3: Yeah, I'm also very interested in, like the capital stack
Speaker 3: for Chinese companies, because over in the US, we all
Speaker 3: know that people are flinging money at anything with the
Speaker 3: word AI in it, but in China it's very different.
Speaker 3: I get the impression that it's like much harder to
Speaker 3: raise enormous sums of capital, and so I'm very curious
Speaker 3: how that limited capital actually influences the development of these
Speaker 3: models and the tech.
Speaker 1: I think it's safe to say that both of us
Speaker 1: have a lot of impressions. Yes, right, impression Manytimes that
Speaker 1: its intro is like, I get the impression, but I
Speaker 1: actually have no idea. So that is a good reason
Speaker 1: to actually bring in our guest, someone who is more
Speaker 1: than quote impressions unquote about the AI tech scene. We're
Speaker 1: going to be speaking to someone whose newsletter I'm a
Speaker 1: big fan of and everyone should read. Are going to
Speaker 1: be speaking to the perfect guest, Grace Shao. She's an independent,
Speaker 1: a researcher, and she has a great substack called AI Prome,
Speaker 1: and she joined us here in our Hong Kong office.
Speaker 1: So Grace, thank you so much for coming on od Lave.
Speaker 2: Thank you so much for having me, Joe and Tracy.
Speaker 1: How did we do on our impression? Quote?
Speaker 2: Those are pretty accurate impressions? I think?
Speaker 3: Okay, good, that was the episode. Like, let's start at
Speaker 3: a basic level. So the big impression, the one that
Speaker 3: everyone knows is Chinese models are open source versus the
Speaker 3: closed frontier models of the US. Why did it develop
Speaker 3: that way?
Speaker 4: Yeah, I think people like to think of these myscal reasons,
Speaker 4: but really it was a very pragmatic business reason to
Speaker 4: start with. To start with, a lot of the labs
Speaker 4: have cited that, you know, for Western companies or Western
Speaker 4: developers to trust them, they needed to open source their
Speaker 4: models to build that trust and credibility. So in many ways,
Speaker 4: it's a branding decision. Then on top of that, I
Speaker 4: think you can see it as a philosophical drive. You know,
Speaker 4: the founder of deep Seagalmen Fung, has openly said he
Speaker 4: wants open source his most frontier research to really help
Speaker 4: propel the whole industry as a whole, and that kind
Speaker 4: of R and D sharing has now formed a layer
Speaker 4: for the whole ecosystem where each of the labs kind
Speaker 4: of integrate each other's kind of breakthroughs. You know, you
Speaker 4: see them congratulate each other even on X when they
Speaker 4: have new models announced, so you can say it's a
Speaker 4: bit more collegial. I wouldn't say they're not competing though, however,
Speaker 4: because of the compute constraint, they're faced with, talent constraint
Speaker 4: and the capital constraint even mentioned, they are a lot
Speaker 4: more conscious with where they want to put their money,
Speaker 4: where they want to put their time in R and
Speaker 4: D and all of that forms the basis of a
Speaker 4: strong open source ecosystem.
Speaker 1: Is the culture as pro sharing and pro open source
Speaker 1: as it was even two years ago now. The Deep
Speaker 1: Seek moment was right around Trump's inauguration in early twenty
Speaker 1: twenty five, so about a year and a half ago.
Speaker 1: Since then, has the culture stayed the same or has
Speaker 1: that sort of competition bug, that intense competition bug that
Speaker 1: we know among American AI labs, has it spread to
Speaker 1: the Chinese labs at all.
Speaker 4: I think the sharing is an unintentional result rather than
Speaker 4: you know, an intentional effort in the beginning to even
Speaker 4: start with. They are for sure extremely competitive, and you know,
Speaker 4: we all know the word involution, so like China Ai
Speaker 4: is do adding as well, that means like there's evolution
Speaker 4: in this ecosystem as well. However, I think bringing up
Speaker 4: deep sea deep sea plays a very interesting role in
Speaker 4: the whole ecosystem. Like you mentioned, V three, propel the
Speaker 4: whole industry forward. Everyone kind of start taking China Ai
Speaker 4: more seriously. You know, it brought a lot of interests
Speaker 4: from investors globally back into the internet companies that Tracy mentioned,
Speaker 4: you know, project that there was a bit of a
Speaker 4: slump for three to five years. However, you know, Jiarpoolzai
Speaker 4: is now public listed in Hong Kong, Mini Max is
Speaker 4: pubably listed in Hong Kong. Moonshot is you know, in
Speaker 4: preparation to go public next year. They are competing with
Speaker 4: each other to capture market share, to capture developer mind share.
Speaker 4: But deep Sea plays an interesting role. I want to
Speaker 4: bring it back to DEEPCV four, so V four, you know,
Speaker 4: on the surface, you know, people said, Okay, it wasn't
Speaker 4: as maybe impressive on evails and performance, They didn't catch
Speaker 4: up with the most front of your labs in the
Speaker 4: US whatnot, Right, But it was a very interesting move
Speaker 4: because what I heard from researchers on the ground in
Speaker 4: Beijing was that the lab actually delayed their release for
Speaker 4: about three to four months because they wanted to re
Speaker 4: engineer a lot of the inference onto Huawei. So I'm
Speaker 4: not saying this completely replaces in video or Kuda, not
Speaker 4: at all, because if you ask any developers, they still
Speaker 4: want to use Kuda if they can. However, it was
Speaker 4: the first effort to really I kind of like did
Speaker 4: one for the team, like they kind of like hunt them.
Speaker 3: It's supposed to be like a signal basically, yeah, we're
Speaker 3: doing this all on like a Chinese stack. Yeah.
Speaker 4: They were like, look, guys, like you can actually do this,
Speaker 4: and they became a shared foundation layer for China's model ecosystem. So,
Speaker 4: because again everything is open source and open weight, other
Speaker 4: labs were able to study what they did to actually
Speaker 4: start inferencing on Huawei stack, and I think that was
Speaker 4: the first step, whether it's signaling or actually you know,
Speaker 4: very pragmatic reason to start shifting some reliance on you know,
Speaker 4: the China AI.
Speaker 2: Stack aside from deep Sea.
Speaker 3: Can you kind of describe the differences or what China
Speaker 3: is trying to do on the actual frontier side, because
Speaker 3: there are there are some I.
