Grace Shao on What the World Should Know About Chinese AI

China's AI industry has changed a lot since DeepSeek released its cheap frontier model last year, and briefly sent US tech stocks falling. After being locked out of the most advanced chips, Chinese companies are now allowed to buy some Nvidia H200s. In fact, many of the big Chinese tech companies — like Baidu — are making a push to become full-stack players, with their own chips, models, and cloud infrastructure. Today's guest is Grace Shao, an independent AI researcher and the author of the AI Proem Substack. She's a bit of an insider when it comes to China's AI industry, and when we were in Hong Kong we spoke with her about the latest in open-source models, the competition among Chinese frontier labs, DeepSeek's place in an increasingly crowded Chinese AI market, China's manufacturing edge, where bottlenecks exist right now (spoiler: it isn't data centers), if Chinese grandmas are actually using OpenClaw, and finally, of course, AI psychosis. Read More: China AI Lab’s 170% Stock Surge Cements Winner-Loser Pair Trade https://www.bloomberg.com/news/articles/2026-06-18/china-s-ai-race-spurs-emergence-of-winner-laggard-pair-trade?utm_medium=referral&utm_source=podcast&utm_campaign=odd_lots&utm_content=article China Plans Mechanism to Evaluate AI Impacts on Job Market https://www.bloomberg.com/news/articles/2026-06-17/china-plans-mechanism-to-evaluate-ai-impacts-on-job-market?utm_medium=referral&utm_source=podcast&utm_campaign=odd_lots&utm_content=article Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots https://www.bloomberg.com/subscriptions/oddlots?in_source=oddlotspodcast Subscribe to the Odd Lots Newsletter http://bloomberg.com/subscriptions/oddlots Join the conversation: discord.gg/oddlots https://discord.gg/oddlots See omnystudio.com/listener https://omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 0:26Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 1:08Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.

• 1:52Speaker 01

  Bloomberg Audio Studios. Podcasts, radio, news.

• 2:08Joe Weisenthal?

  Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

• 2:12Tracy Alloway?

  And I'm Tracy Alloway.

• 2:14Joe Weisenthal?

  Tracy, I love being in Hong Kong. I love it here so much. I love it here so much. I would like come here a few times a year if we could.

• 2:20Tracy Alloway?

  I'm sure you would. I've lived here for like, I guess, almost four years. So it's kind of weird coming back, but Hong Kong has a lot of pluses, like great food, great weather for most of the year, beaches. I once heard someone describe it as Manhattan meets Maui, which I think is like, pretty accurate.

• 2:41Joe Weisenthal?

  Oh, it's so nice. The weather is actually not great.

• 2:44Tracy Alloway?

  It's not great right now.

• 2:45Joe Weisenthal?

  Incidentally, this week, we've come during, I guess it's a monsoon season, right?

• 2:49Tracy Alloway?

  Yeah, it's the rainy season. But oh, well, I like thunderstorms, so I'm enjoying it.

• 2:53Joe Weisenthal?

  Yeah, I'm enjoying it too. Anyway, one thing that has changed since the last time you were in Hong Kong, you left in 2022.

• 3:01Tracy Alloway?

  Yeah.

• 3:01Joe Weisenthal?

  We weren't doing as many AI episodes in those days, to say the least.

• 3:05Tracy Alloway?

  In fact, so I remember one of the big stories when I was here in Hong Kong in 2020 was China's tech crackdown, right?

• 3:13Joe Weisenthal?

  That's right, that's right.

• 3:14Tracy Alloway?

  Right. And like, there was all this concern about whether or not the crackdown was going to destroy China's entrepreneurial spirit. I'm doing air quotes on a podcast. I don't know why. But fast forward six years, and there's entrepreneurism basically everywhere. And we talk a lot about how China is producing all these new AI models.

• 3:37Joe Weisenthal?

  Okay, can I like, say, like, I know very little, I mean, I know very little about AI, but I know even less about Chinese AI. But here are some of my general impressions, which is, A, it seems like there are so many open source. Okay, so I know they're largely open source. It seems like every random company you see, like some toothpaste company, and they'll have produced an LLM. So I'm very curious, like, how they're making money on it. I also get the impression, like, you know, the heads of American AI labs speak in these like, sort of quasi mystical terms, et cetera. It doesn't Feel quite the same here where it feels like a bit more of like yet another technology. But I'm glad you brought up the point about the tech crackdown because at the time,

• 4:21Joe Weisenthal?

  the whole story was like, oh, there needs to be less focus on sort of digital tech and more focus on hard tech. Which has been done extremely, that's been an extraordinarily successful endeavor. And then my last impression, though, is that since the release of ChatGPT in late 2022, that was the moment it's like, no, we really have to also compete on sort of this next era of software and sort of consumer facing tech tech breakthroughs.

• 4:48Tracy Alloway?

  Yeah, overall, the AI scene in China feels much more utilitarian to me. It's more about like the big companies, the Tencent, the Alibaba's sort of using AI for their existing business models rather than this existential thing, which it is in the US where like AI is the business. That's just it.

• 5:09Joe Weisenthal?

  Right. Yeah, that's exactly, AI is sort of weird, like it sort of sits in the middle of what you would call like software and hard tech because we could, we consume it through the browser, right? Sort of the same way, or in many cases through the browser, the same way that we would go to an Amazon or an online gaming or something like that. But it's clearly, you know, it's a scientific endeavor. And so it's sort of is this blend and then you have to figure China is so far ahead of the US when it comes to things like robotics and EVs and batteries. And one thing I don't know anything about is the degree to which that melding of hardware capabilities with AI capabilities, how that influences the direction of the development of the AI tech.

• 5:49Tracy Alloway?

  Yeah, I'm also very interested in like the capital stack for Chinese companies because over in the US, we all know that people are flinging money at anything with the word AI in it. But in China, it's very different. I get the impression that it's like much harder to raise enormous sums of capital. And so I'm very curious how that limited capital actually influences the development of these models and the tech.

