How China Caught U.S. AI — With Grace Shao

Grace Shao is the author of AI Proem. Shao joins Big Technology Podcast to discuss how Chinese AI labs have caught their American rivals despite operating with less advanced computing infrastructure. Tune in to hear how talent, specialization, open-source collaboration, and fierce competition helped models like Kimi K3 approach the U. S. frontier, and what that means for OpenAI, Anthropic, and the business of selling intelligence. We also cover model distillation, China’s compute constraints, the growing importance of AI products, why top researchers are returning to China, and the country’s emerging robotics advantage. Hit play for a clear-eyed look at whether the United States can preserve its AI lead. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Watch the full documentary here: https://www.gravitee.io/ai-agent-documentary Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Stop online threats before they become real-world attacks. Visit ⁠ironwall.com/BIGTECHNOLOGY⁠ and request a free Risk Assessment to see exactly how exposed your executives are Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  How does China keep catching up to the US in AI despite having much fewer state of the art resources at its disposal? And what does it mean if US AI labs permanently lose their lead to China? We'll talk about it with a leading China analyst, Grace Xiao, right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial,

• 0:39Speaker 00

  to go inside a large scale agentic CX transformation, including the business decisions, trade offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation. Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation

• 1:23Speaker 00

  of the tech world and beyond. Today, we're going to talk all about China's rise in AI, which is really now a multi-year phenomenon, and whether the United States can maintain any semblance of a lead over China now that KIMI K3 has effectively equaled, maybe not the frontier frontier of US AI models, but enough frontier to make us question whether the lead will be maintainable at all. And then, of course, what the implications are for the AI race. We're joined, of course, by Grace Xiao. She is the author of AI Pro M on Substack and a leading analyst on China's AI efforts and, of course, a friend of the program. Grace, it's great to see you. Welcome to the show.

• 2:06Speaker 01

  Alex, it's so great to be back. Thanks for having me.

• 2:09Speaker 00

  So let me sort of set the stage here. We had this moment last year where DeepSeek was able to produce really great results on reasoning at a cheaper price and the market flipped out. Okay, we know that happened. But even still, despite the fact that people in the US and Europe, and maybe all over the globe, have known that China is very capable in its AI research, the release of KIMI K3 largely had this reaction of like, how did they do that? Right? It's confused a lot of people, even though it shouldn't be a surprise necessarily because China's done it before. You know, it's sort of happened again.

• 2:51Speaker 00

  And this new model from Moonshot, KIMI K3, we've talked about on the show, it's doing really well. This is from Nathan Lambert's Substack, Interconnects. It comes number two on the Vowls AI Index, number three overall on artificial analysis is Intelligence Index, number one in the front-end Code Arena, and has many more impressive results. So let's just start there. Should we be this surprised? And how did Moonshot do it?

• 3:21Speaker 01

  I can't comment exactly how Moonshot did it, but I'll start with a comment, actually, Nathan even shared with me when we talked about after his China, big China trip, when he visited all the labs. I think a lot of it's in the talent, and people are really, really shocked by the talent. And this is something we talked about as well a year ago, when DeepSeek came out with a whole domestically educated and domestically trained talent pool. Now, I think that's something really underlooked right now. You know, the talent pool in China right now kind of is because the base is so big and then there's such a strong STEM education, which feeds into right now the AI researcher realm.

• 4:03Speaker 01

  And then we see like the leading researchers, really, a lot of them, if not, you know, maybe 40, 50% of them are of Chinese descent or heritage. Chinese talent right now is kind of having a moment, I think. And then I think within AI research, you know, what I've heard from a lot of labs are saying, they're like, look, it's not really rocket science, actually. The R and D itself requires a lot of taste and curiosity and test and error, but it does take talent. And China just has an abundance of very smart mathematicians, physicists, and whatnot that are going into AI. Now, beyond that, I think what's the kind of elephant in the room or the obvious is

• 4:43Speaker 01

  the obvious constraint-driven specialization or the compute constraint, you must say. If anything, in a way, what you've seen is a lot of these Chinese labs faced with compute constraint and in some ways capital constraint. They're forcing them to specialize rather than compete across every dimension. So, you know, for the sake of, you know, DeepSeek, it's really, really focused on the infrastructure, the innovation and engineering and efficiency and compute efficiency. With a lot of the others like KIMI, it's really, really focused on its agentic pushout. You know, MiniMax previously was more focused on multimodality and ZAI focus on coding.

• 5:24Speaker 01

  So in that way, you can see the whole ecosystem is kind of each taking its own pie, not by, I think, design, but because of compute constraint. So they have to selectively choose what they do. And then the last thing is, I think, it's a shared R and D, and I think this is something we can definitely talk about a bit more beyond just the KIMI breakthrough. It's just that the open source ecosystem has really harnessed this pretty collegial competition, and there's a lot of, you know, learning and referencing off of each other.

• 5:55Speaker 00

  Yeah, I don't want to downplay the talent side of things. And I actually want to read a selection from Lambert about his trip to China, where he met the KIMI team. And of course, he is a researcher at the Allen Institute for AI, so he's got chops in the AI world, not just, you know, somebody who would write this if he wasn't impressed by the technical abilities of people. So he wrote, "Meeting some of the core KIMI team on my trip to China, it was clear to me that they had incredible culture, some would say aura, and a freedom to express it within the constraints of a GPU limited environment. Where building models is so much of a scaling game, much of the ability to build a good

• 6:37Speaker 00

  model still comes down to individual execution, motivation, and expression. Having visited them, this result is less surprising. Having visited many AI companies, very few have a culture that you can immediately pick up like this."

• 6:52Speaker 01

  And I just want to double click on that. I think both, you know, DeepSeek and KIMI, if not seen as kind of the top two labs right now coming out of China, both founders have openly talked a lot about management of people, you know, removing distractions, really focused on the pursuit of AGI, not kind of getting distracted into consumer applications or whatnot. I think both, in a way, you know, represent, I think, this generation of Chinese entrepreneurs, where they're not so driven by the media commercialization, but very much driven by a bigger mission. And both of them are very, very much committed to the open source ecosystem.

• 7:30Speaker 00

  Okay, so I was going to ask you what you mean and what Nathan means by culture. So is it, is it that, just basically like a determination to not be distracted and to just kind of focus on the core science part of this building of AI? Is that what it is?