Speaker 4: Think if you really have to have to look at
Speaker 4: the ecosystem, we can kind of put side the big
Speaker 4: tech for now, but looking at the maybe the foremost
Speaker 4: relevant startup labs Deep Seak, Moonshot, who has Kimmi, Zia
Speaker 4: who has GLM, and then Mini Max. They are still
Speaker 4: probably the most committed to frontier research. However, because the
Speaker 4: constraint we mentioned that they face, whether it's compete when
Speaker 4: it is capital or even friendly talent, they have decided
Speaker 4: out of necessity to basically each focus on a different
Speaker 4: vertical in capturing a different kind of business share. So
Speaker 4: Zi is very focused on coding capabilities. So if anything,
Speaker 4: their gl AND plan is much more similar to maybe
Speaker 4: what you think of Claude cloud co wor cloud code
Speaker 4: et cetera, a codex that kind of product. And then
Speaker 4: you look at Minimax, they're really focused on the multimodality capabilities, Moonshot,
Speaker 4: they're really focused on agents and deep sea.
Speaker 2: Again.
Speaker 4: Really is just focused on pushing the frontier and trying
Speaker 4: to play catch up and push the Chinese ecosystem as
Speaker 4: fast as possible.
Speaker 1: It's really crazy to look at some of the ones
Speaker 1: that they have already gone public here and just put
Speaker 1: in So Minimax is public and in US dollar terms,
Speaker 1: it's a twenty billion dollar company. I mean there are
Speaker 1: people in the US who have done nothing but publish
Speaker 1: a paper on archive dot org, who do not even
Speaker 1: have a product yet, who have probably VC backed applied
Speaker 1: valuations of our twenty billion dollars. How do they make money?
Speaker 1: You know again and open source? Okay? Like in these
Speaker 1: four models that you name, do they have different thoughts
Speaker 1: on how they plan to make money or different business models?
Speaker 2: Yeah?
Speaker 4: So China's VC space in general has not been that vibrant,
Speaker 4: frankly since Internet crackdown and a lot of USD funds
Speaker 4: did exit you know, three to five years ago, with
Speaker 4: Saquoia being maybe the most like high profile, right, like
Speaker 4: we all remember that. Now, people forget even in twenty
Speaker 4: twenty two, a lot of these labs that we just
Speaker 4: talked about, they were struggling to even raise money, you know,
Speaker 4: raise capital, and a lot of them spun out of
Speaker 4: academic institutions. You know, you mentioned their value anywhere roughly
Speaker 4: between twenty to thirty billion right now, but they went
Speaker 4: public between like six to eight billion. That's like kind
Speaker 4: of tiny compared to American valuations. Right now, however, they
Speaker 4: are actually making money, you know, the publicly disclosed information
Speaker 4: I think from mini max and DRUP who indicates that
Speaker 4: they were making just as much like in their last month.
Speaker 4: They've made the same amount of money last month as
Speaker 4: they did last year essentially, and their end of your
Speaker 4: AR projection is anywhere between one to one point two
Speaker 4: billion right now.
Speaker 2: So they are making money.
Speaker 4: And how well, just because their open source doesn't mean
Speaker 4: they don't make money. I think people forget. You know,
Speaker 4: we had open source softwares before as well. People are
Speaker 4: paying for managed services, and when you're paying for an
Speaker 4: API through that AI or minimax whatnot, you basically don't
Speaker 4: have to self host, you don't have to get your
Speaker 4: own GPU. You don't have to get you figure out
Speaker 4: your gown compute. You don't have to figure out your
Speaker 4: own guardrails or deployment, your security or monitoring whatnot.
Speaker 1: Right, So, just to be clear, you can self host
Speaker 1: all of these models, but for the most part, they
Speaker 1: do offer the inference part of the stack, and that
Speaker 1: is a profit center for them.
Speaker 2: Yes, exactly, got it all right.
Speaker 3: So there seem to be two major constraints on Chinese AI.
Speaker 3: Maybe energy is a constraint as well, we should talk
Speaker 3: about that. But there's the capital issue, so not as
Speaker 3: much capital available or people aren't flinging it at AI
Speaker 3: companies the way they are in the US. And then secondly,
Speaker 3: there are the export controls on chips and we talked
Speaker 3: about that a little bit, but can you describe how
Speaker 3: those controls are actually I guess influencing the development of
Speaker 3: the models themselves and like I guess optimization.
Speaker 4: So some of these big tech are actually even buying
Speaker 4: out the contracts that data centers have with some of
Speaker 4: these labs, and you know they are taking over compute.
Speaker 4: So these labs essentially are now optimizing for the highest
Speaker 4: quality inference amount, if that makes sense. They don't actually
Speaker 4: have enough even supply, they don't have enough compute power
Speaker 4: to even like meet the demand that's coming through. So
Speaker 4: that's on like how they're servicing clients, how they're changing
Speaker 4: I guess, or how they're optimizing O their training is
Speaker 4: that a lot of them are really focused on the
Speaker 4: post training. And this goes back to so you know
Speaker 4: how open ai has like three buckets where they check
Speaker 4: money at there's like the R and D, there's pre training,
Speaker 4: this post training R and D. A lot of times
Speaker 4: a lot of money is spent, but say like one
Speaker 4: out of ten things stick, but you need a lot
Speaker 4: of compute and resource some people to be figuring out
Speaker 4: where to go. For a lot of these labs in China,
Speaker 4: they frankly don't have that luxury. So they've even given
Speaker 4: me a metaphor and said it's kind of like knowing
Speaker 4: what the answer to the homeworks and working backwards. So
Speaker 4: they will wait till the frontier labs to come out
Speaker 4: with where the right direction is for the next frontier model,
Speaker 4: and they will work backwards and actually focus all their
Speaker 4: resources on post training. So with post training, they will
Speaker 4: optimize a lot of times the data they collect. For example,
Speaker 4: if a data provider like Mercore provides a very very
Speaker 4: niche set of data set for like an open AI whatnot,
Speaker 4: Maybe they will charge them ten twenty mille dollars. The
Speaker 4: Chinese lab will weigh out that exclusivity contract three to
Speaker 4: six months time, let's say, and then pay a fraction
Speaker 4: if not like a tenth of that price the same
Speaker 4: data set, and that kind of plays into that like
Speaker 4: six to nine month leg that we hear about as well.
Speaker 3: That's really interesting. Let's talk about energy then, because the
Speaker 3: story in the US is that electricity is really the
Speaker 3: big constraint on AI use, and you know, you got
Speaker 3: to find a data center that has an electricity hookup,
Speaker 3: and it has to be reliant and all of that.
