Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, Obviously, there's a lot of there's an infinite number
Speaker 2: of angles to artificial intelligence and what's going on in
Speaker 2: the industry. But one thing that I find like striking
Speaker 2: about this that feels very different from other stories in tech,
Speaker 2: and I don't really know what it's about, is why
Speaker 2: you hear it talked about in sort of geopolitical strategic terms,
Speaker 2: and why countries, whether it's the US, China, but also
Speaker 2: Saudi Arabia, Europe, really feel like they need to have
Speaker 2: like their own domestic model or champion. It's almost like
Speaker 2: it's like oil or something where countries have at least
Speaker 2: been convinced that it's very important for them to have
Speaker 2: some sort of homegrown AI.
Speaker 4: Yeah, you're right.
Speaker 3: I hadn't really thought about it, but that's absolutely true.
Speaker 3: People talk about it almost as a strategic resource at
Speaker 3: this point and sort of in existential terms, and you
Speaker 3: never saw anyone talking about like the need for a
Speaker 3: national SaaS strategy or something like that.
Speaker 2: That's exactly what I think right, Like you didn't know
Speaker 2: what it's like, Oh, well, we have to have our
Speaker 2: own Slack or we have to.
Speaker 3: Have our own salesforce.
Speaker 2: We have to have our own British salesforce and the
Speaker 2: Saudi Arabia salesforce. But there's something about AI models and like,
Speaker 2: you know, it's sort of about the chips specifically, and
Speaker 2: you know everyone wants the chips, right, but there also
Speaker 2: seems to be something where like countries want to like
Speaker 2: really develop their own models and have their own open
Speaker 2: AI or whatever it is.
Speaker 3: Yeah, I also wonder how much of it has to
Speaker 3: do with the data and the idea that like the
Speaker 3: data is what you're training the model on, and so
Speaker 3: there's like privacy concerns isn't the right word, but you know,
Speaker 3: countries want to have some control over their own data.
Speaker 3: They don't want full transparency. So I wonder how much
Speaker 3: that feeds into it. But you're right, we have gotten
Speaker 3: used to talking about the AI arms race in one
Speaker 3: way or another, and we should dig into why that is,
Speaker 3: like why is it such a competitive piece of technology?
Speaker 2: Totally? And I'd say, you know, there's two other things here,
Speaker 2: which is that obviously the ultimate in everything these days.
Speaker 2: A lot of it is US China and how US
Speaker 2: models and US companies compared to Chinese models and Chinese companies,
Speaker 2: which I don't think many Americans have much visibility on,
Speaker 2: or at least I certainly don't. I don't really know
Speaker 2: what China's AI strategy is. And then there is of
Speaker 2: course existing policy and how that might change under a
Speaker 2: Trump administration and what that might do both to you
Speaker 2: as China relations in general, and I don't know if
Speaker 2: that's going to be, you know, some sort of shift
Speaker 2: in how Trump might think about AI versus a Biden administration.
Speaker 3: It is true that, like certainly the semiconductor portion of
Speaker 3: AI is something that has been competitive for a while now,
Speaker 3: and we've seen that in both the Trump administration and
Speaker 3: continued under Biden. So there is that kind of commonality
Speaker 3: running through it. But I am very interested in getting
Speaker 3: more of a handle on exactly what China is doing
Speaker 3: in this space and what it sees potentially as like
Speaker 3: an actual threat here totally.
Speaker 2: Well, I'm very excited to say that we have two
Speaker 2: perfect guests who really know all the dimensions of this
Speaker 2: topic extremely well. We're going to be speaking with Jordan
Speaker 2: Schneider He is the founder of the China Talk podcast
Speaker 2: and newsletter, one of my favorite sources for understanding what's
Speaker 2: going on in China. And we're also going to be
Speaker 2: speaking with Kevin Chu. He is the author of the
Speaker 2: Interconnected newsletter and also a hedge fund manager at Interconnected Capital.
Speaker 2: Writes fantastic stuff like sort of really digging into some
Speaker 2: of the technical and tech sides of all this, so
Speaker 2: hopefully we will walk out of here with a little
Speaker 2: more understanding. Jordan and Kevin, thank you so much for
Speaker 2: coming on OUTE.
Speaker 5: Thank you so much for having us first time.
Speaker 4: Long time. What a treat.
Speaker 2: Love when people say that, I love hearing it reminds
Speaker 2: me of like listening to like sports talk radio as
Speaker 2: a kid. I'll just start with the question, what is
Speaker 2: it about AI? And is it inherent or is it
Speaker 2: sort of the industry's marketing job. But what is it
Speaker 2: about AI that unlike a tracing mention, you know, different
Speaker 2: from like CRM software, social networking or whatever that countries
Speaker 2: really seem to prize having some aspect of it be homegrown.
Speaker 5: Yeah, I couldn't help a chuckle a little bit when
Speaker 5: you guys are doing your intro about the national SAS
Speaker 5: or what is your national CRM since I am a
Speaker 5: software investor by day. You know, it's really interesting if
Speaker 5: you think about how AI, when we talk about it
Speaker 5: generally has been the formulated you know, we're really talking
Speaker 5: about generative AI, right, large language models in particular. There
Speaker 5: are a bunch of other AI machine learning use cases
Speaker 5: that we're gonna you know, just stick to the side
Speaker 5: for now. There's something very deeply cultural when you start
Speaker 5: to model at the entire Internet's information. When it comes
Speaker 5: to large language model, right, it's essentially a three dimensional
Speaker 5: space where every single token, where every single word has
Speaker 5: a line that's drawn from one word to the other.
Speaker 5: That kind of formulates knowledge or formulates you know, truth
Speaker 5: in a lot of ways. And I would say, so far,
Speaker 5: given all the dominant products SHAT, GPT, clode, Gemini, etc.
Speaker 5: They've been modeling Internet in general, which has a lot
Speaker 5: of content from the American Internet if you want to
Speaker 5: think about that, or you know, Western European Internet, and
Speaker 5: there are other versions of the Internet in Japan and
Speaker 5: China in particular, where the ecosystem is such a wall
Speaker 5: garden that the way knowledge and culture and thoughts and
Speaker 5: the you know, history is being expressed is very different,
Speaker 5: and I think that is what triggered a lot of
Speaker 5: country to come to an understanding that this AI thing
Speaker 5: isn't just a blown up spreadsheet with good UI that
Speaker 5: you can track, you know, pipeline or you know, have
Speaker 5: a bunch of messages going around to help your workplace
Speaker 5: go a little bit faster. It is a codification of
Speaker 5: your people.
Speaker 2: And collective truth and culture.
Speaker 5: And I will say collectively, your public discourse, maybe even
Speaker 5: an element of your private discourse that's been published on
Speaker 5: the internet is being codified in a particular large language model.