• 7:49Speaker 01

  So I can't overgeneralize every single lab, but for sure, when you meet a lot of these researchers from the labs, there is a sense of, I think the nerdiness comes through, but of course, that's for every researcher. And then for DeepSeek...

• 8:03Speaker 00

  Yeah, welcome to research.

• 8:04Speaker 01

  I think for, you know, Liang Wenfeng, he's talked a lot about, you know, his philosophy, where there's a very low churn rate in the DeepSeek lab, and the philosophy of committing to open source technology, open source R and D, the mission to really pursue AGI in his worldview, and his team really committed to that. So because of that, also, I think, you know, take it, you know, he might be taking it for granted, but the fact that they don't actually have that much pressure to commercialize because they have enough money. He says, like, look, we have enough capital, we are not capital constrained compared to maybe other labs, and we are really, really focused on just doing the best we can, given the constraint we're faced with. I think with KIMI, Yang Zilin also openly kind of talked about a lot about the hardest

• 8:49Speaker 01

  thing about building this business is not the R and D, the hardest thing is managing the organization and finding the right people for the right kind of work and making sure everyone's united. I think even ZAI has talked about how Tang Jie, who is the chairman, he's actually a professor of Yang Zilin and many of the Tsinghua alums. He's currently still a professor at Tsinghua University. He, in many ways, leans on the fact that he kind of is OG in the industry and is able to unite everyone, and there's a sense of unity, a sense of like shared mission. And I think people are a bit undervaluing this case, especially when we're seeing a lot of other labs maybe having a bit of infighting or, you know, internal mission or value misalignment.

• 9:35Speaker 00

  Yeah, now, some people will say, oh, well, this is just like nine nine six. So people in China are out working people in the US where they're doing, you know, nine AM to nine PM, six days a week. How much of that would you ascribe to it?

• 9:49Speaker 01

  I think there's nine nine six everywhere, honestly. I mean, this sounds, this is really controversial. I think if you're passionate about what you're doing, I nine nine six myself, you know, but only selectively when there's days you need to grind. Alex, I'm sure you're staying up right now. It's nine PM in New York. You're doing this recording with me.

• 10:03Speaker 00

  I'm nine nine sixing today.

• 10:04Speaker 01

  Yeah. So I don't think it's a top down mandate from the company, but I think if you're driven by a mission and you're passionate about what you're doing, people are willing to work. However, it's actually interesting. Yang Weifeng even talked a lot about how he does not believe in overtime for the sake of overtime, FaceTime for the sake of over, uh, FaceTime, which really goes against kind of the stereotype of what people think of Chinese corporates. So it goes down to, I don't think it's just pure grinding, but obviously, uh, people are hustling when they need to.

• 10:33Speaker 00

  Right, yeah, I mean, still from the, from the, you know, Kimi K three did so well on so many benchmarks, uh, that it's still, you know, even if the cultural side of things, you know, are where they should be in terms of being focused and trying to get things right. It is still stunning that, that they were able to turn in the results they, they did. And, and I guess that is, you know, you bring up the specialization thing, and I think that's really important, like the large language model can get very large and do a lot of things, like the same model that's going to come up with scientific breakthroughs and help you figure out what you want to order for lunch is also the model that codes. It's the same model, right?

• 11:13Speaker 00

  And so if you decide, hey, um, the, the most valuable thing, uh, is getting coding right, uh, and because coding sort of is the foundation for agentic tasks, then you can start to see potentially the results that Kimi saw when you want to specialize there.

• 11:32Speaker 01

  Definitely. And I think on that note, I, I want to bring up a very interesting phenomenon. I'm sure you saw like a couple of months ago, there was a huge hype around Chinese model, those Chinese companies all pushing out their own frontier models. Like, I don't remember, like, even like delivery app, Meituan, uh, you know, hardware company, Xiaomi. And then I think a lot of investors, uh, US investors would reach out to me and always ask, like, why are these companies all competing on models? And one really interesting thing is, I think, on one, one hand, there is obviously the expansionary nature of Chinese internet companies. We all know Chinese companies love these super apps and they love to get into every single vertical they can get their hands on. So, you know, the Baba's and Tencent of the world do not actually just do what you, you and I know about them.

• 12:17Speaker 01

  Like, they don't just do commerce, or they don't just do social media. They actually do delivery, ride hailing, map, food ordering, like everything under the sun. So there's that culture in China. I think there's a lot of, um, it goes back to open source again. I think there's a lot of, um, um, I guess, advantage for a lot of these companies to jump into the arena because they already had a lot of open source, open weight research available to them. So the barrier to entry was frankly a bit lower.

• 12:44Speaker 01

  And then beyond that, what was really interesting is when I speak to the Mate two CFO, which is a leading, like, creative AI company, um, he was saying, look, we don't really need the most, uh, like, the biggest model, we don't need the best model in that sense, but we need the best model fine tuned for our use case. So we can push our model in every single vertical app we have available. So they really lean in on, you know, fine tuning, uh, open source models for, um, creative use, whether it's image or, uh, video generation, or even video editing, uh, photo editing, et cetera. And I just thought that was something that's really embedded and ingrained it already in a lot of the tech companies in China. Like, a lot of companies are already thinking ahead of time, or maybe a few months ahead

• 13:28Speaker 01

  of this new mainstream narrative in the US we're seeing, where what makes you valuable? And is that your proprietary data? And is that proprietary data better executed within a smaller model that's better used, uh, created for your use case? Or is it better that we actually all pay for the biggest, largest, best model?

• 13:47Speaker 00

  Right, so having that data combining with a smaller purpose-built model can actually deliver similar performance as using the bigger model, um, maybe with less of your data. So, okay, you mentioned a few times, and I think we should talk about it, the benefit of doing open source, right? And you called it a share R and D, um, sort of effort here. And I remember after DeepSeek came out, uh, I spoke with somebody, uh, who knows their stuff in AI, and they were basically like, look, um, with open source, it's every open source research house working together. When you're building a closed model, like OpenAI or Anthropic, uh, you know, have, you're basically building on your own.

• 14:29Speaker 00

  Uh, I mean, of course, they can bring in the open source, uh, innovations, but it's just, it's basically them against the world, where open source, you kind of have the world against everybody, or against the closed models, to be more technical about it, more accurate. So just talk a little bit about that, because I think that's an important point.