• 6:14Joe Weisenthal?

  I think it's safe to say that both of us have a lot of impressions.

• 6:18Tracy Alloway?

  Yes.

• 6:18Joe Weisenthal?

  Right?

• 6:19Tracy Alloway?

  Big impressions.

• 6:20Joe Weisenthal?

  How many times in this intro is like, I could. The impression, but I actually have no idea. So that is a good reason to actually bring in our guest. Someone who is more than quote impressions unquote about the AI tech scene. We're going to be speaking to someone whose newsletter I'm a big fan of and everyone should read. Are going to be speaking to the perfect guest Grace Xiao. She's an independent AI researcher and she has a great Substack called AI Prom. And she joins us here in our Hong Kong office. So Grace, thank you so much for coming on Odd Lots.

• 6:49Speaker 03

  Thank you so much for having me, Joe and Tracy.

• 6:51Joe Weisenthal?

  How did we do on our quote impressions unquote?

• 6:54Tracy Alloway?

  Those are pretty accurate impressions, I think.

• 6:56Joe Weisenthal?

  Okay, good.

• 6:57Tracy Alloway?

  That was the episode. Like, let's start at a basic level. So the big impression, the one that everyone knows is Chinese models are open source versus the closed frontier models of the US. Why did it develop that way?

• 7:14Speaker 03

  Yeah, I think people like to think of these mystical reasons. But really, it was a very pragmatic business reason to start with. To start with, a lot of the labs have cited that, you know, for Western companies or Western developers to trust them, they needed to open source their models to build that trust and credibility. So in many ways, it's a branding decision. Then on top of that, I think you can see it as a philosophical drive. You know, the founder of DeepSeek, Liang Wenfeng, has openly said he wants to open source his most frontier research to really help propel the whole industry as a whole. And that kind of R and D sharing has now formed a layer for the whole ecosystem where each of the labs kind of integrate each other's kind of breakthroughs.

• 7:57Speaker 03

  You know, you see them congratulating each other even on X when they have new models announced. So you can say it's a bit more collegial. I wouldn't say they're not competing, though. However, because of the compute constraint they're faced with, talent constraint, and the capital constraint you even mentioned, they are a lot more conscious with where they want to put their money, where they want to put their time in R and D. And all of that forms the basis of a strong open source ecosystem.

• 8:21Joe Weisenthal?

  Is the culture as pro-sharing and pro-open source as it was even two years ago? Now, the DeepSeek moment was right around Trump's inauguration in early 2025, so about a year and a half ago. Since then, has the culture stayed the same? Or has that sort of competition bug, that intense competition bug that we know among American AI labs, has it spread to the Chinese labs at all?

• 8:52Speaker 03

  I think the sharing is an unintentional result rather than, you know, an intentional effort in the beginning to even start with. They are for sure extremely competitive. And, you know, we all know the word "evolution." So like China AI is "juanning" as well. That means like there's evolution in this ecosystem as well. However, I think bringing up DeepSeek. DeepSeek plays a very interesting role in the whole ecosystem. Like you mentioned, V3 propelled the whole industry forward. Everyone kind of started taking China AI more seriously. You know, it brought a lot of interest from investors globally back into the internet companies that Tracy mentioned. You know, prior to that, there was a bit of a slump for three to five years. However, you know, Jipu, CAI, is now publicly listed in Hong Kong.

• 9:36Speaker 03

  Minimax is publicly listed in Hong Kong. Moonshot is, you know, in preparation to go public next year. They are competing with each other to capture market share, to capture developer mindshare. But DeepSeek plays an interesting role. I want to bring it back to DeepSeek V4. So V4, you know, on the surface, you know, people said, okay, it wasn't as maybe impressive on evals and performance. It didn't catch up with the most frontier labs in the US, whatnot, right? But it was a very interesting move because what I heard from researchers on the ground in Beijing was that the lab actually delayed their release for about three to four months because they wanted to re-engineer a lot of the inference onto Huawei.

• 10:18Speaker 03

  So I'm not saying this completely replaces Nvidia or CUDA. Not at all, because if you ask any developers, they still want to use CUDA if they can. However, it was the first effort to really... I don't kind of like did one for the team like they they kind of like

• 10:35Tracy Alloway?

  Yeah, was that supposed to be like a signal basically?

• 10:38Speaker 03

  Yeah.

• 10:38Tracy Alloway?

  We're doing this all on like a Chinese stack.

• 10:41Speaker 03

  Yeah, they were like, look, guys, like, you can actually do this. And they became a shared foundation layer for China's model ecosystem. So because again, everything is open source and open weight. Other labs were able to study what they did to actually start inferencing on Huawei stack. And I think that was the first step, whether it's signaling or actually, you know, very pragmatic reason to start shifting some reliance on, you know, the China AI stack.

• 11:08Tracy Alloway?

  Aside from DeepSeek, can you kind of describe the differences or what China is trying to do on the actual frontier side? Because there are, there are some.

• 11:18Speaker 03

  I, I think if you really have, have to look at the ecosystem, we can kind of put aside the big tech for now. But looking at the, maybe the foremost relevant startup labs, DeepSeek, Moonshot, who has Kimi, ZAI who has GLM, and then Minimax. They are still probably the most committed to frontier research. However, because the constraint we mentioned that they face, whether it's compute, whether it's capital, or even frankly, talent, they have decided out of necessity to basically each focus on a different vertical in capturing a different kind of business share. So ZAI is very focused on coding capabilities. So if anything, their GLM plan is much more similar to maybe what you think of Claude, Claude Co., or Claude Code, et cetera.

