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

Summary

---

Big Technology Podcast

Episode Title: How China Caught U.S. AI — With Grace Shao

Host: Alex Kantrowitz
Guest: Grace Shao, China AI Analyst & Author, AI Pro E M Substack
Date: July 29, 2026

---

Overview

This episode delves into how China has repeatedly caught up to — and in certain ways, matched — the United States in AI development, culminating in the recent release of the Chinese LLM “Kimi K3” by Moonshot. Host Alex Kantrowitz interviews Grace Shao, a leading China AI analyst, to dissect the drivers behind China’s AI rise, the importance of open source, the constraints and innovations forced by limited resources, key cultural factors, and what this means for AI competition globally. The show also critically contrasts open vs. closed model approaches and explores the business models, geopolitics, and looming questions about the future of AI and robotics.

---

Key Discussion Points & Insights

1. The Surprise of China’s AI Leap (Kimi K3)

• China’s Kimi K3 model performed at or near the frontier of US LLMs, despite hardware and capital constraints ([02:32], [03:43]).
• The achievement sparked surprise and questions about how this was possible.

Notable Quote:

“The release of Kimi K3 largely had this reaction of like, ‘How did they do that?’... It’s confused a lot of people… The new model from Moonshot, Kimi K3… is doing really well.”
– Alex, [02:32]

2. The Talent Engine & Culture

• China’s massive STEM-educated talent pool underpins its AI progress despite resource constraints.
• Instead of “rocket science," success in AI comes down to abundant, motivated researchers: mathematicians, physicists, and engineers now focused on AI ([03:43]).
• Strong lab cultures: Researchers are mission-driven, less distracted by commercial applications, and reward unity and commitment to open-source R&D.

Notable Quote:

“Having visited them, this result is less surprising… Very few have a culture that you can immediately pick up like this.”
– Alex (reading Nathan Lambert), [06:17]

“There is a sense of unity, a sense of shared mission. And I think people are a bit undervaluing this case, especially when we’re seeing other labs having a bit of infighting or… misalignment.”
– Grace, [08:26]

3. Constraint-Driven Specialization & Ecosystem Collaboration

• Limited compute and capital force labs to specialize (e.g., coding, agent functions, multimodality) instead of competing on all fronts ([03:43]).
• The ecosystem is “collegial” — labs share R&D openly, reference each other, and learn rapidly from open innovations ([06:17]).

Notable Quote:

“The open source ecosystem has really harnessed this pretty collegial competition and there’s a lot of… learning and referencing off of each other.”
– Grace, [03:43]

4. Open Source as China’s Secret Weapon

• Open-source models are accelerating progress, letting Chinese (and global) labs “catch up” through collective iteration ([07:14], [15:09]).
• Open sourcing was initially motivated by a need for transparency and to attract developers (especially internationally), but now serves as a competitive flywheel: more users drive more data and quality, and cross-lab congrats and “copy-positivity” are now routine ([15:09]).

Notable Quotes:

“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&D.”
– Grace, [15:09]

5. Shift in the U.S. Attitude Toward Open Source

• Despite initial fears, U.S. labs and tech CEOs (e.g., Nvidia’s Jensen Huang) have rallied behind open source.
• Even “closed” labs like OpenAI and Anthropic are publicly supporting open source, realizing advantages can’t be hoarded ([18:10], [19:42]).

Notable Quote:

“If we don’t open source and we build the strongest open source models in the U.S. 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.”
– Grace, [19:42]

6. The Distillation Debate

• Distillation (training one model using another’s outputs) is viewed as an open secret; there’s “dumb” (copycat) and “smart” (value-add) distillation, with the latter seen as a grey area and common practice globally ([23:00]).
• While some allege Chinese labs “steal” U.S. model outputs, technical experts say the main edge is homegrown talent and execution — not just IP copying ([25:54]).