• 14:48Speaker 01

  Yeah, I think a lot of, even just bringing it back to the kind of the narrative around China versus US AI right now, I feel like it's really about open source versus closed source at this point, right? And I think it was really humbling to even see this morning, uh, Kimi released their weights yesterday, like, last night in Asia time. And in their opening paragraph, they talked about how like, we are still behind the leading most frontier, but we are inching towards it, basically something like, I paraphrase, but it's something like that, or, um, something in, in those realms. And I think what it really shows is open source is able to somewhat now play catch up because they are leaning into basically everyone's intelligence or everyone's R and D.

• 15:29Speaker 01

  And I think it's really played a large role in propelling China's open source ecosystem. And it's something a lot of the leaders we just talked about, like Yang Weifeng or Yang Ziling, um, have really, um, I think, embodied as well as rallied behind. It's essentially in the beginning, it was like a branding strategy for a lot of these labs, and when I spoke to them, because they said, look, if we want more developers on our ecosystem or on our, using our, our APIs, they need to know what's out there, especially since we are Chinese, frankly. And they're like, if we don't put our R and D, you're going to have a lot of accusations of this and that. But we put our, our papers, we put out our research, you can be the judge of it. So that was the initial kind of starting point of open source and how it got, I think,

• 16:14Speaker 01

  a lot of users, especially startups, that might be more cost constrained and less compliance, you know, worried, um, getting on these labs and start getting on these models. And since then, essentially, it's become like a nice virtuous cycle because the more developers on it, the more you learn about, you know, the, you know, the, the use cases and whatnot. You can tweak it, you can make it better. So that's really kind of the original, I think, goal was to just sell their models abroad. Now, I think from there, it's really become an unintentional, unintentional consequence, where, you know, the learnings of each lab will now serve as essentially open learning textbook for each of these labs, and they will openly congratulate each other.

• 16:56Speaker 01

  I believe, I think, when DeepSeek came out with something, ZAI even retweeted them on Twitter, on X, saying, oh, congratulations, this is such, like, you know, uh, genius work, blah, blah, blah, like, we will incorporate it in our, in our own, you know, our R and D and our own infrastructure layers. So, so essentially, you know, um, that, that's really what's been driving it from the commercial sense. And I think back on the culture sense, you know, I think a lot of academics and researchers actually really like to be followed, cited, and it's part of that kind of academic loop. So the people who want to go into private, they need to be cited to go back into academia. And this is something I've spoken to, I've learned from speaking to a lot of the academics. And this allows them to kind of have their work in public.

• 17:41Speaker 01

  So all of that has garnered a very strong open source philosophy, um, base in China.

• 17:48Speaker 00

  Now, a very interesting thing has happened, uh, in the ensuing days. Uh, and we're talking Monday, July 27th, this will come out Wednesday on the 29th. But this is all live and this is happening. The, if you looked at what the advantage of, of, let's say, US and China were, um, China, of course, is this open source ecosystem. The US was leading, or is leading, I would say, still leading, um, at least when you, when it comes to model intelligence. And the advantage that the United States has had is these closed, uh, uh, you know, AI labs, OpenAI and Anthropic, that have pushed the frontier forward again and again and again. And you would think that if you were thinking, what's the strategy going to be for US companies?

• 18:30Speaker 00

  It would be almost a fear of open source and a rededication to closed. But the exact opposite has happened this week, where, uh, you know, basically led by Jensen Huang, the Nvidia CEO, uh, seemingly every US company has come out in favor of open source, uh, even OpenAI has signed on to this letter, uh, saying we shouldn't ban open source. Then after like days of silence, uh, Anthropic basically had to come out with a statement and said, hey, hey, by the way, we never called for the banning of open source. So, so great, gracious, help us understand.

• 19:07Speaker 00

  What do you think about the fact that if open source is China's big advantage, um, how do you then explain what's going on in the US where all these US companies are coming out in full-throated vocal support of open source?

• 19:20Speaker 01

  Well, I mean, there was a huge 180, right? And I think it was quite interesting. But, you know, going back to what something we just touched on already, I think in the last year, low key, a lot of companies have been building on these open source models, right? Because essentially, if you self-host these models or you use them through these inference service providers like fireworks, these models become yours or American, if you want to put it, right? So the whole fear mongering around, does the data, whatever, go to China, doesn't really, that narrative doesn't really work anymore. So then it was very interesting, because I think at the end of the day, it was kind of like, okay, if we don't open source and we build the strongest open source models in the US, then actually, this pie is being eaten by someone else anyway. Like, it's not like we can stop people from using Chinese open source.

• 20:05Speaker 01

  And even despite, you know, Kimi K3 being said is a token hungry model, it's not as cheap as other Chinese models, the task per, I think, the task per token usage is still quite high, it's still cheaper. It's still cheaper than the most frontier models, and it's inching towards frontier. So it's almost like, I want to cynically say it's a business decision, that's one aspect from the labs. So it's like, well, we now need to compete in open source, you know, back then, the margins were extremely high, right? And I think, you know, I can't comment exactly how high the margins are, because they're not disclosed, but, you know, on the China side, DeepSeek's people have talked about how they are not revenue driven, so whatever money they, sorry, they're not profit driven,

• 20:47Speaker 01

  so whatever revenue they make, they want to put that money into R and D, given that they have the quant fund, kind of like, you know, making their money, and I think the philosophy of the founder is not so commercialized. So I think there was like, a bit of a push for open sourcing. Then beyond that is something going back to what we also touched on earlier, which is, why should companies continue to pay for the best intelligence when the intelligence are kind of taking away what makes a company special? So then you see companies more and more mindfully saying, okay, we shouldn't actually give all our data to Claude and GPT, and then in return, pay for intelligence twice, that's what the Microsoft CEO said, right? So I think there's a bit of, I think if you take a step back and all understand what

• 21:30Speaker 01

  were people's perspectives are coming from, and what their own goals are, right?

• 21:36Speaker 00

  That's right, okay. I actually want to get to what the fact, I want to get to the fact that you can now get open source models to do a similar job as some of the frontier models in the US or close to the frontier models for a cheaper cost, what that does to the AI competition. But before I go there, you know, we're not even 30 minutes in, but we've made it 20 minutes in, and I haven't brought up distillation yet. And so I think a lot of our listeners, if you've made it to this point, have, are probably saying to themselves, well, Grace, these are nice explanations, good culture, and, you know, specialization, and I can buy that out of a surface level.