• 12:03Speaker 03

  A Codex, like that kind of product. And then you look at Minimax, they're really focused on the multimodality capabilities. Moonshot, they're really focused on agents. And DeepSeek, again, really is just focused on pushing the frontier and kind of trying to play catch up and push the Chinese ecosystem as fast as possible.

• 12:21Joe Weisenthal?

  It's really crazy to look at some of the ones that have already gone public here. And just to put in, so Minimax is public, and in US dollar terms, it's a $20 billion company. I mean, there are people in the US who have done nothing but publish a paper on archive.org, who do not even have a product yet, who have probably VC-backed implied valuations over $20 billion. How do they make money? You know, again, open source, okay, like in these four models that you named, do they have different thoughts on how they plan to make money or different business models?

• 12:58Speaker 03

  Yeah, so China's VC space in general has not been that vibrant frankly since internet crackdown and a lot of USD funds did exit, you know, three to five years ago with Sequoia being maybe the most like high profile, right? Like we all remember that. Now, people forget, even in 2022, a lot of these labs that we just talked about, they were struggling to even raise money, you know, raise capital. And a lot of them spun out of academic institutions. You know, you mentioned they're valued anywhere roughly between 20 to 30 billion right now, but they went public between like six to eight billion. That's like kind of tiny compared to American valuations right now. However, they are actually making money. You know, the publicly disclosed information, I think from Minimax and Jupu indicates that they

• 13:42Speaker 03

  were making just as much, like in their last month, they've made the same amount of money last month as they did last year, essentially. And their end of year AAR projection is anywhere between one to 1.2 billion right now. So they are making money. And how? Well, just because they're open source doesn't mean they don't make money. I think people forget, you know, we had open source softwares before as well. People are paying for managed services. And when you're paying for an API through ZAI or Minimax, whatnot, you basically don't have to self-host. You don't have to get your own GPU. You don't have to get, you figure out your own compute. You don't have to figure out your own guardrails, your deployment, your security, your monitoring, whatnot, right?

• 14:22Joe Weisenthal?

  So just to be clear, you can self-host all of these models, but for the most part, they do offer that inference part of the stack and that is a profit center for them?

• 14:34Speaker 03

  Yes, exactly.

• 14:35Joe Weisenthal?

  Got it.

• 14:51Joe Weisenthal?

  There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee. So search Barclays Brief wherever you get your podcasts.

• 15:17Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 15:59Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 16:40Tracy Alloway?

  All right, so there seem to be two major constraints on Chinese AI. Maybe energy is a constraint as well. We should talk about that, but there's the capital issue. So not as much capital available or people aren't flinging it at AI companies the way they are in the US. And then secondly, there are the export controls on chips, and we talked about that a little bit, but can you describe how those controls are actually, I guess, influencing the development of the models themselves and like, I guess, optimization?

• 17:13Speaker 03

  So some of these big tech are actually even buying out the contracts that data centers have with some of these labs and, you know, they are taking over the compute. So these labs essentially are now optimizing for the highest quality of.

• 17:26Speaker 03

  Inferenced demand, if that makes sense, they don't actually have enough even supply. They don't have enough compute power to even like meet the demand that's coming through. So that's on like how they're servicing clients, how they're changing, I guess, or how they're optimizing their training is that. A lot of them are really focused on the post-training. And this goes back to, so you know how OpenAI has like three buckets where they chuck money at? There's like the R and D, there's pre-training, there's post-training. R and D, a lot of times, a lot of money is spent, but say like one out of ten things stick. But you need a lot of compute and resource and people to be figuring out where to go. For a lot of these labs in China, they frankly don't have that luxury.

• 18:05Speaker 03

  So they've even given me a metaphor and said, it's kind of like knowing what the answer to the homework is and working backwards. So they will wait till the frontier labs to come out with where the right direction is for the next frontier model. And they will work backwards and actually focus all their resources on post-training.

• 18:22Tracy Alloway?

  Hmm.

• 18:23Speaker 03

  So with post-training, they will optimize a lot of times the data they collect. For example, if a data provider like Merckor provides a very, very niche set of data. Uh, set for like an OpenAI or whatnot, maybe they would charge them ten, twenty million dollars. The Chinese lab will wait out that exclusivity contract. Three to six months' time, let's say, and then pay a fraction, if not like a tenth of that price for the same data set.

• 18:48Tracy Alloway?

  Yeah.

• 18:48Speaker 03

  And that kind of plays into that like six to nine month lag that we we hear about as well.

• 18:52Tracy Alloway?

  That's really interesting. Let's talk about energy then, because the story in the U S is that. Electricity is really the big constraint on A I use. And you know, you got to find a data center that has an electricity hookup and it has to be reliant and all of that. And it seems to be in short supply. Is it a similar story in China?

• 19:12Speaker 03

  Honestly, energy is probably not the biggest bottleneck right now in China. And I think people like to say, well, some people like to say, oh, somehow the Chinese government had foresight on the A I boom. Driving like the energy consumption, but definitely not. I think people forget that China's economic growth over the last three, four decades also meant a rise of urbanization. And a lot of the cities that, you know, we are visiting these days, like at least. are visiting like Beijing, Shanghai, Shenzhen with all these robots and EVs or whatnot. These are all really urbanized within the last two, three decades. And because of that, the grid is very new. And because of that, the government already foresaw that there was going to be a increase in energy demand.

• 19:53Speaker 03

  And, you know, so a lot of the energy plants, you know, the solar plants, hydro plants, whatnot, were actually built out in, you know, anticipation for that. Now, obviously, this has coincided with now the AI boom and it's really helped out. Beyond that, you know, China has an advantage in the fact that they can actually drive top-down mandates and provincial governments will follow suit. This is something quite unique to China because it's not like decided by each state. So when they pushed out the East Data, West Compute, where it's basically a top-down initiative, where they built a ton of renewable energy for cheap in rural mountainous areas in Guizhou province, like even Xinjiang, Inner Mongolia, Sichuan, you know?