Notable Quote:

“If adversarial distillation…contributed, it is at most to a relatively small degree…Chinese companies are extremely good at building models in the same way the leading American companies are, which I would say is even more remarkable given…they can’t get the latest Nvidia chips.”
– Nathan Lambert (read by Alex), [25:54]

7. Business Models & National Strategy

• Open source does not mean anti-commercialization. Labs make money via API access, managed services, and are seeking fundraising and IPOs ([28:18]).
• Open models can still be profitable (e.g., Minimax $1B run rate); focus is on proliferation and influence, not max profit.
• Chinese government supports open source for economic and geopolitical reasons: open source helps commoditize U.S. AI, increases global influence (esp. in Global South), and serves soft power ([30:13], [31:09]).

Notable Quotes:

“Open source is not anti-commercial, but obviously it 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?”
– Grace, [28:18]

“Xi Jinping’s message is saying, ‘Hey, we will be exporting this along our Belt and Road…you can still participate in the AI boom.’”
– Grace, [31:09]

8. The Future: From Model to Product

• As open source closes the performance gap, intelligence itself becomes less proprietary; the product built atop the model becomes the differentiator ([42:36], [44:07]).
• U.S. labs may pivot to products and vertical integrations for stickier revenue, while China experiments with utility-focused AI ‘agents’ over super-app style platforms ([47:31]).

Notable Quotes:

“If the intelligence you’ve developed becomes less proprietary…it’s the product that will matter.”
– Alex, [42:36]

“For me, the actual edge of these products is not intelligence…Their know-how that no one else can replicate is their 20 years of doing business in supply, finding suppliers, matching with merchants…”
– Grace, [44:16]

“It’s almost a flip—China might see a proliferation of niche AI apps, while the U.S. consolidates into super apps. The craziest dynamic.”
– Alex, [47:31]

9. Compute Constraints and Innovation

• China’s main disadvantage is compute (chip) access; increasingly, demand exceeds supply and models are sometimes rate-limited ([49:08]).
• China is trying to build a self-reliant chip stack and may use resource-intensive but functional workarounds, leveraging vast energy resources ([49:08]).

10. Talent Mobility and “Returnees”

• While some star AI scientists (e.g., Kimi K3’s CEO Yang Zilin) were U.S.-trained, personal, family, and lifestyle factors — plus increased opportunities in China — drive their return. For many, quality of life and career potential tip the scales ([52:16], [54:00]).

Notable Quote:

“Now…people want to return to their home country because of familiarity, but also because of very high quality of life…That’s a privilege we have.”
– Grace, [54:00]

11. Robotics: The Next Battleground

• China’s established manufacturing base, supply chain, and speed make it a natural future leader in robotics ([56:35]).
• Robotics is in early stages; deployment, especially of humanoids, is nascent and ROI remains challenging outside logistics and factories ([58:56]).

Notable Quote:

“It’s the same thing. People love to hype about it…He [Pony.ai CEO] gave me a rough number, 10 years again. And I believe him, because I’ve seen a lot of these robots, they frankly can’t do much.”
– Grace, [59:21]

---

Notable Quotes: Quick Reference

• On culture:
  “A sense of unity, a sense of shared mission…" — Grace, [08:26]
• On open source:
  “Open source is able to play catch up because they are leaning into everyone’s intelligence or everyone’s R&D.” — Grace, [15:09]
• On monetization:
  “Open source is not anti-commercial, but obviously it helps you make less money.” — Grace, [28:18]
• On product vs. intelligence:
  “If the intelligence you’ve developed becomes less proprietary…it’s the product that will matter.” — Alex, [42:36]
• On the global dynamic:
  “The AI frontier is becoming more contested, more global, and potentially less proprietary.” — Grace (quoted by Alex), [42:36]