• 22:15Speaker 00

  But we do have evidence that the labs like DeepSeek and Moonshot have distilled, so basically, taken the essence of the big LLMs from Anthropic and potentially OpenAI, and, you know, quote unquote, taken the IP of these closed labs to build their own models. What's your perspective on that?

• 22:39Speaker 01

  Yeah, first of all, I want to say, obviously, no one has come out publicly saying, hey, Grace, I've distilled a model, so I just want to publicly say that.

• 22:46Speaker 00

  Right, there's, it's more like, there's, there's like, some research that indicates that it's happening, but like, neither of these companies have said they've done it.

• 22:54Speaker 01

  Right, right. So I think it's really interesting, and again, just this week, I was listening to the All In podcast, I'm sure you, like, I think even Freeberg and Sax, these people who are quite, I wouldn't say anti-China, but have a tough stance on the national security angle on China AI, were saying, look, distillation is a practice that's been widely used in product iteration and R and D, even, you know, in its Google days, I think Freeberg was saying that. But beyond that, what's something I found the most enlightening, the framing was provided by Google DeepMind's Yao Shunyu, he's a researcher at Google, and he said there's smart distillation and dumb distillation. Dumb distillation is something what, you know, the layman think of when distillation happens, essentially, you know, I literally take, you know, Model A's answer and plug

• 23:37Speaker 01

  it into Model B essentially, and then Model A will just spit out what, Model B would just say, spit out what Model A said. And it's so obvious, it's like copycat, and I don't think anyone is quite literally doing that, because frankly, it's just too unsophisticated. Now, there's smart distillation, which is kind of operating in the gray area, it is again, not IP theft, it's not breaking the law. However, it could be breaking what is considered, you know, you know, your own term services, I think labs should be doing better, you know, KYCs, but in that sense, essentially, it's like what enterprises are doing in fine tuning their own models. So say, if I'm a enterprise and I self-host an open source model, I'm going to fine tune it with my own data, I'm going to build harness around it, I'm going to make it the best

• 24:21Speaker 01

  model for my own use case. And often, I also use the frontier model to guide that kind of less frontier model in terms of getting its homework done, instead of kind of getting to the right direction, or even for synthetic data training. So when you're looking at smart distillation, it's a lot more murkier, and it's really hard to say what is right or wrong. And I think it goes back to the mainstream narrative that we're hearing right now, even the happening in the US, what is distillation, when we also are hearing potentially, you know, the thinking machines model, distilling on Chinese models. So it's quite funny where everything's kind of going in full circle, and it goes back to my point of open sourcing R and D, where it's really, open sourcing R and D really

• 25:05Speaker 01

  pushes the whole industry forward as a unit. And if you really believe in AI is really going to help and transform our economy, how we work, our basic infrastructure, then pushing it forward together, propelling it together will make more sense. But if you believe this layer of models should be capturing all the value and you should be selling intelligence, then of course, you want closed model and you don't want people distilling your models.

• 25:33Speaker 00

  Yeah, let's, let's read from Nathan Lambert one more time, just for the heck of it, because I think he has a good perspective here as well, and you've, you highlight this actually on your Substack, so I think it was sort of downstream from your finding, Grace. He writes, "It is clearly the strongest open model ever released," writing about Kimi K3. "It should be clear, looking at this model, that if adversarial distillation from the closed frontier models in the US contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and came away with the wrong conclusion, the Chinese AI labs are only producing good models due to IP theft, are in for an awakening. The Chinese companies are extremely good at building models in the same way the leading

• 26:18Speaker 00

  American companies are." Which I would say is even more remarkable, given the fact that they can't get the latest Nvidia chips, right? So they have to work off of generations previous.

• 26:30Speaker 01

  They couldn't get the latest chips, and they couldn't get, you know, access to Fable, but the window was too short for when Kimi K3 came out. And I think, look, Nathan is the technical expert here, if he believes in that, I would really, you know, trust his judgment, I do really appreciate and respect his work.

• 26:49Speaker 00

  Okay, so, you know, we spoke about this the last time you were on the show, last year, and we got to speak about it again, because, you know, you mentioned, all right, so like DeepSeek, you know, hedge fund funded, not really interested in the profit.

• 27:07Speaker 00

  I think that, and Moonshot is owned by Alibaba, funded by them?

• 27:13Speaker 01

  Uh, they have some backing, financial back, actually, a lot of the labs have a bit of financial backing right now, but we can, we can talk about their financial, like, like a breakdown.

• 27:24Speaker 00

  Yeah, so that sort of sparks the question, why are they doing this? Like, why are they doing open source? It seems like if you're doing all this innovation, right, they are giving away the weights. All these companies are downloading the weights and building their own applications, and doing it with like, probably USB, you know, if in the US, you're doing with US based consultants, you're not doing it with Moonshot's consultants. What is the economic advantage of open sourcing all of this technology?

• 27:56Speaker 01

  So I do want to start with, obviously, a lot of these labs are actually looking for fundraising, because obviously, training models is extremely expensive game they're playing, or tasks they're trying to complete, Pete. You know, ZI went public earlier this year, Minimax went public earlier, this is both on Hong Kong Stock Exchange, Moonshot is in the pipeline, supposedly rumored for the next, within the next six months. DeepSeek supposedly also looking at the Starboard in China. Now, that out of the way, they need money, right? It's not like they don't need money. However, I think there's a misunderstanding around open source monetization. You're still paying APIs for managed services, and, you know, you're still paying potentially fireworks for inference service, and if you pay for fireworks inference service, they

• 28:40Speaker 01

  usually have a commercial agreement with like, say, the Kimi provider, where there's some kind of a break, break as well.

• 28:48Speaker 01

  Open source is not anti-commercial, but obviously, it makes, helps you make less money. Now, it goes back to, are you that money hungry, or do you want the technology to proliferate and diffuse more, but you can still make money? And I think this is where people are also realizing, actually, kind of calling some of the closed frontier labs a bit of hypocrisy right now, because actually, labs monetize, you know, like I said, through API access, managed services, a lot of times, you know, you and I probably will not be buying our own GPU and deploying our own models and running security debugging, monitoring. So there's a lot of need for still buying that API. Now, beyond that, I think, you know, if you look at, I think, Minimax, ZI, I think

• 29:32Speaker 01

  ZI's run rate, AR is already something like 1 billion now, and then Minimax projected to be 1 billion, or to 1.2 billion by the end of the year. So, yes, not as lucrative as maybe Anthropic or OpenAI, they're not not making money, in fact, they're making a lot of money still.