Speaker 3: It seems to be in short supply. Is it a
Speaker 3: similar story in China?
Speaker 4: Honestly, energy is probably not the biggest bottleneck right now
Speaker 4: in China. And I think people like to say, well,
Speaker 4: some people like to say, oh, somehow the Chinese government
Speaker 4: had foresight on the AI boom, driving like the energy consumption,
Speaker 4: but definitely not. I think people forget that China's economic
Speaker 4: growth over the last three to four decades also meant
Speaker 4: a rise of urbanization and a lot of the cities
Speaker 4: that you know, we are visiting these days, like at
Speaker 4: least westerns are visiting, like Beijing, China High Engine with
Speaker 4: all these robots and evs or whatnot. These were all
Speaker 4: really urbanized within the last two three decades. And because
Speaker 4: of that, the grid is very new, and because of that,
Speaker 4: the government already foresaw that there was going to be
Speaker 4: a increase in energy demand, and you know, so a
Speaker 4: lot of the energy plants, you know, the solar plants,
Speaker 4: hydro plants, whatnot, were actually built out in you know,
Speaker 4: anticipation for that. Now obviously this has coincided with now
Speaker 4: the AI boom, and it's really helped out beyond that.
Speaker 4: You know, China has an advantage in the fact that
Speaker 4: they can actually drive top down mandates and provincial governments
Speaker 4: will follow suit. This is something quite unique to China
Speaker 4: because it's not like decided about each state. So when
Speaker 4: they pushed out the East State to West compute where
Speaker 4: it's basically a top down initiative where they built a
Speaker 4: ton of renewable energy for cheap in rural mountainous areas
Speaker 4: in guat Zo Province like even Sinzong, Inner Mongolia Suchwan
Speaker 4: you know, those were like very easily executed, frankly, and
Speaker 4: then ninety percent of the population actually sit on the
Speaker 4: eastern coastal lines, like we think about Beijing, Tianjing, Shanghai,
Speaker 4: sh Engine, that's all on east, so that's where the
Speaker 4: data comes from. So that kind of optimization has also
Speaker 4: really helped them, you know, with the low that is
Speaker 4: the demand right now, I want to.
Speaker 1: Get back to something you said. So first of all,
Speaker 1: just to clarify, you mentioned companies like Mercore that sell
Speaker 1: proprietary data that they are able to collect and manufacture
Speaker 1: in various ways. Then they sell it to an open
Speaker 1: aira So a company like Merkcore will hire a bunch
Speaker 1: of people to say, build power points, and then they'll
Speaker 1: collect the data on how they do that, and that
Speaker 1: is fresh data that they can sell. So those have
Speaker 1: exclusivity windows, after which they can then sell them to anyone.
Speaker 4: I'm not saying Marcos specifically, but supposedly there are these
Speaker 4: data providers that do the cell and they have exclusivity windows,
Speaker 4: and then the Chinese labs kind of weigh that out
Speaker 4: so they can pay like maybe a million dollars versus
Speaker 4: like time and folk same data side.
Speaker 1: So This gives to something generally speaking, which is that
Speaker 1: people are around the world correctly like quite impressed by
Speaker 1: how high quality the Chinese models are, even if they're behind.
Speaker 1: But then you have things like that, and then you
Speaker 1: also have accusations from the likes of Anthropic that they're
Speaker 1: distilling models and that they're finding ways to collect the
Speaker 1: outputs of American models for training. So then you could say, well, yeah, sure,
Speaker 1: that's like, it's great this open source model and it
Speaker 1: can stay close to the edge. But then the encounter
Speaker 1: is that they can only be so advanced because there's
Speaker 1: this extremely capital intensive closed source model in the US
Speaker 1: that's really establishing the frontier, and that these Chinese companies
Speaker 1: wouldn't be anywhere near where they were if they weren't
Speaker 1: sort of I guess you would say drafting off the
Speaker 1: American labs.
Speaker 4: Yeah, I think the compute constraint on the capital constraint
Speaker 4: Israel and frankly, like no one's hiding that or pretending
Speaker 4: that that's not an issue for them right now, like
Speaker 4: deep Sea has openly said they even were struggling right
Speaker 4: like they needed more compute. I think on the distillation
Speaker 4: allegations or accusation it is quite interesting. Like recently, I've
Speaker 4: been thinking about this a lot and thinking about what
Speaker 4: it means for distillation and what it means for the
Speaker 4: models to catch up. Right. So, there was this one
Speaker 4: quote from y'all showing you who is a Google Deep
Speaker 4: Mind researcher. He said, there're smart distillation and dumb disolation.
Speaker 4: Dumb distillation is something I think most of us were
Speaker 4: frankly non technical think about. It's like, okay, you take
Speaker 4: like a thousand queries, you take the answers of whatever
Speaker 4: claud gives you, right, and then you kind of force
Speaker 4: copy that into your said model, and then you forcefully
Speaker 4: make them basically like get the exact same answer. Smart
Speaker 4: distillation is like you using the frontier model almost as
Speaker 4: a partner to help you with the judgment, for the
Speaker 4: valuation and even the data labeling itself. So you're using
Speaker 4: it as almost a teacher for your own model. It
Speaker 4: guides it little bit versus really copy pasting the answer,
Speaker 4: if that makes sense, And that part of it is
Speaker 4: frankly not that unethical or like you know that frown
Speaker 4: upon right now, because that is what enterprises do when
Speaker 4: they're fine to me, So it's all kind of a
Speaker 4: bit of a mercury area.
Speaker 2: To be honest.
Speaker 3: Okay, so you mentioned data, just then talk to us
Speaker 3: about what the Chinese data set actually looks like, because
Speaker 3: I imagine, if you're a ten cent I mean you've
Speaker 3: got we chat, right, that must be a whole load
Speaker 3: of data on which to actually like build your AI.
Speaker 3: But on the other hand, I imagine, like there are
Speaker 3: some restrictions around the Internet. Obviously, what does it actually
Speaker 3: look like here?
Speaker 2: So I actually split that into two parts.