---

Timestamps for Key Segments

• 02:32 — Kimi K3’s achievement, China’s surprise parity in AI
• 03:43–06:17 — The role of talent, specialization, and the open source ecosystem
• 08:26 — Importance of lab culture and unity
• 15:09 — Rise and mechanics of China’s open-source R&D
• 18:10–19:42 — Shift in U.S. open source attitudes
• 23:00–26:51 — Distillation debate and IP concerns
• 28:18 — Open source monetization & business model breakdown
• 31:09 — Chinese government’s open strategy in global AI
• 42:36–44:16 — Shift from closed models to value in products
• 49:08 — Compute constraints and the path forward
• 52:16–54:00 — Mobility of top Chinese AI talent
• 56:35–58:56 — State and outlook for robotics in China

---

Conclusion: Implications for the AI Race

• China’s AI labs are thriving through resourcefulness, specialization, and open-source collaboration — not just “stealing” U.S. innovation.
• The future likely favors pragmatic, productized AI over monolithic, closed models.
• Open source is eating AI’s core value proposition, driving both commercial opportunity and geopolitical intrigue.
• As AI commoditizes, the competitive battleground will shift to vertical solutions, local adaptations, and the coming robotics wave.

For more from Grace Shao, visit aiprom.substack.com.

---

Transcript

[A]

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 USAI Labs permanently lose their lead to China? We'll talk about it with a leading China analyst, Grace Shao, right after this. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results, not just ideas. When pace and performance matter most, PwC combines market insights and deep sector experience with AI, cloud and emerging tech to accelerate your transformation and drive measurable ROI. From strategy to execution, PwC can help you anticipate what's next, outpace disruption and compete. For more information, visit pwc.com Insurance isn't

[B]

one size fits all, and shopping for it shouldn't feel like squeezing into something that just doesn't fit. That's why drivers have enjoyed Progressive's Name your price tool for years. With the name your price tool, you tell them what you want to pay and they show you options that fit your budget enough. Hunting for discounts, trying to calculate rates, and tinkering with coverages. Maybe you're picking out your very first policy, or maybe you're just looking for something that works better for you and your family. Either way, they make it simple to see your options. No guesswork, no surprises. Ready to see how easy and fun shopping for car insurance can be? Visit progressive.com and give the name your price tool a try. Take the stress out of shopping and find coverage that fits your life on your terms. Progressive Casualty Insurance Company and Affiliates Price and Coverage Match Limited by State Law

[A]

welcome to Big Technology Podcast, a show for cool headed and nuanced conversation 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 Kimmy 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. She is the author of AI Pro M on Substack and a leading analyst on China's AI efforts and of course the front of the program. Grace, it's great to see you. Welcome to the show.

[B]

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

[A]

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, you know, despite the fact that people in the US and Europe and maybe all over the globe have known that China has, is very capable in its AI research, the release of Kimmy 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. And this, this new model From Moonshot, Kimmy K3, we've talked about it on the show, it's doing really well. This is from Nathan Lambert's substack Interconnects. It comes number two on the VAL's AI index. Number three overall on artificial analysis is intelligence index. Number one in the front end coned arena and has many more impressive results. So let's just start there. Should we be this surprised and, and how did Moonshot do it?

[B]

I can't comment on exactly how Moonshot did it, but I'll start with a comment. Actually, Nathan even shared with me when we talked about after his big China trip when he visited all the labs. I think it's 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 Deep Seek came out with a whole domestically educated and domestically trained talent pool. Now I think that's something really under looked 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, you know, researcher realm. And then we see like the leading researchers, really, 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 that 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 the obvious constraint driven specialization or the computer, like, you know, 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, Deep Sea, 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 agent Push Out. You know, Minimax previously was more focused on multimodality and ZI focused on coding. 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 a 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 harness this pretty collegial competition and there's a lot of, you know, learning and referencing off of each other.

[A]

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, 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, of a scaling game, much of the ability to build a good 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.

[B]

And I just want to double click on that. I think both, you know, Deep Sea 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 focus on the pursuit of AGI, not kind of getting distracted into consumer applications or whatnot. I think both in 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.

[A]

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?

[B]

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 Deep Sleep.

[A]

Welcome to research.