• 29:51Speaker 00

  Right, there's also this view that like, well, if you think about national competitiveness, and we've seen governments get involved. I mean, Xi Jinping gave a speech about the virtues of open source. The US government seems to be touching AI every week now. You know, I think in part for a desire to mitigate the harms, but also because they view it as important, you know, from a national strategic perspective to have the lead. So there is this view that like, you know, the companies in China can open source because for the government in China, it's, you know, basically the best possible outcome is to commoditize this like leading industry in the United States. Your thoughts?

• 30:34Speaker 01

  Okay, first, I think the argument around subsidization is really funny because I don't know if the government is that rich, frankly, just chucking billions and billions at every lab. So definitely, I don't think they're like, yeah.

• 30:46Speaker 00

  They have a lot of money, though.

• 30:48Speaker 01

  There's a lot of subsidization on energy and data centers, but it's definitely if you talk to these labs, they're still, they're private companies. And in fact, some, most of them actually don't want to take government money because there's some hindrance as well, right? Like, and when they go public and etc. on their structure. Now, that side, I think it was really interesting that President Xi Jinping attended W A C, which is like the world, it's called the World AI Conference that hosted annually in Shanghai. It's been around since 2018, but it honestly didn't really get much traction until maybe last year when post deep sea takeoff. And now like, there's floods of American investors, American policy, think tank people all going in. And I think what was really interesting

• 31:28Speaker 01

  is, to your point, I think governments are viewing AI as a very strategic driver. Now, it could be a driver, I think it's a few fold, a driver for economic prosperity, economic growth, of course. It's a driver for, I think, soft power and diplomacy, of course. And now also, obviously, a very fundamental point on technological competition. In terms of what she said, I think the highlights was really about, you know, open openness and inclusivity, which is cannot be actually mistaken for quite literally embrace the open source. I think he talked a lot about openness, inclusivity, that was echoing what even the previous leader that attended, which is, I think, I think it was the premier that

• 32:13Speaker 01

  attended W A C last year. It's a lot of the messaging towards the global south, because I think there's a lot of worry around, you know, countries that frankly, don't have the talent or compute, or even just the raw material, whatever needed to right now participate on the model layer. They don't want to be left behind. And Xi Jinping's message is saying, hey, we will be exporting this along our Belt and Road, essentially, that you can still be participate in the AI boom or the next wave of infrastructure upgrade.

• 32:44Speaker 00

  Yeah, I mean, I'll just say, you know, and then go to break unless you want to comment on it. Like, it's definitely in China's interest for this all to just commoditize. Even if it's not the direct strategy, I'm sure they're, they're quite happy to see

• 33:01Speaker 00

  the US industry, after all these billions of dollars have been put towards the developing of models, at least sweating a bit. So there will have to be, you know, some, some adjustment on the US side, because this is sort of, if you're thinking about it from the closed model standpoint, it's not what you want. It's probably why we see, we saw Anthropic spend all that time, you know, waffling, or not waffling, but just not responding to this open source, you know, sort of moment of praise in the US. And so that sort of leaves us to what the, what the competitive dynamics of AI looks like, assuming this is, continues

• 33:42Speaker 00

  to be the rule that, that open source, you know, it used to be that the thought was open source was a year behind, like the US frontier models. Now it seems like it's just months. So what does the competition look like? We'll cover that when we come back, right after this. Hi, everyone. Alex Kantrowitz here. I want to tell you about a documentary I have made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents, I sat down with MIT Professor Ramash Roskar, former White House CIO Theresa Payton, Michelin's Group Chief Data and AI Officer Ambika Roger Gopal, and Sharon Gai, a former executive at Alibaba. They each offer unique insights into this

• 34:27Speaker 00

  evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey. You can watch the full documentary at the link in the show notes.

• 34:47Speaker 00

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• 36:47Speaker 00

  And we're back here on Big Technology Podcast with Grace Xiao. You should check out her Substack. It's AI Proem. So it's A I P R O E M dot Substack dot com, where she covers this world in great depth and with great clarity. So highly recommend you sign up. Grace, let's talk about what the competition, by the way, feel free to comment on what I, what I shared, you know, before the break, but also like, what does the competition in AI look like right now? If you have, you know, basically this world, let's say we get to the world where open source commoditizes the closed models, and, you know, the whole

• 37:28Speaker 00

  plan was to sort of have these closed models and sell AGI on a meter. But if you can't do that, then what happens to AI?

• 37:37Speaker 01

  I think for sure, there has been a bit of a global reckoning, I think, on twofold. One is the need for governance because of how, how much these AI models can do and the potential risk that's been talked about, especially in the mainstream narrative in the US, right? And I think that's really sent kind of fear a bit around the world. Now, given that, I think, following up on what we just talked about, I think it is in China's, China's interest. And it was reiterated at W A C, where AI governance will be another focus. And this ties to, I think, the whole open source thing, because it says, basically, they're signaling, let's build around the industry and find guardrails to basically, in some way, control or contain this technology, because they still see this

• 38:22Speaker 01

  technology as similar to any other technology in that sense. It's not like this new mythical creature that we cannot contain. And then, from there, let's export it to the world from a very, from a high level. From a business level, I think this is the second point I was going to touch on, which is, it's been really interesting. I think even a year ago when we spoke on Big Technology Podcast, a lot of companies were really, really gunning for the US market. It was seen as, if I want to sell, the US market is always going to be the most lucrative, enterprise companies are willing to pay, blah, blah, blah. You know, if we make it in the US, we've made it, right? That was the, like, holy grail. And there's been a complete change of mind recently, when I spoke into quite a few of

• 39:05Speaker 01

  the leading Chinese products, whether they're on like coding agents, what, whatnot. It's really focused on potentially going to, you know, Southeast Asia, potentially going to Europe, because they're saying, okay, first of all, geopolitical headwinds is not, like, it's no joke, it's not going to be easy to sell to US. There's obviously a grueling competition in the US domestic market. But there's a lot of desire from other markets that want Chinese technology providers. They're saying, maybe some of them don't want to pay that massive premium from US tech. And maybe some of them are also losing a bit of interest from, you know, the very scary narratives that they're hearing from the US as well. And some, and then on top of that, many of them are looking to build on top of open

• 39:49Speaker 01

  source models, and they need a support to help them kind of build that, you know, infrastructure around it. So it's been very interesting to hear that kind of mentality shift.