Speaker 4: Just to add to that and bring up the US
Speaker 4: China context. So you know, if we take as a
Speaker 4: premise that the US and China are in a strategic competition,
Speaker 4: what nations do when they try to when they compete
Speaker 4: is they try to, you know, maximize national power. What
Speaker 4: is national power composed of how big your economy is
Speaker 4: and how powerful your military is. Now, you know, there's
Speaker 4: still a lot of debate to be about just how
Speaker 4: important AI might be to the future of militaries and
Speaker 4: the future of productivity growth, But it is the technology
Speaker 4: today that has the most potential to really change long
Speaker 4: term military and economic trajectories. And I think we've seen
Speaker 4: over the past few years the realization, maybe starting with
Speaker 4: the October seventh, twenty twenty two export controls, that having
Speaker 4: a completely open exchange between the US and China is
Speaker 4: too much of a risk for American policy makers to stomach.
Speaker 4: And sort of in response to that, simultaneously and particularly
Speaker 4: in response to the October seventh export controls, you've seen
Speaker 4: trying to really aggressively start to invest and support both
Speaker 4: their semiconductor as well as broader AI ecosystems.
Speaker 3: Wait, so I want to press on this point that
Speaker 3: Kevin also brought up. But the idea of like AI
Speaker 3: as a means of like cementing cultural and informational control.
Speaker 3: Did anyone play around with the what was it called
Speaker 3: like the study?
Speaker 4: She thought app?
Speaker 3: Yeah, it was like the open AI app that was
Speaker 3: trained on. Xi Jinping thought, here's.
Speaker 4: The funny thing is that app is like actually kind
Speaker 4: of vaporware. We tried to find it on China Talk
Speaker 4: you guys should all subscribe to and couldn't. And I
Speaker 4: think this is one of the really interesting pieces of
Speaker 4: the Chinese government and how they're specifically playing on the
Speaker 4: sort of AI model side of things. As I've sort
Speaker 4: of if you look at a lot of the Chinese
Speaker 4: government sponsored projects so far, particularly on the model generation
Speaker 4: or like national data set side, and you try to
Speaker 4: get to ground truth on them, there's really not a
Speaker 4: lot of there there. But that's not to say that
Speaker 4: the Chinese sort of VC backed model ecosystem isn't the
Speaker 4: real deal. We've seen companies raise multiple billions of dollars
Speaker 4: and be able to publish models, which are you know,
Speaker 4: maybe six months behind the leading edge when you when
Speaker 4: you're looking at open AI or anthropic and the sort
Speaker 4: of delta between the government back stuff that you know,
Speaker 4: can't pay for the best engineers or what have you
Speaker 4: and these you know, DRIPUAI, Moonshot, Ernie it Baydu. Those
Speaker 4: folks are really running just as hard as what you're
Speaker 4: seeing in the West.
Speaker 3: Let me ask a related question, but in terms of
Speaker 3: Chinese AI models, what do we know about the data
Speaker 3: sets that they're being trained on and are there any
Speaker 3: limitations that are introduced by the fact that you know,
Speaker 3: they are being developed within what is ostensibly all walled,
Speaker 3: you know, the Great Firewall of China Internet. Do they
Speaker 3: still have access to X China data sets that they
Speaker 3: can pull in even if those aren't being sort of
Speaker 3: publicly dispensed to normal people.
Speaker 5: So I think they definitely do. The one thing that
Speaker 5: all these Chinese tech companies, the model makers in particular,
Speaker 5: are very aware of, and I would say any tech
Speaker 5: company in China has to be aware of, is the
Speaker 5: redline that cannot cross when it comes to certain things
Speaker 5: that their product may generate, right, And I think this
Speaker 5: is one of the reasons why building a general of
Speaker 5: AI application or company in China is so hard. Yes,
Speaker 5: they do have access to you know, Wikipedia or the
Speaker 5: Common Crawl, any of the kind of the standard table
Speaker 5: stake data sets that you use to train all the
Speaker 5: large language models so far, and then they can inject
Speaker 5: some of the data that is available inside the Chinese
Speaker 5: Internet kind of garden a wall garden, if you will.
Speaker 5: But there are two interesting things about the Chinese Internet right.
Speaker 5: One is I'm assuming everybody who's listening to this is
Speaker 5: aware of the level of censorship that's in public discourse
Speaker 5: on the Chinese Internet. That does not stop Chinese nedicence
Speaker 5: from wanting to express themselves in a certain way. So
Speaker 5: if you are in a ficianadel of the Chinese language.
Speaker 5: You will see a lot of contortions of linguistic kind
Speaker 5: of acrobats being used in all kinds of ways to
Speaker 5: publish public in China that you can convey certain meanings,
Speaker 5: but when it comes to modeling the language actually becomes
Speaker 5: very difficult to model the accuracy of the language. So
Speaker 5: that's number one and number two because of again the
Speaker 5: red line of censorship moving at all times, so it's
Speaker 5: not like a static target. A lot of contents actually
Speaker 5: get deleted on the Chinese Internet. Things just disappear, you know,
Speaker 5: if you don't archive of a certain article, when you
Speaker 5: read it two days later, it might be gone for
Speaker 5: you know, reasons that a lot of people can't even
Speaker 5: decipher even if you think about this all the time,
Speaker 5: and that reduces the quantity. And I would say the
Speaker 5: quality of training data that exists on their Chinese Internet
Speaker 5: in particular, that I think is almost built in disadvantage
Speaker 5: to a lot of Chinese model makers. They're building their
Speaker 5: own lom you know, despite all odds there of course,
Speaker 5: still continuing their effort to do so.
Speaker 2: One thing I like about some of the AI conversations
Speaker 2: is they get into the realm of linguistic and linguistic theory.
Speaker 2: Can I just press you on that point about the
Speaker 2: nature of the language itself and the effect that that
Speaker 2: has on building a high quality model.
Speaker 5: Yeah, so, I would say two things that's interesting about
Speaker 5: their Chinese language in particular is that one, there are
Speaker 5: a lot of homonyms. There are a lot of sounds.
Speaker 5: There are a lot of characters that sound almost exactly
Speaker 5: the same. That means, you know, nothing similar.
Speaker 3: The famous example is the whole like It's either a
Speaker 3: poem or a very long sentence with like ma like force.
Speaker 3: And all these ma can be depending on the intonation,
Speaker 3: can like mean a bunch of different things. You can
Speaker 3: construct a whole poem or sentence out of that single syllable.