Speaker 4: On the data itself, people often think China is so
Speaker 4: data intensive and you just have mass amount of data
Speaker 4: to use for AI training. However, actually people forget again
Speaker 4: China's enterprise build out or you know whatever. The knowledge
Speaker 4: work economy is very new and not as sophisticated frankly
Speaker 4: as American ecosystem or the Western ecosystem, if you have
Speaker 4: to put it that way. So data is often unstructured,
Speaker 4: and data thus a lot of the specific needs for
Speaker 4: you know, the kind of training we're seeing today is
Speaker 4: not as vibrant, or the data ecosystem is not as
Speaker 4: sophisticated as what American data providers kind of can provide,
Speaker 4: such as mercre like we just mentioned now on the
Speaker 4: big tech side, it's bit interesting. So I'm glad you
Speaker 4: brought up Tencent, because Tencent actually just announced last week
Speaker 4: that they are working in the works of creating an
Speaker 4: agent that can be plugged into we Chat. This has
Speaker 4: been very controversial and it has actually had a lot
Speaker 4: of pushback even internally, because WeChat's product manager, Alan Jung
Speaker 4: has been famously or notoriously known to be kind of
Speaker 4: hard to work with if you want to push something
Speaker 4: with a WeChat because he's so protective of that user experience.
Speaker 4: It's his baby, right, And Tencent, like you said, has
Speaker 4: we Chat, which is a super app, has more than
Speaker 4: one point four billion MAU globally, like mostly one point
Speaker 4: three million people in China and the Chinese ask for
Speaker 4: globally or people who work with China. That is immense value.
Speaker 4: But the risk and compliance risk of potentially of agent
Speaker 4: going rogue within that chatbot, or that of agent going
Speaker 4: rogue in executing you know, whether it's a purchase whatnot,
Speaker 4: is that risk is very high. So they've been working
Speaker 4: on that. And on top of that, Tencent itself has
Speaker 4: been lagging behind compared to other big tech in their
Speaker 4: own proprietary models, and they've really been trying to really
Speaker 4: play catch up.
Speaker 2: They actually last.
Speaker 4: Year poached someone from open Ai who is a researcher
Speaker 4: called y'all shoam U as well has the same name
Speaker 4: as the other researcher we just mentioned, to lead this
Speaker 4: whole initiative, and their whole goal is to basically build
Speaker 4: a ten cent agent native model, and that is their
Speaker 4: biggest goal because end of the day, like you said
Speaker 4: in the very beginning, their goal is to optimize their
Speaker 4: existing businesses already and bring AI to the mass consumer
Speaker 4: as fast as they can.
Speaker 1: You know, you mentioned poaching a research from open Ai,
Speaker 1: and it's like the way I see it, AR will
Speaker 1: definitely be built by the Chinese. The question is whether
Speaker 1: it be built by the Chinese working the American labs
Speaker 1: or whether it will be built by Chinese working and
Speaker 1: Chinese labs. Has there been a gathering of steam of
Speaker 1: researchers from that had been working at American labs going
Speaker 1: to Chinese labs or are they still sort of one
Speaker 1: off and somewhat rare.
Speaker 4: I think even during the Internet era, we saw a
Speaker 4: lot of Chinese nationals or Chinese I think people returning
Speaker 4: to China right. I think this it's easy to blanket
Speaker 4: statement as geopolitical headwinds. People are scared, but realistically, I
Speaker 4: think most people are just trying to take care of
Speaker 4: their families and live a good life, right, yea. I
Speaker 4: hate to sound so crass about it, but you know,
Speaker 4: sometimes it's what your package look like and over generalize.
Speaker 4: I've heard from many researchers say, look, if my wife
Speaker 4: is a lawyer in China, my wife is a nurse
Speaker 4: in China, my wife is a teacher in China, that
Speaker 4: kind of employment opportunity is very, very hard to actually
Speaker 4: you transfer to.
Speaker 2: A new market.
Speaker 4: They're like, if I can get a similar package and
Speaker 4: a growth opportunity in one of the big labs in China,
Speaker 4: I would pick that over living in the US. And
Speaker 4: on top of that, I think that something is lost
Speaker 4: in the nuance is my parents immigrated to North America
Speaker 4: thirty plus years ago. It was a very clean cut,
Speaker 4: like quality of life. It's just like objectively better in
Speaker 4: any city in North America compared to any city in China.
Speaker 4: Now that's kind of a personal debate, right, because it
Speaker 4: depends on what you really value. If you want to
Speaker 4: be close to the city center. You want that fast paced,
Speaker 4: like techno evy futuristic lifestyle, China actually gives that to you.
Speaker 4: And then on top of that, if you want to
Speaker 4: be close to your family, it's a very personal reason.
Speaker 4: So I've met a lot of research I actually decided
Speaker 4: to come back to China or this part of the
Speaker 4: world simply because they wanted to do it for personal
Speaker 4: family reasons.
Speaker 3: Are they paid as much as they are in the US?
Speaker 3: Because we get headlines all the time about you know,
Speaker 3: so and so is joining whatever company, and people treat
Speaker 3: that new whose like sports stars, right, like teams trading
Speaker 3: their best players. Is it a similar thing here?
Speaker 4: I think you definitely get less of that sports star
Speaker 4: vibe or mentality. Here. They're still getting paid like hafty amounts.
Speaker 4: How much they don't usually disclose, but at least even
Speaker 4: in the Internet era, like a byte dance product manager
Speaker 4: can make just as much as a meta product manager. Similarly,
Speaker 4: if you're like an average AI researcher, you're probably making
Speaker 4: a similar amount. Although the star star players, like the
Speaker 4: ones that are signing one hundred million bonuses, I don't
Speaker 4: know if we had anything like that big like in China,
Speaker 4: but look, they made their money with the IPOs, they
Speaker 4: made the money recently with all this AI boom. It's
Speaker 4: just on a maybe slightly smaller scale. Doesn't mean that
Speaker 4: they're not making much more than the frankly average person.
Speaker 1: Tracy, Can I say something that might be sort of
Speaker 1: a sacrilege for a podcast host to say? Uh, Okay,
Speaker 1: I'm only speaking for myself here, not necessarily speaking for
Speaker 1: the team, But it occurred to me like, I'm not
Speaker 1: really sure if I'd be that interested in, say, getting
Speaker 1: the CEO of like an American AI lab on the
Speaker 1: podcast as a guest. I don't know what I would
Speaker 1: ask them, because like, like do I really want to
Speaker 1: hear like Sam Altman or Dabi Sasabi's or whatever like
Speaker 1: the future of work and all this stuff or like
Speaker 1: these all the big you know, I love doing AI episodes.