• 39:57Speaker 00

  Yeah, you also, I mean, you put it basically, the problem for the US closed source or closed model developers, you put it very clearly. Essential risk to the frontier labs is not that their models suddenly become useless, it's that frontier level capabilities, capability becomes increasingly difficult to monetize at premium prices, when open weight alternatives can perform most tasks at a fraction of the cost. I mean, you come at this from a business standpoint.

• 40:25Speaker 00

  If that becomes the reality, right, what do you even do if you're a closed source company like a closed

• 40:31Speaker 01

  I think you can still, you can still charge. I think, you know, for certain government agencies, certain companies, you know, Fortune 500s that might have very strict regulation compliance or rules, whatever. I think for certain sensitive sectors, if you were to wanna use American tech stack, it still makes a lot of sense because maybe the money does not, is not a main fact considering factor. Does that make sense? But for a startup for SME, like every penny matters and you're gonna wanna fi- find the best model for your ROI. So I, I do think end of the day, majority of the world actually runs in a very pragmatic lens because you gotta pay your bills and you gotta make sure your business is generating money.

• 41:12Speaker 01

  So when you are buying for intelligence, that intelligence needs to make sense and justify the cost of it. And I think what we saw a couple of months ago was a sudden awakening or realization that a lot of the token maxing wasn't making investment sense because, you know, spending a million dollars per person on token usage is mental when their salary is maybe like 200K and their revenue generation is even lower than that. Do you know what I mean? Like it doesn't make any business sense. So I do think businesses will look at this very differently and it's putting pressure on the closed models. But I think people who are working at the most frontier or even like, I'm just pulling a name out of my head, but like a Jane Street.

• 41:55Speaker 01

  If you're gonna spend $2 million per head, but you're gonna generate like 20 million on each, you know, bet, like on each investment, then that money is justified to Jane Street probably. But I think, you know, it goes back to how do you justify that cost?

• 42:13Speaker 00

  Right. And I think you also, I mean, you, you effectively have the answer in your piece or, or maybe a different answer. Uh, or maybe an answer that expands upon this in your piece that I think is, you know, sort of says it all, right? So you say, uh, and this is your reaction to Kimi K3. You say Kimi K3, but you can basically say this about this entire open source, uh, moment. It, you say it's the latest evidence that the AI frontier is becoming more contested, more global and potentially less proprietary. And I think we talked about this a little bit last time also. What happens when the AI intelligence itself becomes more proprietary, uh, becomes less proprietary? The thing that matters is the product, right? So I asked earlier, what do the AI labs do? And we've been on this a bit on the show recently.

• 42:57Speaker 00

  If you're, if you're, um, if the intelligence that you've developed becomes less proprietary, um, if it becomes more of a commodity and less of something that you can hoard, your products are, are just what matter. And we, we saw this with, with DeepSeek and we're seeing it again here. I think that if there was a belief that you could build a trillion dollar company just by selling intelligence from the API alone, uh, not gonna happen, most likely. Uh, but what could happen is you, if you're the developer of the intelligence, you can build products with that intelligence. Uh, and, and sort of return to your investors that way. Your thoughts?

• 43:36Speaker 01

  Yeah, definitely. I think, you know, um, on DeepSeek, first of all, I don't think they would push out products because like I said, I don't think they're trying to commercialize or

• 43:44Speaker 00

  No, no, I'm talking about OpenAI and Anthropic. I think the DeepSeek's and the Kimi K2's of the world are thrilled to build just the models.

• 43:52Speaker 01

  But, but I do think, I, I agree with you. I think there's a lot of stickiness when, in the products. And I think, you know, Claude, Co-Work, uh, these products like are still very, very sticky and there's still gonna be a lot of potential for them to continue to grow. Like I recently spoke to designers where I thought maybe like, do you use Claude design or do you still switch back to Figma? Cause Figma is gonna add on AI. Surprisingly, the answer was that if I'm gonna do everything within Claude already, I'm already using it as my think, thinking partner. I'm already using it for coding. I'm already using it for all the other tasks beyond my actual day-to-day design work, then I will also use Claude for design. So it's almost adopting a super app kind of mentality. It's like, I wanna capture all. Now, however, I have the reverse, kinda like the counter argument as well.

• 44:37Speaker 01

  So I went to visit Alibaba recently and I was really fascinated with a product that's just really, really not under, in the radar. Frankly, I don't even think their own company is really valuing it. It's called Axios. And it's actually just a very simplistic agent interface built on top of their supply network. And why it really amazed me, it was because the, the, the know-how and the actual edge they have is exact, not as intelligent. It's because like we said, frankly, finding a supplier for, I don't know, a glass of glass or a lipstick or a microphone is not difficult, right? But what their actual proprietary strength or their know-how that no one else can replicate is their 20 years of like kind of

• 45:20Speaker 01

  doing business in supply, finding suppliers, matching with merchants and then helping merchants sell it. So they're a to B to C business and now they basically built an agent on top of the 1688, which is their wholesaler website. And like I was talking to their people, like their, their representatives and they're showing me the interface. It looked like Co-Work, um, a bit glossier, like prettier because you know how Chinese apps love to have like a zillion different buttons? So there's colorfully colored and it has all these buttons. And if you are just an SME or like a drop, drop shipper or like a Shopify brand owner, all you need to go do is go on the platform and say, I'm looking for insert eight.

• 46:01Speaker 01

  Like you can make merch for big technology and they say, I want merch for big technology. This is my, uh, these are people I've interviewed. This is my vibe. I don't know what color I want. I don't know what size. I want something universal. Help me think it through. And they will literally go through their database and try to find you the right optimal kind of product. And then they will source it and they'll talk to the suppliers for you, find the best price for you, then give you the three to five, like, you know, options and then you can pick. So, so for me, I was like, wow, the actual edge of these products are not in intelligence. It's opposite of Claude, what Claude is doing or GPT is doing. They're not going into every vertical. They are simply doing this one thing, but doing it very well. And it's kinda similar to what I talked about in May 2 as well, which is the creative industry.

• 46:44Speaker 01

  They're like, we build these models and they're gonna be the best models for you to use to optimize your e-commerce product placement or to filter your job of face and make your job line more shizzled or whatever. But they're not for you to build, you know, use to create the next, you know, Hollywood blockbuster or whatever. So I think there's more and more, um, awareness around that now.