[B]

I think for, you know, liangwanfeng, he's talked a lot about his philosophy where there's a very low churn rate in the Deep Sleep 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 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, 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 Kimmy Yang Zhiling also openly kind of talked about a lot about the hardest thing about building this business is not the R and D. The hardest thing is organizing, managing the organization and finding the right people for the right kind of work and making sure everyone's united. I think even Zi has talked about how Tang Jie, who is the chairman, he's actually a pre. He's a professor of Yang Zhiling 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 an 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 valley misalignment. Yeah.

[A]

Now some people will say, oh, well, this is just like 9, 9, 6. So people in China are out working people in the US where they're doing, you know, 9am to 9pm Six days a week. How much of that would you ascribe to it?

[B]

I think there's 996 everywhere, honestly. I mean, this sounds. This is really controversial. I think if you're passionate about what you're doing I996 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 9pm in New York. You're doing this recording with me today. 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 are passionate about what you're doing, people are willing to work. However, it's actually interesting. Yama Fung even talked a lot about how he does not believe in overtime for the sake of overtime, FaceTime for the sake of over 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 people are hustling when they need to.

[A]

Right? Yeah. I mean still, from the, from the, you know, Kimmy K3 did so well on so many benchmarks 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 a lot 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. And so if you decide, hey, the, the most valuable thing is getting coding. Right? And because coding sort of is the foundation for agentic tasks, then you can start to see potentially the results that Kimmy saw when you want to specialize there.

[B]

Definitely. And I think on that note, I want to bring up a very interesting phenomenon I'm sure you saw like a couple months ago there was a huge hype around Chinese model. Chinese companies all pushing out their own frontier models. Like I don't remember like even like delivery app Meituan, you know, hardware company Xiaomi. And then I think a lot of investors, 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 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 Babas and Tencent of the world do not actually just do what you and I know about them. 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 it goes back to open source again. I think there's a lot of, 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. And then beyond that, what was really interesting is when I speak to the MEITU cfo which is a leading like creative AI company, he was saying, look, we don't really need the most like the biggest model, we don't need the best model in that sense, but we need the best model fine tune for our use case. So we can push out our model in every single vertical app we have available. So they really lean in on, you know, fine tuning open source models for creative use. What is image or video generation or even video editing, photo editing, et cetera. And I just thought that was something that's really embedded and ingrained 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 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 to better execute it within a smaller model that's better used, created for your use case or is it better that we actually all pay for the biggest, largest, best model?

[A]

Right. So having that data combining with a smaller purpose built model can actually deliver similar performance as using the bigger model, 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 sort of effort here. And I remember after Deep Seek came out I spoke with somebody who knows their stuff in AI and they were basically like look, with open source it's every open source research house working together. When you're building a closed model like OpenAI or Anthropic, you know, have you're basically building on your own. I mean of course they can bring in the open source 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.

[B]

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 a closed source at this point. Right. And I think it was really humbling to even see this morning. Kimmy 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 basically something like a paraphrase, but it's something like that or something 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. 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, language or Yang Jilin have really, 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 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 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 a lot of users, especially startups that might be more cost constrained and less compliance, you know, worried getting on these labs, I started 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 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 unintentioned, 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. I believe, I think when Deep Seat came out with something, CI even retweeted them on Twitter on X saying, oh, Congratulations. This is such like, you know, 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 layer. So, so essentially, you know, 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 follow sighted 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. So all of that has garnered a very strong open source philosophy base in China.

[A]

Now a very interesting thing has happened in the ensuing days and we're talking Monday, July 27, 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, China of course is this open source ecosystem the US was leading or is leading, I would say still leading at least when you, when it comes to model intelligence. And the advantage that the United States has had is these closed, 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 U.S. companies? It would be almost a fear of open source and a rededication to closed. But the exact opposite has happened this week where, you know, basically led by Jensen Huang, the Nvidia CEO, seemingly every US company has come out in favor of open source. Even OpenAI has signed on to this letter saying we shouldn't ban open source. Then after like days of silence, Anthropic basically had to come out with a statement said hey, hey, by the way, we never called for the banning of open source. So Grace, just help us understand, what do you think about the fact that if open source is China's big advantage, 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?