• 20:38Speaker 03

  Those were like very easily executed, frankly. And then ninety percent of the population actually sit on the eastern coastal lines. Like we think about Beijing, Tianjin, Shanghai, Shenzhen. That's all on east, so that's where the data comes from. So that kind of optimization has also really helped them, you know, with the load that is like the demand right now.

• 20:59Joe Weisenthal?

  I want to get back to something you said. So first of all, just to clarify, you mentioned companies like Merckor that sell proprietary data that they are able to collect and manufacture in various ways. Then they sell it to an open air. So a company like Merckor will hire a bunch of people to say, build PowerPoints, and then they'll collect the data on how they do that, and that is fresh data that they can sell. So those have exclusivity windows, after which they can then sell them to anyone?

• 21:28Speaker 03

  I'm not saying Merckor specifically, but supposedly there are these data providers that do this. And they have exclusivity windows and then the Chinese labs kind of weigh that out. So they can pay like maybe a million dollars versus like ten million for the same dataset.

• 21:42Joe Weisenthal?

  So this gets to something generally speaking, which is that people Are around the world correctly, like quite impressed by how high quality the Chinese models are, even if they're behind. But then you have things like that. And then you also have accusations from the likes of Anthropic that they're distilling models and that they're finding ways to collect the outputs of American models for training. So then you could say, well, yes, sure, that's like, it's great. That's open source model and it can stay close to the edge. But then the counter is that they can only be so advanced because there is this extremely capital intensive closed source model in the US that's really establishing the frontier

• 22:25Joe Weisenthal?

  and that these Chinese companies wouldn't be anywhere near where they were if they weren't sort of, I guess you would say, drafting off the American labs.

• 22:33Speaker 03

  Yeah, I think the compute constraint and the capital constraint is real. And frankly, like, no one's hiding that or pretending that that's not an issue for them right now. Like, Deep Sea has openly said they even were struggling, right? Like, they needed more compute. I think on the distillation allegations or accusation, it is quite interesting. Like, recently, I've been thinking about this a lot and thinking about what it means for distillation and what it means for the models to catch up, right? So there was this one quote from Yao Shunyu, who is a Google DeepMind researcher. He said, "There is smart distillation and dumb distillation." Dumb distillation is something I think most of us, where frankly, non-technical think about. It's like, okay, you take like a thousand queries. You take the answers of whatever Claude gives you, right?

• 23:17Speaker 03

  And then you kind of force copy that into your said model. And then you forcefully make them basically like, get the exact same answer. Smart distillation is like you using the frontier model almost as a partner to help you with the judgment, for the valuation, and even the data labeling itself. So you're using it as almost a teacher for your own model.

• 23:36Joe Weisenthal?

  Okay.

• 23:37Speaker 03

  It guides it a little bit versus really copy pasting the answer, if that makes sense. And that part of it is frankly, not that unethical or like, you know, that frowned upon right now because that is what enterprises do when they're fine tuning. So it's all kind of a bit of a murky area, to be honest.

• 23:54Joe Weisenthal?

  Okay.

• 23:54Tracy Alloway?

  So you mentioned data just then. Talk to us about what the Chinese data set actually looks like. Because I imagine if you're a Tencent, I mean, you've got WeChat, right? That must be a whole load of data on which to actually like build your AI. But on the other hand, I imagine like there are some restrictions around the internet, obviously. What does it actually look like here?

• 24:17Speaker 03

  So I actually split that into two parts. On the data itself, people often think China is so data intensive and you just have a massive amount of data to use for AI training. However, actually, people forget, again, China's enterprise buildup or, you know, whatever, the knowledge work economy is very new and not as sophisticated, frankly, as the American ecosystem or the Western ecosystem, if you have to put it that way. So data is often unstructured and data thus, a lot of the specific needs for, you know, the kind of training we're seeing today is not as vibrant or the data ecosystem is not as sophisticated as what American data providers kind of can provide, such as Merckor, like we just mentioned. Now, on the big tech side, it's a bit interesting.

• 25:01Speaker 03

  So I'm glad you brought up Tencent because Tencent actually just announced last week that they are working in the works of creating an agent that can be plugged into WeChat. This has been very controversial and it has actually had a lot of pushback even internally because WeChat's product manager, Alan Zhang, has been famously or notoriously known to be kind of hard to work with if you want to push something within WeChat because he's so protective of that user experience. It's his baby, right? And Tencent, like you said, has WeChat, which is a super app, has more than 1.4 billion MAU globally, like mostly 1.3 billion people in China and the Chinese diaspora globally or people who work with China.

• 25:43Speaker 03

  That is immense value, but the risk and compliance risk of potentially of agent going rogue within that chatbot or that of agent going rogue in executing, you know, whether it's a purchase or whatnot, is very, that risk is very high. So they've been working on that. And on top of that, Tencent itself has been lagging behind compared to other big tech in their own Prioritizing models and they've really been trying to really play catch up. They actually last year poached someone from OpenAI, who is a researcher called Yao Shun Yu as well, his same name as the other researcher we just mentioned, to lead this whole initiative and their whole goal is to basically build a Tencent agent native model and that is their biggest goal because end of the day, like you said in the very beginning,

• 26:28Speaker 03

  their goal is to optimize their existing businesses already and bring AI to the mass consumer as fast as they can.

• 26:35Joe Weisenthal?

  You know, you mentioned poaching a researcher from OpenAI and it's like the way I see it, AI will definitely be built by the Chinese. The question is whether it will be built by the Chinese working in the American labs or whether it will be built by Chinese working in Chinese labs. Has there been a gathering of steam of researchers from that had been working at American labs going to Chinese labs or are they still sort of one off and somewhat rare?