Speaker 5: That's right like ma ma ma right the four tones
Speaker 5: of the standard Chinese Mandarin language. And if you just
Speaker 5: put that into the tech context, you know, Jack Ma Mayun,
Speaker 5: the founder of Ali Baba, has been either a celebrated
Speaker 5: entrepreneur or a villain, depending on how you think about
Speaker 5: his current public stature. Right. That makes the modeling of
Speaker 5: any let's just say, publicly available information about Jack Ma
Speaker 5: pretty difficult when you're also building a generative application on
Speaker 5: top of it. And there are a lot of these
Speaker 5: examples in the Chinese linguistic case. That makes not just
Speaker 5: the modeling difficult on a technical sense, but also what
Speaker 5: can or you cannot generate based on the red lines
Speaker 5: that you may or may not have to cross.
Speaker 4: Yeah, So, just to play Devil's advocate here for a second,
Speaker 4: GPT four was trained on zero point two percent Mandarin
Speaker 4: and is ninety nine percent as smart in Chinese as
Speaker 4: it is in English. And when you look at what
Speaker 4: some folks out of the leading Chinese AI labs are saying,
Speaker 4: like they're perfectly happy getting the vast majority of their
Speaker 4: training data from English and sort of pourting it over
Speaker 4: to Chinese. So there is a little challenge there. But
Speaker 4: I also think it's it's the other thing on this
Speaker 4: sort of are the model is going to be lobotomized
Speaker 4: because they will be able to be too politically sensitive.
Speaker 4: I mean, look, I feel like this is basically a
Speaker 4: solved problem. If you try to make Chatgypt say something
Speaker 4: racist or sexist, it basically won't. And if Chinese internet
Speaker 4: companies have data sets on anything, it's all the posts
Speaker 4: that they've censored over the past twenty years, you know.
Speaker 4: Having also tried to do this exercise of getting Chinese
Speaker 4: models to say politically racy stuff. It is a pretty
Speaker 4: hard slog. So you know, on the plus side, I think,
Speaker 4: you know, not all data that's going to matter for
Speaker 4: the future of AI and it's economic and military impact
Speaker 4: is going to be linguistic and text based. And you know,
Speaker 4: being the world's manufacturing center, putting cameras into all those
Speaker 4: factories may help you train a sort of like AI
Speaker 4: manufacturing robot a lot better. So there are real trade
Speaker 4: offs here, and it's not just like China is sitting
Speaker 4: behind the eight bombs.
Speaker 3: I just realized how old I am because I remember
Speaker 3: when China's censors like actually physically used to cross things
Speaker 3: out in newspapers that were like important in Beijing. This
Speaker 3: would have been around like two thousand and four, two
Speaker 3: thousand and five. It used to be done by hand.
Speaker 3: One thing I wanted to ask, since Kevin brought up
Speaker 3: the sort of like vagaries of Chinese politics where one
Speaker 3: day something is really in and the next day something
Speaker 3: is very much out, we have seen that bleed into
Speaker 3: financial markets and specifically you know, funding for technology, primarily
Speaker 3: internet technology SaaS in recent years, but also into housing
Speaker 3: and things like that with the three redlines policy. So
Speaker 3: I'm curious, like, what does the funding environment actually look
Speaker 3: like for AI at the moment? Is there a lot
Speaker 3: of like government support for developing this technology?
Speaker 2: And just while you're answering that, like who are some
Speaker 2: of the companies, Yeah, you shouldn't, like who are the
Speaker 2: open AI and anthropic of China that we should know
Speaker 2: about it?
Speaker 5: Yeah?
Speaker 3: And how are they raising money? Like is most of
Speaker 3: it coming from vcs, vcs or public vironments?
Speaker 5: Yeah? So when it comes to the AI model makers, right,
Speaker 5: I'll put that in one category of new startups, which
Speaker 5: actually kind of came up around the same time as
Speaker 5: all the other ones that we already know here in
Speaker 5: the US as well. You have Dripai, which is, you know,
Speaker 5: purported to be one of the company that's closest to
Speaker 5: open ai. You know, they're avow to all so develop AGI.
Speaker 5: They have strong academic roots from Qinghuai University. You have
Speaker 5: Moonsha that I think Jordan mentioned. You have zero one
Speaker 5: dot Ai, which is a lee hai Fu's a new
Speaker 5: venture building models as well. And I would say they're
Speaker 5: probably six or seven Chinese unicorns that currently exist that
Speaker 5: receive funding mostly from your Chinese standard vcs. These are
Speaker 5: vcs that are just like the Sequoias and the and
Speaker 5: reasons of the world. Right, you have Jung Fund, you
Speaker 5: have a Hillhouse Capital, you have home Shin who was
Speaker 5: formerly a Sequoia capital China, and a few others pouring
Speaker 5: money into this particular lane because one thing I think
Speaker 5: we should back up is that this is one of
Speaker 5: the very few lanes that vcs in China can actually
Speaker 5: put a lot of money into and expect the kind
Speaker 5: of return that you would expect from a pure financial
Speaker 5: VC because a lot of other parts of the Chinese
Speaker 5: tech ecosystem has been battered down quite a bit because
Speaker 5: of all the differences in the policy crackdown that happened
Speaker 5: in the last few years or so. So those are
Speaker 5: some of the big model maker players, and a lot
Speaker 5: of them are attracting large corporate venture investments from Alibaba
Speaker 5: in particular, but also Huaiwi, ten Cent, all the big
Speaker 5: dogs pouring into it. Very similar again to what we
Speaker 5: have here in the US, where Microsoft, Google and Amazon
Speaker 5: all have like their player on the field, if you will,
Speaker 5: when it comes to startups, and even some of the
Speaker 5: deal structures are similar I think one of Ali Baba's
Speaker 5: deal is that half of the investment they're making has
Speaker 5: to go into cloud credits to use the GPU service
Speaker 5: that are in Alibaba Cloud to be able to train
Speaker 5: their model as a way to entice, but also to
Speaker 5: tie them into Ali Baba's on ecosystem. So actually they're
Speaker 5: probably more similarities, I would say, than differences when it
Speaker 5: comes to the AI model makers funded by vcs, But
Speaker 5: that's about a different as similar as it gets.
Speaker 2: Jordan, you mentioned the robots and the idea that another
Speaker 2: form of data is just all the visual information that's
Speaker 2: inside a factory, and we did an episode on sort
Speaker 2: of the connection with AI and robotics with Josh Wolf
Speaker 2: and we talked about some of these ideas. But you know,
Speaker 2: you mentioned you know you're talking about okay, they also
Speaker 2: want to achieve AGI. Are there distinct aspects though of
Speaker 2: the strategy that is like oh no, like okay, we
Speaker 2: are thinking about AI in the chatbot sense, So that
Speaker 2: is the way most people in the US think about
Speaker 2: what these AI companies have delivered. Is it the same
Speaker 2: with these Chinese powerhouses or are they thinking about different
Speaker 2: forms of expression maybe in a more industrial manner. Et
Speaker 2: cetera than the sort of chatbot idiom.