[B]

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 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 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. And even despite, you know, Kimmy 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 on 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, deep seats people have talked about how they are not revenue driven. So whatever money they start, they're not profit driven. 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 I take a step back and all understand where people's perspectives are coming from and what their own goals are. Right?

[A]

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, 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 could buy that at a surface level. But we, we do have evidence that the labs like Deep Seek and, 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?

[B]

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.

[A]

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.

[B]

Right. Right. So, 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 Sachs, 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's days. I think Freebie was saying that. But beyond that, what something I found the most enlightening. The FR framing was provided by Google DeepMind's Yao Shun Yu. He's a researcher at Google and he said there's smart distillation and dumb distillation. Dumb distillation is something what the layman think of when distillation happens. Essentially I literally take Model A's answer and plug it into model B essentially. And then model A will just spit out. Model B would just say spit out what Model A said. And it's so obvious, it's like copycatting. 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 an enterprise and I self host an open source model, I'm going to fine tune it with my own data, I'm build harness around it, I'm going to make it the best model for my own use case. And often I 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 the thinking machines model distilling on Chinese models. So it's quite funny where everything's kind of going a 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 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 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.

[A]

Yeah. 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 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 Kimmy 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 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.

[B]

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 Kimmy 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.

[A]

Okay, so you know we, 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 it. I think that and Moonshot is owned by Alibaba, funded by them.

[B]

They have some backing financial back. Actually a lot of the labs have a bit of financial backing right now. But we can talk about their financial breakdown.

[A]

Yeah, so it sort of sparks the question why are they doing this? 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 Moonshots consultants. What is the economic advantage of open sourcing all of this technology?

[B]

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 are trying to complete. Pete, you know the went public earlier this year, mini maximum public earlier this both on Hong Kong stock exchange. Moonshot is in the pipeline supposedly rumored for next within the next six months. Deep sea 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 usually have a commercial agreement with like say the Kimi provider where there's some kind of a break as well. 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 if you look at, I think minimax AI, I think the AI'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 making money. In fact, they're making a lot of money still.

[A]

Right. There's, 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 strategics perspective to have the lead. So there is this view that like, you know, the companies in China can't 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?

[B]

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

[A]

yeah, they have a lot of money though.

[B]

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 et cetera and their structure. Now that side. I think it was really interesting that President Xi Jinping attended wac, 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 seat 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 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 openness and inclusivity, which cannot be actually mistaken for quite literally embracing open source. I think he talked a lot about openness, inclusivity to that was echoing what even the previous leader that attended, which is, I think it was the premier that attended WSA 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 infrastructure upgrade.

[A]

Yeah, I mean, I'll just say, you know, and then we'll go to brick. 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 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 it. 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 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 Cantrowicz here. I want to tell you about a documentary I've 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 Ramesh Raskar, former White House CIO Theresa Payton, Michelin's Group Chief Data and AI Officer Ambika Rajagopal and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this 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. This episode is brought to you by deeplighting. When I sat down with DeepL's founder Jarak Kutliovsky on YouTube recently, we got into the case for specialized AI DeepL voices. What it looks like when the stakes are real time conversation and honestly, it's something I wish I'd had for my own cross border interviews. Turning a language barrier into a non issue DeepL voice delivers live translation in over 40 languages for virtual meetings and in person conversations, helping people speak in their preferred language without losing flow or nuance. Whether you're meeting with a customer, negotiating with a supplier, or collaborating with global colleagues, it keeps pace with you in real time, easily handling the technical terms, acronyms and product names specific to your business. So what you actually mean never gets lost in translation. And for the builders listening, DeepL's Voice API lets you embed real time speech transcription and translation directly into your products. So go check it out for yourself. You can try DeepL Voice for free at DeepL.com tryvoice that's DeepL.com tryvoice Today's executives are more threatened, more exposed, and more vulnerable than ever before. Corporations spend billions on workplace security, but what happens when a threat finds your executives outside the office? 70% of attacks on executives happen at home or away from the office, and Ironwall understands a terrifying reality. If someone has a grievance against your company, the first place they turn to is Google. It takes them about five minutes to find one of your executive's home addresses online. And if their personal information is sitting on the open web, they're far too easy to find. The team at Ironwall knows this better than anyone. They've protected some of the most targeted executives and individuals on the planet for almost two decades. Protect your people with continuous personal data removal, proactive prevention tools, and emergency support. So when someone goes looking for your executives, Ironwall ensures they hit a dead end. Go to ironwall.com bigtechnology Fill in the quick form and request your free risk assessment. The team will show you just how exposed your executives are and how to lock it down before a threat reaches their front door. That's ironwall.com bigtechnology stop online threats before they become real world attacks. And we're back here on big Technology podcast with Grace Shao. You should check out her substack. It's AI Pro M. So it's a I P r o e m.substack.com where she covers this this world in great depth and with great clarity. So highly recommend you sign up, Grace. Let's talk about what the competition feel. 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 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?