• 47:07Speaker 00

  It's so interesting. What you're describing is almost a flip of what we've seen in the consumer internet up until this point, where China had the super app and the US had a bunch of disparate apps. And I think maybe because of China's, uh, embrace of open source and because of the US closed AI model, uh, we might end up seeing a proliferation of individual AI apps in China while the US goes to super apps. It's like the craziest dynamic.

• 47:33Speaker 01

  That's so funny. We just coined something, Alex. We need to IP this. We called it first.

• 47:38Speaker 00

  That's good. That's good. Let's do, uh, we'll do a co, uh, co byline on, on Substack. That'd be fun.

• 47:45Speaker 00

  Okay. We have to talk more about compute because, um, yes, uh, you know, you could say Moonshot was able to design Kimi K2. Uh, or K3. I keep calling it K2 because I get the mountain stuck in my head. K3. Um, and there was a Kimi K2. Uh, but with the compute they had, but and you talked about they get paid when people wanna, you know, use the model off their API. The issue is that they couldn't really sustain a lot of demand, right? They had to take the model off basically or not. They had to limit new signups, uh, because of their compute constraints. And so if I'm, let's say, let's say I'm gonna try to channel like Greg Brockman from OpenAI.

• 48:28Speaker 00

  You know, he might say China can go and ship all the parody AI they want. If they can't deliver it with enough compute, it doesn't matter and compute is gonna be the thing that makes OpenAI win in this race. Uh, what do you think about that?

• 48:44Speaker 01

  Honestly, I think compute is the obvious constraint. I don't think anyone's hiding from that. None of the lab leaders are hiding from it. And the fact that you, like you said, Kimi literally posted on X saying that they needed to, you know, reassess basically who they serve. They're not the first to talk about it. I think last year around Chinese, oh, this year around Chinese near. GLM faced something similar. DeepSeek has faced something similar when demand is literally higher than supply. Uh, which is funny because, you know, the, the argument in mainstream is always about, is there enough demand for AI? There is. Now, on the China side is there's not enough supply for AI. So I, I, like, as in the intelligence side, I like the compute side.

• 49:24Speaker 01

  So I think, um, it's, it's not a secret that China's trying to figure out how to build their, like self-reliant tech stack. Um, it's not a secret. Huawei is working very closely with DeepSeek on how to optimize hardware and software and try to figure out this. Now, I, I am not a semi-expert, so I can't comment too much more on the technicalities. But I think even Elon Musk recently came out during a Economist interview saying, he said something in the lines of, I believe China will figure out Baccary and it's closer than we think. So I wouldn't give a number on it, but when I hear from other experts, like I've spoken to Paul Triolo, who is a expert in semi,

• 50:07Speaker 01

  semi semiconductors and especially China's supply chain on this space. He also believes like China's gonna find probably solutions, you know, it, it might not be the smallest chip, you know, but they could potentially find other ways to optimize the chip, even if it uses more energy. And it goes back to what we talked about even last episode when we spoke, it was like China's energy infrastructure side is not a problem. So if you have enough electricity and power to be powering these chips, even if you need to use double amount of energy, that's not a kind of a bottleneck for, uh, the AI compute side. However, obviously the argument is then how sustainable is it in the long run for the environment and everything?

• 50:47Speaker 01

  But I think it's an ongoing R and D, um, and, and potentially we'll see more breakthroughs coming, coming in the next few months or years.

• 50:56Speaker 00

  Now, I'm going to ask you a question that you've called ridiculous in your writing, but I feel like we shouldn't let it go unaddressed, which is, you know, we started this conversation about, you know, you talked about how China has a lot of homegrown AI talent. And that is true, but the wrinkle in the Kimi K3 story is the Moonshot CEO Yang Zilin did a lot of his graduate work or his graduate work at Carnegie Mellon. And in fact, his like Carnegie Mellon advisors were celebrating his breakthroughs on X in the days after the release. So the question that you've called ridiculous is why didn't he stay in the US?

• 51:39Speaker 00

  I think for, you know, you, audience outside of China, it is an interesting question. And so I'm just gonna ask it to you. Why did he, why did he leave and decide to do this elsewhere?

• 51:52Speaker 01

  I can't exactly tell you exactly what he said, obviously, but I think multiple star researchers have returned to China over the years. There's obviously various different layers of this. At a very high level, people love to say, obviously geopolitical headwinds is not making easier for Chinese nationals or Chinese ethnic people. I think the rise of, you know, racism, frankly, even during COVID made people feel uncomfortable at a personal level. That we don't know that could potentially attribute it to it, right? But then there's also just the personal reason, I think, is the main driver for most people, honestly. Like I've spoken to researchers where hate to over generalize, but their wives or spouse or whoever are maybe teachers, lawyers, you know, healthcare professionals in China.

• 52:36Speaker 01

  And those are not very transferable skill, like not very transferable credentials. You know, previously what we saw seen in immigration or immigrant families is that people who got like, who are doctors will maybe go to the US and have to retrain, redo their residency or even, you know, frankly, not be able to practice anymore. So there are a lot of personal reasons driving a lot of researchers and tech professionals saying, hey, I would actually rather be close to my home and then I can have my spouse or family do whatever they want and they can still build their own career. There's number one, then there's obviously the family kind of point. I think, you know, again, people really want to be close to family. I think it's not that hard to understand, right? There's that.

• 53:18Speaker 01

  And then on top of that, I think is the more nuanced thing where I get some hate from. But look, I was raised in Canada. I was educated in the US. I think a lot of my peers around me are similarly like that. And I think there is a choice for people frankly, like us, and it's a privilege we have. And when you think about it, where you want to stay, there is obviously the visa requirement. But then there is also the quality of life and your, your own native culture, right? So where I sit in Hong Kong, I can natively be both Chinese and Western. I think it's very actually accepted. I think someone like Yang Zilin or, you know, Yao Shun at Tencent, who's a former OpenAI researcher, you know, their native language is Chinese, their native culture is Chinese. And then it goes back to the quality of life.

• 54:03Speaker 01

  I'm sure people love to say, oh, but you know, why would you want to stay in Asia, blah, blah, blah. The fact is, I think 30 years ago, for any average Chinese immigrant to go to North America, it is a no brainer because the quality of life in almost any major city is going to be higher than a major city in China. But that kind of, I'm talking about in politics, I'm talking about individual taste, individual lifestyle, the day to day, me buying coffee and living in a nice apartment, things like this, the quality of life is no longer justifiable. And I think it's really a trade off people decide. So just to bring it back to myself, my parents immigrated to North America more than 30 years ago. That was a very easy decision for them back then.