Speaker 5: So Joe one thing that this is more of a
Speaker 5: recent development. So I think just the last week, Shanghai
Speaker 5: hosted its annual World AI conference, right, and during this
Speaker 5: conference is a big show and there I believe twenty
Speaker 5: five different brands of humanoid robots that were on display
Speaker 5: in the expo you know, hall of this center. So
Speaker 5: that illustrates I think to you that I think the
Speaker 5: direction I think Tracy asked about this, you know, where's
Speaker 5: the funding going to?
Speaker 3: Right?
Speaker 5: I think if the national government were to have a say,
Speaker 5: a lot of the funding ought to be go into
Speaker 5: AI applications that are what I call heart tech things
Speaker 5: that you can actually touch, things that actually do certain
Speaker 5: things in the real world, whether it's in a car factory,
Speaker 5: in a manufacturing shop, or even actual robots that will
Speaker 5: be in your home. And I think that is the
Speaker 5: direction that a lot of Chinese companies that are not
Speaker 5: kind of in the model building world are going into.
Speaker 5: And I think there's actually one very important, kind of
Speaker 5: larger existential crisis that China's dealing with and a lot
Speaker 5: of people don't talk about in the context of AI,
Speaker 5: which is that by the end of this century, the
Speaker 5: UN is projecting that the entire population of China will
Speaker 5: go down to around eight hundred million to about one
Speaker 5: point four billion right now, so roughly have and half
Speaker 5: of that eight hundred million people will be people who
Speaker 5: are sixty years or older. So demography is destiny in
Speaker 5: a lot of ways. And if you're a Chinese policymaker
Speaker 5: steering into this abyss that we're just going to basically
Speaker 5: rent out of people, then you know, having a lot
Speaker 5: of AI robotics is almost a national imperative for China
Speaker 5: to solve its own demographic problem with or without, you know,
Speaker 5: everything else going on around the world.
Speaker 3: Okay, So one of the benefits of having a command
Speaker 3: economy essentially is that Chinese policy makers can come out
Speaker 3: and say, we want everyone to direct their money away
Speaker 3: from I don't know, online internet e commerce platforms and
Speaker 3: into hard technology like semiconductors or developing some new chatbot
Speaker 3: or something like that. But the downside is that you
Speaker 3: can get excess capital to put it politely crowded into
Speaker 3: these things. Is that a concern when it comes to AI,
Speaker 3: or is it that the technology is so strategically important
Speaker 3: and so competitive that it's not really a concern at
Speaker 3: the moment, you know.
Speaker 4: So I think it's an interesting contrast you can look
Speaker 4: at between sort of the AI model makers and the
Speaker 4: semiconductor and the broader semiconductor ecosystem. So what Kevin laid out,
Speaker 4: which is what I see as well, is basically like
Speaker 4: a largely private sector driven thing with the same kind
Speaker 4: of boom and bust or boom and like question mark
Speaker 4: that we're current living living into in the West, happening
Speaker 4: in China.
Speaker 3: Now.
Speaker 4: You know, what's happened in the Chinese semiconductor ecosystem over
Speaker 4: the past decade or so is really different. So you know,
Speaker 4: if you're talking about the US Chips Act, which didn't
Speaker 4: come online until twenty twenty two, and it's going to
Speaker 4: spend roughly seventy five billion dollars, like we've seen that
Speaker 4: level of investment doubled or even trebled over the past
Speaker 4: ten years in the Chinese government, you know, she is
Speaker 4: really semiconductor and hardware and industrial pilled. And I think
Speaker 4: he also from a long time ago saw the writing
Speaker 4: on the wall of these you know, of these export
Speaker 4: restrictions coming on as uh as semi conductors became more
Speaker 4: and more, more and more and more strategic technology.
Speaker 2: She she should ping. Sounds like he could be the
Speaker 2: perfect guest if anyone wants to disseminate that. If anyone's listening,
Speaker 2: someone who's.
Speaker 3: To talk about semi conduct if he if he's.
Speaker 2: Hardware and chip pilled, it sounds like he would be
Speaker 2: a great guest. So let's talk a little bit more.
Speaker 2: We know that the Biden administration has imposed, along with
Speaker 2: some allies, various limitations on the export of advanced chips
Speaker 2: and so forth. But why don't one of you just
Speaker 2: sort of give us, like the broad outline beyond or
Speaker 2: including the chip limitations of US policy towards you know,
Speaker 2: international AI policy towards China, and how we're actively implementing
Speaker 2: the strategic aspect or the geopolitical aspect of AI.
Speaker 4: Again. You know, I started with this idea that AI
Speaker 4: is a strategic, dual use technology that has big implications
Speaker 4: both for the future of economic growth as well as
Speaker 4: the future of military power. So in September of twenty
Speaker 4: twenty two, a few weeks before the October seventh Export controls,
Speaker 4: Jake Sulivan gave a speech where he said that it's
Speaker 4: not good enough to just be in the lead, but
Speaker 4: that the US needs to have as far a lead
Speaker 4: as possible in critical emerging strategic technologies, AI being first
Speaker 4: and foremost on that list. So we've seen a number
Speaker 4: of different efforts on you know, from both a data
Speaker 4: and algorithmic as well as a compute perspective, to make
Speaker 4: sure that America can continue to run faster than China.
Speaker 4: So from export controls, you have limitations on the types
Speaker 4: of chips which you can be exported into China. We've
Speaker 4: had EUV restrictions so that China can't make the most
Speaker 4: advanced chips domestically, the types of chips that the US
Speaker 4: is no longer allowing Chinese firms to buy in country,
Speaker 4: We're starting to see more and more restrict you know,
Speaker 4: there's movement the water around restrictions of letting Chinese firms
Speaker 4: access those types of chips abroad and with foreign cloud providers.
Speaker 4: And then there's also starting to be some interesting movement
Speaker 4: around restrictions when it comes to both data and algorithms.