• 27:02Speaker 03

  I think even during the internet era, we saw a lot of Chinese nationals or Chinese ethnic people returning to China, right? I think this, it's easy to blanket statement as geopolitical headwinds, people are scared. But realistically, I think most people are just trying to take care of their families and live a good life, right? I hate to sound so crass about it, but you know, sometimes it's what your package looks like. And to over generalize, I've heard from many researchers say, look, if my wife is a lawyer in China, my wife is a nurse in China, my wife is a teacher in China, that kind of employment opportunity is very, very hard to actually transfer to a new market. If I can get a similar package and a growth opportunity in one of the big labs in China, I will pick that over living in the US.

• 27:47Speaker 03

  And on top of that, I think that something is lost in the nuance is my parents immigrated to North America thirty plus years ago. It was a very clean cut like quality of life. It's just like objectively better in any city in North America compared to any city in China. Now that's kind of a personal debate, right? Because it depends on what you really value. If you want to be close to city center, you want that fast paced like techno, EV futuristic lifestyle, China actually gives that to you. And then on top of that. If you want to be close to your family, it's a very personal reason. So I met a lot of researchers who actually decided to come back to China or this part of the world, simply because they wanted to do it for personal family reasons.

• 28:28Tracy Alloway?

  Hmm. Are they paid as much as they are in the US? Because we get headlines all the time about, you know, so and so is joining whatever company and people treat that news like sports stars, right? Like teams trading their best players. Is it a similar thing here?

• 28:45Speaker 03

  I think you definitely get less of that sports star vibe or mentality here. They're still getting paid like hefty amounts. How much? They don't usually disclose, but at least even in the internet era, like a ByteDance product manager can make just as much as a Meta product manager. Similarly, if you're like an average AI researcher, you're probably making a similar amount. Although the star star players, like the ones that are signing a hundred million bonuses, I don't know if we had anything like that big like in China. But look, they made their money with the IPOs. They made their money recently with all that this AI boom. It's just on a maybe slightly smaller scale. Doesn't mean that they're not making much more than the frankly, average person.

• 29:27Joe Weisenthal?

  Tracy, can I say something that might be sort of sacrilege for a podcast host to say?

• 29:33Tracy Alloway?

  Uh, okay.

• 29:35Joe Weisenthal?

  This, I'm only speaking for myself here. I'm not necessarily speaking for the team. But it occurred to me like, I'm not really sure if I'd be that interested in say, getting the CEO of like an American AI lab on the podcast as a guest. I don't know what I would ask them because like, like, do I really want to hear like Sam Altman or Damis Hasabi or whatever, like the future of work and all this stuff or like these, all the big, you know, I love doing AI episodes. I just feel like at that level, I would rather talk to sort of like someone in actually the engine room rather than this sort of big picture person who may have some degree of AI psychosis and just like as on it speaks in like the biggest generalities.

• 30:20Tracy Alloway?

  Okay. Well, now Sam Altman needs to like invite himself on the show just to test your. Your commitment to not having AI CEOs.

• 30:29Joe Weisenthal?

  No, I would, I would do it, but I like, let's just agree here that if we ever like get one of the really big like lab CEOs, let's just ask the very like sort of mundane questions about operations and now like, what are we all gonna do and other, you know, is, what is the meaning of life gonna be when we don't have jobs? Cause I'm so sick of those conversations. They may be important at some point, but Grace, I'm sort of curious from your perception, like, it does feel like the heads of the American AI labs have some degree of AI psychosis themselves. Either they talk about all white collar employment is gonna disappear, or that they're gonna build a monster that if done wrong is gonna be out of control and that they're not, you know, it'll escape,

• 31:12Joe Weisenthal?

  it'll escape the sandbox. Is there the same sort of existential discourse in the Chinese AI community?

• 31:21Speaker 03

  Yeah, I think to start with, in the AI community themselves, I would say people are a lot more pragmatic. And I think recently I was talking to Nathan Lambert, who is open source researcher who just came to China, visited all the labs. He said, look, I was shocked to see majority of labs are so young, as in like a lot of the researchers are still students. A lot of them are interns and the core research teams are maybe led by a handful of people. And then these people are academics by training. So maybe they're a bit less commercial. Maybe you can say they're less like sophisticated to manipulate the market, whatever you wanna call them. So I definitely feel like there's less of that kind of psychosis or high level narrative going around.

• 32:02Speaker 03

  However, I would say that there is obviously anxiety from the public in some degree, not as much of a back, back, uh, pushback. But recently there was a very interesting court case in Hangzhou, which is, you know, home to Alibaba and a lot of these AI labs. Basically, a company tried to like lay off a person saying you are being replaced by AI. And the court literally ruled, say, that is not allowed and you, no company can use AI as an excuse to lay off or replace, or even cut short their contract time. So that was a really swift reaction from regulators, and I think it really did serve as a calming factor for the public.

• 32:43Speaker 03

  Obviously, I also think I want to preface the fact that the knowledge worker economy makes up less of the overall economy in China as well. So that kind of fear maybe doesn't feel as imminent, but that conversation is being had. But I do think in Asia in general, not only in China, you look at South Korea, Singapore, all these countries are approaching AI in a very pragmatic way. And the tire moms are trying to train up the kids to be AI native. The students are trying to train themselves up to be AI native. People are preparing for the future versus pushing back on the future.

• 33:17Joe Weisenthal?

  That's interesting. You know, in the US, obviously, companies announce that they're laying people off and they cite AI even frequently when there's no evidence that AI had anything to do with it. So it's interesting they're not allowed to do it. In defense of American AI labs, most of them lose a lot of money, and yet they actually do spend, at least the big ones, spend quite a decent amount on so-called like alignment research, safety research, making sure the AIs don't go rogue, et cetera. How big of a part of the Chinese labs, how much did they spend on, I guess, what they would, what you would put, what the American labs would put into the safety bucket?