Speaker 1: I just feel like at that level I would rather
Speaker 1: talk to us or like someone in actually the engine
Speaker 1: room rather than this sort of big picture a person
Speaker 1: who may have some degree of AI psychosis and just
Speaker 1: like as on a speaks in like the biggest generalities.
Speaker 3: Okay, well, now Sam Altman needs to like invite himself
Speaker 3: on the show just to test your your commitment to
Speaker 3: not having AI CEOs.
Speaker 1: No, I would, I would do it, But I like,
Speaker 1: let's just agree here that if we ever get one
Speaker 1: of the really big like lab CEOs, let's just ask
Speaker 1: the very like sort of mundane questions about operations and now,
Speaker 1: like what are we all going to do? And you know,
Speaker 1: is what is the meaning of life going to be
Speaker 1: when we don't have jobs? Because I'm so sick of
Speaker 1: those conversations. They may be important at some point, but Grace,
Speaker 1: I'm sort of curious from your perception, like it does
Speaker 1: feel like the heads of the American AI labs have
Speaker 1: some degree of AI psychosis themselves. Either they talk about
Speaker 1: all white color employment is going to disappear, or that
Speaker 1: they're going to build a monster that if done wrong,
Speaker 1: is going to be out of control and that they're
Speaker 1: not you know, it'll escape, that it'll escape the sandbox.
Speaker 1: Is there the same sort of existential discourse in the
Speaker 1: Chinese AI community?
Speaker 4: Yeah? I think to start with in the AI community themselves,
Speaker 4: I would say people are a lot more pragmatic and
Speaker 4: I think recently I was talking to Nathan Lambert, who
Speaker 4: is open source researcher who just came to China visited
Speaker 4: all the labs. He said, Look, I was shocked to
Speaker 4: see majority of labs are so as in like a
Speaker 4: lot of the researchers are still students, a lot of
Speaker 4: them are interns, and the core research teams are maybe
Speaker 4: led by a handful of people. And then these people
Speaker 4: are academics by training. So maybe they're a bit less commercial.
Speaker 2: Maybe you can.
Speaker 4: Say they're less like sophisticated to make manimally the market,
Speaker 4: whatever you want to call them. So I definitely feel
Speaker 4: like there's less of that kind of psychosis or high
Speaker 4: level narrative going around.
Speaker 2: However, I would.
Speaker 4: Say that there is obviously anxiety from the public in
Speaker 4: some degree, not as much of a back pushback. But
Speaker 4: recently there was a very interesting court case in Hongjo,
Speaker 4: which is you know, home to Ali Baba and a
Speaker 4: lot of these al abs. Basically, a company try to
Speaker 4: like lay off a person saying you are being replaced
Speaker 4: with AI, and the court literally rules say that is
Speaker 4: not allowed and no company can use AI as an
Speaker 4: excuse to lay off or replace or even cut short
Speaker 4: their contract time. So that was a really swift reaction
Speaker 4: from regulators, and I think it really did serve as a.
Speaker 2: Calming factor for the public.
Speaker 4: Obviously, I also think I want to preface the fact
Speaker 4: that the knowledge worker economy makes.
Speaker 2: Up less of the whoverall.
Speaker 4: Economy in China as well, so that kind of fear
Speaker 4: maybe doesn't feel as imminent, but that conversation is being had.
Speaker 4: But I do think in Asia in general, not only
Speaker 4: in China. You look at South Korea, Singapore, all these
Speaker 4: countries are approaching AI in a very pragmatic way, and
Speaker 4: the tire moms are trying to train up the kids
Speaker 4: to be AI native, the students are trying to train
Speaker 4: themselves up to be AA native. People are preparing for
Speaker 4: the future versus pushing back on the future.
Speaker 1: That's interesting. You know, in the US, obviously, companies announced
Speaker 1: that they're laying people off, and they cite AI even
Speaker 1: frequently when there's no evidence that AI had anything to
Speaker 1: do with it. So it's interesting they're not allowed to
Speaker 1: do it. In defense of American AI labs, most of
Speaker 1: them lose a lot of money, and yet they actually
Speaker 1: do spend, at least the one spend there quite a
Speaker 1: decent amount on so called like alignment research, safety research,
Speaker 1: making sure the AIS don't go rogue et cetera. How
Speaker 1: big of a part of the Chinese labs, how much
Speaker 1: did they spend on I guess what they would you
Speaker 1: would put but the American labors would put into the
Speaker 1: safety bucket.
Speaker 4: I do have to say, I'm not a policy expert,
Speaker 4: so I don't work with a lot of the safety
Speaker 4: people as much. But there are organizations in China that
Speaker 4: are definitely working, like the regulators as well as the
Speaker 4: private sector working together. And for some context, in China,
Speaker 4: there are various moving parts in the government. There's MIT,
Speaker 4: the CAC, et cetera. These agencies basically some are to
Speaker 4: propel economic development. So in this case AI diffusion, the
Speaker 4: whole idea of AI plus AI plus every single sector
Speaker 4: you can think of, some act more like a guardrail
Speaker 4: as a protector, so they are working.
Speaker 2: Hand in hand.
Speaker 4: And on top of that, every single AI jen AI
Speaker 4: application as well as LM company have to go through
Speaker 4: the National Registry in China, so they actually disclose what
Speaker 4: is being trained, what is you know, the potential risk
Speaker 4: that said, I think right now, you know, no one
Speaker 4: really knows what the real impact of AI will be
Speaker 4: on economy, but you know definitely that fear mongering narrative
Speaker 4: is not mainstream in China.
Speaker 3: How would you describe I guess the approach of the
Speaker 3: Chinese government to AI in general, because it feels like
Speaker 3: the trade off for maybe not being on the cutting
Speaker 3: edge with frontier models is well, you're further along with
Speaker 3: sovereign AI and the government maybe has like a better
Speaker 3: handle on what the labs are actually doing.
Speaker 4: The Chinese government probably sees this in a much more
Speaker 4: pragmatic way. You know, just like how there was an
Speaker 4: Internet plus policy fifteen years ago, there's now an AI
Speaker 4: plus policy. When deep seak moment took off, you know,
Speaker 4: there was a frenzy of private companies even in home appliances,
Speaker 4: trying to embed deep seat, integrate deep sea. I'm just like,
Speaker 4: what is a AI vacuum going to do for you?