Speaker 4: On the data side, you're starting to see new data
Speaker 4: walls be lifted with regards to electric vehicles in particular,
Speaker 4: we'll maybe see more coming forward, and then from an
Speaker 4: algorithmic perspective, you know, two weeks ago open aiy said
Speaker 4: that they were no longer comfortable using a giving API
Speaker 4: access to developers based in the PRC, and that may
Speaker 4: be a harbinger of something larger. Congress recently gave this
Speaker 4: the power to explicitly stop algorithmic exports. So the wall
Speaker 4: slowly but surely is coming up, and the Chinese developers
Speaker 4: and semiconductor ecosystems response to this is really interesting because
Speaker 4: you're seeing very aggressive investment on the hardware side. You
Speaker 4: see stock piling of chips and even billions of dollars
Speaker 4: of the best of what Nvidia can legally export to them. Smuggling, too,
Speaker 4: is starting to be a real thing we recently saw
Speaker 4: some mainstream coverage of, so you know, there will be
Speaker 4: kind of gray areas and people trying to play corner
Speaker 4: cases to get more technology and access. Industrial espionage as
Speaker 4: well as something that starts starting to bubble up a
Speaker 4: bit more. The scandal of sorts with Leopold Ashenbrett are
Speaker 4: getting fired, only for two weeks later Open ai to
Speaker 4: bring on General Nakasoni, the former head of the NSA,
Speaker 4: to shore up their internal controls, I think is an
Speaker 4: interesting leading indicator of what's to come on that front.
Speaker 3: One of the things that you sometimes hear about restrictions on,
Speaker 3: for instance, semiconductor exports to China is the idea that
Speaker 3: by cutting off the country from all this external technology,
Speaker 3: you're only going to accelerate its own technological progress and
Speaker 3: its own development of leading edge chips and things like that.
Speaker 3: How much credence do you lend to those types of arguments.
Speaker 5: I think it's a moving target right now. I think
Speaker 5: logically you would expect China to put all of this
Speaker 5: resources into building the best GPU possible, right if we
Speaker 5: all think AI is going to be the next a
Speaker 5: big paradigm shift for national competitiveness, not just market competitiveness
Speaker 5: yet right now, because of I will say, the effectiveness
Speaker 5: of US expert control, two rounds of export control, and
Speaker 5: also working with allies around the world to solidify expert control,
Speaker 5: it's actually been a very difficult time for China to
Speaker 5: catch up from the hardware side, both in terms of
Speaker 5: designing the best GPU possible, but also to manufacture the
Speaker 5: chips in bulk using its own chip boundry SMIC because
Speaker 5: you know, China's been cut off from TSMC to be
Speaker 5: able to meet demand that China itself, its own companies
Speaker 5: need to build its own product, let alone expanding to
Speaker 5: you know, other parts around the world. And that's why
Speaker 5: you see all these behaviors still of either Chinese developers
Speaker 5: using VPNs and other ways to access open ais APIs,
Speaker 5: or you have these sort of smuggling of prevented or
Speaker 5: prohibited in media chips into China to be able to
Speaker 5: keep the building of the infrastructure going. And I think
Speaker 5: that goes back to what you were talking about Tracy earlier,
Speaker 5: is that there is a limitation to how much the
Speaker 5: national government wants certain things to happen and whether that
Speaker 5: could actually happen or not when it comes to especially
Speaker 5: building hard tech in China. When it comes to AI
Speaker 5: infrastructure in particular, we see a lot of pretty poor
Speaker 5: examples of government money being wasted in chip manufacturing already
Speaker 5: in a few years before AI was even really a thing,
Speaker 5: and now it's in a very tough spot. I think
Speaker 5: for a lot of people in China to be able
Speaker 5: to overcome the current hurdle, and I think this gap,
Speaker 5: this specifically hardware GPU imposed gap, will only widen when
Speaker 5: Nvidia mass produced. It is a black Well chip, which
Speaker 5: is the next generation of GPU that's going to you know,
Speaker 5: be produced in bulk starting next year, which has a
Speaker 5: huge performance kind of advantage, you know, roughly thirty two
Speaker 5: times more than the A chips, which is what opening
Speaker 5: I used to train GPT for to be able to
Speaker 5: you know, close that gap. So I think unfortunately for
Speaker 5: the Chinese technology builders, the gap will only widen.
Speaker 3: Jordan, I think you've talked about this before as well,
Speaker 3: the idea that if China is restricted in the amount
Speaker 3: of compute that it has access to, maybe there's a
Speaker 3: scenario where its own aid has just become really really
Speaker 3: efficient in one way or another.
Speaker 4: Yeah. I mean this is you know, when you're under
Speaker 4: constraints and there's money to be made, and you know,
Speaker 4: engineers will find a way, right, So I on the
Speaker 4: compute side, right. You know, it's interesting thinking about Blackwell.
Speaker 4: Only about ten percent of the gains that it is
Speaker 4: making from the past generation came from a node size.
Speaker 4: The vast majority of the gains that they've been able
Speaker 4: to squeeze into the Blackwell system are from stuff like
Speaker 4: like optics and interconnects and that stuff. High bandwidth memory.
Speaker 4: That's type of stuff is a lot more difficult to
Speaker 4: export control than just one machine coming out of the Netherlands.
Speaker 4: So you know, it's difficult. There's tens of billions of
Speaker 4: dollars of R and D that's been on this stuff.
Speaker 4: But it's sort of an open question to me how
Speaker 4: difficult re engineering all the innovations that went into Blackwell
Speaker 4: is going to be versus re engineering an e UV machine,
Speaker 4: which like seems to be next to impossible. This is
Speaker 4: an important point just to jump in here. Like when
Speaker 4: people when in pop culture, when people talk about chips,
Speaker 4: it's almost always like node size and smaller and smaller.
Speaker 3: But this idea of like the vibrant pop culture of
Speaker 3: semi people. But this is I'm not sure we're quite there.
Speaker 2: This is how like ignorant people like myself if I
Speaker 2: think like, oh, well, you know, the cutting edge chip,
Speaker 2: it's just in my mind smaller, nodest. But this is
Speaker 2: like a really important point that you hit on that
Speaker 2: when actually when we're talking about say in video GPUs,
Speaker 2: there are other dimensions that are actually much more important
Speaker 2: than node size.
Speaker 5: That's right. And you know, if you look at all
Speaker 5: the new products in the video is releasing to the
Speaker 5: market is not a chip. Of course, you can buy
Speaker 5: a chip, but you know it's really eight chips together
Speaker 5: connected with high bandwidth memory and the interconnects and all
Speaker 5: the other elements that goes into a system. And then
Speaker 5: next generation the packaging will be thirty six chip and
Speaker 5: then seventy two chip. And of course there's a good
Speaker 5: reason for that because all these models, if you believe
Speaker 5: that larger models become smarter models, then they need massive
Speaker 5: amounts of all processing to be able to train all
Speaker 5: these model officially and to deploy them officially. So that's
Speaker 5: why you tie up all these high performing GPUs together,
Speaker 5: and I think that's what makes this hardware gap even
Speaker 5: more difficult from a system level, for even a cloud
Speaker 5: level to really overcome. I think, you know, before, we've
Speaker 5: been talking a lot about the chip war between different countries.
Speaker 5: I think we're vastly quickly evolving into a cloud war
Speaker 5: between different countries competing on the AI and technology in general,
Speaker 5: because it's all about the cloud and the system and
Speaker 5: how officially can you acquire and then utilize them. That
Speaker 5: makes a big difference.