• 54:47Speaker 01

  They were educated in North America. They stayed on, right? No one actually questioned, why would you return to China or anything like that? And but now actually, if you meet very, very talented researchers, finance professionals, journals, whatever, right? People want to return to their home country because of familiarity, but also because of very high quality of life. And then on top of that, I think Yang Zilin, knowing that he is a genius that he is, probably had some more, you know, relationship in China to help him build this out and his own peers, his own team to build out. Imagine, you know, moving to a new country in your twenties and trying to build that kind of relationship and, and build up that reputation.

• 55:29Speaker 00

  Totally. Okay, Grace, my last question for you, I don't want to let you out of here without addressing what the next step in this is going to be. And I think we all know it's robotics. You know, we spoke about it last, last time that you were here, that China has this advantage because they've been building a lot of hardware and now they have AI development. So just give us like a quick look at how robotics has progressed in the last year in China. I recently saw, I think it was in Shanghai, a bunch of robots fighting each other in a UFC. I don't know if they were like telecontrolled or autonomous, but it does seem like there's, there's some progress being made. And obviously it's a place we got to pay attention to. So give us the update on that.

• 56:11Speaker 01

  Yeah, there's definitely a rise of so-called like tech investor tourism in the Shenzhen GBA area, which is the Greater Bay Area that connects to Huaishin in Hong Kong, because that's, that's where all the manufacturing of robotics happen right now. So for sure, I think China right now is, has a huge advantage in the supply chain of robotics, because given the last 30 years of being, you know, the manufacturing hub of essentially everything under the sun, any robotics company that need, will have some kind of a supply and touch point in China and likely from that GBA area to start with. Then beyond that, I think, you know, a lot of the speed is an advantage when I speak to people on the ground, production line can roll off almost 50% faster than other countries.

• 56:54Speaker 01

  Even if you look at like an EV car or something, when I spoke to people in the EV industry, they're saying, you know, a seeker company, a seeker car can roll off the production line within a year and a half from design to production versus maybe three to five years for a traditional OEM. That kind of Shenzhen speed transfers into robots. It's significantly cheaper. It's said that, you know, a lot of these robots, whether they're humanoid or industrial robots, the raw kind of hardware itself can be like at least 50% cheaper than produced elsewhere. So all of these have really kind of emboldened China's hardware space or robotic space. And beyond that, what's really interesting is you're seeing a lot of companies in the

• 57:37Speaker 01

  EV space or in other, other areas that were autonomous driving, they're now inching towards and expanding their range into humanoid robots. So it's very interesting. However, that said, I will preface saying, I think, you know, humanoid robots right now, it's still in a very nascent state. Of course, the dexterity and the mobility has improved significantly from what we've even seen last year or 10 years ago significantly. But the real life use cases are very, very minimal. Because if you think about how much it takes for us to even like lift up an arm like that, like I look like a T-Rex, but you know, it's not easy. And what is the real use case of this? But this takes training. And then the real, real, real bottleneck for these, all these companies, whether you're

• 58:19Speaker 01

  talking about autonomous driving or industrial robotics or humanoid robotics with, in terms of AI integration, is that they don't have enough physical data. And I think that's where the world models come in and we're seeing a lot of competition going in there right now.

• 58:33Speaker 00

  Yeah, that makes me relieved. I think, you know, I have appreciated the fast progress that we've seen up over the past couple of years, but if we had a robotics intelligence explosion alongside this like LLM explosion that we're having right now, I don't know if we could handle it. I mean, I'm sure we'd figure it out at a certain point and they, they will get there. The researchers will get there on robotics, but kind of, I don't know.

• 58:57Speaker 01

  I think it takes more time. You know, I just spoke to Pony AI CEO a couple of weeks ago, and he, he basically was, you know, when Silicon Valley said, okay, autonomous driving is going to reach us in the next three to five years, he said 10 years. And it took him 10 years. So he, I think he founded the company in 2016. Now 2026, they're deploying hundreds of cars in their fleets. He was saying, I was like, what about robotics? He's like, look, it's the same thing. People love to hype about it. They're saying, look, humanoid robots are going to be deployed in the next three to five years in the mass market. He gave me a rough number 10 years again, and I, I believe him because I've seen a lot of these robots. They, they frankly can't do much. And then it's also the again ROI, like if you're going to buy a robot and help you

• 59:40Speaker 01

  restock bottles on your convenience shop, convenience store shop, those cost about 700K, so almost 100K USD. In markets, especially like Asia, across Asia, where labor definitely does not cost 100K for a year, how do you justify that? And they do extremely so and often like make mistakes where you can actually create employment. I don't think they're taking anyone's jobs anytime soon. I think industrial robots in manufacturing warehouses where actually there is already a labor shortage globally, they will see more use cases and more mass adoption, but this is not going to be seen by the consumer eyes. In fact, they're already being deployed, right? Like the six axes arms, the things that lift things, logistical use cases where autonomous

• 1:00:24Speaker 01

  vehicles like that look like, I think the company was called Neolex, they, they look like little boxes are already on wheels and shoveling things and taking things and putting things back in shelves in warehouses. These are, I think, use cases where they're actually complementing the current workforce because a lot of times these are not really fun jobs. These are labor's jobs that people don't want to do already. And frankly, the intelligence of it is very low, so they can just repeat program it.

• 1:00:52Speaker 00

  Yeah, I believe in the potential for these things, but right now, nothing makes me happier, happier than seeing a humanoid robot brought on stage for a demo and just falling and totally collapsing. I mean, I have great joy when that happens, so. But eventually, get it right eventually, just like, let's take our time on that one. All right, Grace, the, the website, I should say it again, aiproem.substack.com, P R O A I P R O E M dot substack.com. Grace, you've done it again, you know, two years in a row. Great opportunity to speak with you and help us understand everything going on with the Chinese AI movement, which I think will only get more interesting from here. So thank you so much for coming on.

• 1:01:36Speaker 01

  Thanks for having me again, Alex.

• 1:01:38Speaker 00

  All right, we'll have to do it again soon. Thank you, everybody, for listening and watching, and we'll see you next time on Big Technology Podcast.

• 1:01:47Speaker 02

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