• 33:56Speaker 03

  I do have to say, I'm not a policy expert, so I don't work with a lot of the safety people as much, but there are organizations in China that are definitely working, like the regulators, as well as the private sector, working together. And for some context, in China, there are various moving parts in the government, there's MIT, the CAC, et cetera. These agencies, basically, some are to propel economic development. So in this case, AI diffusion, the whole idea of AI plus, AI plus every single sector you can think of, some act more like a guardrail as a protector. So they are working hand in hand. And on top of that, every single AI, gen AI application, as well as LLM company, have

• 34:40Speaker 03

  to go through the national registry in China. So they actually disclose what is being trained. What is, you know, the potential risk? That said, I think right now, you know, no one really knows what the real impact of AI will be on economy. But, you know, definitely that fear mongering narrative is not mainstream in China.

• 35:17Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.

• 36:00Speaker 01

  But according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 36:40Tracy Alloway?

  How would you describe, I guess, the approach of the Chinese government to AI in general? Because it feels like the trade-off for maybe not being on the cutting edge with frontier models is, well, you're further along with sovereign AI and the government maybe has like a better handle on what the labs are actually doing.

• 37:00Speaker 03

  The Chinese government probably sees this in a much more pragmatic way, you know, just like how there was Internet Plus policy 15 years ago, there's now an AI Plus policy. When DeepSeek moment took off, you know, there was a frenzy of private companies, even in home appliances, trying to embed DeepSeek, integrate DeepSeek. I'm just like, what is AI vacuum going to do for you? Or AI, like, electric toothbrush going to do for you? It was wild, right? But the government picked that up. And I think what the Chinese government, going back to what we talked about earlier, is that they have the advantage of having the ability to push things down from top down. And at the very high level, they're seeing AI as an economic driver to propel maybe

• 37:44Speaker 03

  efficiency, to address some of the labor shortage that's coming as the population continues to age and decline. It also addresses a lot of issues where a lot of the young people don't want to work in manufacturing roles, they want to be automated, actually, and they want to work in urban areas. So they have that now. And then each of the provincial governments will take that as kind of like a KPI. They're like, all right, let's go ask like VCs essentially and go find, you know, future DeepSeek and fund them. However, how much these companies want to take government money is a different discussion. A lot of them would then even support them by providing, you know, infrastructure like buildings, offices, even like some of them are heard like dormitory for these young

• 38:28Speaker 03

  entrepreneurs, and then give them money and capital to try things out. There's also these AI pilot zones being rolled out across the country. I think now about 11 or 12 of them where, you know, people can try out new AI products. I met with the largest AI developer community founder in China a couple of weeks ago, and she was saying there's more than a couple hundred thousand developers in this. Ecosystem and they are working with regulators and the private sector. So if say ByteDance have a new product, they might go to them first and say, can you try this out? And then they will report and debug and see what hap- what's happening. And then tell the whoever local government that's funding them with providing with infrastructure say, hey, this product might come out. Do you wanna be part of it? Do you wanna give it money? Do you wanna provide it with whatever resource you want to?

• 39:13Speaker 03

  So there is kind of this like cohesive ecosystem where they kind of all dance together. How much these companies actually want to take state money? I think that's debatable. However, as AI and robotics become more and more sensitive and being recognized as not only an economic driver, but potentially a military use or geopolitical, I guess, talking point. At this point, it is becoming more and more nationalized, not only in China, but globally in the US and so on.

• 39:42Joe Weisenthal?

  Robotics is obviously an area where China is just straight up ahead of the United States, or at least according to all the videos on my Twitter and Instagram feed of humanoid robots and so forth. How much does, you know, when we were all kids, when we thought of like AI, I think we thought of robots, right? We thought about the T one thousand or some version of it. And now when we think of AI, most people think of chatbots, but that's just one aspect of AI. For the advanced labs, whether talking about the DeepSeek or the Minimax's or GLM and so forth, like, are they actively working hand in hand with some of the the unitries and advanced robotics companies to figure out

• 40:24Joe Weisenthal?

  how you can actually have that, the true AI robot of the future?

• 40:28Speaker 03

  Yeah, I think China, having been the manufacturing hub of like literally everything under the sun over the last like three decades, has definitely a, an advantage have owning all the supply chain, right? And it's like not only just owning the supply chain, but you know, there are literally regions where that whole supply from raw material to like the end result, end product from OEM is all within like, say, fifty kilometers of each other. So what we're seeing is a lot of American investors and entrepreneurs coming into China. To kind of get a sense of that. And like you mentioned, because we've been so fixing the software, I think China having a very strong hardware background is now thinking about how can we actually integrate the software into the hardware.

• 41:11Speaker 03

  How ready that is to the mass market, I frankly don't think it's really there yet. So recently I just met with some robotic companies. They actually can't just plug in a mini max, you know, that's like for them, they need to actually get physical data. They this is where like, you know, now all the hype is on world models, physical AI, you know, that is a completely different set of kind of technology essentially. Where without the three D data that these models need right now, the bottleneck right now is that. You know, these hardware is these humanoid, quadrupeds, dogs, whatever you want to call them, they cannot be powered by L L Ms. That's number one. Number two is despite that China being very strong on hardware.

• 41:54Speaker 03

  The bottleneck is actually a lot of times in the integration as well as the battery solutions. You know, you think of China having very strong battery solutions, but most of these gadgets can't last more than like, say, two hours. And there's no one that's really come out with a better solution so far. What I've seen the most creative thing so far is like, you know, those glasses you wear, like the meta glasses. They kind of die within two hours, but China like iFly Tech or Rocket, that's kind of a newer player startup. They created these battery capsules where you can just like stick on to your glasses. It's very lightweight, doesn't really affect your user experience, and that's actually able to kind of extend it by a few hours. So. To go back to your question, is China trying to do physical AI? Definitely.

• 42:39Speaker 03

  What is their edge? I think it's still in manufacturing. Is their software good enough? I don't think anyone really has good enough software right now as of now.