Speaker 3: Tell me if I'm wrong here, But my sense also
Speaker 3: with like semiconductor rivalry, maybe to a lesser extent, but
Speaker 3: certainly with the AI rivalry, is that it's mostly a
Speaker 3: competition between the US and China. Like, as far as
Speaker 3: I can tell, Europe isn't necessarily obsessing about developing national
Speaker 3: chat GPT versions as much as you know the US
Speaker 3: and China are. Why Why is that so?
Speaker 4: You know, I think from a from the semiconductor perspective, right,
Speaker 4: like it's really China and the rest of the world.
Speaker 4: There is a very integrated global ecosystem in which you
Speaker 4: have European players, East Asian players, South Asian players, the
Speaker 4: US that are all kind of working together to push,
Speaker 4: you know, to push node sides and improve the sort
Speaker 4: and develop the sorts of technologies which are going into
Speaker 4: something like Blackwell and China, you know, has kind of
Speaker 4: been cut off or is at some levels being cut
Speaker 4: off from that broader ecosystem and having to start to
Speaker 4: domesticate a lot of the technologies that they were able
Speaker 4: to formally rely on from other countries. Now, Kevin, I
Speaker 4: don't know if you want to talk to the like
Speaker 4: every country with its own model and own cloud thing,
Speaker 4: because I think that's a really interesting I'm curious about that.
Speaker 5: Yeah, I think you know not to beat on Europe
Speaker 5: too much, but I do think given just the regulatory
Speaker 5: I was ay bias right to wars releasing new technology
Speaker 5: really quickly. In general, it just isn't a very hospitable
Speaker 5: environment to be able to build a product legit CHATGPT,
Speaker 5: which was really released I believe on a whim or
Speaker 5: on a quick competitive note. It wasn't something that's super
Speaker 5: thought through. But because of the environment that we have
Speaker 5: in America, we allow that sort of things to happen
Speaker 5: and then we think about regulation later. Coming back to
Speaker 5: the national angle, I think there is a strong sort
Speaker 5: of motivation, coming back to the cultural point that every
Speaker 5: country does want its own national champion to some extent.
Speaker 5: I think French France has Misstraw, which it kind of
Speaker 5: puts up as its own national champion. It's a very
Speaker 5: strong open source AI model maker that has a lot
Speaker 5: of strong relationship with Microsoft as well. I think there's
Speaker 5: another company out of Germany that also is a model maker.
Speaker 5: So there are a few examples out there. Even Japan
Speaker 5: has a I believe sakana Ai, which is its own
Speaker 5: possible startup national champion to model, Japan's you know, culture
Speaker 5: or truth or knowledge or whatnot into a national model,
Speaker 5: and India has a few others as well. And the
Speaker 5: Middle East is another very important region that is playing
Speaker 5: an increasingly large role, I think, as the government actually
Speaker 5: pushed out some really good open source model about a
Speaker 5: year ago called Falcon that series which has been able
Speaker 5: to put the Middle East on the map when it
Speaker 5: comes to national development of AM models. So there is
Speaker 5: a lot going on, but certainly not nearly as much
Speaker 5: as the US China competition. Besides that, actually.
Speaker 3: You just reminded me this is something I've always wondered
Speaker 3: as well, but what is the state of open source
Speaker 3: in China? Like, is it as much of a thing
Speaker 3: as it is in the States or elsewhere? What does
Speaker 3: that actually look like in terms of sharing code and
Speaker 3: things like that.
Speaker 5: So it's very much a thing in fact. So I
Speaker 5: used to work at a GitHub, which you know, is
Speaker 5: one of the largest a code repository of open source
Speaker 5: code and to drive in a natural expansion, so I
Speaker 5: got to work with a lot of Chinese open source
Speaker 5: developers and it's actually the very few, i would say
Speaker 5: vibrant communities that really embrace a lot of openness transparency.
Speaker 5: They have this really nerdy holiday on October twenty fourth,
Speaker 5: which if you you know binary is you know to
Speaker 5: to Day, It's ten exactly, But basically it's a called
Speaker 5: Programmer Day and it's a completely grassroots holiday in China
Speaker 5: that celebrates open source development. They have, you know, a
Speaker 5: huge party, a huge convention. They talk about open source sharing.
Speaker 5: They all use you know, GitHub or hugging Face or
Speaker 5: all your your usual open source collaboration platform. So the
Speaker 5: open source kind of movement, if you will, has really
Speaker 5: deep roots in China and that has been around for
Speaker 5: a couple of decades already before we have this kind
Speaker 5: of open source closed source model dichotomy that's happening in AI.
Speaker 5: In fact, a few of the nonprofits that are kind
Speaker 5: of government affiliated in China has released open source models
Speaker 5: as well as a way to showcase what they have
Speaker 5: done as a way to kind of both compete but
Speaker 5: also to collaborate with academics and researchers elsewhere.
Speaker 4: Yeah, you know, just coming a little more on the
Speaker 4: government open source connection. You know, it's it's easier to
Speaker 4: be like open source is a good strategy, particularly if
Speaker 4: you're behind. We've seen a lot of investment on the
Speaker 4: hardware side with Risk five as well as on as
Speaker 4: well as as Kevin mentioned, on the software side. And
Speaker 4: you know, I think it's it's still an open question,
Speaker 4: but a lot of there's a lot of chatter on
Speaker 4: the Chinese Internet about just to what extent the Chinese
Speaker 4: model makers are dependent on Meta continuing to drop open
Speaker 4: source models in the Lama series. And you know, we
Speaker 4: may if and when Zuckerberg decides that he's done playing
Speaker 4: the open source game, we may see a very different
Speaker 4: trajectory for the Chinese model makers if they're not able
Speaker 4: to kind of you know, get their algorithmic and hardware
Speaker 4: shops in order before then.
Speaker 2: So we just have a few minutes left. But you know,
Speaker 2: we talked about the current state of US restrictions towards
Speaker 2: China or how the US thinks about AI and the
Speaker 2: geopolitical context. There are chip export controls, there's more being done,
Speaker 2: you know, when it comes to cracking down an industrial espionage.
Speaker 2: There have been a lot of stories, including several broken
Speaker 2: by Bloomberg about you know, academic collaboration and you know,
Speaker 2: trying limiting who can talk to whom. And we know
Speaker 2: that there's a lot of issues in academia related to
Speaker 2: computer science and tech and Chinese researchers in the US,
Speaker 2: et cetera. We have this framework, but it might all
Speaker 2: change because the president, the party of the presidency might
Speaker 2: change in a few months. What do we know, if anything,
Speaker 2: about the Trump view towards AI and AI in the
Speaker 2: geopolitical context.