[B]

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 U.S. right. And I think that's really sent kind of fear a bit around the world. Now 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 WSC 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 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 gr. There has been a complete change of mind recently when I spoken to quite a few of the leading Chinese products, whether they're on like coding agents, whatnot. It's really focused on potentially going to 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 source models and they need the 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.

[A]

Yeah, you also, you 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 now that their models suddenly become useless, is 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, if that becomes the reality, right. What do you even do if you're a closed source company?

[B]

Like a closed source, I think you can still charge, I think, you know, for certain government agencies, certain companies, you know, Fortune 500 that might have very strict regulation, compliance or rules, whatever. I think for certain sensitive sectors, if you were to want to use American tech stack, it still makes a lot of sense because maybe the money does not is not a main considering factor. Does that make sense? But for a startup, for SME, like every penny matters and you're going to want to find the best model for your roi. So I do think end of the day, majority of the world actually runs in a very pragmatic lens because you got to pay your bills and you got to make sure your business is generating money. 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 months 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 Jane street, if you're going to spend $2 million per head, but you're going to 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?

[A]

Right. And I think you also, I mean, you, you effectively have the answer in your piece or maybe a different answer 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, and this is your reaction to Kimi K3. You say Kimmy K3, but you can basically say this about this entire open source moment. 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, 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. If you're, if you're, if the intelligence that you've developed becomes less proprietary, if it becomes more of a commodity and less of something that you can hoard, your products are just what matter. And we saw this 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, not going to happen, most likely. But what could happen is if you're the developer of the intelligence, you can build products with that intelligence and sort of return to your investors that way. Your thoughts?

[B]

Yeah, definitely. I think, you know, 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.

[A]

No, no, I'm talking about OpenAI and anthropic. I think the Deep Seeks and the Kimmy K2s of the world are thrilled to build just the models.

[B]

But I do think I agree with you. I think there's a lot of stickiness when in the products and I think, you know, Claude, coworkers, these products like are still very, very sticky and there's still going to 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 cloud design or do you still switch back to Figma? Because Figma isn't going to add on AI. Surprisingly, the answer was that if I'm going to do everything within cloud 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. Like I want to capture all now however, I have the reverse kind of like the counter argument as well. So I went to visit Alibaba recently and I was really fascinated with a product that is really, really not under in the radar. And frankly I don't even think their own company is really valuing it. It's called Azio 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 know how and the actual edge they have is exact not as intelligent 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 doing business in supply, finding suppliers, matching with merchants and then helping merchants sell it. So they're a 2B 2C business. And now they basically built an agent on top of the 1688 which is their wholesaler website and I was talking to their people like their representatives and they're showing me the interface. It looked like cowork, a bit glossier, like prettier because you know how Chinese apps love to have like a zillion different buttons. So there it's colorfully colded and it has all these buttons and if you are just an SME or like dropship dropshipper or like a Shopify brand owner, all you need to do is go on the platform and say I'm looking for insert a. Like you can make merch for big technology and I want merch for big technology. This is my, 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 supplies 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 an 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 kind of similar to what I talked about in May Tool as well, which is the creative industry. They're like, we build these models and they're going to be the best models for you to use to optimize your E commerce product placement or to filter your job of and make your jawline more chiseled 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 awareness around that now.