• 42:46Tracy Alloway?

  Wait, I'm just going to press you on this. So if if we fast forward ten years, like what would you say is most likely to be China's comparative advantage? Is it like the cheap, open source, super optimized models? Is it software, AI software that's like integrated with like industry and existing business? Or is it robotics and the sort of hardware side of AI?

• 43:10Joe Weisenthal?

  Ten years is a long time.

• 43:11Speaker 03

  Ten years is a long time.

• 43:13Joe Weisenthal?

  Sorry.

• 43:13Tracy Alloway?

  Well, we want to challenge you.

• 43:16Speaker 03

  A lot of these companies didn't exist ten years ago, or not even five years ago.

• 43:19Tracy Alloway?

  Yeah, that's fair. Okay, I can shorten the timeframe. In three years.

• 43:23Speaker 03

  All right. So, don't chase me down if I'm wrong in three years, but I think, you know, there's two parts. One is I think I agree with you. Hardware side, China is definitely gonna have, I think, more breakthroughs and have a lot of edge. Not only is the supply chain all domestically there, I think something overlooked by people is the fact that a lot of the know-how is also there. And that's not easy to transfer overnight. You know Patrick McGee's book recently, and his Apple book, saying how Apple tried to move this whole supply chain to India. The biggest bottleneck is actually these like highly skilled laborist jobs that actually are so technical that cannot be even trained in one generation. It took decades to really train up the local community labor force, whatnot. So that's still there.

• 44:08Speaker 03

  Now, because of that ecosystem, a lot of these robots, home appliances, whatnot, these tech gadgets are produced at less than fifty percent of the cost of where you could produce that anywhere else in the world. They are also extremely innovative. I've talked to people at EV companies, just for example, to ship out a new model from ideation to production to hitting the like floors. That takes maybe less than fifteen months.

• 44:35Tracy Alloway?

  Wow.

• 44:35Speaker 03

  But for a traditional OEM, like wait for at least three to five years, right? So there is the hardware side. I think another very underappreciated fact on the Chinese open source model is that people don't realize. So, a year ago when I spoke to startups in Silicon Valley, they were the most cost-conscious, frankly, less compliance-conscious and gives very little care about geopolitics. They were building on top of quit. Now, it's actually a lot of AI Native American enterprises building on Chinese open source.

• 45:06Tracy Alloway?

  Yeah.

• 45:06Speaker 03

  Because we're seeing headlines on The ROI is not like showing it's extremely expensive for these token maxing projects, whatnot. So Harvey Cursor, they've talked about using a hybrid model where they will build majority on GLM or Kimi, but kind of like what we talked about earlier where they use like Opus to act as a judge or a guidance. So I think that's something where we'll continue to see. And these companies are generating a lot of revenue and going back to the fact that like a lot of them don't even have enough capability to support the demand that's coming through.

• 45:42Joe Weisenthal?

  That's such a fascinating idea and it makes a lot of sense that for at the application layer, that probably a lot of different models can go into it. You know, by the way, I was talking to someone at a dinner recently and he said he thinks that AI writing will get a lot better when AI is embedded in humanoid robots because then the will have this sort of groundedness in the real world where I have no idea if this is true. But he said the reason, his theory was that the reason why AI writing is still so weird is because it's in this disembodied data centers and that as soon as they're really in robots, then they'll have a sort of real world groundedness. All right, I have one last question. You know, after OpenClaw came out, I started seeing, again, I, my

• 46:25Joe Weisenthal?

  entire Twitter feed and Instagram feed, I have done this to myself, but all I, all I do all day is consume Chinese propaganda. But um, I started seeing all these videos, like all these grandmothers and stuff like setting up their claws and stuff like that. And I saw these videos and I said to myself, I just don't believe this. I think this is fake news. I do not actually believe that there's all these 80 year old grandmas or whatever, really excited about setting up their open claw or whatever. Are those real? Like, what's the deal with that?

• 46:53Speaker 03

  I think that was definitely a bit of a hype.

• 46:55Joe Weisenthal?

  I knew it.

• 46:56Speaker 03

  No, no, but I will say there were grandmas lining up to get it done.

• 47:00Joe Weisenthal?

  Okay.

• 47:00Speaker 03

  Um, I'll answer this twofold. On the, on the kind of, the surface is I think Chinese aunties, uncles, whatnot, they are just much more open to technology because, you know, you go around, whether it's by force or by nature, you know, you can't really navigate. Modern Chinese life without being on Alipay and WeChat.

• 47:20Tracy Alloway?

  You really can't, you can't like buy Starbucks in Beijing without like having WePay.

• 47:24Speaker 03

  After covid, I went back to, I think Shanghai for the first time. And I was sitting at a restaurant, just like waiting for someone to like he- help me order food. No one came because they're just like, why are you so like, why are you a caveman? Don't you know how to like scan the QR code on your table? Okay, so that tangent aside, I think the overall optimism around technology is very different from the West. Because in the last twenty thirty years, a lot of rural areas in China literally could not access resources, information, goods, whatever that, you know, like big cities could. Not until these super apps came about. So a lot of people don't have TVs in their homes and they live in a village and their maybe annual household income is like a thousand dollars.

• 48:10Speaker 03

  But they will have a smartphone and that smartphone will be able to actually enable them to get micro loans, to purchase goods, you know, to help their kids access information online, whatever that is. So technology is very much kind of um, accepted and respected and actually like big tech is loved. Like if you work for one of big tech, you are like a pride of the family. So there's that very culture aspect of it. Then is like going back to the super apps. So I think the open claw frenzy was interesting because some people say it was the first agent that, you know, Chinese people could get their hands on, like a Western agent that they can't get their hands on. Because, you know, Anthropic and OpenAI doesn't actually operate in China, you can't access that.