Speaker 5: So I think the interesting about Trump is that during
Speaker 5: his presidency, his first term, he actually did have worked
Speaker 5: on national AI initiative of something which I believe is
Speaker 5: also by Eric Schmid, the former CEO of Google, and
Speaker 5: he signed on to some kind of a national initiative
Speaker 5: plan in twenty twenty, but it was mid of COVID,
Speaker 5: none of us had any time to pay attention that.
Speaker 5: It's like didn't go anywhere. But he did have some
Speaker 5: kind of history with AI. But based on the most
Speaker 5: recent kind of the Republican National Conventions Platform just being released,
Speaker 5: one of the lining items is that they will overturn
Speaker 5: Biden's AI executive order, so Trump can put his own spin,
Speaker 5: or his own branding, or his own substance on AI
Speaker 5: when he if he does become a president again. So
Speaker 5: we can expect a lot of kind of volatility on
Speaker 5: that front. And another thing that I would say that
Speaker 5: I think is the most consistent thing about Trump is
Speaker 5: his unilateralism. Right He's always been about America first, American only,
Speaker 5: and right now there is a pretty good rhythm of
Speaker 5: global AI governance that's happening, starting with in the UK
Speaker 5: a the Bletchley Park Declaration, to a summit in South
Speaker 5: Korea earlier this year, to then another summit in Paris
Speaker 5: in February of next year where you have world leaders
Speaker 5: including China and all the other relevant departments and ministries
Speaker 5: around the world coming together to talk about AI governance
Speaker 5: in a global sense. If I were to put my
Speaker 5: money in the betting market about Trump, I think he
Speaker 5: will probably blow a lot of that out of the
Speaker 5: water to go to his unilateral instinct and really put
Speaker 5: America stamp on this, and it's hard to predict what
Speaker 5: that will look like.
Speaker 4: Trump went on the All In podcast, maybe the podcast
Speaker 4: that must not be named one.
Speaker 2: No, it's fun, but.
Speaker 5: It was.
Speaker 4: It was really remarkable because they asked him about China
Speaker 4: and AI and he had, like, you know, a moderately
Speaker 4: sophisticated answer, sort of shocking. He you know, identified that
Speaker 4: it's a race between the US and China. And he
Speaker 4: also said that energy is really important to data centers,
Speaker 4: and we got to make that happen. You know, if
Speaker 4: you edited down his comments from five minutes to like
Speaker 4: thirty seconds, it was pretty coherent. But it was a
Speaker 4: remarkable thing that, like he was talking to someone about
Speaker 4: this and they got like two halfway decent talking points
Speaker 4: in his head. So, you know, how he's going to
Speaker 4: approach technology in China, as I think, is a really
Speaker 4: open question. On the one hand, we had this very
Speaker 4: like well thought out, sophisticated playbook that he ran to
Speaker 4: basically get the world to turn on Huawei. On the
Speaker 4: other hand, you know, we had a lot of weird,
Speaker 4: trumpy stuff, like with Zte, the Commerce Department's basically going
Speaker 4: to kill it. She called him, said that it was
Speaker 4: going to cost too many Chinese jobs, and then he
Speaker 4: went on and tweeted the next day saying too many
Speaker 4: jobs lost in China, like we're going to give Zte
Speaker 4: a reprieve. So he's really of two minds on all
Speaker 4: this stuff. Maybe someone talks him into being scared about inflation,
Speaker 4: who knows, But I think there's a very wide range
Speaker 4: of outcomes both on the sort of his domestic approach
Speaker 4: to artificial intelligence as well as he's as well as
Speaker 4: how he's going to think about technology competition in China.
Speaker 2: You said something there that actually I wanted to ask,
Speaker 2: and this is just about the sort of technological arms
Speaker 2: rate again, but obviously within the within the US and
Speaker 2: the data centers, there's a lot of anxiety about access
Speaker 2: to electricity and how are we going to supply that?
Speaker 2: And we all know the US is not in a
Speaker 2: good state these days, are like building things and adding
Speaker 2: a lot of energy capacity. Is that a when you
Speaker 2: think about US versus China in that respect? Could that
Speaker 2: be a comparative advantage for the Chinese companies? I mean,
Speaker 2: I know China continues to add more nuclear generation and
Speaker 2: they're probably more liberal in terms of adding more coal
Speaker 2: power and other things like that. Could they end up
Speaker 2: within an advantage based on their electricity grid on solving
Speaker 2: the power side of AI.
Speaker 4: You know, this might be a bit of a red herring.
Speaker 4: I am like cautiously optimistic that Republicans and Democrats will
Speaker 4: end up kind of giving some you know, NIPA exceptions
Speaker 4: for these data centers, which, like everyone is now agreeing
Speaker 4: is this like incredibly important national strategic resource. I mean,
Speaker 4: I think the bookcase with China, right is if they're
Speaker 4: stuck with less efficient ships and just how to build
Speaker 4: data centers twice the size that are twice as expensive,
Speaker 4: then like, yeah, maybe they can like throw on a
Speaker 4: few more coal plants and nuclear nuclear reactors to get
Speaker 4: them to get them going. But I am cautiously optimistic
Speaker 4: that in video will continue to make increasingly power efficient
Speaker 4: chips and you know, state, local and national regulators will
Speaker 4: understand that this is something worth bending the rules on,
Speaker 4: which is sort of what you've seen on the sort
Speaker 4: of semiconductor fabrication build out over the past few years
Speaker 4: with the Chips Act.
Speaker 5: And if I can add one more points to what
Speaker 5: you just said, Jordan, I think a lot of the
Speaker 5: focus that we've talked about has been the algorithm, the
Speaker 5: model how power for the GPU is. But a lot
Speaker 5: of engineering sources has been devoted and will continue to
Speaker 5: be to reduce the power hungriness of AI model because
Speaker 5: one of the big things that's a hugely hotly debated
Speaker 5: topic is that all these tokens that we're generating for
Speaker 5: all these apps are just way too expensive, right, the
Speaker 5: expense comes from the compute and then the power that's
Speaker 5: being consumed to it. So there will be a lot
Speaker 5: of engineering that is going to accomplish when it comes
Speaker 5: to reducing the costs and the need for energy over time.
Speaker 5: So I don't think the energy trade is a long
Speaker 5: term investment thesis, but maybe a short term arbitrage as
Speaker 5: far as how much extra power we needed just to
Speaker 5: feed the hungry AI Denta center when all the engineering
Speaker 5: is go into reducing that dependence, because everyone sees how
Speaker 5: that could be detrimental in a lot of ways.
Speaker 2: Jordan and Kevin, thank you so much for coming on
Speaker 2: a latch. That was a fantastic our goal for the
Speaker 2: episode of Understanding this more I feel I do. I
Speaker 2: do so thank you both so much for coming on that.