[A]

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 embrace of open source and because of the US closed AI model, 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.

[B]

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

[A]

That's good, that's good. Let's do. We'll do a co. Co byline on. On Substack. That'd be fun. Okay, we have to talk more about compute because yes, you know, you could say Moonshot was able to design Kimik 2 or K3. I keep calling it K2 because I get the mountain stuck in my head. K3. And there was a Kimmy K2 but with the compute they had. But. And you talked about they get paid when people want to 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 because of their compute constraints. And so if I'm, let's say, let's say I'm going to try to channel like Greg Brockman from OpenAI. 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 going to be the thing that makes OpenAI win in this race. What do you think about that?

[B]

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 like you said, Kimmy literally posted on X saying that they needed to, you know, reassess basically who they serve, they're not the first to talk about. I think last year around Chinese New Year, this year around Chinese New Year GLM faced something similar. Deep Sea has faced something similar when demand is literally higher than supply. Which is funny because the argument in mainstream is always about is there enough demand for AI. There is now on the China side there's not enough supply for AI as in the intelligence side like the compute side. So I think it's not a secret that China is trying to figure out how to build their self reliant tech stack. It's not a secret. Huawei is working very closely with Deep seek 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 bloggery 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 semi semiconductors and especially China supply chain on this space, he also believes like China's going to find probably solutions. You know, 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 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 the AI compute side. However obviously the argument is then how sustainable is it in the long run for the environment and everything. But I think it's an ongoing R and D and potentially we'll see more breakthroughs coming in the next few months or years.

[A]

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 that is true. But the wrinkle in the Kimmy K3 story is the moonshot CEO Yang Zilin did a lot of his graduate work 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 their release. So. So the question that you've called ridiculous is why didn't he stay in the US 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 leave and decide to do this elsewhere?

[B]

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 it easier for Chinese nationals or Chinese ethnic people. I think the rise of racism, frankly, 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 overgeneralize, but their wives or spouse or whoever are maybe teachers, lawyers, you know, healthcare professionals in China. 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, 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. 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 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's 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 Zhiling or you know, Yao Shun Yu 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 life. I'm sure people love to say, oh, but you know, why would you want to stay in Asia? 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 not talking about politics, I'm talking about individual taste and individual lifestyle. The day to day of 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. They were educated it in North America. They stayed on. Right. No one actually questioned why would you return to China or anything like that. But now actually if you meet very, very top 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 life. And then on top of that, I think Yang Zhilein, 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 20s and trying to build that kind of relationship and, and build up that reputation.

[A]

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.

[B]

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 high Shenzhen 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. 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 secret company, a Zeeker 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 EV space or in other areas that 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 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 ten 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 little 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 talking about autonomous driving or industrial robotics or humanoid robotics in terms of AI integration, is that they don't have enough physical data And I think that that's where the world models come in and we're seeing a lot of competition going in there right now.

[A]

Yeah, that makes me relieved. I, I think, you know, I have appreciated the fast progress that we've seen up over the past couple 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, I think

[B]

it takes more time. You know, I just spoke to Pony AI CEO a couple 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 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, human robots are going to be deployed in next two to five years in the mass market. He gave me a rough number, 10 years again. And 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 re like staff restock bottles on your convenience shop, convenience store shop. Those cost about 700 case, 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 axis arms, the things that lift things. Logistical use cases where autonomous 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.

[A]

Yeah, I believe in the potential for these things, but right now, nothing makes me happier than seeing a humanoid robot brought on stage for a demo and just falling and got 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. Aiprom.substack.com P R O A I P R O E M.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, 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.

[B]

Thanks for having me again, Alex.

[A]

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.