• 48:52Speaker 03

  So when Tencent and Alibaba tried to embed open claw products into their own like series of products or business products, whatever, offerings, it got people really excited. And because of these super app uh models that they have, it was a very natural way for people to access them. There's a functional adjacency to, you know, the search bar and then opening up an open claw. And then trying to like run, like, you know, manage your mini programs within Tencent, WeChat, and then trying to order something. So all of that kind of took off. But that said, actually, the Chinese government Again, regulators acted very swiftly. In the beginning, I think local government like Wuxi tried to even encourage local businesses to embrace OpenClaw.

• 49:38Speaker 03

  But the Beijing government immediately said, guys, actually be very, very careful of your data privacy, your security, banks shouldn't do this, SOEs shouldn't do this. Be mindful of what you're doing with this technology. And then the big tech kind of rolled back a bit of their marketing. And you can see, actually hilariously, there were advertisements for helping these aunties and uncles how to uninstall these OpenClaw on their gadgets. So anyway, that's a bit of a kind of background on that.

• 50:05Joe Weisenthal?

  All right, Grace Xiao, thank you so much for coming on Odd Lots. Great to connect with you here in Hong Kong. And we'll have you back in three years. No, hopefully before then, we'll have you back in at least, certainly in three years to see how your predictions held up.

• 50:19Speaker 03

  Thanks so much.

• 50:32Joe Weisenthal?

  Tracy, that was fun. There was a lot of interesting ideas in a fairly short conversation. But one thing specifically, and then that like sort of stands out to me, is thinking about some of these application companies, how much it makes sense for them to sort of, yeah, they'll use like a state of the art American closed model for like some of the work. But then other just, you know, almost as capable models underneath. So you have like a legal AI app, like a Harvey or something. I'm sure some of these open source models work well for some tasks within that context. And how much it makes sense to sort of combine them under one app layer.

• 51:12Tracy Alloway?

  Yeah, well, you don't need the cutting edge model for everything, right? But like, If you can get some of the cutting edge model combined with like the cheapness of the open source thing, like that seems like a pretty good deal for a lot of companies.

• 51:28Joe Weisenthal?

  Yeah, definitely. I also think like, how is the massive manufacturing edge that China has not going to just keep compounding itself? I mean, this is like the multi-trillion dollar question of the entire world, but it really does. I mean, when you think about, okay, there's this sort of presumably natural synergy. There's all this real world physical data that Chinese manufacturing companies can theoretically get from their various robotic vacuum cleaners and so forth, and then feed those into certain models that then we call AI. That really does seem like a potential leg up that, you know, just on the data collection alone from all those physical things, like a huge potential

• 52:12Joe Weisenthal?

  edge over the next several years.

• 52:14Tracy Alloway?

  Yeah, although it was very interesting Grace was saying that it's not as developed or as structured as it is in the US.

• 52:20Joe Weisenthal?

  Yeah.

• 52:20Tracy Alloway?

  Because I I had thought the same thing, like if everyone's talking over WeChat, if everyone's paying over WePay, you must have oodles and oodles of data. But yeah, that was interesting. The ecosystem thing I think is really important, and it just seems like really hard to get an ecosystem kind of going from scratch.

• 52:39Joe Weisenthal?

  Yeah.

• 52:39Tracy Alloway?

  And I remember, maybe it was with Dan Wong, but someone describing how like if you go to Shenzhen, you can basically start like an entire company manufacturing a physical thing because every single supplier is there. You just go from like one storefront to another storefront to another storefront. I don't think that can be replicated anywhere in the US.

• 53:02Joe Weisenthal?

  Yeah, I mean, this is a bit of a tangent, but you know, this is economists talk about, quote, agglomeration all the time. The advantage is exactly that of having everything there and then you build these deep networks. One thing I find to be a little strange, and again, this is a tangent, is how San Francisco concentrated AI is, even though at the American level, it is sort of pure like desk work, right? Like it's not the sort of. We need the bolts manufacturer, we need the servo's manufacturer, we need the robo vision manufacturer, and yet it's still so agglomerated, or the fact that finance is so agglomerated in New York City. I find that, or uh, conglomerated, I find that to be a little odd. So um, but yeah,

• 53:45Tracy Alloway?

  I thought Agglomerated is a good word.

• 53:47Joe Weisenthal?

  Agglomerated. I don't know whether it's conglomerated or agglomerated. I'm just gonna say glomerated. We know that there are certain industries in the US that are quote, "agglomerated" one way or another. But um, yeah, I did find that to be, and man, these like, minimax, $20 billion company, that's like, nothing compared to the valuations that we see with American companies. Pretty wild stuff. Also, the point, we should do more, I mean, there's a million, we gotta do, we have, and there are plenty of non-AI things to do, so you can't just keep saying, we should do an episode on X or Y, but data markets, and like, the idea of like, okay, these Chinese companies can buy very pricey proprietary data after some exclusivity window. There's some interesting stuff to be done there.

• 54:32Tracy Alloway?

  Yeah, the monetization was really interesting to hear. Okay, shall we leave it there for now?

• 54:36Joe Weisenthal?

  Let's leave it there.

• 54:37Tracy Alloway?

  This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

• 54:43Joe Weisenthal?

  And I'm Joe Wasandall. You can follow me at the Stalwart. Follow our guest Grace Chow. She's at Grace MZ Chow, and check out her Substack, AI Prom. Follow our producers, Carmen Rodriguez at Carmen Arment, Dashal Bennett at Dashbot, Cale Brooks at CaleBrooks, and Kevin Lozano at KevinLloydLozano. And for more Odd Lots content, go to Bloomberg.com/oddlots for the daily newsletter and all of our episodes. And you can chat about all these topics 24/7 in our Discord, discord.gg/oddlots.

• 55:13Tracy Alloway?

  And if you like Odd Lots, if you want us to do an episode on data markets, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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