Speaker 2: Happy Tracy, I thought that was great. I thought, I mean,
Speaker 2: there were just a bunch of things that like, I,
Speaker 2: you know, even setting aside the geopolitical aspects that I
Speaker 2: just didn't know about what the AI you know, space
Speaker 2: looks like in China, and if that was very helpful, right, I'm.
Speaker 3: Sort of obsessed with the programmer's day now. I want
Speaker 3: to learn more about the holiday. Do you think all
Speaker 3: the open source programmers they like all get together to
Speaker 3: celebrate and do a dance, maybe a byte dance.
Speaker 2: Yeah, oh boom, come on, okay, Tracy, you get We'll
Speaker 2: leave that one in.
Speaker 3: Thank you, so the producers will add that cricket noise
Speaker 3: that they have on hand.
Speaker 2: I am also interested in the holiday. I do think
Speaker 2: so Kevin's first answer, I just thought it was like
Speaker 2: really clarifying about why a country might think that having
Speaker 2: a national version is important right now, because if and
Speaker 2: I hadn't really thought about it in those terms, because
Speaker 2: on some level, right, like it's easy enough, like if
Speaker 2: it's you know, Lama, you can use that anywhere on
Speaker 2: you know, various chips and stuff. But the idea that
Speaker 2: like the AI is going to like codify truth as
Speaker 2: it means in the context of that country, and you know,
Speaker 2: of China their version of what's true. Kevin gave the
Speaker 2: Jackmaw example, which is that one day he may be
Speaker 2: a hero and one day he may have a villain
Speaker 2: or maybe a villain, like that's always changing, and so
Speaker 2: there may be some impulse to have the model sort
Speaker 2: of correctly reflect what is state policy. But this idea
Speaker 2: that like it captures you know, creates this version of
Speaker 2: the national identity, culture and language is very interesting.
Speaker 3: No, absolutely, and I think China is sort of the
Speaker 3: extreme example of that. And that's sort of what I
Speaker 3: was alluding to, not very well in the intro when
Speaker 3: I was talking about data control, but like, there is
Speaker 3: this cultural element to AI where you are using something
Speaker 3: like chat GPT, or maybe not at the moment, but
Speaker 3: you could imagine a future where you're using something like
Speaker 3: chat GPT in the same way that you're using something
Speaker 3: like Wikipedia, where it's almost like the codified public version
Speaker 3: of events, right, And so if you are a government
Speaker 3: like China, why wouldn't you want to have some degree
Speaker 3: of control over what that's spitting out and the narrative
Speaker 3: that it's building.
Speaker 2: I was also really intrigued that, you know, we talk
Speaker 2: about chip wars all the time, but the idea that
Speaker 2: there is a distinction that is useful to make between
Speaker 2: a chip war and a cloud war, and that okay,
Speaker 2: there is you know, we all know that, like it
Speaker 2: was supposedly you know, smaller nodes or whatever are better,
Speaker 2: although I think in the context of GPUs that's less important,
Speaker 2: but that so much of the technology is about the interconnect,
Speaker 2: and we talked about this with Corewave, that there is
Speaker 2: a distinct element to building up a very powerful GPU
Speaker 2: cloud versus a traditional cloud because because of the cables
Speaker 2: and the packaging, et cetera, and so the idea that
Speaker 2: it's more about more than just like the chip itself
Speaker 2: and you know, the size of that chip so to speak.
Speaker 3: No, absolutely, And one thing that really stood out from
Speaker 3: that conversation, and I'm thinking back, this actually happened when
Speaker 3: I was in Hong Kong and I was, you know,
Speaker 3: coordinating some of the news coverage there. But I remember
Speaker 3: the big crackdown on the sort of like retail internet
Speaker 3: companies like Ali Baba, and I remember at the time
Speaker 3: policymakers were very very specific and very deliberate and very
Speaker 3: open about trying to direct more capital, you know, less
Speaker 3: disorderly capital flowing into SaaS, more capital going into hardware
Speaker 3: and making things like chips. It was almost, you know,
Speaker 3: for lack of a better tech analogy, it was like
Speaker 3: flipping a switch, right. They basically said, like, no more
Speaker 3: money going into retail tech, everything going into semiconductor manufacturing
Speaker 3: and all that stuff. So it was it was interesting
Speaker 3: to hear like both the challenges and the opportunities from
Speaker 3: that perspective in China.
Speaker 2: By the way, I don't know if it was The
Speaker 2: Financial Times, but that was the first whoever came up
Speaker 2: with chech shept. That was very clever. That was very
Speaker 2: well done. I first saw that in that that word
Speaker 2: that in the ft. But that's a pretty clever term.
Speaker 2: That was a good one.
Speaker 3: I'm disappointed to find out it's actually vaporware, although no,
Speaker 3: I know, perhaps not surprised. Okay, shall we leave it there.
Speaker 2: Let's leave it there.
Speaker 3: This has been another episode of the auth Lots podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at The Stalwart.
Speaker 2: Follow our guests Jordan Schneider He's at Jordan sc h
Speaker 2: NYC and check out his China Talk newsletter and podcast.
Speaker 2: And Kevin Schoue He's at Kevin Sshoue and check out
Speaker 2: his writing at Interconnected and follow our producers Carmen Rodriguez
Speaker 2: at Carmen armand dash Ol Bennett at Dashbot and kel
Speaker 2: Brooks at kel Brooks. Thank you to our producer Moses
Speaker 2: Ondam and from our Oddlots content go to Bloomberg dot
Speaker 2: com slash od lots where you have transcripts, a blog,
Speaker 2: and a newsletter and you can chat about all of
Speaker 2: these topics twenty four to seven in our discord discord
Speaker 2: dot gg slash odd lots, we have an AI room
Speaker 2: in there. Maybe when this comes out, we'll see if
Speaker 2: either Kevin or Jordan or both would be up for
Speaker 2: doing an AMA in there. The day it comes out.
Speaker 2: I'm sure people have a lot of questions, so we'll
Speaker 2: try to make that. Try to make that happen.
Speaker 3: And if you enjoy authlots, if if you want us to,
Speaker 3: I don't know, start a pop culture semiconductor spin off show,
Speaker 3: then please leave us a positive review on your favorite
Speaker 3: podcast platform. And remember, if you are a Bloomberg subscriber,
Speaker 3: you can listen to all of our episodes absolutely ad free.
Speaker 3: All you need to do is connect your Bloomberg account
Speaker 3: with Apple Podcasts. In order to do that, just find
Speaker 3: the Bloomberg channel on Apple Podcasts and follow the instructions there.
Speaker 3: Thanks for listening.