Speaker 4: Or AI like electric truth rush for you? It was wild, right,
Speaker 4: but the government picked that up. And I think what
Speaker 4: the Chinese government, going back to what we talked about earlier,
Speaker 4: is that they have the advantage of having the ability
Speaker 4: to push things down from top down and at the
Speaker 4: very high level, they're seeing AI as an economic driver
Speaker 4: to propel maybe efficiency, to address some of the labor
Speaker 4: shortage that is coming as the population could continues to
Speaker 4: age and decline. It also addresses a lot of issues
Speaker 4: where a lot of the young people don't want to
Speaker 4: work in manufacturing roles. They want to be automated actually,
Speaker 4: and they want to work in urban areas. So they
Speaker 4: have that now, and then each of the provincial governments
Speaker 4: will take that as kind of like a KPI. They're like,
Speaker 4: all right, let's go ask like vcs essentially and go
Speaker 4: find you know, future deep seeks and fund them. However,
Speaker 4: how much these companies want to take government money is
Speaker 4: a different discussion. A lot of them will then even
Speaker 4: support them by providing you know, infrastructure like buildings, offices,
Speaker 4: even like some of them heard like dormitory for these
Speaker 4: young entrepreneurs, and then give them money and capital try
Speaker 4: things out. There's also these AI pilot zones being rolled
Speaker 4: out across the country. I think now about eleven or
Speaker 4: twelve of them, where you know, people can try out
Speaker 4: new AI products. I met with the largest AI developer
Speaker 4: community founder in China a couple of weeks ago, and
Speaker 4: she was saying, there's more than a couple hundred thousand
Speaker 4: developers in this ecosystem and they are working with.
Speaker 2: Regulators and the private sector.
Speaker 4: So if say bitdowns have a new product, they might
Speaker 4: go to them first and say can you try this out?
Speaker 4: And then they will report and debug and see what's happening,
Speaker 4: and then tell the whoever low government that's funding them
Speaker 4: with providing with the infrastructure, say, hey, this product might
Speaker 4: come out. Do you want to be part of it?
Speaker 4: Do you want to give it money? Do you want
Speaker 4: to provide it with whatever resource you want to? So
Speaker 4: there is kind of this like cohesive ecosystem where they
Speaker 4: kind of all dance together. How much these companies actually
Speaker 4: want to take state money, I think that's debatable. However,
Speaker 4: as AI and robot has become morem when sensitive and
Speaker 4: being recognized as not only an economic driver but potentially
Speaker 4: a military use or geopolitical I guess talking point. At
Speaker 4: this point, it is becoming more and more nationalized, not
Speaker 4: only in China but globally in the US and so on.
Speaker 1: Robotics is obviously an area where China is just straight
Speaker 1: up ahead of the United States, or at least according
Speaker 1: to all the videos on my Twitter and Instagram feed
Speaker 1: of humanoid robots and so forth. How much does you
Speaker 1: know when we were all kids when we thought of
Speaker 1: like AI, I think we thought of robots, right, we
Speaker 1: thought about fifteen one thousand or some version of it.
Speaker 1: And now when we think of AI, most people think
Speaker 1: of chatbots. But that's just one aspect of AI. For
Speaker 1: the advanced labage, whether we're talking about the deep sea
Speaker 1: so then Mini Max's or GLM and so forth, like,
Speaker 1: are they actively working hand in hand with some of
Speaker 1: the unit trees and advanced robotics companies to figure out
Speaker 1: how you can actually have that the true AI robot
Speaker 1: of the future.
Speaker 4: Yeah, I think China, having been the manufacturing hub of
Speaker 4: like literally everything in the sun over the last like
Speaker 4: three decades, has definitely an advantage of owning all the
Speaker 4: supply chain, right. And it's like not only just owning
Speaker 4: the supply chain, but you know, there are literally regions
Speaker 4: where that whole supply from raw material to like the
Speaker 4: result end product from OEM is all within like say,
Speaker 4: fifty kilometers of each other. So what we're seeing is
Speaker 4: a lot of American investors and entrepreneurs coming into China
Speaker 4: to kind of get a sense of that. And like
Speaker 4: you mentioned, because we've been so fixed in software, I
Speaker 4: think China having a very strong hardware background is now
Speaker 4: thinking about how can we actually integrate the software into
Speaker 4: the hardware. How ready that is to the mass market.
Speaker 4: I frankly don't think it's really there yet. So recently
Speaker 4: I just met with some robotic companies. They actually can't
Speaker 4: just plug in a Mini Max. You know, that's like
Speaker 4: for them, they need to actually get physical data. They
Speaker 4: This is where like you know, now all the hype
Speaker 4: is on world models physical AI. You know, that is
Speaker 4: a complete different set of kind of technology essentially, where
Speaker 4: without the three D data that these models need right now,
Speaker 4: the bottomneck right now is that you know, these hardware
Speaker 4: is these humanoids, quadrupids, dogs, whatever you want to call them,
Speaker 4: they cannot be powered by lms.
Speaker 2: That's number one.
Speaker 4: Number two is despite that China being very strong on hardware,
Speaker 4: the bottleneck is actually a lot of times in the
Speaker 4: integration as well as the battery solutions. You know, you
Speaker 4: think of China having very strong battery solutions, but most
Speaker 4: of these gadgets can't last more than like say two hours,
Speaker 4: and there's no one that's really come out with a
Speaker 4: better solution so far. What I've seen the most creative
Speaker 4: things so far is like you know, those glasses you wear,
Speaker 4: like the metaglasses, they kind of die within two hours.
Speaker 4: But China, like I Fly Tech or Rocket that's kind
Speaker 4: of a newer player startup, they created these battery capsules
Speaker 4: where you can just like stick on to your glasses.
Speaker 4: It's very lightweight, doesn't really affect your user experience, and
Speaker 4: that's actually able to kind of extend it by a
Speaker 4: few hours. So to go back to your question, is
Speaker 4: China trying to do physical AI?
Speaker 2: Definitely?
Speaker 4: What is their edge? I think it's still in manufacturing.
Speaker 4: Is their software good enough? I don't think anyone really
Speaker 4: has good enough software right now as of now.
Speaker 2: Wait, I'm just going to press you on this.
Speaker 3: So if we fast forward ten years, like, what would
Speaker 3: you say is most likely to be China's comparative advantage.
Speaker 3: Is it like the cheap, open source super optimized models.
Speaker 3: Is it software AI software that's like integrated with like
Speaker 3: industry and existing business or is it robotics and the
Speaker 3: sort of hardware side of AI.
Speaker 2: I think any.
Speaker 3: But we want to challenge here.
Speaker 2: A lot of these companies that exist ten years ago
Speaker 2: or not even five years.
Speaker 3: That's fair, okay, I can shorten the timeframe in three years.
Speaker 4: All right, So don't chase me down if I'm wrong
Speaker 4: in three years. But I think, you know, there's two parts.
Speaker 4: One is I think I agree with the hardware side.
Speaker 4: China is definitely going to have I think, more breakthroughs
Speaker 4: and have a lot of edge. Not only is the
Speaker 4: supply chain all domestically there, I think something overlooked by
Speaker 4: people is the fact that a lot of the know
Speaker 4: how is also there, and that's not easy to transfer overnight.
Speaker 4: You know Patrick McGee's book recently in his Apple book
Speaker 4: saying how Apple tried to move this whole supply chain
Speaker 4: to India. The biggest bottom neck is actually these like
Speaker 4: highly skilled laborist jobs that actually are so technical that
Speaker 4: cannot be even trained in one generation. It took decades
Speaker 4: to really train up the local community labor force whatnot.
Speaker 4: So that's still there now because of that ecosystem.
Speaker 2: A lot of these.
Speaker 4: Robots home appliance is whatnot. These tech gadgets are produced
Speaker 4: at less than fifty percent of the costs of where
Speaker 4: you could produce that anywhere else in the world. They
Speaker 4: are also extremely innovative. I've talked to people at ev
Speaker 4: companies just for example, to ship out a new model
Speaker 4: from ideation to production to hitting the floors that takes
Speaker 4: maybe less than fifteen months. Wow, But for traditional OEM
Speaker 4: like way for at least three to five years. Right,
Speaker 4: So there's the hardware side. I think another very underappreciated
Speaker 4: fact on the Chinese open source model is that people
Speaker 4: don't realize. So a year ago when I spoke to
Speaker 4: startups as semlic Valley, they were the most cost conscious,
Speaker 4: frankly less compliance conscious and gives very little the care
Speaker 4: about geopolitics. They were building on top of QUID. Now
Speaker 4: it's actually all a lot of Ai Native American enterprises
Speaker 4: building on Chinese open source because we're seeing headlines on
Speaker 4: the ROI is not like showing it's extremely expensive for
Speaker 4: these token maxing projects whatnot. So Harvey Cursor, they've talked
Speaker 4: about using a hybrid model where they will build majority
Speaker 4: on GLM or KIMMI, but kind of like what we
Speaker 4: talked about earlier where they use like Opus to act
Speaker 4: as a judge or a guidance. So I think that's
Speaker 4: something where we're continue to see and these companies are
Speaker 4: generating a lot of revenue, and going back to the
Speaker 4: fact that like a lot of them don't even have
Speaker 4: enough capability to support the demand that's coming through.
Speaker 1: That's such a fascinating idea, and it makes a lot
Speaker 1: of sense that at the application layer that probably a
Speaker 1: lot of different models can go into it.
Speaker 4: You know.
Speaker 1: By the way, I was talking to someone out a
Speaker 1: dinner recently and he said he thinks that AI writing
Speaker 1: will get a lot better when AI is embedded in
Speaker 1: humanoid robots, because then the will have this sort of
Speaker 1: groundedness in the real world where I have no idea
Speaker 1: this is true, he said. The reason his theory was
Speaker 1: that the reason why AI writing is still so weird
Speaker 1: is because it's in this disembodied data centers, and that is,
Speaker 1: as soon as they're really in robots, then they'll have
Speaker 1: a sort of real world groundedness. A right, have one
Speaker 1: last question, you know, after open Claw came out, I
Speaker 1: started seeing again my entire Twitter feed and answergram feed.
Speaker 1: I have done this to myself, but all I do
Speaker 1: all day is consume Chinese propaganada. But I started seeing
Speaker 1: all these videos, like all these grandmothers and stuff like
Speaker 1: setting up their claws and stuff like that. And I
Speaker 1: saw these videos and I said to myself, I just
Speaker 1: don't believe this. I think this is fake news. I
Speaker 1: do not actually believe that there's all these eighty year
Speaker 1: old Grammas or whatever really excited about setting up their
Speaker 1: open claw or whatever. Are those real? Like, what's the
Speaker 1: deal with that?
Speaker 3: Yeah?
Speaker 2: I think that was definitely a bit of a hype.
Speaker 1: I knew it.
Speaker 4: No, No, but I will say there were Grammas lining
Speaker 4: up to get it done.
Speaker 2: Okay, I'll answer this twofold on the kind of the
Speaker 2: surface is.
Speaker 4: I think Chinese aunties, uncles whatnot. They are just much
Speaker 4: more open to technology because you know, you go around,
Speaker 4: whether it's by force or by nature. You know, you
Speaker 4: can't really navigate modern Chinese life without being on alipay.
Speaker 1: No, you really can't.
Speaker 3: You can't like buy Starbucks in Beijing without like having
Speaker 3: we paid.
Speaker 4: After COVID, I went back to I think Shanghai for
Speaker 4: the first time, and I was sitting at a restaurant
Speaker 4: just like waiting for someone to help me order food.
Speaker 4: No one came because they're just like, why are you
Speaker 4: so Like, why are you a caveman? Don't you know
Speaker 4: how to like scan the QR code on your table? Okay,
Speaker 4: So that Tangent's side, I think the overall optimism around.
Speaker 4: Technology is very different from the West because in the
Speaker 4: last twenty thirty years, a lot of rural eras in
Speaker 4: China literally could not access resources, information, goods, whatever that,
Speaker 4: you know, like big cities could not until these super
Speaker 4: apps came about. So a lot of people don't have
Speaker 4: TVs in their homes and they live in a village
Speaker 4: and there may be annual household income is like one
Speaker 4: thousand dollars, but they will have a smartphone and that
Speaker 4: smartphone will be able to actually enable them to get
Speaker 4: micro loans to purchase goods, you know, to help their
Speaker 4: kids access information online whatever that is. So technology is
Speaker 4: very much kind of accepted and respected and actually like
Speaker 4: big tech is loved, like if you work for one
Speaker 4: of big tech, you are like a pride of the family.
Speaker 4: So there's that very culture aspect of it. Then is
Speaker 4: like going back to the super apps. So I think
Speaker 4: the open Claw frenzy was interesting because some people say
Speaker 4: it was the first agent that you know, Chinese people
Speaker 4: could get their hands on, like a Western agent that
Speaker 4: they can get their hands on because you know, anthropic
Speaker 4: and open it doesn't actually operated in China. You can't
Speaker 4: access that. So when Tensan and Ali Baba try to
Speaker 4: embed open claw products into their own like series of
Speaker 4: products or business whatever offerings. It got people really excited,
Speaker 4: and because of these super app models that they have,
Speaker 4: it was a very natural way for people to access them.
Speaker 4: There's a functional adjacency to you know, the search bar
Speaker 4: and then opening up an open claw and then trying
Speaker 4: to like run like you know, manage your mini programs
Speaker 4: within ten cent we chat and then try to order something.
Speaker 4: So all of that kind of took off. But that said,
Speaker 4: actually the Chinese government again regulators acted very swiftly in
Speaker 4: the beginning. I think local government, like we see try
Speaker 4: to even encourage local businesses to embrace open claw. But
Speaker 4: the Beijing government immediately said, guys, actually be very very
Speaker 4: careful of your data privacy. Your security banks didn't do
Speaker 4: this as so you shouldn't do this. Be mindful of
Speaker 4: what you're doing with this technology. And then the big
Speaker 4: tech kind of rolled back a bit of their marketing
Speaker 4: and you can see, actually hilariously there were advertisements for
Speaker 4: helping these aunties and uncles how to uninstall these open claw.
Speaker 2: On their gadget. So anyway, that's a bit of a
Speaker 2: kind of background on that.
Speaker 1: All right, Graceha, thank you so much for coming on
Speaker 1: odd lot. It's great to connect with you here in
Speaker 1: Hong Kong. And we'll have you back in three years, no,
Speaker 1: hopefully before then. We'll have you back at least certainly
Speaker 1: in three years to see how your predictions held up.
Speaker 2: Thanks so much, Tracy.
Speaker 1: That was fun. There was a lot of interesting ideas
Speaker 1: in a fairly short conversation. But one thing specifically and
Speaker 1: then that like sort of stands out to me is
Speaker 1: thinking about some of these application companies, how much it
Speaker 1: makes sense for them to sort of Yeah, they'll use
Speaker 1: like a state of the art American closed model for
Speaker 1: like some of the work, but then other just you know,
Speaker 1: almost as capable models underneath, so you have like a
Speaker 1: legal AI app like a Harvey or something. Sure, some
Speaker 1: of these open source models work well for some tasks
Speaker 1: within that context, And how much it makes sense to
Speaker 1: sort of combine them under one app layer.
Speaker 3: Yeah, Well, you don't need the cutting edge model for everything, right,
Speaker 3: but like if you can get some of the cutting
Speaker 3: edge model combined with like the cheapness of the open
Speaker 3: source thing, like that seems like a pretty good deal
Speaker 3: for a lot of companies.
Speaker 1: Yeah, definitely. I also think like, how is the massive
Speaker 1: manufacturing edge that China has not going to just keep
Speaker 1: compounding itself. I mean, this is like the multi trillion
Speaker 1: dollar question of the entire world. But it really does.
Speaker 1: I mean, when you think about, Okay, there's a sort
Speaker 1: of presumably natural synergy, there's all this real world physical
Speaker 1: data that Chinese manufacturing companies can theoretically get from their
Speaker 1: various robotic vacuum cleaners and so forth, and then feed
Speaker 1: those into certain model then we call AI. That really
Speaker 1: does seem like a potential leg up that you know,
Speaker 1: just done the data collection alone from all those physical things,
Speaker 1: like a huge potential edge over the next several years.
Speaker 3: Yeah, although it was very interesting, Grace was saying that
Speaker 3: it's not as developed or as structured as it is
Speaker 3: in the US, because I had thought the same thing, like,
Speaker 3: if everyone's talking over we chat, if everyone's paying over
Speaker 3: we pay, you must have oodles and oodles of data.
Speaker 3: But yeah, that was interesting. The ecosystem thing, I think
Speaker 3: is really important, and it just seems like really hard
Speaker 3: to get an ecosystem kind of going from scratch. And
Speaker 3: I remember maybe it was with Dan Wong, but someone
Speaker 3: describing how like, if you go to shen Zen, you
Speaker 3: can basically start like an entire company manufacturing a physical
Speaker 3: thing because every single supplier is there. You just go
Speaker 3: from like one storefront to another storefront to another storefront.
Speaker 3: I don't think that can be replicated anywhere in the US.
Speaker 1: I mean, this is a bit of a tangent. But
Speaker 1: you know this is economists talk about quote agglomeration all
Speaker 1: the time. The advantage is exactly that of having everything
Speaker 1: there and then you build these deep networks. One thing
Speaker 1: I find to be a little strange, and again this
Speaker 1: is a tangent, is how San Francisco concentrated AI is,
Speaker 1: even though at the American level it is sort of
Speaker 1: pure like desk work right Like, it's not the sort
Speaker 1: of we need the bolts manufacturer, we need the servos manufacturer,
Speaker 1: we need robovision manufacturer, and yet it's still so agglomerated.
Speaker 1: Or the fact that finance is so agglomerated in New
Speaker 1: York City. I find that or conglomerated. I find that
Speaker 1: to be a little odd. So, but yeah, I don't
Speaker 1: conglomerated is a conglomerated I don't know whether it's conglomerated
Speaker 1: or agglomerated. I'm just gonna say glomerated. We know that
Speaker 1: we are certain industries in the US that are quote
Speaker 1: conglomerated one way or another. But yeah, I did find
Speaker 1: that to be And man, these like Mini Mac billion
Speaker 1: dollar company that's like nothing compared to the valuations that
Speaker 1: we see with American companies. Pretty wild stuff. Also, the
Speaker 1: point we should do more, I mean, there's a million
Speaker 1: we gotta do we have and there are plenty of
Speaker 1: non a I things to do, so you can't just
Speaker 1: keep saying we should do an episode on X or Y.
Speaker 1: But data markets and like the idea of like, okay,
Speaker 1: these Chinese companies can buy very pricey proprietary data after
Speaker 1: some exclusivity window. There's some interesting stuffing to down there.
Speaker 3: Yeah, the monetization was really interesting to hear.
Speaker 2: Okay, shall we leave it there for now?
Speaker 1: Let's leave it there.
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